From 12f261f4480d66071fb3fcace277d71a9fab214c Mon Sep 17 00:00:00 2001 From: Chenhongyi Yang Date: Sun, 11 Dec 2022 22:15:52 +0000 Subject: [PATCH] first commit --- CITATION.cff | 9 + CONTRIBUTING.md | 61 + LICENSE | 203 ++ MANIFEST.in | 4 + README.md | 197 ++ configs/_base_/datasets/cifar100_bs16.py | 36 + configs/_base_/datasets/cifar10_bs16.py | 35 + configs/_base_/datasets/cub_bs8_384.py | 54 + configs/_base_/datasets/cub_bs8_448.py | 54 + configs/_base_/datasets/imagenet21k_bs128.py | 43 + .../imagenet_bs128_poolformer_medium_224.py | 71 + .../imagenet_bs128_poolformer_small_224.py | 71 + .../_base_/datasets/imagenet_bs256_rsb_a12.py | 53 + .../_base_/datasets/imagenet_bs256_rsb_a3.py | 53 + configs/_base_/datasets/imagenet_bs32.py | 40 + .../datasets/imagenet_bs32_pil_bicubic.py | 48 + .../datasets/imagenet_bs32_pil_resize.py | 40 + configs/_base_/datasets/imagenet_bs64.py | 40 + .../_base_/datasets/imagenet_bs64_autoaug.py | 43 + .../datasets/imagenet_bs64_convmixer_224.py | 71 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mmcls/models/backbones/poolformer.py create mode 100644 mmcls/models/backbones/regnet.py create mode 100644 mmcls/models/backbones/repmlp.py create mode 100644 mmcls/models/backbones/repvgg.py create mode 100644 mmcls/models/backbones/res2net.py create mode 100644 mmcls/models/backbones/resnest.py create mode 100644 mmcls/models/backbones/resnet.py create mode 100644 mmcls/models/backbones/resnet_cifar.py create mode 100644 mmcls/models/backbones/resnext.py create mode 100644 mmcls/models/backbones/seresnet.py create mode 100644 mmcls/models/backbones/seresnext.py create mode 100644 mmcls/models/backbones/shufflenet_v1.py create mode 100644 mmcls/models/backbones/shufflenet_v2.py create mode 100644 mmcls/models/backbones/swin_transformer.py create mode 100644 mmcls/models/backbones/swin_transformer_v2.py create mode 100644 mmcls/models/backbones/t2t_vit.py create mode 100644 mmcls/models/backbones/timm_backbone.py create mode 100644 mmcls/models/backbones/tnt.py create mode 100644 mmcls/models/backbones/twins.py create mode 100644 mmcls/models/backbones/van.py create mode 100644 mmcls/models/backbones/vgg.py create mode 100644 mmcls/models/backbones/vision_transformer.py create mode 100644 mmcls/models/builder.py create mode 100644 mmcls/models/classifiers/__init__.py create mode 100644 mmcls/models/classifiers/base.py create mode 100644 mmcls/models/classifiers/image.py create mode 100644 mmcls/models/heads/__init__.py create mode 100644 mmcls/models/heads/base_head.py create mode 100644 mmcls/models/heads/cls_head.py create mode 100644 mmcls/models/heads/conformer_head.py create mode 100644 mmcls/models/heads/deit_head.py create mode 100644 mmcls/models/heads/efficientformer_head.py create mode 100644 mmcls/models/heads/linear_head.py create mode 100755 mmcls/models/heads/multi_label_csra_head.py create mode 100644 mmcls/models/heads/multi_label_head.py create mode 100644 mmcls/models/heads/multi_label_linear_head.py create mode 100644 mmcls/models/heads/stacked_head.py create mode 100644 mmcls/models/heads/vision_transformer_head.py create mode 100644 mmcls/models/losses/__init__.py create mode 100644 mmcls/models/losses/accuracy.py create mode 100644 mmcls/models/losses/asymmetric_loss.py create mode 100644 mmcls/models/losses/cross_entropy_loss.py create mode 100644 mmcls/models/losses/focal_loss.py create mode 100644 mmcls/models/losses/label_smooth_loss.py create mode 100644 mmcls/models/losses/seesaw_loss.py create mode 100644 mmcls/models/losses/utils.py create mode 100644 mmcls/models/necks/__init__.py create mode 100644 mmcls/models/necks/gap.py create mode 100644 mmcls/models/necks/gem.py create mode 100644 mmcls/models/necks/hr_fuse.py create mode 100644 mmcls/models/utils/__init__.py create mode 100644 mmcls/models/utils/attention.py create mode 100644 mmcls/models/utils/augment/__init__.py create mode 100644 mmcls/models/utils/augment/augments.py create mode 100644 mmcls/models/utils/augment/builder.py create mode 100644 mmcls/models/utils/augment/cutmix.py create mode 100644 mmcls/models/utils/augment/identity.py create mode 100644 mmcls/models/utils/augment/mixup.py create mode 100644 mmcls/models/utils/augment/resizemix.py create mode 100644 mmcls/models/utils/augment/utils.py create mode 100644 mmcls/models/utils/channel_shuffle.py create mode 100644 mmcls/models/utils/embed.py create mode 100644 mmcls/models/utils/helpers.py create mode 100644 mmcls/models/utils/inverted_residual.py create mode 100644 mmcls/models/utils/layer_scale.py create mode 100644 mmcls/models/utils/make_divisible.py create mode 100644 mmcls/models/utils/position_encoding.py create mode 100644 mmcls/models/utils/se_layer.py create mode 100644 mmcls/utils/__init__.py create mode 100644 mmcls/utils/collect_env.py create mode 100644 mmcls/utils/device.py create mode 100644 mmcls/utils/distribution.py create mode 100644 mmcls/utils/logger.py create mode 100644 mmcls/utils/setup_env.py create mode 100644 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tools/deployment/test.py create mode 100644 tools/deployment/test_torchserver.py create mode 100644 tools/dist_test.sh create mode 100644 tools/dist_train.sh create mode 100644 tools/dist_train_arm.sh create mode 100644 tools/kfold-cross-valid.py create mode 100644 tools/misc/print_config.py create mode 100644 tools/misc/verify_dataset.py create mode 100644 tools/slurm_test.sh create mode 100644 tools/slurm_train.sh create mode 100644 tools/test.py create mode 100644 tools/train.py create mode 100644 tools/visualizations/vis_cam.py create mode 100644 tools/visualizations/vis_lr.py create mode 100644 tools/visualizations/vis_pipeline.py diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..0c0d773 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,9 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +title: "OpenMMLab's Image Classification Toolbox and Benchmark" +authors: + - name: "MMClassification Contributors" +version: 0.15.0 +date-released: 2020-07-09 +repository-code: "https://github.com/open-mmlab/mmclassification" +license: Apache-2.0 diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..8a0c632 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,61 @@ +# Contributing to OpenMMLab + +All kinds of contributions are welcome, including but not limited to the following. + +- Fix typo or bugs +- Add documentation or translate the documentation into other languages +- Add new features and components + +## Workflow + +1. fork and pull the latest OpenMMLab repository (MMClassification) +2. checkout a new branch (do not use master branch for PRs) +3. commit your changes +4. create a PR + +```{note} +If you plan to add some new features that involve large changes, it is encouraged to open an issue for discussion first. +``` + +## Code style + +### Python + +We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style. + +We use the following tools for linting and formatting: + +- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools. +- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports. +- [yapf](https://github.com/google/yapf): A formatter for Python files. +- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files. +- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files. +- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring. + +Style configurations can be found in [setup.cfg](./setup.cfg). + +We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`, +fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit. +The config for a pre-commit hook is stored in [.pre-commit-config](https://github.com/open-mmlab/mmclassification/blob/master/.pre-commit-config.yaml). + +After you clone the repository, you will need to install initialize pre-commit hook. + +```shell +pip install -U pre-commit +``` + +From the repository folder + +```shell +pre-commit install +``` + +After this on every commit check code linters and formatter will be enforced. + +```{important} +Before you create a PR, make sure that your code lints and is formatted by yapf. +``` + +### C++ and CUDA + +We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html). diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..f731325 --- /dev/null +++ b/LICENSE @@ -0,0 +1,203 @@ +Copyright (c) OpenMMLab. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2020 MMClassification Authors. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..17ddc8c --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,4 @@ +include requirements/*.txt +include mmcls/.mim/model-index.yml +recursive-include mmcls/.mim/configs *.py *.yml +recursive-include mmcls/.mim/tools *.py *.sh diff --git a/README.md b/README.md new file mode 100644 index 0000000..75c9cf5 --- /dev/null +++ b/README.md @@ -0,0 +1,197 @@ +# GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation +

+ +

+ +GPViT is a high-resolution non-hierarchical vision transformer architecture designed for high-performing visual recognition. This repository contains the official PyTorch implementation of our paper: + +[GPViT: A High Resolution Non-Hierarchical Vision Transformer with Group Propagation, *Chenhongyi Yang**, *Jiarui Xu**, *Shalini De Mello*, *Elliot J. Crowley*, *Xiaolong Wang*.](TBD) + +## Usage + +### Environment Setup +Our code base is built upon the MM-series toolkits. Specifically, classification is based on [MMClassification](); object detection is based on [MMDetection](); and semantic segmentation is based on [MMSegmentation](). Users can follow the official site of those toolkit to set up their environments. We also provide a sample setting up script as following: + +```shell +conda create -n gpvit python=3.7 -y +source activate gpvit +pip install torch==1.7.1+cu101 torchvision==0.8.2+cu101 -f https://download.pytorch.org/whl/torch_stable.html +pip install -U openmim +mim install mmcv-full==1.4.8 +pip install timm +pip install lmdb # for ImageNet experiments +pip install -v -e . +cd downstream/mmdetection # setup object detection and instance segmentation +pip install -v -e . +cd ../mmsegmentation # setup semantic segmentation +pip install -v -e . +``` + +### Data Preparation +Please follow [MMClassification](), [MMDetection]() and [MMSegmentation]() to set up the ImageNet, COCO and ADE20K datasets. For ImageNet experiment, we convert the dataset to LMDB format to accelerate training and testing. For example, you can convert you own dataset by running: +```shell +python tools/dataset_tools/create_lmdb_dataset.py \ + --train-img-dir data/imagenet/train \ + --train-out data/imagenet/imagenet_lmdb/train \ + --val_img_dir data/imagenet/val \ + --val-out data/imagenet/imagenet_lmdb/val +``` +After setting up, the datasets file structure should be as follows: +``` +GPViT +|-- data +| |-- imagenet +| | |-- imagenet_lmdb +| | | |-- train +| | | | |-- data.mdb +| | | | |-- lock.mdb +| | | |-- val +| | | | |-- data.mdb +| | | | |-- lock.mdb +| | |-- meta +| | | |- ... +|-- downstream +| |-- mmsegmentation +| | |-- data +| | | |-- ade +| | | | |-- ADEChallengeData2016 +| | | | | |-- annotations +| | | | | | |-- ... +| | | | | |-- images +| | | | | | |-- ... +| | | | | |-- objectInfo150.txt +| | | | | |-- sceneCategories.txt +| | |-- ... +| |-- mmdetection +| | |-- data +| | | |-- coco +| | | | |-- train2017 +| | | | | |-- ... +| | | | |-- val2017 +| | | | | |-- ... +| | | | |-- annotations +| | | | | |-- instances_train2017.json +| | | | | |-- instances_val2017.json +| | | | | |-- ... +| | |-- ... +|-- ... +``` + +### ImageNet classification +#### Training GPViT +```shell +# Example: Training GPViT-L1 model +zsh tool/dist_train.sh configs/gpvit/gpvit_l1.py 16 +``` +#### Testing GPViT +```shell +# Example: Testing GPViT-L1 model +zsh tool/dist_test.sh configs/gpvit/gpvit_l1.py work_dirs/gpvit_l1/epoch_300.pth 16 --metrics accuracy +``` +### COCO Object Detection and Instance Segmentation + +#### Training GPViT based Mask R-CNN +```shell +# Example: Training GPViT-L1 models with 1x and 3x+MS schedules +zsh tools/dist_train.sh configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_1x.py 16 +zsh tools/dist_train.sh configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_3x.py 16 +``` + +#### Training GPViT based RetinaNet +```shell +# Example: Training GPViT-L1 models with 1x and 3x+MS schedules +zsh tools/dist_train.sh configs/gpvit/retinanet/gpvit_l1_retinanet_1x.py 16 +zsh tools/dist_train.sh configs/gpvit/retinanet/gpvit_l4_retinanet_3x.py 16 +``` + +#### Testing GPViT based Mask R-CNN +```shell +# Example: Testing GPViT-L1 Mask R-CNN 1x model +zsh tools/dist_test.sh configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_1x.py work_dirs/gpvit_l1_maskrcnn_1x/epoch_12.pth 16 --eval bbox segm +``` + +#### Testing GPViT based RetinaNet +```shell +# Example: Testing GPViT-L1 RetinaNet 1x model +zsh tools/dist_test.sh configs/gpvit/retinanet/gpvit_l1_retinanet_1x.py work_dirs/gpvit_l1_retinanet_1x/epoch_12.pth 16 --eval bbox +``` + +### ADE20K semantic segmentation +#### Training GPViT based semantic segmentation models +```shell +# Example: Training GPViT-L1 based SegFormer and UperNet models +zsh tools/dist_train.sh configs/gpvit/gpvit_l1_segformer.py 16 +zsh tools/dist_train.sh configs/gpvit/gpvit_l1_upernet.py 16 +``` +#### Testing GPViT based semantic segmentation models +```shell +# Example: Testing GPViT-L1 based SegFormer and UperNet models +zsh tools/dist_test.sh configs/gpvit/gpvit_l1_segformer.py work_dirs/gpvit_l1_segformer/iter_160000.pth 16 --eval mIoU +zsh tools/dist_test.sh configs/gpvit/gpvit_l1_upernet.py work_dirs/gpvit_l1_upernet/iter_160000.pth 16 --eval mIoU +``` + +## Benchmark results + +### ImageNet-1k classification +| Model | #Params (M) | Top-1 Acc | Top-5 Acc | Config | Model | +|:--------:|:-----------:|:---------:|:---------:|:----------:|:---------:| +| GPViT-L1 | 9.3 | 80.5 | 95.4 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/configs/gpvit/gpvit_l1.py) | [model]() | +| GPViT-L2 | 23.8 | 83.4 | 96.6 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/configs/gpvit/gpvit_l2.py) | [model]() | +| GPViT-L3 | 36.2 | 84.1 | 96.9 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/configs/gpvit/gpvit_l3.py) | [model]() | +| GPViT-L4 | 75.4 | 84.3 | 96.9 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/configs/gpvit/gpvit_l4.py) | [model]() | + +### COCO Mask R-CNN 1x Schedule +| Model | #Params (M) | AP Box | AP Mask | Config | Model | +|:--------:|:-----------:|:------:|:-------:|:----------:|:---------:| +| GPViT-L1 | 33 | 48.1 | 42.7 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_1x.py) | [model]() | +| GPViT-L2 | 50 | 49.9 | 43.9 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_1x.py) | [model]() | +| GPViT-L3 | 64 | 50.4 | 44.4 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_1x.py) | [model]() | +| GPViT-L4 | 109 | 51.0 | 45.0 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_1x.py) | [model]() | + +### COCO Mask R-CNN 3x+MS Schedule +| Model | #Params (M) | AP Box | AP Mask | Config | Model | +|:--------:|:-----------:|:------:|:-------:|:----------:|:---------:| +| GPViT-L1 | 33 | 50.2 | 44.3 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_3x.py) | [model]() | +| GPViT-L2 | 50 | 51.4 | 45.1 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_3x.py) | [model]() | +| GPViT-L3 | 64 | 51.6 | 45.2 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_3x.py) | [model]() | +| GPViT-L4 | 109 | 52.1 | 45.7 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_3x.py) | [model]() | + +### COCO RetinaNet 1x Schedule +| Model | #Params (M) | AP Box | Config | Model | +|:--------:|:-----------:|:------:|:----------:|:---------:| +| GPViT-L1 | 21 | 45.8 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_1x.py) | [model]() | +| GPViT-L2 | 37 | 48.0 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_1x.py) | [model]() | +| GPViT-L3 | 52 | 48.3 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_1x.py) | [model]() | +| GPViT-L4 | 96 | 48.7 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_1x.py) | [model]() | + +### COCO RetinaNet 3x+MS Schedule +| Model | #Params (M) | AP Box | Config | Model | +|:--------:|:-----------:|:------:|:----------:|:---------:| +| GPViT-L1 | 21 | 48.1 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_3x.py) | [model]() | +| GPViT-L2 | 37 | 49.0 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_3x.py) | [model]() | +| GPViT-L3 | 52 | 49.4 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_3x.py) | [model]() | +| GPViT-L4 | 96 | 49.8 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_3x.py) | [model]() | + +### ADE20K UperNet +| Model | #Params (M) | mIoU | Config | Model | +|:--------:|:-----------:|:----:|:----------:|:---------:| +| GPViT-L1 | 37 | 49.1 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l1_upernet.py) | [model]() | +| GPViT-L2 | 53 | 50.2 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l2_upernet.py) | [model]() | +| GPViT-L3 | 66 | 51.7 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l3_upernet.py) | [model]() | +| GPViT-L4 | 107 | 52.5 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l14_upernet.py) | [model]() | + +### ADE20K SegFormer +| Model | #Params (M) | mIoU | Config | Model | +|:--------:|:-----------:|:----:|:----------:|:---------:| +| GPViT-L1 | 9 | 46.9 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l1_segformer.py) | [model]() | +| GPViT-L2 | 24 | 49.2 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l2_segformer.py) | [model]() | +| GPViT-L3 | 36 | 50.8 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l3_segformer.py) | [model]() | +| GPViT-L4 | 76 | 51.3 | [config](https://github.com/ChenhongyiYang/GPViT/blob/main/downstream/mmsegmentation/configs/gpvit/gpvit_l14_segformer.py) | [model]() | + + + +## Citation +``` +TBD +``` + diff --git a/configs/_base_/datasets/cifar100_bs16.py b/configs/_base_/datasets/cifar100_bs16.py new file mode 100644 index 0000000..d4f8db7 --- /dev/null +++ b/configs/_base_/datasets/cifar100_bs16.py @@ -0,0 +1,36 @@ +# dataset settings +dataset_type = 'CIFAR100' +img_norm_cfg = dict( + mean=[129.304, 124.070, 112.434], + std=[68.170, 65.392, 70.418], + to_rgb=False) +train_pipeline = [ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=16, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/cifar100', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/cifar100', + pipeline=test_pipeline, + test_mode=True), + test=dict( + type=dataset_type, + data_prefix='data/cifar100', + pipeline=test_pipeline, + test_mode=True)) diff --git a/configs/_base_/datasets/cifar10_bs16.py b/configs/_base_/datasets/cifar10_bs16.py new file mode 100644 index 0000000..0d28adf --- /dev/null +++ b/configs/_base_/datasets/cifar10_bs16.py @@ -0,0 +1,35 @@ +# dataset settings +dataset_type = 'CIFAR10' +img_norm_cfg = dict( + mean=[125.307, 122.961, 113.8575], + std=[51.5865, 50.847, 51.255], + to_rgb=False) +train_pipeline = [ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=16, + workers_per_gpu=2, + train=dict( + type=dataset_type, data_prefix='data/cifar10', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/cifar10', + pipeline=test_pipeline, + test_mode=True), + test=dict( + type=dataset_type, + data_prefix='data/cifar10', + pipeline=test_pipeline, + test_mode=True)) diff --git a/configs/_base_/datasets/cub_bs8_384.py b/configs/_base_/datasets/cub_bs8_384.py new file mode 100644 index 0000000..4acad24 --- /dev/null +++ b/configs/_base_/datasets/cub_bs8_384.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'CUB' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=510), + dict(type='RandomCrop', size=384), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=510), + dict(type='CenterCrop', crop_size=384), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data_root = 'data/CUB_200_2011/' +data = dict( + samples_per_gpu=8, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'images.txt', + image_class_labels_file=data_root + 'image_class_labels.txt', + train_test_split_file=data_root + 'train_test_split.txt', + data_prefix=data_root + 'images', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'images.txt', + image_class_labels_file=data_root + 'image_class_labels.txt', + train_test_split_file=data_root + 'train_test_split.txt', + data_prefix=data_root + 'images', + test_mode=True, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'images.txt', + image_class_labels_file=data_root + 'image_class_labels.txt', + train_test_split_file=data_root + 'train_test_split.txt', + data_prefix=data_root + 'images', + test_mode=True, + pipeline=test_pipeline)) + +evaluation = dict( + interval=1, metric='accuracy', + save_best='auto') # save the checkpoint with highest accuracy diff --git a/configs/_base_/datasets/cub_bs8_448.py b/configs/_base_/datasets/cub_bs8_448.py new file mode 100644 index 0000000..9e909a1 --- /dev/null +++ b/configs/_base_/datasets/cub_bs8_448.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'CUB' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=600), + dict(type='RandomCrop', size=448), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=600), + dict(type='CenterCrop', crop_size=448), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data_root = 'data/CUB_200_2011/' +data = dict( + samples_per_gpu=8, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'images.txt', + image_class_labels_file=data_root + 'image_class_labels.txt', + train_test_split_file=data_root + 'train_test_split.txt', + data_prefix=data_root + 'images', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'images.txt', + image_class_labels_file=data_root + 'image_class_labels.txt', + train_test_split_file=data_root + 'train_test_split.txt', + data_prefix=data_root + 'images', + test_mode=True, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'images.txt', + image_class_labels_file=data_root + 'image_class_labels.txt', + train_test_split_file=data_root + 'train_test_split.txt', + data_prefix=data_root + 'images', + test_mode=True, + pipeline=test_pipeline)) + +evaluation = dict( + interval=1, metric='accuracy', + save_best='auto') # save the checkpoint with highest accuracy diff --git a/configs/_base_/datasets/imagenet21k_bs128.py b/configs/_base_/datasets/imagenet21k_bs128.py new file mode 100644 index 0000000..b81a746 --- /dev/null +++ b/configs/_base_/datasets/imagenet21k_bs128.py @@ -0,0 +1,43 @@ +# dataset settings +dataset_type = 'ImageNet21k' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=128, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet21k/train', + pipeline=train_pipeline, + recursion_subdir=True), + val=dict( + type=dataset_type, + data_prefix='data/imagenet21k/val', + ann_file='data/imagenet21k/meta/val.txt', + pipeline=test_pipeline, + recursion_subdir=True), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet21k/val', + ann_file='data/imagenet21k/meta/val.txt', + pipeline=test_pipeline, + recursion_subdir=True)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs128_poolformer_medium_224.py b/configs/_base_/datasets/imagenet_bs128_poolformer_medium_224.py new file mode 100644 index 0000000..667e58a --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs128_poolformer_medium_224.py @@ -0,0 +1,71 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(236, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=128, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs128_poolformer_small_224.py b/configs/_base_/datasets/imagenet_bs128_poolformer_small_224.py new file mode 100644 index 0000000..76aee7e --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs128_poolformer_small_224.py @@ -0,0 +1,71 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=128, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs256_rsb_a12.py b/configs/_base_/datasets/imagenet_bs256_rsb_a12.py new file mode 100644 index 0000000..7596855 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs256_rsb_a12.py @@ -0,0 +1,53 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=7, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(236, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=256, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs256_rsb_a3.py b/configs/_base_/datasets/imagenet_bs256_rsb_a3.py new file mode 100644 index 0000000..aee640d --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs256_rsb_a3.py @@ -0,0 +1,53 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=160), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=6, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(236, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=256, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs32.py b/configs/_base_/datasets/imagenet_bs32.py new file mode 100644 index 0000000..8a54659 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs32.py @@ -0,0 +1,40 @@ +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs32_pil_bicubic.py b/configs/_base_/datasets/imagenet_bs32_pil_bicubic.py new file mode 100644 index 0000000..d66c1bd --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs32_pil_bicubic.py @@ -0,0 +1,48 @@ +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(256, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs32_pil_resize.py b/configs/_base_/datasets/imagenet_bs32_pil_resize.py new file mode 100644 index 0000000..22b74f7 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs32_pil_resize.py @@ -0,0 +1,40 @@ +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64.py b/configs/_base_/datasets/imagenet_bs64.py new file mode 100644 index 0000000..b9f866a --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64.py @@ -0,0 +1,40 @@ +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_autoaug.py b/configs/_base_/datasets/imagenet_bs64_autoaug.py new file mode 100644 index 0000000..a1092a3 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_autoaug.py @@ -0,0 +1,43 @@ +_base_ = ['./pipelines/auto_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='AutoAugment', policies={{_base_.auto_increasing_policies}}), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_convmixer_224.py b/configs/_base_/datasets/imagenet_bs64_convmixer_224.py new file mode 100644 index 0000000..afd7113 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_convmixer_224.py @@ -0,0 +1,71 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(233, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_mixer_224.py b/configs/_base_/datasets/imagenet_bs64_mixer_224.py new file mode 100644 index 0000000..a005436 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_mixer_224.py @@ -0,0 +1,48 @@ +# dataset settings +dataset_type = 'ImageNet' + +# change according to https://github.com/rwightman/pytorch-image-models/blob +# /master/timm/models/mlp_mixer.py +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) + +# training is not supported for now +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224, backend='cv2'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', size=(256, -1), backend='cv2', interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_pil_resize.py b/configs/_base_/datasets/imagenet_bs64_pil_resize.py new file mode 100644 index 0000000..95d0e1f --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_pil_resize.py @@ -0,0 +1,40 @@ +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_pil_resize_autoaug.py b/configs/_base_/datasets/imagenet_bs64_pil_resize_autoaug.py new file mode 100644 index 0000000..2a9a4de --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_pil_resize_autoaug.py @@ -0,0 +1,53 @@ +_base_ = [ + 'pipelines/auto_aug.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='AutoAugment', policies={{_base_.policy_imagenet}}), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(256, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_swin_224.py b/configs/_base_/datasets/imagenet_bs64_swin_224.py new file mode 100644 index 0000000..4a059a3 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_swin_224.py @@ -0,0 +1,71 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(256, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_swin_224_lmdb.py b/configs/_base_/datasets/imagenet_bs64_swin_224_lmdb.py new file mode 100644 index 0000000..7d79449 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_swin_224_lmdb.py @@ -0,0 +1,80 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFileLMDB', + file_client_args={ + 'backend': 'lmdb', + 'db_path': 'data/imagenet/imagenet_lmdb/train' + }), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFileLMDB', + file_client_args={ + 'backend': 'lmdb', + 'db_path': 'data/imagenet/imagenet_lmdb/val' + }), + dict( + type='Resize', + size=(256, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='', + ann_file='data/imagenet/meta/train.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_swin_256.py b/configs/_base_/datasets/imagenet_bs64_swin_256.py new file mode 100644 index 0000000..1f73683 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_swin_256.py @@ -0,0 +1,71 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=256, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(292, -1), # ( 256 / 224 * 256 ) + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=256), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_swin_384.py b/configs/_base_/datasets/imagenet_bs64_swin_384.py new file mode 100644 index 0000000..d263939 --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_swin_384.py @@ -0,0 +1,43 @@ +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=384, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=384, backend='pillow', interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=10, metric='accuracy') diff --git a/configs/_base_/datasets/imagenet_bs64_t2t_224.py b/configs/_base_/datasets/imagenet_bs64_t2t_224.py new file mode 100644 index 0000000..1190d6f --- /dev/null +++ b/configs/_base_/datasets/imagenet_bs64_t2t_224.py @@ -0,0 +1,71 @@ +_base_ = ['./pipelines/rand_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=4, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) + +evaluation = dict(interval=1, metric='accuracy', save_best='auto') diff --git a/configs/_base_/datasets/pipelines/auto_aug.py b/configs/_base_/datasets/pipelines/auto_aug.py new file mode 100644 index 0000000..5a10f7e --- /dev/null +++ b/configs/_base_/datasets/pipelines/auto_aug.py @@ -0,0 +1,96 @@ +# Policy for ImageNet, refers to +# https://github.com/DeepVoltaire/AutoAugment/blame/master/autoaugment.py +policy_imagenet = [ + [ + dict(type='Posterize', bits=4, prob=0.4), + dict(type='Rotate', angle=30., prob=0.6) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], + [ + dict(type='Posterize', bits=5, prob=0.6), + dict(type='Posterize', bits=5, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8) + ], + [ + dict(type='Solarize', thr=256 / 9 * 6, prob=0.6), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Posterize', bits=6, prob=0.8), + dict(type='Equalize', prob=1.)], + [ + dict(type='Rotate', angle=10., prob=0.2), + dict(type='Solarize', thr=256 / 9, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.6), + dict(type='Posterize', bits=5, prob=0.4) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0., prob=0.4) + ], + [ + dict(type='Rotate', angle=30., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Equalize', prob=0.0), + dict(type='Equalize', prob=0.8)], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0.2, prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0.8, prob=0.8), + dict(type='Solarize', thr=256 / 9 * 2, prob=0.8) + ], + [ + dict(type='Sharpness', magnitude=0.7, prob=0.4), + dict(type='Invert', prob=0.6) + ], + [ + dict( + type='Shear', + magnitude=0.3 / 9 * 5, + prob=0.6, + direction='horizontal'), + dict(type='Equalize', prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], +] diff --git a/configs/_base_/datasets/pipelines/rand_aug.py b/configs/_base_/datasets/pipelines/rand_aug.py new file mode 100644 index 0000000..f2bab3c --- /dev/null +++ b/configs/_base_/datasets/pipelines/rand_aug.py @@ -0,0 +1,43 @@ +# Refers to `_RAND_INCREASING_TRANSFORMS` in pytorch-image-models +rand_increasing_policies = [ + dict(type='AutoContrast'), + dict(type='Equalize'), + dict(type='Invert'), + dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)), + dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)), + dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)), + dict( + type='SolarizeAdd', + magnitude_key='magnitude', + magnitude_range=(0, 110)), + dict( + type='ColorTransform', + magnitude_key='magnitude', + magnitude_range=(0, 0.9)), + dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)), + dict( + type='Brightness', magnitude_key='magnitude', + magnitude_range=(0, 0.9)), + dict( + type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + direction='horizontal'), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + direction='vertical'), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.45), + direction='horizontal'), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.45), + direction='vertical') +] diff --git a/configs/_base_/datasets/stanford_cars_bs8_448.py b/configs/_base_/datasets/stanford_cars_bs8_448.py new file mode 100644 index 0000000..636b2e1 --- /dev/null +++ b/configs/_base_/datasets/stanford_cars_bs8_448.py @@ -0,0 +1,46 @@ +# dataset settings +dataset_type = 'StanfordCars' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=512), + dict(type='RandomCrop', size=448), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=512), + dict(type='CenterCrop', crop_size=448), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data_root = 'data/stanfordcars' +data = dict( + samples_per_gpu=8, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix=data_root, + test_mode=False, + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix=data_root, + test_mode=True, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_prefix=data_root, + test_mode=True, + pipeline=test_pipeline)) + +evaluation = dict( + interval=1, metric='accuracy', + save_best='auto') # save the checkpoint with highest accuracy diff --git a/configs/_base_/datasets/voc_bs16.py b/configs/_base_/datasets/voc_bs16.py new file mode 100644 index 0000000..73fa0bc --- /dev/null +++ b/configs/_base_/datasets/voc_bs16.py @@ -0,0 +1,41 @@ +# dataset settings +dataset_type = 'VOC' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=16, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/VOCdevkit/VOC2007/', + ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/VOCdevkit/VOC2007/', + ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + data_prefix='data/VOCdevkit/VOC2007/', + ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', + pipeline=test_pipeline)) +evaluation = dict( + interval=1, metric=['mAP', 'CP', 'OP', 'CR', 'OR', 'CF1', 'OF1']) diff --git a/configs/_base_/default_runtime.py b/configs/_base_/default_runtime.py new file mode 100644 index 0000000..ba965a4 --- /dev/null +++ b/configs/_base_/default_runtime.py @@ -0,0 +1,16 @@ +# checkpoint saving +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=100, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable + +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] diff --git a/configs/_base_/models/conformer/base-p16.py b/configs/_base_/models/conformer/base-p16.py new file mode 100644 index 0000000..157dcc9 --- /dev/null +++ b/configs/_base_/models/conformer/base-p16.py @@ -0,0 +1,22 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='Conformer', arch='base', drop_path_rate=0.1, init_cfg=None), + neck=None, + head=dict( + type='ConformerHead', + num_classes=1000, + in_channels=[1536, 576], + init_cfg=None, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/conformer/small-p16.py b/configs/_base_/models/conformer/small-p16.py new file mode 100644 index 0000000..1729808 --- /dev/null +++ b/configs/_base_/models/conformer/small-p16.py @@ -0,0 +1,22 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='Conformer', arch='small', drop_path_rate=0.1, init_cfg=None), + neck=None, + head=dict( + type='ConformerHead', + num_classes=1000, + in_channels=[1024, 384], + init_cfg=None, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/conformer/small-p32.py b/configs/_base_/models/conformer/small-p32.py new file mode 100644 index 0000000..593aba1 --- /dev/null +++ b/configs/_base_/models/conformer/small-p32.py @@ -0,0 +1,26 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='Conformer', + arch='small', + patch_size=32, + drop_path_rate=0.1, + init_cfg=None), + neck=None, + head=dict( + type='ConformerHead', + num_classes=1000, + in_channels=[1024, 384], + init_cfg=None, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/conformer/tiny-p16.py b/configs/_base_/models/conformer/tiny-p16.py new file mode 100644 index 0000000..dad8eca --- /dev/null +++ b/configs/_base_/models/conformer/tiny-p16.py @@ -0,0 +1,22 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='Conformer', arch='tiny', drop_path_rate=0.1, init_cfg=None), + neck=None, + head=dict( + type='ConformerHead', + num_classes=1000, + in_channels=[256, 384], + init_cfg=None, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/convmixer/convmixer-1024-20.py b/configs/_base_/models/convmixer/convmixer-1024-20.py new file mode 100644 index 0000000..a8f4d51 --- /dev/null +++ b/configs/_base_/models/convmixer/convmixer-1024-20.py @@ -0,0 +1,11 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='ConvMixer', arch='1024/20'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/convmixer/convmixer-1536-20.py b/configs/_base_/models/convmixer/convmixer-1536-20.py new file mode 100644 index 0000000..9ad8209 --- /dev/null +++ b/configs/_base_/models/convmixer/convmixer-1536-20.py @@ -0,0 +1,11 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='ConvMixer', arch='1536/20'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/convmixer/convmixer-768-32.py b/configs/_base_/models/convmixer/convmixer-768-32.py new file mode 100644 index 0000000..1cba528 --- /dev/null +++ b/configs/_base_/models/convmixer/convmixer-768-32.py @@ -0,0 +1,11 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='ConvMixer', arch='768/32', act_cfg=dict(type='ReLU')), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/convnext/convnext-base.py b/configs/_base_/models/convnext/convnext-base.py new file mode 100644 index 0000000..7fc5ce7 --- /dev/null +++ b/configs/_base_/models/convnext/convnext-base.py @@ -0,0 +1,23 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ConvNeXt', + arch='base', + out_indices=(3, ), + drop_path_rate=0.5, + gap_before_final_norm=True, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.), + ]), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/convnext/convnext-large.py b/configs/_base_/models/convnext/convnext-large.py new file mode 100644 index 0000000..4d9e37c --- /dev/null +++ b/configs/_base_/models/convnext/convnext-large.py @@ -0,0 +1,23 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ConvNeXt', + arch='large', + out_indices=(3, ), + drop_path_rate=0.5, + gap_before_final_norm=True, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.), + ]), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/convnext/convnext-small.py b/configs/_base_/models/convnext/convnext-small.py new file mode 100644 index 0000000..989ad1d --- /dev/null +++ b/configs/_base_/models/convnext/convnext-small.py @@ -0,0 +1,23 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ConvNeXt', + arch='small', + out_indices=(3, ), + drop_path_rate=0.4, + gap_before_final_norm=True, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.), + ]), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/convnext/convnext-tiny.py b/configs/_base_/models/convnext/convnext-tiny.py new file mode 100644 index 0000000..0b692ab --- /dev/null +++ b/configs/_base_/models/convnext/convnext-tiny.py @@ -0,0 +1,23 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ConvNeXt', + arch='tiny', + out_indices=(3, ), + drop_path_rate=0.1, + gap_before_final_norm=True, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.), + ]), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/convnext/convnext-xlarge.py b/configs/_base_/models/convnext/convnext-xlarge.py new file mode 100644 index 0000000..0c75e32 --- /dev/null +++ b/configs/_base_/models/convnext/convnext-xlarge.py @@ -0,0 +1,23 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ConvNeXt', + arch='xlarge', + out_indices=(3, ), + drop_path_rate=0.5, + gap_before_final_norm=True, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['LayerNorm'], val=1., bias=0.), + ]), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/densenet/densenet121.py b/configs/_base_/models/densenet/densenet121.py new file mode 100644 index 0000000..0a14d30 --- /dev/null +++ b/configs/_base_/models/densenet/densenet121.py @@ -0,0 +1,11 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='DenseNet', arch='121'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/densenet/densenet161.py b/configs/_base_/models/densenet/densenet161.py new file mode 100644 index 0000000..61a0d83 --- /dev/null +++ b/configs/_base_/models/densenet/densenet161.py @@ -0,0 +1,11 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='DenseNet', arch='161'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2208, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/densenet/densenet169.py b/configs/_base_/models/densenet/densenet169.py new file mode 100644 index 0000000..779ea17 --- /dev/null +++ b/configs/_base_/models/densenet/densenet169.py @@ -0,0 +1,11 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='DenseNet', arch='169'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1664, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/densenet/densenet201.py b/configs/_base_/models/densenet/densenet201.py new file mode 100644 index 0000000..2909af0 --- /dev/null +++ b/configs/_base_/models/densenet/densenet201.py @@ -0,0 +1,11 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='DenseNet', arch='201'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1920, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/efficientnet_b0.py b/configs/_base_/models/efficientnet_b0.py new file mode 100644 index 0000000..d9ba685 --- /dev/null +++ b/configs/_base_/models/efficientnet_b0.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b0'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b1.py b/configs/_base_/models/efficientnet_b1.py new file mode 100644 index 0000000..63e15c8 --- /dev/null +++ b/configs/_base_/models/efficientnet_b1.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b1'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b2.py b/configs/_base_/models/efficientnet_b2.py new file mode 100644 index 0000000..5edcfa5 --- /dev/null +++ b/configs/_base_/models/efficientnet_b2.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b2'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1408, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b3.py b/configs/_base_/models/efficientnet_b3.py new file mode 100644 index 0000000..c7c6d6d --- /dev/null +++ b/configs/_base_/models/efficientnet_b3.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b3'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b4.py b/configs/_base_/models/efficientnet_b4.py new file mode 100644 index 0000000..06840ed --- /dev/null +++ b/configs/_base_/models/efficientnet_b4.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b4'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1792, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b5.py b/configs/_base_/models/efficientnet_b5.py new file mode 100644 index 0000000..a86eebd --- /dev/null +++ b/configs/_base_/models/efficientnet_b5.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b5'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b6.py b/configs/_base_/models/efficientnet_b6.py new file mode 100644 index 0000000..4eada1d --- /dev/null +++ b/configs/_base_/models/efficientnet_b6.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b6'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2304, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b7.py b/configs/_base_/models/efficientnet_b7.py new file mode 100644 index 0000000..1d84ba4 --- /dev/null +++ b/configs/_base_/models/efficientnet_b7.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b7'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2560, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_b8.py b/configs/_base_/models/efficientnet_b8.py new file mode 100644 index 0000000..c950064 --- /dev/null +++ b/configs/_base_/models/efficientnet_b8.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='EfficientNet', arch='b8'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2816, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_em.py b/configs/_base_/models/efficientnet_em.py new file mode 100644 index 0000000..abecdbe --- /dev/null +++ b/configs/_base_/models/efficientnet_em.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + # `em` means EfficientNet-EdgeTPU-M arch + backbone=dict(type='EfficientNet', arch='em', act_cfg=dict(type='ReLU')), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/efficientnet_es.py b/configs/_base_/models/efficientnet_es.py new file mode 100644 index 0000000..911ba4a --- /dev/null +++ b/configs/_base_/models/efficientnet_es.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + # `es` means EfficientNet-EdgeTPU-S arch + backbone=dict(type='EfficientNet', arch='es', act_cfg=dict(type='ReLU')), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/hornet/hornet-base-gf.py b/configs/_base_/models/hornet/hornet-base-gf.py new file mode 100644 index 0000000..7544970 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-base-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='base-gf', drop_path_rate=0.5), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-base.py b/configs/_base_/models/hornet/hornet-base.py new file mode 100644 index 0000000..8276414 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-base.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='base', drop_path_rate=0.5), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-large-gf.py b/configs/_base_/models/hornet/hornet-large-gf.py new file mode 100644 index 0000000..a5b5511 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-large-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='large-gf', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-large-gf384.py b/configs/_base_/models/hornet/hornet-large-gf384.py new file mode 100644 index 0000000..fbb5478 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-large-gf384.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='large-gf384', drop_path_rate=0.4), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ]) diff --git a/configs/_base_/models/hornet/hornet-large.py b/configs/_base_/models/hornet/hornet-large.py new file mode 100644 index 0000000..26d99e1 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-large.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='large', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-small-gf.py b/configs/_base_/models/hornet/hornet-small-gf.py new file mode 100644 index 0000000..42d9d11 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-small-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='small-gf', drop_path_rate=0.4), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-small.py b/configs/_base_/models/hornet/hornet-small.py new file mode 100644 index 0000000..e803976 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-small.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='small', drop_path_rate=0.4), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-tiny-gf.py b/configs/_base_/models/hornet/hornet-tiny-gf.py new file mode 100644 index 0000000..0e417d0 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-tiny-gf.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='tiny-gf', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hornet/hornet-tiny.py b/configs/_base_/models/hornet/hornet-tiny.py new file mode 100644 index 0000000..068d7d6 --- /dev/null +++ b/configs/_base_/models/hornet/hornet-tiny.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HorNet', arch='tiny', drop_path_rate=0.2), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-6) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/hrnet/hrnet-w18.py b/configs/_base_/models/hrnet/hrnet-w18.py new file mode 100644 index 0000000..f7fbf29 --- /dev/null +++ b/configs/_base_/models/hrnet/hrnet-w18.py @@ -0,0 +1,15 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HRNet', arch='w18'), + neck=[ + dict(type='HRFuseScales', in_channels=(18, 36, 72, 144)), + dict(type='GlobalAveragePooling'), + ], + head=dict( + type='LinearClsHead', + in_channels=2048, + num_classes=1000, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/hrnet/hrnet-w30.py b/configs/_base_/models/hrnet/hrnet-w30.py new file mode 100644 index 0000000..babcaca --- /dev/null +++ b/configs/_base_/models/hrnet/hrnet-w30.py @@ -0,0 +1,15 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HRNet', arch='w30'), + neck=[ + dict(type='HRFuseScales', in_channels=(30, 60, 120, 240)), + dict(type='GlobalAveragePooling'), + ], + head=dict( + type='LinearClsHead', + in_channels=2048, + num_classes=1000, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/hrnet/hrnet-w32.py b/configs/_base_/models/hrnet/hrnet-w32.py new file mode 100644 index 0000000..2c1e980 --- /dev/null +++ b/configs/_base_/models/hrnet/hrnet-w32.py @@ -0,0 +1,15 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HRNet', arch='w32'), + neck=[ + dict(type='HRFuseScales', in_channels=(32, 64, 128, 256)), + dict(type='GlobalAveragePooling'), + ], + head=dict( + type='LinearClsHead', + in_channels=2048, + num_classes=1000, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/hrnet/hrnet-w40.py b/configs/_base_/models/hrnet/hrnet-w40.py new file mode 100644 index 0000000..83f65d8 --- /dev/null +++ b/configs/_base_/models/hrnet/hrnet-w40.py @@ -0,0 +1,15 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HRNet', arch='w40'), + neck=[ + dict(type='HRFuseScales', in_channels=(40, 80, 160, 320)), + dict(type='GlobalAveragePooling'), + ], + head=dict( + type='LinearClsHead', + in_channels=2048, + num_classes=1000, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/hrnet/hrnet-w44.py b/configs/_base_/models/hrnet/hrnet-w44.py new file mode 100644 index 0000000..e75dc0f --- /dev/null +++ b/configs/_base_/models/hrnet/hrnet-w44.py @@ -0,0 +1,15 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HRNet', arch='w44'), + neck=[ + dict(type='HRFuseScales', in_channels=(44, 88, 176, 352)), + dict(type='GlobalAveragePooling'), + ], + head=dict( + type='LinearClsHead', + in_channels=2048, + num_classes=1000, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/hrnet/hrnet-w48.py b/configs/_base_/models/hrnet/hrnet-w48.py new file mode 100644 index 0000000..f060495 --- /dev/null +++ b/configs/_base_/models/hrnet/hrnet-w48.py @@ -0,0 +1,15 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HRNet', arch='w48'), + neck=[ + dict(type='HRFuseScales', in_channels=(48, 96, 192, 384)), + dict(type='GlobalAveragePooling'), + ], + head=dict( + type='LinearClsHead', + in_channels=2048, + num_classes=1000, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/hrnet/hrnet-w64.py b/configs/_base_/models/hrnet/hrnet-w64.py new file mode 100644 index 0000000..844c3fe --- /dev/null +++ b/configs/_base_/models/hrnet/hrnet-w64.py @@ -0,0 +1,15 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='HRNet', arch='w64'), + neck=[ + dict(type='HRFuseScales', in_channels=(64, 128, 256, 512)), + dict(type='GlobalAveragePooling'), + ], + head=dict( + type='LinearClsHead', + in_channels=2048, + num_classes=1000, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/mlp_mixer_base_patch16.py b/configs/_base_/models/mlp_mixer_base_patch16.py new file mode 100644 index 0000000..5ebd17f --- /dev/null +++ b/configs/_base_/models/mlp_mixer_base_patch16.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='MlpMixer', + arch='b', + img_size=224, + patch_size=16, + drop_rate=0.1, + init_cfg=[ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + ), +) diff --git a/configs/_base_/models/mlp_mixer_large_patch16.py b/configs/_base_/models/mlp_mixer_large_patch16.py new file mode 100644 index 0000000..ff10713 --- /dev/null +++ b/configs/_base_/models/mlp_mixer_large_patch16.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='MlpMixer', + arch='l', + img_size=224, + patch_size=16, + drop_rate=0.1, + init_cfg=[ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + ), +) diff --git a/configs/_base_/models/mobilenet_v2_1x.py b/configs/_base_/models/mobilenet_v2_1x.py new file mode 100644 index 0000000..6ebff1e --- /dev/null +++ b/configs/_base_/models/mobilenet_v2_1x.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='MobileNetV2', widen_factor=1.0), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/mobilenet_v3_large_imagenet.py b/configs/_base_/models/mobilenet_v3_large_imagenet.py new file mode 100644 index 0000000..5318f50 --- /dev/null +++ b/configs/_base_/models/mobilenet_v3_large_imagenet.py @@ -0,0 +1,16 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='MobileNetV3', arch='large'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='StackedLinearClsHead', + num_classes=1000, + in_channels=960, + mid_channels=[1280], + dropout_rate=0.2, + act_cfg=dict(type='HSwish'), + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + init_cfg=dict( + type='Normal', layer='Linear', mean=0., std=0.01, bias=0.), + topk=(1, 5))) diff --git a/configs/_base_/models/mobilenet_v3_small_cifar.py b/configs/_base_/models/mobilenet_v3_small_cifar.py new file mode 100644 index 0000000..5dbe980 --- /dev/null +++ b/configs/_base_/models/mobilenet_v3_small_cifar.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='MobileNetV3', arch='small'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='StackedLinearClsHead', + num_classes=10, + in_channels=576, + mid_channels=[1280], + act_cfg=dict(type='HSwish'), + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/mobilenet_v3_small_imagenet.py b/configs/_base_/models/mobilenet_v3_small_imagenet.py new file mode 100644 index 0000000..af6cc1b --- /dev/null +++ b/configs/_base_/models/mobilenet_v3_small_imagenet.py @@ -0,0 +1,16 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='MobileNetV3', arch='small'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='StackedLinearClsHead', + num_classes=1000, + in_channels=576, + mid_channels=[1024], + dropout_rate=0.2, + act_cfg=dict(type='HSwish'), + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + init_cfg=dict( + type='Normal', layer='Linear', mean=0., std=0.01, bias=0.), + topk=(1, 5))) diff --git a/configs/_base_/models/mvit/mvitv2-base.py b/configs/_base_/models/mvit/mvitv2-base.py new file mode 100644 index 0000000..c75e78e --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-base.py @@ -0,0 +1,19 @@ +model = dict( + type='ImageClassifier', + backbone=dict(type='MViT', arch='base', drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=768, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/mvit/mvitv2-large.py b/configs/_base_/models/mvit/mvitv2-large.py new file mode 100644 index 0000000..aa4a325 --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-large.py @@ -0,0 +1,23 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='MViT', + arch='large', + drop_path_rate=0.5, + dim_mul_in_attention=False), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=1152, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/mvit/mvitv2-small.py b/configs/_base_/models/mvit/mvitv2-small.py new file mode 100644 index 0000000..bb9329d --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-small.py @@ -0,0 +1,19 @@ +model = dict( + type='ImageClassifier', + backbone=dict(type='MViT', arch='small', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=768, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/mvit/mvitv2-tiny.py b/configs/_base_/models/mvit/mvitv2-tiny.py new file mode 100644 index 0000000..7ca85dc --- /dev/null +++ b/configs/_base_/models/mvit/mvitv2-tiny.py @@ -0,0 +1,19 @@ +model = dict( + type='ImageClassifier', + backbone=dict(type='MViT', arch='tiny', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + in_channels=768, + num_classes=1000, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/poolformer/poolformer_m36.py b/configs/_base_/models/poolformer/poolformer_m36.py new file mode 100644 index 0000000..276a721 --- /dev/null +++ b/configs/_base_/models/poolformer/poolformer_m36.py @@ -0,0 +1,22 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='PoolFormer', + arch='m36', + drop_path_rate=0.1, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + ]), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/poolformer/poolformer_m48.py b/configs/_base_/models/poolformer/poolformer_m48.py new file mode 100644 index 0000000..8c006ac --- /dev/null +++ b/configs/_base_/models/poolformer/poolformer_m48.py @@ -0,0 +1,22 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='PoolFormer', + arch='m48', + drop_path_rate=0.1, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + ]), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/poolformer/poolformer_s12.py b/configs/_base_/models/poolformer/poolformer_s12.py new file mode 100644 index 0000000..b7b3600 --- /dev/null +++ b/configs/_base_/models/poolformer/poolformer_s12.py @@ -0,0 +1,22 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='PoolFormer', + arch='s12', + drop_path_rate=0.1, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + ]), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/poolformer/poolformer_s24.py b/configs/_base_/models/poolformer/poolformer_s24.py new file mode 100644 index 0000000..822ab5b --- /dev/null +++ b/configs/_base_/models/poolformer/poolformer_s24.py @@ -0,0 +1,22 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='PoolFormer', + arch='s24', + drop_path_rate=0.1, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + ]), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/poolformer/poolformer_s36.py b/configs/_base_/models/poolformer/poolformer_s36.py new file mode 100644 index 0000000..489f222 --- /dev/null +++ b/configs/_base_/models/poolformer/poolformer_s36.py @@ -0,0 +1,22 @@ +# Model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='PoolFormer', + arch='s36', + drop_path_rate=0.1, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + ]), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/regnet/regnetx_1.6gf.py b/configs/_base_/models/regnet/regnetx_1.6gf.py new file mode 100644 index 0000000..b81f0ad --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_1.6gf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_1.6gf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=912, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/regnet/regnetx_12gf.py b/configs/_base_/models/regnet/regnetx_12gf.py new file mode 100644 index 0000000..383d4f8 --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_12gf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_12gf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2240, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/regnet/regnetx_3.2gf.py b/configs/_base_/models/regnet/regnetx_3.2gf.py new file mode 100644 index 0000000..67d4541 --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_3.2gf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_3.2gf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1008, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/regnet/regnetx_4.0gf.py b/configs/_base_/models/regnet/regnetx_4.0gf.py new file mode 100644 index 0000000..01419c6 --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_4.0gf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_4.0gf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1360, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/regnet/regnetx_400mf.py b/configs/_base_/models/regnet/regnetx_400mf.py new file mode 100644 index 0000000..ef518b9 --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_400mf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_400mf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=384, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/regnet/regnetx_6.4gf.py b/configs/_base_/models/regnet/regnetx_6.4gf.py new file mode 100644 index 0000000..44e6222 --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_6.4gf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_6.4gf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1624, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/regnet/regnetx_8.0gf.py b/configs/_base_/models/regnet/regnetx_8.0gf.py new file mode 100644 index 0000000..2929826 --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_8.0gf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_8.0gf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1920, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/regnet/regnetx_800mf.py b/configs/_base_/models/regnet/regnetx_800mf.py new file mode 100644 index 0000000..210f760 --- /dev/null +++ b/configs/_base_/models/regnet/regnetx_800mf.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='RegNet', arch='regnetx_800mf'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=672, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/repmlp-base_224.py b/configs/_base_/models/repmlp-base_224.py new file mode 100644 index 0000000..7db0077 --- /dev/null +++ b/configs/_base_/models/repmlp-base_224.py @@ -0,0 +1,18 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='RepMLPNet', + arch='B', + img_size=224, + out_indices=(3, ), + reparam_conv_kernels=(1, 3), + deploy=False), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/repvgg-A0_in1k.py b/configs/_base_/models/repvgg-A0_in1k.py new file mode 100644 index 0000000..093ffb7 --- /dev/null +++ b/configs/_base_/models/repvgg-A0_in1k.py @@ -0,0 +1,15 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='RepVGG', + arch='A0', + out_indices=(3, ), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py b/configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py new file mode 100644 index 0000000..5bb07db --- /dev/null +++ b/configs/_base_/models/repvgg-B3_lbs-mixup_in1k.py @@ -0,0 +1,23 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='RepVGG', + arch='B3', + out_indices=(3, ), + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2560, + loss=dict( + type='LabelSmoothLoss', + loss_weight=1.0, + label_smooth_val=0.1, + mode='classy_vision', + num_classes=1000), + topk=(1, 5), + ), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000, + prob=1.))) diff --git a/configs/_base_/models/res2net101-w26-s4.py b/configs/_base_/models/res2net101-w26-s4.py new file mode 100644 index 0000000..3bf64c5 --- /dev/null +++ b/configs/_base_/models/res2net101-w26-s4.py @@ -0,0 +1,18 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + deep_stem=False, + avg_down=False, + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/res2net50-w14-s8.py b/configs/_base_/models/res2net50-w14-s8.py new file mode 100644 index 0000000..5875142 --- /dev/null +++ b/configs/_base_/models/res2net50-w14-s8.py @@ -0,0 +1,18 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='Res2Net', + depth=50, + scales=8, + base_width=14, + deep_stem=False, + avg_down=False, + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/res2net50-w26-s4.py b/configs/_base_/models/res2net50-w26-s4.py new file mode 100644 index 0000000..be8fdb5 --- /dev/null +++ b/configs/_base_/models/res2net50-w26-s4.py @@ -0,0 +1,18 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='Res2Net', + depth=50, + scales=4, + base_width=26, + deep_stem=False, + avg_down=False, + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/res2net50-w26-s6.py b/configs/_base_/models/res2net50-w26-s6.py new file mode 100644 index 0000000..281b136 --- /dev/null +++ b/configs/_base_/models/res2net50-w26-s6.py @@ -0,0 +1,18 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='Res2Net', + depth=50, + scales=6, + base_width=26, + deep_stem=False, + avg_down=False, + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/res2net50-w26-s8.py b/configs/_base_/models/res2net50-w26-s8.py new file mode 100644 index 0000000..b4f62f3 --- /dev/null +++ b/configs/_base_/models/res2net50-w26-s8.py @@ -0,0 +1,18 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='Res2Net', + depth=50, + scales=8, + base_width=26, + deep_stem=False, + avg_down=False, + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/res2net50-w48-s2.py b/configs/_base_/models/res2net50-w48-s2.py new file mode 100644 index 0000000..8675c91 --- /dev/null +++ b/configs/_base_/models/res2net50-w48-s2.py @@ -0,0 +1,18 @@ +model = dict( + type='ImageClassifier', + backbone=dict( + type='Res2Net', + depth=50, + scales=2, + base_width=48, + deep_stem=False, + avg_down=False, + ), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnest101.py b/configs/_base_/models/resnest101.py new file mode 100644 index 0000000..97f7749 --- /dev/null +++ b/configs/_base_/models/resnest101.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeSt', + depth=101, + num_stages=4, + stem_channels=128, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + num_classes=1000, + reduction='mean', + loss_weight=1.0), + topk=(1, 5), + cal_acc=False)) +train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000)) diff --git a/configs/_base_/models/resnest200.py b/configs/_base_/models/resnest200.py new file mode 100644 index 0000000..4610017 --- /dev/null +++ b/configs/_base_/models/resnest200.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeSt', + depth=200, + num_stages=4, + stem_channels=128, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + num_classes=1000, + reduction='mean', + loss_weight=1.0), + topk=(1, 5), + cal_acc=False)) +train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000)) diff --git a/configs/_base_/models/resnest269.py b/configs/_base_/models/resnest269.py new file mode 100644 index 0000000..ad365d0 --- /dev/null +++ b/configs/_base_/models/resnest269.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeSt', + depth=269, + num_stages=4, + stem_channels=128, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + num_classes=1000, + reduction='mean', + loss_weight=1.0), + topk=(1, 5), + cal_acc=False)) +train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000)) diff --git a/configs/_base_/models/resnest50.py b/configs/_base_/models/resnest50.py new file mode 100644 index 0000000..15269d4 --- /dev/null +++ b/configs/_base_/models/resnest50.py @@ -0,0 +1,23 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeSt', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + num_classes=1000, + reduction='mean', + loss_weight=1.0), + topk=(1, 5), + cal_acc=False)) +train_cfg = dict(mixup=dict(alpha=0.2, num_classes=1000)) diff --git a/configs/_base_/models/resnet101.py b/configs/_base_/models/resnet101.py new file mode 100644 index 0000000..1147cd4 --- /dev/null +++ b/configs/_base_/models/resnet101.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnet101_cifar.py b/configs/_base_/models/resnet101_cifar.py new file mode 100644 index 0000000..a84d470 --- /dev/null +++ b/configs/_base_/models/resnet101_cifar.py @@ -0,0 +1,16 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet_CIFAR', + depth=101, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=10, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/resnet152.py b/configs/_base_/models/resnet152.py new file mode 100644 index 0000000..94a718c --- /dev/null +++ b/configs/_base_/models/resnet152.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=152, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnet152_cifar.py b/configs/_base_/models/resnet152_cifar.py new file mode 100644 index 0000000..55c0cc6 --- /dev/null +++ b/configs/_base_/models/resnet152_cifar.py @@ -0,0 +1,16 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet_CIFAR', + depth=152, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=10, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/resnet18.py b/configs/_base_/models/resnet18.py new file mode 100644 index 0000000..7c66758 --- /dev/null +++ b/configs/_base_/models/resnet18.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnet18_cifar.py b/configs/_base_/models/resnet18_cifar.py new file mode 100644 index 0000000..7b9cf1e --- /dev/null +++ b/configs/_base_/models/resnet18_cifar.py @@ -0,0 +1,16 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet_CIFAR', + depth=18, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=10, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/resnet34.py b/configs/_base_/models/resnet34.py new file mode 100644 index 0000000..100ee28 --- /dev/null +++ b/configs/_base_/models/resnet34.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=34, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnet34_cifar.py b/configs/_base_/models/resnet34_cifar.py new file mode 100644 index 0000000..55d033b --- /dev/null +++ b/configs/_base_/models/resnet34_cifar.py @@ -0,0 +1,16 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet_CIFAR', + depth=34, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=10, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/resnet34_gem.py b/configs/_base_/models/resnet34_gem.py new file mode 100644 index 0000000..5c0e0d3 --- /dev/null +++ b/configs/_base_/models/resnet34_gem.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=34, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GeneralizedMeanPooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnet50.py b/configs/_base_/models/resnet50.py new file mode 100644 index 0000000..129a2bb --- /dev/null +++ b/configs/_base_/models/resnet50.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnet50_cifar.py b/configs/_base_/models/resnet50_cifar.py new file mode 100644 index 0000000..33b66d5 --- /dev/null +++ b/configs/_base_/models/resnet50_cifar.py @@ -0,0 +1,16 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet_CIFAR', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=10, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) diff --git a/configs/_base_/models/resnet50_cifar_cutmix.py b/configs/_base_/models/resnet50_cifar_cutmix.py new file mode 100644 index 0000000..73c38be --- /dev/null +++ b/configs/_base_/models/resnet50_cifar_cutmix.py @@ -0,0 +1,18 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet_CIFAR', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='MultiLabelLinearClsHead', + num_classes=10, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)), + train_cfg=dict( + augments=dict(type='BatchCutMix', alpha=1.0, num_classes=10, + prob=1.0))) diff --git a/configs/_base_/models/resnet50_cifar_mixup.py b/configs/_base_/models/resnet50_cifar_mixup.py new file mode 100644 index 0000000..3de14f3 --- /dev/null +++ b/configs/_base_/models/resnet50_cifar_mixup.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet_CIFAR', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='MultiLabelLinearClsHead', + num_classes=10, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=1., num_classes=10, prob=1.))) diff --git a/configs/_base_/models/resnet50_cutmix.py b/configs/_base_/models/resnet50_cutmix.py new file mode 100644 index 0000000..fb79088 --- /dev/null +++ b/configs/_base_/models/resnet50_cutmix.py @@ -0,0 +1,18 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='MultiLabelLinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)), + train_cfg=dict( + augments=dict( + type='BatchCutMix', alpha=1.0, num_classes=1000, prob=1.0))) diff --git a/configs/_base_/models/resnet50_label_smooth.py b/configs/_base_/models/resnet50_label_smooth.py new file mode 100644 index 0000000..b6f7937 --- /dev/null +++ b/configs/_base_/models/resnet50_label_smooth.py @@ -0,0 +1,18 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnet50_mixup.py b/configs/_base_/models/resnet50_mixup.py new file mode 100644 index 0000000..8ff9522 --- /dev/null +++ b/configs/_base_/models/resnet50_mixup.py @@ -0,0 +1,18 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='MultiLabelLinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0, use_soft=True)), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000, + prob=1.))) diff --git a/configs/_base_/models/resnetv1c50.py b/configs/_base_/models/resnetv1c50.py new file mode 100644 index 0000000..3b973e2 --- /dev/null +++ b/configs/_base_/models/resnetv1c50.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNetV1c', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnetv1d101.py b/configs/_base_/models/resnetv1d101.py new file mode 100644 index 0000000..1e56223 --- /dev/null +++ b/configs/_base_/models/resnetv1d101.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNetV1d', + depth=101, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnetv1d152.py b/configs/_base_/models/resnetv1d152.py new file mode 100644 index 0000000..58cc73b --- /dev/null +++ b/configs/_base_/models/resnetv1d152.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNetV1d', + depth=152, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnetv1d50.py b/configs/_base_/models/resnetv1d50.py new file mode 100644 index 0000000..015aaa3 --- /dev/null +++ b/configs/_base_/models/resnetv1d50.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNetV1d', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnext101_32x4d.py b/configs/_base_/models/resnext101_32x4d.py new file mode 100644 index 0000000..1c89fb6 --- /dev/null +++ b/configs/_base_/models/resnext101_32x4d.py @@ -0,0 +1,19 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeXt', + depth=101, + num_stages=4, + out_indices=(3, ), + groups=32, + width_per_group=4, + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnext101_32x8d.py b/configs/_base_/models/resnext101_32x8d.py new file mode 100644 index 0000000..2bb63f3 --- /dev/null +++ b/configs/_base_/models/resnext101_32x8d.py @@ -0,0 +1,19 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeXt', + depth=101, + num_stages=4, + out_indices=(3, ), + groups=32, + width_per_group=8, + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnext152_32x4d.py b/configs/_base_/models/resnext152_32x4d.py new file mode 100644 index 0000000..d392eff --- /dev/null +++ b/configs/_base_/models/resnext152_32x4d.py @@ -0,0 +1,19 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeXt', + depth=152, + num_stages=4, + out_indices=(3, ), + groups=32, + width_per_group=4, + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/resnext50_32x4d.py b/configs/_base_/models/resnext50_32x4d.py new file mode 100644 index 0000000..0604262 --- /dev/null +++ b/configs/_base_/models/resnext50_32x4d.py @@ -0,0 +1,19 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNeXt', + depth=50, + num_stages=4, + out_indices=(3, ), + groups=32, + width_per_group=4, + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/seresnet101.py b/configs/_base_/models/seresnet101.py new file mode 100644 index 0000000..137a6f9 --- /dev/null +++ b/configs/_base_/models/seresnet101.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SEResNet', + depth=101, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/seresnet50.py b/configs/_base_/models/seresnet50.py new file mode 100644 index 0000000..e5f6bfc --- /dev/null +++ b/configs/_base_/models/seresnet50.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SEResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/seresnext101_32x4d.py b/configs/_base_/models/seresnext101_32x4d.py new file mode 100644 index 0000000..cc8a62c --- /dev/null +++ b/configs/_base_/models/seresnext101_32x4d.py @@ -0,0 +1,20 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SEResNeXt', + depth=101, + num_stages=4, + out_indices=(3, ), + groups=32, + width_per_group=4, + se_ratio=16, + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/seresnext50_32x4d.py b/configs/_base_/models/seresnext50_32x4d.py new file mode 100644 index 0000000..0cdf7cb --- /dev/null +++ b/configs/_base_/models/seresnext50_32x4d.py @@ -0,0 +1,20 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SEResNeXt', + depth=50, + num_stages=4, + out_indices=(3, ), + groups=32, + width_per_group=4, + se_ratio=16, + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/shufflenet_v1_1x.py b/configs/_base_/models/shufflenet_v1_1x.py new file mode 100644 index 0000000..f0f9d1f --- /dev/null +++ b/configs/_base_/models/shufflenet_v1_1x.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='ShuffleNetV1', groups=3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=960, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/shufflenet_v2_1x.py b/configs/_base_/models/shufflenet_v2_1x.py new file mode 100644 index 0000000..190800e --- /dev/null +++ b/configs/_base_/models/shufflenet_v2_1x.py @@ -0,0 +1,12 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='ShuffleNetV2', widen_factor=1.0), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/swin_transformer/base_224.py b/configs/_base_/models/swin_transformer/base_224.py new file mode 100644 index 0000000..e16b4e6 --- /dev/null +++ b/configs/_base_/models/swin_transformer/base_224.py @@ -0,0 +1,22 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformer', arch='base', img_size=224, drop_path_rate=0.5), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/swin_transformer/base_384.py b/configs/_base_/models/swin_transformer/base_384.py new file mode 100644 index 0000000..ce78981 --- /dev/null +++ b/configs/_base_/models/swin_transformer/base_384.py @@ -0,0 +1,16 @@ +# model settings +# Only for evaluation +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformer', + arch='base', + img_size=384, + stage_cfgs=dict(block_cfgs=dict(window_size=12))), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/swin_transformer/large_224.py b/configs/_base_/models/swin_transformer/large_224.py new file mode 100644 index 0000000..747d00e --- /dev/null +++ b/configs/_base_/models/swin_transformer/large_224.py @@ -0,0 +1,12 @@ +# model settings +# Only for evaluation +model = dict( + type='ImageClassifier', + backbone=dict(type='SwinTransformer', arch='large', img_size=224), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/swin_transformer/large_384.py b/configs/_base_/models/swin_transformer/large_384.py new file mode 100644 index 0000000..7026f81 --- /dev/null +++ b/configs/_base_/models/swin_transformer/large_384.py @@ -0,0 +1,16 @@ +# model settings +# Only for evaluation +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformer', + arch='large', + img_size=384, + stage_cfgs=dict(block_cfgs=dict(window_size=12))), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/swin_transformer/small_224.py b/configs/_base_/models/swin_transformer/small_224.py new file mode 100644 index 0000000..7873986 --- /dev/null +++ b/configs/_base_/models/swin_transformer/small_224.py @@ -0,0 +1,23 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformer', arch='small', img_size=224, + drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/swin_transformer/tiny_224.py b/configs/_base_/models/swin_transformer/tiny_224.py new file mode 100644 index 0000000..2d68d66 --- /dev/null +++ b/configs/_base_/models/swin_transformer/tiny_224.py @@ -0,0 +1,22 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformer', arch='tiny', img_size=224, drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/swin_transformer_v2/base_256.py b/configs/_base_/models/swin_transformer_v2/base_256.py new file mode 100644 index 0000000..f711a9c --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/base_256.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='base', + img_size=256, + drop_path_rate=0.5), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/swin_transformer_v2/base_384.py b/configs/_base_/models/swin_transformer_v2/base_384.py new file mode 100644 index 0000000..5fb9aea --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/base_384.py @@ -0,0 +1,17 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='base', + img_size=384, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/swin_transformer_v2/large_256.py b/configs/_base_/models/swin_transformer_v2/large_256.py new file mode 100644 index 0000000..fe557c3 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/large_256.py @@ -0,0 +1,16 @@ +# model settings +# Only for evaluation +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='large', + img_size=256, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/swin_transformer_v2/large_384.py b/configs/_base_/models/swin_transformer_v2/large_384.py new file mode 100644 index 0000000..a626c40 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/large_384.py @@ -0,0 +1,16 @@ +# model settings +# Only for evaluation +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='large', + img_size=384, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1536, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5))) diff --git a/configs/_base_/models/swin_transformer_v2/small_256.py b/configs/_base_/models/swin_transformer_v2/small_256.py new file mode 100644 index 0000000..8808f09 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/small_256.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='small', + img_size=256, + drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/swin_transformer_v2/tiny_256.py b/configs/_base_/models/swin_transformer_v2/tiny_256.py new file mode 100644 index 0000000..d40e394 --- /dev/null +++ b/configs/_base_/models/swin_transformer_v2/tiny_256.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SwinTransformerV2', + arch='tiny', + img_size=256, + drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/t2t-vit-t-14.py b/configs/_base_/models/t2t-vit-t-14.py new file mode 100644 index 0000000..91dbb67 --- /dev/null +++ b/configs/_base_/models/t2t-vit-t-14.py @@ -0,0 +1,41 @@ +# model settings +embed_dims = 384 +num_classes = 1000 + +model = dict( + type='ImageClassifier', + backbone=dict( + type='T2T_ViT', + img_size=224, + in_channels=3, + embed_dims=embed_dims, + t2t_cfg=dict( + token_dims=64, + use_performer=False, + ), + num_layers=14, + layer_cfgs=dict( + num_heads=6, + feedforward_channels=3 * embed_dims, # mlp_ratio = 3 + ), + drop_path_rate=0.1, + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ]), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=num_classes, + in_channels=embed_dims, + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + mode='original', + ), + topk=(1, 5), + init_cfg=dict(type='TruncNormal', layer='Linear', std=.02)), + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, prob=0.5, num_classes=num_classes), + dict(type='BatchCutMix', alpha=1.0, prob=0.5, num_classes=num_classes), + ])) diff --git a/configs/_base_/models/t2t-vit-t-19.py b/configs/_base_/models/t2t-vit-t-19.py new file mode 100644 index 0000000..8ab139d --- /dev/null +++ b/configs/_base_/models/t2t-vit-t-19.py @@ -0,0 +1,41 @@ +# model settings +embed_dims = 448 +num_classes = 1000 + +model = dict( + type='ImageClassifier', + backbone=dict( + type='T2T_ViT', + img_size=224, + in_channels=3, + embed_dims=embed_dims, + t2t_cfg=dict( + token_dims=64, + use_performer=False, + ), + num_layers=19, + layer_cfgs=dict( + num_heads=7, + feedforward_channels=3 * embed_dims, # mlp_ratio = 3 + ), + drop_path_rate=0.1, + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ]), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=num_classes, + in_channels=embed_dims, + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + mode='original', + ), + topk=(1, 5), + init_cfg=dict(type='TruncNormal', layer='Linear', std=.02)), + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, prob=0.5, num_classes=num_classes), + dict(type='BatchCutMix', alpha=1.0, prob=0.5, num_classes=num_classes), + ])) diff --git a/configs/_base_/models/t2t-vit-t-24.py b/configs/_base_/models/t2t-vit-t-24.py new file mode 100644 index 0000000..5990960 --- /dev/null +++ b/configs/_base_/models/t2t-vit-t-24.py @@ -0,0 +1,41 @@ +# model settings +embed_dims = 512 +num_classes = 1000 + +model = dict( + type='ImageClassifier', + backbone=dict( + type='T2T_ViT', + img_size=224, + in_channels=3, + embed_dims=embed_dims, + t2t_cfg=dict( + token_dims=64, + use_performer=False, + ), + num_layers=24, + layer_cfgs=dict( + num_heads=8, + feedforward_channels=3 * embed_dims, # mlp_ratio = 3 + ), + drop_path_rate=0.1, + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ]), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=num_classes, + in_channels=embed_dims, + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + mode='original', + ), + topk=(1, 5), + init_cfg=dict(type='TruncNormal', layer='Linear', std=.02)), + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, prob=0.5, num_classes=num_classes), + dict(type='BatchCutMix', alpha=1.0, prob=0.5, num_classes=num_classes), + ])) diff --git a/configs/_base_/models/tnt_s_patch16_224.py b/configs/_base_/models/tnt_s_patch16_224.py new file mode 100644 index 0000000..5e13d07 --- /dev/null +++ b/configs/_base_/models/tnt_s_patch16_224.py @@ -0,0 +1,29 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='TNT', + arch='s', + img_size=224, + patch_size=16, + in_channels=3, + ffn_ratio=4, + qkv_bias=False, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.1, + first_stride=4, + num_fcs=2, + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ]), + neck=None, + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=384, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + topk=(1, 5), + init_cfg=dict(type='TruncNormal', layer='Linear', std=.02))) diff --git a/configs/_base_/models/twins_pcpvt_base.py b/configs/_base_/models/twins_pcpvt_base.py new file mode 100644 index 0000000..473d7ee --- /dev/null +++ b/configs/_base_/models/twins_pcpvt_base.py @@ -0,0 +1,30 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='PCPVT', + arch='base', + in_channels=3, + out_indices=(3, ), + qkv_bias=True, + norm_cfg=dict(type='LN', eps=1e-06), + norm_after_stage=[False, False, False, True], + drop_rate=0.0, + attn_drop_rate=0., + drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/twins_svt_base.py b/configs/_base_/models/twins_svt_base.py new file mode 100644 index 0000000..cabd373 --- /dev/null +++ b/configs/_base_/models/twins_svt_base.py @@ -0,0 +1,30 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='SVT', + arch='base', + in_channels=3, + out_indices=(3, ), + qkv_bias=True, + norm_cfg=dict(type='LN'), + norm_after_stage=[False, False, False, True], + drop_rate=0.0, + attn_drop_rate=0., + drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/van/van_b0.py b/configs/_base_/models/van/van_b0.py new file mode 100644 index 0000000..5fa977e --- /dev/null +++ b/configs/_base_/models/van/van_b0.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b0', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=256, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/van/van_b1.py b/configs/_base_/models/van/van_b1.py new file mode 100644 index 0000000..a27a50b --- /dev/null +++ b/configs/_base_/models/van/van_b1.py @@ -0,0 +1,21 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b1', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) diff --git a/configs/_base_/models/van/van_b2.py b/configs/_base_/models/van/van_b2.py new file mode 100644 index 0000000..41b0484 --- /dev/null +++ b/configs/_base_/models/van/van_b2.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b2', drop_path_rate=0.1), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b3.py b/configs/_base_/models/van/van_b3.py new file mode 100644 index 0000000..d32b12c --- /dev/null +++ b/configs/_base_/models/van/van_b3.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b3', drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b4.py b/configs/_base_/models/van/van_b4.py new file mode 100644 index 0000000..417835c --- /dev/null +++ b/configs/_base_/models/van/van_b4.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b4', drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=512, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b5.py b/configs/_base_/models/van/van_b5.py new file mode 100644 index 0000000..fe8b923 --- /dev/null +++ b/configs/_base_/models/van/van_b5.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b5', drop_path_rate=0.2), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_b6.py b/configs/_base_/models/van/van_b6.py new file mode 100644 index 0000000..a0dfb3c --- /dev/null +++ b/configs/_base_/models/van/van_b6.py @@ -0,0 +1,13 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VAN', arch='b6', drop_path_rate=0.3), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + init_cfg=None, # suppress the default init_cfg of LinearClsHead. + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + cal_acc=False)) diff --git a/configs/_base_/models/van/van_base.py b/configs/_base_/models/van/van_base.py new file mode 100644 index 0000000..5c2bcf0 --- /dev/null +++ b/configs/_base_/models/van/van_base.py @@ -0,0 +1 @@ +_base_ = ['./van-b2.py'] diff --git a/configs/_base_/models/van/van_large.py b/configs/_base_/models/van/van_large.py new file mode 100644 index 0000000..bc9536c --- /dev/null +++ b/configs/_base_/models/van/van_large.py @@ -0,0 +1 @@ +_base_ = ['./van-b3.py'] diff --git a/configs/_base_/models/van/van_small.py b/configs/_base_/models/van/van_small.py new file mode 100644 index 0000000..3973c22 --- /dev/null +++ b/configs/_base_/models/van/van_small.py @@ -0,0 +1 @@ +_base_ = ['./van-b1.py'] diff --git a/configs/_base_/models/van/van_tiny.py b/configs/_base_/models/van/van_tiny.py new file mode 100644 index 0000000..ace9ebb --- /dev/null +++ b/configs/_base_/models/van/van_tiny.py @@ -0,0 +1 @@ +_base_ = ['./van-b0.py'] diff --git a/configs/_base_/models/vgg11.py b/configs/_base_/models/vgg11.py new file mode 100644 index 0000000..2b6ee14 --- /dev/null +++ b/configs/_base_/models/vgg11.py @@ -0,0 +1,10 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VGG', depth=11, num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vgg11bn.py b/configs/_base_/models/vgg11bn.py new file mode 100644 index 0000000..cb4c64e --- /dev/null +++ b/configs/_base_/models/vgg11bn.py @@ -0,0 +1,11 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VGG', depth=11, norm_cfg=dict(type='BN'), num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vgg13.py b/configs/_base_/models/vgg13.py new file mode 100644 index 0000000..a938910 --- /dev/null +++ b/configs/_base_/models/vgg13.py @@ -0,0 +1,10 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VGG', depth=13, num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vgg13bn.py b/configs/_base_/models/vgg13bn.py new file mode 100644 index 0000000..b12173b --- /dev/null +++ b/configs/_base_/models/vgg13bn.py @@ -0,0 +1,11 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VGG', depth=13, norm_cfg=dict(type='BN'), num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vgg16.py b/configs/_base_/models/vgg16.py new file mode 100644 index 0000000..93ce864 --- /dev/null +++ b/configs/_base_/models/vgg16.py @@ -0,0 +1,10 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VGG', depth=16, num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vgg16bn.py b/configs/_base_/models/vgg16bn.py new file mode 100644 index 0000000..765e34f --- /dev/null +++ b/configs/_base_/models/vgg16bn.py @@ -0,0 +1,11 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VGG', depth=16, norm_cfg=dict(type='BN'), num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vgg19.py b/configs/_base_/models/vgg19.py new file mode 100644 index 0000000..6f4ab06 --- /dev/null +++ b/configs/_base_/models/vgg19.py @@ -0,0 +1,10 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='VGG', depth=19, num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vgg19bn.py b/configs/_base_/models/vgg19bn.py new file mode 100644 index 0000000..c468b5d --- /dev/null +++ b/configs/_base_/models/vgg19bn.py @@ -0,0 +1,11 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VGG', depth=19, norm_cfg=dict(type='BN'), num_classes=1000), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vit-base-p16.py b/configs/_base_/models/vit-base-p16.py new file mode 100644 index 0000000..bb42bed --- /dev/null +++ b/configs/_base_/models/vit-base-p16.py @@ -0,0 +1,25 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VisionTransformer', + arch='b', + img_size=224, + patch_size=16, + drop_rate=0.1, + init_cfg=[ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ]), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=1000, + in_channels=768, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, + mode='classy_vision'), + )) diff --git a/configs/_base_/models/vit-base-p32.py b/configs/_base_/models/vit-base-p32.py new file mode 100644 index 0000000..ad550ef --- /dev/null +++ b/configs/_base_/models/vit-base-p32.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VisionTransformer', + arch='b', + img_size=224, + patch_size=32, + drop_rate=0.1, + init_cfg=[ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ]), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vit-large-p16.py b/configs/_base_/models/vit-large-p16.py new file mode 100644 index 0000000..9716230 --- /dev/null +++ b/configs/_base_/models/vit-large-p16.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VisionTransformer', + arch='l', + img_size=224, + patch_size=16, + drop_rate=0.1, + init_cfg=[ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ]), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/vit-large-p32.py b/configs/_base_/models/vit-large-p32.py new file mode 100644 index 0000000..f9491bb --- /dev/null +++ b/configs/_base_/models/vit-large-p32.py @@ -0,0 +1,24 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VisionTransformer', + arch='l', + img_size=224, + patch_size=32, + drop_rate=0.1, + init_cfg=[ + dict( + type='Kaiming', + layer='Conv2d', + mode='fan_in', + nonlinearity='linear') + ]), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/models/wide-resnet50.py b/configs/_base_/models/wide-resnet50.py new file mode 100644 index 0000000..a2913b9 --- /dev/null +++ b/configs/_base_/models/wide-resnet50.py @@ -0,0 +1,20 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + stem_channels=64, + base_channels=128, + expansion=2, + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) diff --git a/configs/_base_/schedules/cifar10_bs128.py b/configs/_base_/schedules/cifar10_bs128.py new file mode 100644 index 0000000..f134dbc --- /dev/null +++ b/configs/_base_/schedules/cifar10_bs128.py @@ -0,0 +1,6 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[100, 150]) +runner = dict(type='EpochBasedRunner', max_epochs=200) diff --git a/configs/_base_/schedules/cub_bs64.py b/configs/_base_/schedules/cub_bs64.py new file mode 100644 index 0000000..93cce6a --- /dev/null +++ b/configs/_base_/schedules/cub_bs64.py @@ -0,0 +1,13 @@ +# optimizer +optimizer = dict( + type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=5, + warmup_ratio=0.01, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/_base_/schedules/imagenet_bs1024_adamw_conformer.py b/configs/_base_/schedules/imagenet_bs1024_adamw_conformer.py new file mode 100644 index 0000000..92f1801 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs1024_adamw_conformer.py @@ -0,0 +1,29 @@ +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.cls_token': dict(decay_mult=0.0), + }) + +# for batch in each gpu is 128, 8 gpu +# lr = 5e-4 * 128 * 8 / 512 = 0.001 +optimizer = dict( + type='AdamW', + lr=5e-4 * 128 * 8 / 512, + weight_decay=0.05, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=5 * 1252, + warmup_by_epoch=False) + +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py b/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py new file mode 100644 index 0000000..2ad035c --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs1024_adamw_swin.py @@ -0,0 +1,30 @@ +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0) + }) + +# for batch in each gpu is 128, 8 gpu +# lr = 5e-4 * 128 * 8 / 512 = 0.001 +optimizer = dict( + type='AdamW', + lr=5e-4 * 1024 / 512, + weight_decay=0.05, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=5.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=20, + warmup_by_epoch=True) + +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/_base_/schedules/imagenet_bs1024_coslr.py b/configs/_base_/schedules/imagenet_bs1024_coslr.py new file mode 100644 index 0000000..ee84e7a --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs1024_coslr.py @@ -0,0 +1,12 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.8, momentum=0.9, weight_decay=5e-5) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=5, + warmup_ratio=0.1, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py b/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py new file mode 100644 index 0000000..99fbdda --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py @@ -0,0 +1,17 @@ +# optimizer +optimizer = dict( + type='SGD', + lr=0.5, + momentum=0.9, + weight_decay=0.00004, + paramwise_cfg=dict(norm_decay_mult=0)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='poly', + min_lr=0, + by_epoch=False, + warmup='constant', + warmup_iters=5000, +) +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/_base_/schedules/imagenet_bs2048.py b/configs/_base_/schedules/imagenet_bs2048.py new file mode 100644 index 0000000..93fdebf --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs2048.py @@ -0,0 +1,12 @@ +# optimizer +optimizer = dict( + type='SGD', lr=0.8, momentum=0.9, weight_decay=0.0001, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=2500, + warmup_ratio=0.25, + step=[30, 60, 90]) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/_base_/schedules/imagenet_bs2048_AdamW.py b/configs/_base_/schedules/imagenet_bs2048_AdamW.py new file mode 100644 index 0000000..6d4f208 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs2048_AdamW.py @@ -0,0 +1,20 @@ +# optimizer +# In ClassyVision, the lr is set to 0.003 for bs4096. +# In this implementation(bs2048), lr = 0.003 / 4096 * (32bs * 64gpus) = 0.0015 +optimizer = dict(type='AdamW', lr=0.0015, weight_decay=0.3) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# specific to vit pretrain +paramwise_cfg = dict( + custom_keys={ + '.backbone.cls_token': dict(decay_mult=0.0), + '.backbone.pos_embed': dict(decay_mult=0.0) + }) +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=10000, + warmup_ratio=1e-4) +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/_base_/schedules/imagenet_bs2048_coslr.py b/configs/_base_/schedules/imagenet_bs2048_coslr.py new file mode 100644 index 0000000..b9e77f2 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs2048_coslr.py @@ -0,0 +1,12 @@ +# optimizer +optimizer = dict( + type='SGD', lr=0.8, momentum=0.9, weight_decay=0.0001, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=2500, + warmup_ratio=0.25) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/_base_/schedules/imagenet_bs2048_rsb.py b/configs/_base_/schedules/imagenet_bs2048_rsb.py new file mode 100644 index 0000000..e021cb0 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs2048_rsb.py @@ -0,0 +1,12 @@ +# optimizer +optimizer = dict(type='Lamb', lr=0.005, weight_decay=0.02) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=1.0e-6, + warmup='linear', + # For ImageNet-1k, 626 iters per epoch, warmup 5 epochs. + warmup_iters=5 * 626, + warmup_ratio=0.0001) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/_base_/schedules/imagenet_bs256.py b/configs/_base_/schedules/imagenet_bs256.py new file mode 100644 index 0000000..3b5d198 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs256.py @@ -0,0 +1,6 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[30, 60, 90]) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/_base_/schedules/imagenet_bs256_140e.py b/configs/_base_/schedules/imagenet_bs256_140e.py new file mode 100644 index 0000000..caba157 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs256_140e.py @@ -0,0 +1,6 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[40, 80, 120]) +runner = dict(type='EpochBasedRunner', max_epochs=140) diff --git a/configs/_base_/schedules/imagenet_bs256_200e_coslr_warmup.py b/configs/_base_/schedules/imagenet_bs256_200e_coslr_warmup.py new file mode 100644 index 0000000..49456b2 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs256_200e_coslr_warmup.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=25025, + warmup_ratio=0.25) +runner = dict(type='EpochBasedRunner', max_epochs=200) diff --git a/configs/_base_/schedules/imagenet_bs256_coslr.py b/configs/_base_/schedules/imagenet_bs256_coslr.py new file mode 100644 index 0000000..779b479 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs256_coslr.py @@ -0,0 +1,6 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='CosineAnnealing', min_lr=0) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/_base_/schedules/imagenet_bs256_epochstep.py b/configs/_base_/schedules/imagenet_bs256_epochstep.py new file mode 100644 index 0000000..2347a04 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs256_epochstep.py @@ -0,0 +1,6 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.045, momentum=0.9, weight_decay=0.00004) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', gamma=0.98, step=1) +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/_base_/schedules/imagenet_bs4096_AdamW.py b/configs/_base_/schedules/imagenet_bs4096_AdamW.py new file mode 100644 index 0000000..75b00d8 --- /dev/null +++ b/configs/_base_/schedules/imagenet_bs4096_AdamW.py @@ -0,0 +1,24 @@ +# specific to vit pretrain +paramwise_cfg = dict(custom_keys={ + '.cls_token': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0) +}) + +# optimizer +optimizer = dict( + type='AdamW', + lr=0.003, + weight_decay=0.3, + paramwise_cfg=paramwise_cfg, +) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=10000, + warmup_ratio=1e-4, +) +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/_base_/schedules/stanford_cars_bs8.py b/configs/_base_/schedules/stanford_cars_bs8.py new file mode 100644 index 0000000..dee252e --- /dev/null +++ b/configs/_base_/schedules/stanford_cars_bs8.py @@ -0,0 +1,7 @@ +# optimizer +optimizer = dict( + type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0005, nesterov=True) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[40, 70, 90]) +runner = dict(type='EpochBasedRunner', max_epochs=100) diff --git a/configs/conformer/README.md b/configs/conformer/README.md new file mode 100644 index 0000000..5b7d96b --- /dev/null +++ b/configs/conformer/README.md @@ -0,0 +1,37 @@ +# Conformer + +> [Conformer: Local Features Coupling Global Representations for Visual Recognition](https://arxiv.org/abs/2105.03889) + + + +## Abstract + +Within Convolutional Neural Network (CNN), the convolution operations are good at extracting local features but experience difficulty to capture global representations. Within visual transformer, the cascaded self-attention modules can capture long-distance feature dependencies but unfortunately deteriorate local feature details. In this paper, we propose a hybrid network structure, termed Conformer, to take advantage of convolutional operations and self-attention mechanisms for enhanced representation learning. Conformer roots in the Feature Coupling Unit (FCU), which fuses local features and global representations under different resolutions in an interactive fashion. Conformer adopts a concurrent structure so that local features and global representations are retained to the maximum extent. Experiments show that Conformer, under the comparable parameter complexity, outperforms the visual transformer (DeiT-B) by 2.3% on ImageNet. On MSCOCO, it outperforms ResNet-101 by 3.7% and 3.6% mAPs for object detection and instance segmentation, respectively, demonstrating the great potential to be a general backbone network. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------: | :-----------------------------------------------------------------------: | +| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) | +| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) | +| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) | +| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) | + +*Models with * are converted from the [official repo](https://github.com/pengzhiliang/Conformer). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@article{peng2021conformer, + title={Conformer: Local Features Coupling Global Representations for Visual Recognition}, + author={Zhiliang Peng and Wei Huang and Shanzhi Gu and Lingxi Xie and Yaowei Wang and Jianbin Jiao and Qixiang Ye}, + journal={arXiv preprint arXiv:2105.03889}, + year={2021}, +} +``` diff --git a/configs/conformer/conformer-base-p16_8xb128_in1k.py b/configs/conformer/conformer-base-p16_8xb128_in1k.py new file mode 100644 index 0000000..29ed58b --- /dev/null +++ b/configs/conformer/conformer-base-p16_8xb128_in1k.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/conformer/base-p16.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_conformer.py', + '../_base_/default_runtime.py' +] + +data = dict(samples_per_gpu=128) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/conformer/conformer-small-p16_8xb128_in1k.py b/configs/conformer/conformer-small-p16_8xb128_in1k.py new file mode 100644 index 0000000..c40ed04 --- /dev/null +++ b/configs/conformer/conformer-small-p16_8xb128_in1k.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/conformer/small-p16.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_conformer.py', + '../_base_/default_runtime.py' +] + +data = dict(samples_per_gpu=128) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/conformer/conformer-small-p32_8xb128_in1k.py b/configs/conformer/conformer-small-p32_8xb128_in1k.py new file mode 100644 index 0000000..aaa1189 --- /dev/null +++ b/configs/conformer/conformer-small-p32_8xb128_in1k.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/conformer/small-p32.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_conformer.py', + '../_base_/default_runtime.py' +] + +data = dict(samples_per_gpu=128) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/conformer/conformer-tiny-p16_8xb128_in1k.py b/configs/conformer/conformer-tiny-p16_8xb128_in1k.py new file mode 100644 index 0000000..76a264c --- /dev/null +++ b/configs/conformer/conformer-tiny-p16_8xb128_in1k.py @@ -0,0 +1,9 @@ +_base_ = [ + '../_base_/models/conformer/tiny-p16.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_conformer.py', + '../_base_/default_runtime.py' +] + +data = dict(samples_per_gpu=128) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/conformer/metafile.yml b/configs/conformer/metafile.yml new file mode 100644 index 0000000..4efe05f --- /dev/null +++ b/configs/conformer/metafile.yml @@ -0,0 +1,78 @@ +Collections: + - Name: Conformer + Metadata: + Training Data: ImageNet-1k + Architecture: + - Layer Normalization + - Scaled Dot-Product Attention + - Dropout + Paper: + URL: https://arxiv.org/abs/2105.03889 + Title: "Conformer: Local Features Coupling Global Representations for Visual Recognition" + README: configs/conformer/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.19.0/mmcls/models/backbones/conformer.py + Version: v0.19.0 + +Models: + - Name: conformer-tiny-p16_3rdparty_8xb128_in1k + In Collection: Conformer + Config: configs/conformer/conformer-tiny-p16_8xb128_in1k.py + Metadata: + FLOPs: 4899611328 + Parameters: 23524704 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.31 + Top 5 Accuracy: 95.60 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth + Converted From: + Weights: https://drive.google.com/file/d/19SxGhKcWOR5oQSxNUWUM2MGYiaWMrF1z/view?usp=sharing + Code: https://github.com/pengzhiliang/Conformer/blob/main/models.py#L65 + - Name: conformer-small-p16_3rdparty_8xb128_in1k + In Collection: Conformer + Config: configs/conformer/conformer-small-p16_8xb128_in1k.py + Metadata: + FLOPs: 10311309312 + Parameters: 37673424 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.32 + Top 5 Accuracy: 96.46 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth + Converted From: + Weights: https://drive.google.com/file/d/1mpOlbLaVxOfEwV4-ha78j_1Ebqzj2B83/view?usp=sharing + Code: https://github.com/pengzhiliang/Conformer/blob/main/models.py#L73 + - Name: conformer-small-p32_8xb128_in1k + In Collection: Conformer + Config: configs/conformer/conformer-small-p32_8xb128_in1k.py + Metadata: + FLOPs: 7087281792 + Parameters: 38853072 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.96 + Top 5 Accuracy: 96.02 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth + - Name: conformer-base-p16_3rdparty_8xb128_in1k + In Collection: Conformer + Config: configs/conformer/conformer-base-p16_8xb128_in1k.py + Metadata: + FLOPs: 22892078080 + Parameters: 83289136 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.82 + Top 5 Accuracy: 96.59 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth + Converted From: + Weights: https://drive.google.com/file/d/1oeQ9LSOGKEUaYGu7WTlUGl3KDsQIi0MA/view?usp=sharing + Code: https://github.com/pengzhiliang/Conformer/blob/main/models.py#L89 diff --git a/configs/convmixer/README.md b/configs/convmixer/README.md new file mode 100644 index 0000000..763bad3 --- /dev/null +++ b/configs/convmixer/README.md @@ -0,0 +1,42 @@ +# ConvMixer + +> [Patches Are All You Need?](https://arxiv.org/abs/2201.09792) + + + +## Abstract + + + +Although convolutional networks have been the dominant architecture for vision tasks for many years, recent experiments have shown that Transformer-based models, most notably the Vision Transformer (ViT), may exceed their performance in some settings. However, due to the quadratic runtime of the self-attention layers in Transformers, ViTs require the use of patch embeddings, which group together small regions of the image into single input features, in order to be applied to larger image sizes. This raises a question: Is the performance of ViTs due to the inherently-more-powerful Transformer architecture, or is it at least partly due to using patches as the input representation? In this paper, we present some evidence for the latter: specifically, we propose the ConvMixer, an extremely simple model that is similar in spirit to the ViT and the even-more-basic MLP-Mixer in that it operates directly on patches as input, separates the mixing of spatial and channel dimensions, and maintains equal size and resolution throughout the network. In contrast, however, the ConvMixer uses only standard convolutions to achieve the mixing steps. Despite its simplicity, we show that the ConvMixer outperforms the ViT, MLP-Mixer, and some of their variants for similar parameter counts and data set sizes, in addition to outperforming classical vision models such as the ResNet. + + + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :------------------------------------------------------------------------: | +| ConvMixer-768/32\* | 21.11 | 19.62 | 80.16 | 95.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-768-32_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-768-32_3rdparty_10xb64_in1k_20220323-bca1f7b8.pth) | +| ConvMixer-1024/20\* | 24.38 | 5.55 | 76.94 | 93.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1024-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1024-20_3rdparty_10xb64_in1k_20220323-48f8aeba.pth) | +| ConvMixer-1536/20\* | 51.63 | 48.71 | 81.37 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convmixer/convmixer-1536-20_10xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1536_20_3rdparty_10xb64_in1k_20220323-ea5786f3.pth) | + +*Models with * are converted from the [official repo](https://github.com/locuslab/convmixer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@misc{trockman2022patches, + title={Patches Are All You Need?}, + author={Asher Trockman and J. Zico Kolter}, + year={2022}, + eprint={2201.09792}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/configs/convmixer/convmixer-1024-20_10xb64_in1k.py b/configs/convmixer/convmixer-1024-20_10xb64_in1k.py new file mode 100644 index 0000000..58694d6 --- /dev/null +++ b/configs/convmixer/convmixer-1024-20_10xb64_in1k.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/convmixer/convmixer-1024-20.py', + '../_base_/datasets/imagenet_bs64_convmixer_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=0.01) + +runner = dict(type='EpochBasedRunner', max_epochs=150) diff --git a/configs/convmixer/convmixer-1536-20_10xb64_in1k.py b/configs/convmixer/convmixer-1536-20_10xb64_in1k.py new file mode 100644 index 0000000..17a7559 --- /dev/null +++ b/configs/convmixer/convmixer-1536-20_10xb64_in1k.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/convmixer/convmixer-1536-20.py', + '../_base_/datasets/imagenet_bs64_convmixer_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=0.01) + +runner = dict(type='EpochBasedRunner', max_epochs=150) diff --git a/configs/convmixer/convmixer-768-32_10xb64_in1k.py b/configs/convmixer/convmixer-768-32_10xb64_in1k.py new file mode 100644 index 0000000..fa4c060 --- /dev/null +++ b/configs/convmixer/convmixer-768-32_10xb64_in1k.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/convmixer/convmixer-768-32.py', + '../_base_/datasets/imagenet_bs64_convmixer_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=0.01) + +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/convmixer/metafile.yml b/configs/convmixer/metafile.yml new file mode 100644 index 0000000..7831d74 --- /dev/null +++ b/configs/convmixer/metafile.yml @@ -0,0 +1,61 @@ +Collections: + - Name: ConvMixer + Metadata: + Training Data: ImageNet-1k + Architecture: + - 1x1 Convolution + - LayerScale + Paper: + URL: https://arxiv.org/abs/2201.09792 + Title: Patches Are All You Need? + README: configs/convmixer/README.md + +Models: + - Name: convmixer-768-32_10xb64_in1k + Metadata: + FLOPs: 19623051264 + Parameters: 21110248 + In Collections: ConvMixer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.16 + Top 5 Accuracy: 95.08 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-768-32_3rdparty_10xb64_in1k_20220323-bca1f7b8.pth + Config: configs/convmixer/convmixer-768-32_10xb64_in1k.py + Converted From: + Weights: https://github.com/tmp-iclr/convmixer/releases/download/v1.0/convmixer_768_32_ks7_p7_relu.pth.tar + Code: https://github.com/locuslab/convmixer + - Name: convmixer-1024-20_10xb64_in1k + Metadata: + FLOPs: 5550112768 + Parameters: 24383464 + In Collections: ConvMixer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 76.94 + Top 5 Accuracy: 93.36 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1024-20_3rdparty_10xb64_in1k_20220323-48f8aeba.pth + Config: configs/convmixer/convmixer-1024-20_10xb64_in1k.py + Converted From: + Weights: https://github.com/tmp-iclr/convmixer/releases/download/v1.0/convmixer_1024_20_ks9_p14.pth.tar + Code: https://github.com/locuslab/convmixer + - Name: convmixer-1536-20_10xb64_in1k + Metadata: + FLOPs: 48713170944 + Parameters: 51625960 + In Collections: ConvMixer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.37 + Top 5 Accuracy: 95.61 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convmixer/convmixer-1536_20_3rdparty_10xb64_in1k_20220323-ea5786f3.pth + Config: configs/convmixer/convmixer-1536-20_10xb64_in1k.py + Converted From: + Weights: https://github.com/tmp-iclr/convmixer/releases/download/v1.0/convmixer_1536_20_ks9_p7.pth.tar + Code: https://github.com/locuslab/convmixer diff --git a/configs/convnext/README.md b/configs/convnext/README.md new file mode 100644 index 0000000..7db8136 --- /dev/null +++ b/configs/convnext/README.md @@ -0,0 +1,59 @@ +# ConvNeXt + +> [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545v1) + + + +## Abstract + + + +The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. + + + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------: | :---------------------------------------------------------------------: | +| ConvNeXt-T\* | From scratch | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) | +| ConvNeXt-S\* | From scratch | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) | +| ConvNeXt-B\* | From scratch | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) | +| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) | +| ConvNeXt-L\* | From scratch | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) | +| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) | +| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) | + +*Models with * are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +### Pre-trained Models + +The pre-trained models on ImageNet-1k or ImageNet-21k are used to fine-tune on the downstream tasks. + +| Model | Training Data | Params(M) | Flops(G) | Download | +| :-----------: | :-----------: | :-------: | :------: | :-----------------------------------------------------------------------------------------------------------------------------------: | +| ConvNeXt-T\* | ImageNet-1k | 28.59 | 4.46 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128-noema_in1k_20220222-2908964a.pth) | +| ConvNeXt-S\* | ImageNet-1k | 50.22 | 8.69 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128-noema_in1k_20220222-fa001ca5.pth) | +| ConvNeXt-B\* | ImageNet-1k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128-noema_in1k_20220222-dba4f95f.pth) | +| ConvNeXt-B\* | ImageNet-21k | 88.59 | 15.36 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_in21k_20220124-13b83eec.pth) | +| ConvNeXt-L\* | ImageNet-21k | 197.77 | 34.37 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_in21k_20220124-41b5a79f.pth) | +| ConvNeXt-XL\* | ImageNet-21k | 350.20 | 60.93 | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_3rdparty_in21k_20220124-f909bad7.pth) | + +*Models with * are converted from the [official repo](https://github.com/facebookresearch/ConvNeXt).* + +## Citation + +```bibtex +@Article{liu2022convnet, + author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, + title = {A ConvNet for the 2020s}, + journal = {arXiv preprint arXiv:2201.03545}, + year = {2022}, +} +``` diff --git a/configs/convnext/convnext-base_32xb128_in1k.py b/configs/convnext/convnext-base_32xb128_in1k.py new file mode 100644 index 0000000..6c0450a --- /dev/null +++ b/configs/convnext/convnext-base_32xb128_in1k.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/convnext/convnext-base.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=128) + +optimizer = dict(lr=4e-3) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/convnext/convnext-large_64xb64_in1k.py b/configs/convnext/convnext-large_64xb64_in1k.py new file mode 100644 index 0000000..1faae25 --- /dev/null +++ b/configs/convnext/convnext-large_64xb64_in1k.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/convnext/convnext-large.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/convnext/convnext-small_32xb128_in1k.py b/configs/convnext/convnext-small_32xb128_in1k.py new file mode 100644 index 0000000..d820fc6 --- /dev/null +++ b/configs/convnext/convnext-small_32xb128_in1k.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/convnext/convnext-small.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=128) + +optimizer = dict(lr=4e-3) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/convnext/convnext-tiny_32xb128_in1k.py b/configs/convnext/convnext-tiny_32xb128_in1k.py new file mode 100644 index 0000000..46d0185 --- /dev/null +++ b/configs/convnext/convnext-tiny_32xb128_in1k.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/convnext/convnext-tiny.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=128) + +optimizer = dict(lr=4e-3) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/convnext/convnext-xlarge_64xb64_in1k.py b/configs/convnext/convnext-xlarge_64xb64_in1k.py new file mode 100644 index 0000000..7284901 --- /dev/null +++ b/configs/convnext/convnext-xlarge_64xb64_in1k.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/convnext/convnext-xlarge.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/convnext/metafile.yml b/configs/convnext/metafile.yml new file mode 100644 index 0000000..823f332 --- /dev/null +++ b/configs/convnext/metafile.yml @@ -0,0 +1,221 @@ +Collections: + - Name: ConvNeXt + Metadata: + Training Data: ImageNet-1k + Architecture: + - 1x1 Convolution + - LayerScale + Paper: + URL: https://arxiv.org/abs/2201.03545v1 + Title: A ConvNet for the 2020s + README: configs/convnext/README.md + Code: + Version: v0.20.1 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/convnext.py + +Models: + - Name: convnext-tiny_3rdparty_32xb128_in1k + Metadata: + FLOPs: 4457472768 + Parameters: 28589128 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.05 + Top 5 Accuracy: 95.86 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth + Config: configs/convnext/convnext-tiny_32xb128_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-tiny_3rdparty_32xb128-noema_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 4457472768 + Parameters: 28589128 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.81 + Top 5 Accuracy: 95.67 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128-noema_in1k_20220222-2908964a.pth + Config: configs/convnext/convnext-tiny_32xb128_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-small_3rdparty_32xb128_in1k + Metadata: + FLOPs: 8687008512 + Parameters: 50223688 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.13 + Top 5 Accuracy: 96.44 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth + Config: configs/convnext/convnext-small_32xb128_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-small_3rdparty_32xb128-noema_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 8687008512 + Parameters: 50223688 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.11 + Top 5 Accuracy: 96.34 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128-noema_in1k_20220222-fa001ca5.pth + Config: configs/convnext/convnext-small_32xb128_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-base_3rdparty_32xb128_in1k + Metadata: + FLOPs: 15359124480 + Parameters: 88591464 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.85 + Top 5 Accuracy: 96.74 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth + Config: configs/convnext/convnext-base_32xb128_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-base_3rdparty_32xb128-noema_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 15359124480 + Parameters: 88591464 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.71 + Top 5 Accuracy: 96.60 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128-noema_in1k_20220222-dba4f95f.pth + Config: configs/convnext/convnext-base_32xb128_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-base_3rdparty_in21k + Metadata: + Training Data: ImageNet-21k + FLOPs: 15359124480 + Parameters: 88591464 + In Collections: ConvNeXt + Results: null + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_in21k_20220124-13b83eec.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-base_in21k-pre-3rdparty_32xb128_in1k + Metadata: + Training Data: + - ImageNet-21k + - ImageNet-1k + FLOPs: 15359124480 + Parameters: 88591464 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 85.81 + Top 5 Accuracy: 97.86 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth + Config: configs/convnext/convnext-base_32xb128_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-large_3rdparty_64xb64_in1k + Metadata: + FLOPs: 34368026112 + Parameters: 197767336 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.30 + Top 5 Accuracy: 96.89 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth + Config: configs/convnext/convnext-large_64xb64_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-large_3rdparty_in21k + Metadata: + Training Data: ImageNet-21k + FLOPs: 34368026112 + Parameters: 197767336 + In Collections: ConvNeXt + Results: null + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_in21k_20220124-41b5a79f.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-large_in21k-pre-3rdparty_64xb64_in1k + Metadata: + Training Data: + - ImageNet-21k + - ImageNet-1k + FLOPs: 34368026112 + Parameters: 197767336 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.61 + Top 5 Accuracy: 98.04 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth + Config: configs/convnext/convnext-large_64xb64_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-xlarge_3rdparty_in21k + Metadata: + Training Data: ImageNet-21k + FLOPs: 60929820672 + Parameters: 350196968 + In Collections: ConvNeXt + Results: null + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_3rdparty_in21k_20220124-f909bad7.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth + Code: https://github.com/facebookresearch/ConvNeXt + - Name: convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k + Metadata: + Training Data: + - ImageNet-21k + - ImageNet-1k + FLOPs: 60929820672 + Parameters: 350196968 + In Collections: ConvNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.97 + Top 5 Accuracy: 98.20 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth + Config: configs/convnext/convnext-xlarge_64xb64_in1k.py + Converted From: + Weights: https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth + Code: https://github.com/facebookresearch/ConvNeXt diff --git a/configs/cspnet/README.md b/configs/cspnet/README.md new file mode 100644 index 0000000..10eb9d0 --- /dev/null +++ b/configs/cspnet/README.md @@ -0,0 +1,41 @@ +# CSPNet + +> [CSPNet: A New Backbone that can Enhance Learning Capability of CNN](https://arxiv.org/abs/1911.11929) + + + +## Abstract + + + +Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet. Source code is at this https URL. + + + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :---------------------------------------------------------------------: | +| CSPDarkNet50\* | From scratch | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) | +| CSPResNet50\* | From scratch | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) | +| CSPResNeXt50\* | From scratch | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) | + +*Models with * are converted from the [timm repo](https://github.com/rwightman/pytorch-image-models). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@inproceedings{wang2020cspnet, + title={CSPNet: A new backbone that can enhance learning capability of CNN}, + author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Wu, Yueh-Hua and Chen, Ping-Yang and Hsieh, Jun-Wei and Yeh, I-Hau}, + booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops}, + pages={390--391}, + year={2020} +} +``` diff --git a/configs/cspnet/cspdarknet50_8xb32_in1k.py b/configs/cspnet/cspdarknet50_8xb32_in1k.py new file mode 100644 index 0000000..cf2ce73 --- /dev/null +++ b/configs/cspnet/cspdarknet50_8xb32_in1k.py @@ -0,0 +1,65 @@ +_base_ = [ + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='CSPDarkNet', depth=53), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(288, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=256), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/cspnet/cspresnet50_8xb32_in1k.py b/configs/cspnet/cspresnet50_8xb32_in1k.py new file mode 100644 index 0000000..f4cfbf8 --- /dev/null +++ b/configs/cspnet/cspresnet50_8xb32_in1k.py @@ -0,0 +1,66 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='CSPResNet', depth=50), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1024, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(288, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=256), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/cspnet/cspresnext50_8xb32_in1k.py b/configs/cspnet/cspresnext50_8xb32_in1k.py new file mode 100644 index 0000000..a82ab75 --- /dev/null +++ b/configs/cspnet/cspresnext50_8xb32_in1k.py @@ -0,0 +1,65 @@ +_base_ = [ + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='CSPResNeXt', depth=50), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(256, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/cspnet/metafile.yml b/configs/cspnet/metafile.yml new file mode 100644 index 0000000..8c4a78e --- /dev/null +++ b/configs/cspnet/metafile.yml @@ -0,0 +1,64 @@ +Collections: + - Name: CSPNet + Metadata: + Training Data: ImageNet-1k + Architecture: + - Cross Stage Partia Stage + Paper: + URL: https://arxiv.org/abs/1911.11929 + Title: 'CSPNet: A New Backbone that can Enhance Learning Capability of CNN' + README: configs/cspnet/README.md + Code: + Version: v0.22.0 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.22.0/mmcls/models/backbones/cspnet.py + +Models: + - Name: cspdarknet50_3rdparty_8xb32_in1k + Metadata: + FLOPs: 5040000000 + Parameters: 27640000 + In Collections: CSPNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.05 + Top 5 Accuracy: 95.07 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth + Config: configs/cspnet/cspdarknet50_8xb32_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspdarknet53_ra_256-d05c7c21.pth + Code: https://github.com/rwightman/pytorch-image-models + - Name: cspresnet50_3rdparty_8xb32_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 3480000000 + Parameters: 21620000 + In Collections: CSPNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.55 + Top 5 Accuracy: 94.68 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth + Config: configs/cspnet/cspresnet50_8xb32_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnet50_ra-d3e8d487.pth + Code: https://github.com/rwightman/pytorch-image-models + - Name: cspresnext50_3rdparty_8xb32_in1k + Metadata: + FLOPs: 3110000000 + Parameters: 20570000 + In Collections: CSPNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.96 + Top 5 Accuracy: 94.96 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth + Config: configs/cspnet/cspresnext50_8xb32_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnext50_ra_224-648b4713.pth + Code: https://github.com/rwightman/pytorch-image-models diff --git a/configs/csra/README.md b/configs/csra/README.md new file mode 100644 index 0000000..fa677cf --- /dev/null +++ b/configs/csra/README.md @@ -0,0 +1,36 @@ +# CSRA + +> [Residual Attention: A Simple but Effective Method for Multi-Label Recognition](https://arxiv.org/abs/2108.02456) + + + +## Abstract + +Multi-label image recognition is a challenging computer vision task of practical use. Progresses in this area, however, are often characterized by complicated methods, heavy computations, and lack of intuitive explanations. To effectively capture different spatial regions occupied by objects from different categories, we propose an embarrassingly simple module, named class-specific residual attention (CSRA). CSRA generates class-specific features for every category by proposing a simple spatial attention score, and then combines it with the class-agnostic average pooling feature. CSRA achieves state-of-the-art results on multilabel recognition, and at the same time is much simpler than them. Furthermore, with only 4 lines of code, CSRA also leads to consistent improvement across many diverse pretrained models and datasets without any extra training. CSRA is both easy to implement and light in computations, which also enjoys intuitive explanations and visualizations. + +
+ +
+ +## Results and models + +### VOC2007 + +| Model | Pretrain | Params(M) | Flops(G) | mAP | OF1 (%) | CF1 (%) | Config | Download | +| :------------: | :------------------------------------------------: | :-------: | :------: | :---: | :-----: | :-----: | :-----------------------------------------------: | :-------------------------------------------------: | +| Resnet101-CSRA | [ImageNet-1k](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) | 23.55 | 4.12 | 94.98 | 90.80 | 89.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/csra/resnet101-csra_1xb16_voc07-448px.py) | [model](https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.log.json) | + +## Citation + +```bibtex +@misc{https://doi.org/10.48550/arxiv.2108.02456, + doi = {10.48550/ARXIV.2108.02456}, + url = {https://arxiv.org/abs/2108.02456}, + author = {Zhu, Ke and Wu, Jianxin}, + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {Residual Attention: A Simple but Effective Method for Multi-Label Recognition}, + publisher = {arXiv}, + year = {2021}, + copyright = {arXiv.org perpetual, non-exclusive license} +} +``` diff --git a/configs/csra/metafile.yml b/configs/csra/metafile.yml new file mode 100644 index 0000000..f1fa622 --- /dev/null +++ b/configs/csra/metafile.yml @@ -0,0 +1,29 @@ +Collections: + - Name: CSRA + Metadata: + Training Data: PASCAL VOC 2007 + Architecture: + - Class-specific Residual Attention + Paper: + URL: https://arxiv.org/abs/1911.11929 + Title: 'Residual Attention: A Simple but Effective Method for Multi-Label Recognition' + README: configs/csra/README.md + Code: + Version: v0.24.0 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/heads/multi_label_csra_head.py + +Models: + - Name: resnet101-csra_1xb16_voc07-448px + Metadata: + FLOPs: 4120000000 + Parameters: 23550000 + In Collections: CSRA + Results: + - Dataset: PASCAL VOC 2007 + Metrics: + mAP: 94.98 + OF1: 90.80 + CF1: 89.16 + Task: Multi-Label Classification + Weights: https://download.openmmlab.com/mmclassification/v0/csra/resnet101-csra_1xb16_voc07-448px_20220722-29efb40a.pth + Config: configs/csra/resnet101-csra_1xb16_voc07-448px.py diff --git a/configs/csra/resnet101-csra_1xb16_voc07-448px.py b/configs/csra/resnet101-csra_1xb16_voc07-448px.py new file mode 100644 index 0000000..5dc5dd6 --- /dev/null +++ b/configs/csra/resnet101-csra_1xb16_voc07-448px.py @@ -0,0 +1,75 @@ +_base_ = ['../_base_/datasets/voc_bs16.py', '../_base_/default_runtime.py'] + +# Pre-trained Checkpoint Path +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth' # noqa +# If you want to use the pre-trained weight of ResNet101-CutMix from +# the originary repo(https://github.com/Kevinz-code/CSRA). Script of +# 'tools/convert_models/torchvision_to_mmcls.py' can help you convert weight +# into mmcls format. The mAP result would hit 95.5 by using the weight. +# checkpoint = 'PATH/TO/PRE-TRAINED_WEIGHT' + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(3, ), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint, prefix='backbone')), + neck=None, + head=dict( + type='CSRAClsHead', + num_classes=20, + in_channels=2048, + num_heads=1, + lam=0.1, + loss=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))) + +# dataset setting +img_norm_cfg = dict(mean=[0, 0, 0], std=[255, 255, 255], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=448, scale=(0.7, 1.0)), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=448), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + # map the difficult examples as negative ones(0) + train=dict(pipeline=train_pipeline, difficult_as_postive=False), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +# the lr of classifier.head is 10 * base_lr, which help convergence. +optimizer = dict( + type='SGD', + lr=0.0002, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(custom_keys={'head': dict(lr_mult=10)})) + +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='step', + step=6, + gamma=0.1, + warmup='linear', + warmup_iters=1, + warmup_ratio=1e-7, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/deit/README.md b/configs/deit/README.md new file mode 100644 index 0000000..e310365 --- /dev/null +++ b/configs/deit/README.md @@ -0,0 +1,52 @@ +# DeiT + +> [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) + + + +## Abstract + +Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers are pre-trained with hundreds of millions of images using an expensive infrastructure, thereby limiting their adoption. In this work, we produce a competitive convolution-free transformer by training on Imagenet only. We train them on a single computer in less than 3 days. Our reference vision transformer (86M parameters) achieves top-1 accuracy of 83.1% (single-crop evaluation) on ImageNet with no external data. More importantly, we introduce a teacher-student strategy specific to transformers. It relies on a distillation token ensuring that the student learns from the teacher through attention. We show the interest of this token-based distillation, especially when using a convnet as a teacher. This leads us to report results competitive with convnets for both Imagenet (where we obtain up to 85.2% accuracy) and when transferring to other tasks. We share our code and models. + +
+ +
+ +## Results and models + +### ImageNet-1k + +The teacher of the distilled version DeiT is RegNetY-16GF. + +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------: | :--------------------------------------------------------------: | +| DeiT-tiny | From scratch | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) | +| DeiT-tiny distilled\* | From scratch | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) | +| DeiT-small | From scratch | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) | +| DeiT-small distilled\* | From scratch | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) | +| DeiT-base | From scratch | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) | +| DeiT-base\* | From scratch | 86.57 | 16.86 | 81.79 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth) | +| DeiT-base distilled\* | From scratch | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) | +| DeiT-base 384px\* | ImageNet-1k | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) | +| DeiT-base distilled 384px\* | ImageNet-1k | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/tree/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) | + +*Models with * are converted from the [official repo](https://github.com/facebookresearch/deit). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +```{warning} +MMClassification doesn't support training the distilled version DeiT. +And we provide distilled version checkpoints for inference only. +``` + +## Citation + +``` +@InProceedings{pmlr-v139-touvron21a, + title = {Training data-efficient image transformers & distillation through attention}, + author = {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve}, + booktitle = {International Conference on Machine Learning}, + pages = {10347--10357}, + year = {2021}, + volume = {139}, + month = {July} +} +``` diff --git a/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py b/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py new file mode 100644 index 0000000..c8bdfb5 --- /dev/null +++ b/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py @@ -0,0 +1,9 @@ +_base_ = './deit-base_ft-16xb32_in1k-384px.py' + +# model settings +model = dict( + backbone=dict(type='DistilledVisionTransformer'), + head=dict(type='DeiTClsHead'), + # Change to the path of the pretrained model + # init_cfg=dict(type='Pretrained', checkpoint=''), +) diff --git a/configs/deit/deit-base-distilled_pt-16xb64_in1k.py b/configs/deit/deit-base-distilled_pt-16xb64_in1k.py new file mode 100644 index 0000000..6716583 --- /dev/null +++ b/configs/deit/deit-base-distilled_pt-16xb64_in1k.py @@ -0,0 +1,10 @@ +_base_ = './deit-small_pt-4xb256_in1k.py' + +# model settings +model = dict( + backbone=dict(type='DistilledVisionTransformer', arch='deit-base'), + head=dict(type='DeiTClsHead', in_channels=768), +) + +# data settings +data = dict(samples_per_gpu=64, workers_per_gpu=5) diff --git a/configs/deit/deit-base_ft-16xb32_in1k-384px.py b/configs/deit/deit-base_ft-16xb32_in1k-384px.py new file mode 100644 index 0000000..db44416 --- /dev/null +++ b/configs/deit/deit-base_ft-16xb32_in1k-384px.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs64_swin_384.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VisionTransformer', + arch='deit-base', + img_size=384, + patch_size=16, + ), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=1000, + in_channels=768, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + # Change to the path of the pretrained model + # init_cfg=dict(type='Pretrained', checkpoint=''), +) + +# data settings +data = dict(samples_per_gpu=32, workers_per_gpu=5) diff --git a/configs/deit/deit-base_pt-16xb64_in1k.py b/configs/deit/deit-base_pt-16xb64_in1k.py new file mode 100644 index 0000000..24c13dc --- /dev/null +++ b/configs/deit/deit-base_pt-16xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = './deit-small_pt-4xb256_in1k.py' + +# model settings +model = dict( + backbone=dict( + type='VisionTransformer', arch='deit-base', drop_path_rate=0.1), + head=dict(type='VisionTransformerClsHead', in_channels=768), +) + +# data settings +data = dict(samples_per_gpu=64, workers_per_gpu=5) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/deit/deit-small-distilled_pt-4xb256_in1k.py b/configs/deit/deit-small-distilled_pt-4xb256_in1k.py new file mode 100644 index 0000000..3b1fac2 --- /dev/null +++ b/configs/deit/deit-small-distilled_pt-4xb256_in1k.py @@ -0,0 +1,7 @@ +_base_ = './deit-small_pt-4xb256_in1k.py' + +# model settings +model = dict( + backbone=dict(type='DistilledVisionTransformer', arch='deit-small'), + head=dict(type='DeiTClsHead', in_channels=384), +) diff --git a/configs/deit/deit-small_pt-4xb256_in1k.py b/configs/deit/deit-small_pt-4xb256_in1k.py new file mode 100644 index 0000000..550f080 --- /dev/null +++ b/configs/deit/deit-small_pt-4xb256_in1k.py @@ -0,0 +1,44 @@ +# In small and tiny arch, remove drop path and EMA hook comparing with the +# original config +_base_ = [ + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='VisionTransformer', + arch='deit-small', + img_size=224, + patch_size=16), + neck=None, + head=dict( + type='VisionTransformerClsHead', + num_classes=1000, + in_channels=384, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + ), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=.02), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.), + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# data settings +data = dict(samples_per_gpu=256, workers_per_gpu=5) + +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.cls_token': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0) + }) +optimizer = dict(paramwise_cfg=paramwise_cfg) diff --git a/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py b/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py new file mode 100644 index 0000000..175f980 --- /dev/null +++ b/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py @@ -0,0 +1,7 @@ +_base_ = './deit-small_pt-4xb256_in1k.py' + +# model settings +model = dict( + backbone=dict(type='DistilledVisionTransformer', arch='deit-tiny'), + head=dict(type='DeiTClsHead', in_channels=192), +) diff --git a/configs/deit/deit-tiny_pt-4xb256_in1k.py b/configs/deit/deit-tiny_pt-4xb256_in1k.py new file mode 100644 index 0000000..43df6e1 --- /dev/null +++ b/configs/deit/deit-tiny_pt-4xb256_in1k.py @@ -0,0 +1,7 @@ +_base_ = './deit-small_pt-4xb256_in1k.py' + +# model settings +model = dict( + backbone=dict(type='VisionTransformer', arch='deit-tiny'), + head=dict(type='VisionTransformerClsHead', in_channels=192), +) diff --git a/configs/deit/metafile.yml b/configs/deit/metafile.yml new file mode 100644 index 0000000..ddd4c67 --- /dev/null +++ b/configs/deit/metafile.yml @@ -0,0 +1,153 @@ +Collections: + - Name: DeiT + Metadata: + Training Data: ImageNet-1k + Architecture: + - Layer Normalization + - Scaled Dot-Product Attention + - Attention Dropout + - Multi-Head Attention + Paper: + URL: https://arxiv.org/abs/2012.12877 + Title: "Training data-efficient image transformers & distillation through attention" + README: configs/deit/README.md + Code: + URL: v0.19.0 + Version: https://github.com/open-mmlab/mmclassification/blob/v0.19.0/mmcls/models/backbones/deit.py + +Models: + - Name: deit-tiny_pt-4xb256_in1k + Metadata: + FLOPs: 1080000000 + Parameters: 5720000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 74.50 + Top 5 Accuracy: 92.24 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth + Config: configs/deit/deit-tiny_pt-4xb256_in1k.py + - Name: deit-tiny-distilled_3rdparty_pt-4xb256_in1k + Metadata: + FLOPs: 1080000000 + Parameters: 5720000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 74.51 + Top 5 Accuracy: 91.90 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth + Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L108 + Config: configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py + - Name: deit-small_pt-4xb256_in1k + Metadata: + FLOPs: 4240000000 + Parameters: 22050000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.69 + Top 5 Accuracy: 95.06 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth + Config: configs/deit/deit-small_pt-4xb256_in1k.py + - Name: deit-small-distilled_3rdparty_pt-4xb256_in1k + Metadata: + FLOPs: 4240000000 + Parameters: 22050000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.17 + Top 5 Accuracy: 95.40 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth + Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L123 + Config: configs/deit/deit-small-distilled_pt-4xb256_in1k.py + - Name: deit-base_pt-16xb64_in1k + Metadata: + FLOPs: 16860000000 + Parameters: 86570000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.76 + Top 5 Accuracy: 95.81 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth + Config: configs/deit/deit-base_pt-16xb64_in1k.py + - Name: deit-base_3rdparty_pt-16xb64_in1k + Metadata: + FLOPs: 16860000000 + Parameters: 86570000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.79 + Top 5 Accuracy: 95.59 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_pt-16xb64_in1k_20211124-6f40c188.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth + Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L93 + Config: configs/deit/deit-base_pt-16xb64_in1k.py + - Name: deit-base-distilled_3rdparty_pt-16xb64_in1k + Metadata: + FLOPs: 16860000000 + Parameters: 86570000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.33 + Top 5 Accuracy: 96.49 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth + Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L138 + Config: configs/deit/deit-base-distilled_pt-16xb64_in1k.py + - Name: deit-base_3rdparty_ft-16xb32_in1k-384px + Metadata: + FLOPs: 49370000000 + Parameters: 86860000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.04 + Top 5 Accuracy: 96.31 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth + Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L153 + Config: configs/deit/deit-base_ft-16xb32_in1k-384px.py + - Name: deit-base-distilled_3rdparty_ft-16xb32_in1k-384px + Metadata: + FLOPs: 49370000000 + Parameters: 86860000 + In Collection: DeiT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 85.55 + Top 5 Accuracy: 97.35 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth + Code: https://github.com/facebookresearch/deit/blob/f5123946205daf72a88783dae94cabff98c49c55/models.py#L168 + Config: configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py diff --git a/configs/densenet/README.md b/configs/densenet/README.md new file mode 100644 index 0000000..f07f25c --- /dev/null +++ b/configs/densenet/README.md @@ -0,0 +1,41 @@ +# DenseNet + +> [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) + + + +## Abstract + +Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L+1)/2 direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) | +| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) | +| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) | +| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) | + +*Models with * are converted from [pytorch](https://pytorch.org/vision/stable/models.html), guided by [original repo](https://github.com/liuzhuang13/DenseNet). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@misc{https://doi.org/10.48550/arxiv.1608.06993, + doi = {10.48550/ARXIV.1608.06993}, + url = {https://arxiv.org/abs/1608.06993}, + author = {Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q.}, + keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {Densely Connected Convolutional Networks}, + publisher = {arXiv}, + year = {2016}, + copyright = {arXiv.org perpetual, non-exclusive license} +} +``` diff --git a/configs/densenet/densenet121_4xb256_in1k.py b/configs/densenet/densenet121_4xb256_in1k.py new file mode 100644 index 0000000..08d65ae --- /dev/null +++ b/configs/densenet/densenet121_4xb256_in1k.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/densenet/densenet121.py', + '../_base_/datasets/imagenet_bs64.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=256) + +runner = dict(type='EpochBasedRunner', max_epochs=90) diff --git a/configs/densenet/densenet161_4xb256_in1k.py b/configs/densenet/densenet161_4xb256_in1k.py new file mode 100644 index 0000000..4581d1d --- /dev/null +++ b/configs/densenet/densenet161_4xb256_in1k.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/densenet/densenet161.py', + '../_base_/datasets/imagenet_bs64.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=256) + +runner = dict(type='EpochBasedRunner', max_epochs=90) diff --git a/configs/densenet/densenet169_4xb256_in1k.py b/configs/densenet/densenet169_4xb256_in1k.py new file mode 100644 index 0000000..6179293 --- /dev/null +++ b/configs/densenet/densenet169_4xb256_in1k.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/densenet/densenet169.py', + '../_base_/datasets/imagenet_bs64.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=256) + +runner = dict(type='EpochBasedRunner', max_epochs=90) diff --git a/configs/densenet/densenet201_4xb256_in1k.py b/configs/densenet/densenet201_4xb256_in1k.py new file mode 100644 index 0000000..897a141 --- /dev/null +++ b/configs/densenet/densenet201_4xb256_in1k.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/densenet/densenet201.py', + '../_base_/datasets/imagenet_bs64.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=256) + +runner = dict(type='EpochBasedRunner', max_epochs=90) diff --git a/configs/densenet/metafile.yml b/configs/densenet/metafile.yml new file mode 100644 index 0000000..84366b2 --- /dev/null +++ b/configs/densenet/metafile.yml @@ -0,0 +1,76 @@ +Collections: + - Name: DenseNet + Metadata: + Training Data: ImageNet-1k + Architecture: + - DenseBlock + Paper: + URL: https://arxiv.org/abs/1608.06993 + Title: Densely Connected Convolutional Networks + README: configs/densenet/README.md + +Models: + - Name: densenet121_4xb256_in1k + Metadata: + FLOPs: 2881695488 + Parameters: 7978856 + In Collections: DenseNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 74.96 + Top 5 Accuracy: 92.21 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth + Config: configs/densenet/densenet121_4xb256_in1k.py + Converted From: + Weights: https://download.pytorch.org/models/densenet121-a639ec97.pth + Code: https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py + - Name: densenet169_4xb256_in1k + Metadata: + FLOPs: 3416860160 + Parameters: 14149480 + In Collections: DenseNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 76.08 + Top 5 Accuracy: 93.11 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth + Config: configs/densenet/densenet169_4xb256_in1k.py + Converted From: + Weights: https://download.pytorch.org/models/densenet169-b2777c0a.pth + Code: https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py + - Name: densenet201_4xb256_in1k + Metadata: + FLOPs: 4365236736 + Parameters: 20013928 + In Collections: DenseNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.32 + Top 5 Accuracy: 93.64 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth + Config: configs/densenet/densenet201_4xb256_in1k.py + Converted From: + Weights: https://download.pytorch.org/models/densenet201-c1103571.pth + Code: https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py + - Name: densenet161_4xb256_in1k + Metadata: + FLOPs: 7816363968 + Parameters: 28681000 + In Collections: DenseNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.61 + Top 5 Accuracy: 93.83 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth + Config: configs/densenet/densenet161_4xb256_in1k.py + Converted From: + Weights: https://download.pytorch.org/models/densenet161-8d451a50.pth + Code: https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py diff --git a/configs/efficientformer/README.md b/configs/efficientformer/README.md new file mode 100644 index 0000000..ecd6b49 --- /dev/null +++ b/configs/efficientformer/README.md @@ -0,0 +1,47 @@ +# EfficientFormer + +> [EfficientFormer: Vision Transformers at MobileNet Speed](https://arxiv.org/abs/2206.01191) + + + +## Abstract + +Vision Transformers (ViT) have shown rapid progress in computer vision tasks, achieving promising results on various benchmarks. However, due to the massive number of parameters and model design, e.g., attention mechanism, ViT-based models are generally times slower than lightweight convolutional networks. Therefore, the deployment of ViT for real-time applications is particularly challenging, especially on resource-constrained hardware such as mobile devices. Recent efforts try to reduce the computation complexity of ViT through network architecture search or hybrid design with MobileNet block, yet the inference speed is still unsatisfactory. This leads to an important question: can transformers run as fast as MobileNet while obtaining high performance? To answer this, we first revisit the network architecture and operators used in ViT-based models and identify inefficient designs. Then we introduce a dimension-consistent pure transformer (without MobileNet blocks) as a design paradigm. Finally, we perform latency-driven slimming to get a series of final models dubbed EfficientFormer. Extensive experiments show the superiority of EfficientFormer in performance and speed on mobile devices. Our fastest model, EfficientFormer-L1, achieves 79.2% top-1 accuracy on ImageNet-1K with only 1.6 ms inference latency on iPhone 12 (compiled with CoreML), which runs as fast as MobileNetV2×1.4 (1.6 ms, 74.7% top-1), and our largest model, EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work proves that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------: | :------------------------------------------------------------------------: | +| EfficientFormer-l1\* | 12.19 | 1.30 | 80.46 | 94.99 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) | +| EfficientFormer-l3\* | 31.41 | 3.93 | 82.45 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) | +| EfficientFormer-l7\* | 82.23 | 10.16 | 83.40 | 96.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) | + +*Models with * are converted from the [official repo](https://github.com/snap-research/EfficientFormer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@misc{https://doi.org/10.48550/arxiv.2206.01191, + doi = {10.48550/ARXIV.2206.01191}, + + url = {https://arxiv.org/abs/2206.01191}, + + author = {Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian}, + + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + + title = {EfficientFormer: Vision Transformers at MobileNet Speed}, + + publisher = {arXiv}, + + year = {2022}, + + copyright = {Creative Commons Attribution 4.0 International} +} +``` diff --git a/configs/efficientformer/efficientformer-l1_8xb128_in1k.py b/configs/efficientformer/efficientformer-l1_8xb128_in1k.py new file mode 100644 index 0000000..f5db2bf --- /dev/null +++ b/configs/efficientformer/efficientformer-l1_8xb128_in1k.py @@ -0,0 +1,24 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +model = dict( + type='ImageClassifier', + backbone=dict( + type='EfficientFormer', + arch='l1', + drop_path_rate=0, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-5) + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='EfficientFormerClsHead', in_channels=448, num_classes=1000)) diff --git a/configs/efficientformer/efficientformer-l3_8xb128_in1k.py b/configs/efficientformer/efficientformer-l3_8xb128_in1k.py new file mode 100644 index 0000000..e920f78 --- /dev/null +++ b/configs/efficientformer/efficientformer-l3_8xb128_in1k.py @@ -0,0 +1,24 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +model = dict( + type='ImageClassifier', + backbone=dict( + type='EfficientFormer', + arch='l3', + drop_path_rate=0, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-5) + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='EfficientFormerClsHead', in_channels=512, num_classes=1000)) diff --git a/configs/efficientformer/efficientformer-l7_8xb128_in1k.py b/configs/efficientformer/efficientformer-l7_8xb128_in1k.py new file mode 100644 index 0000000..a59e3a7 --- /dev/null +++ b/configs/efficientformer/efficientformer-l7_8xb128_in1k.py @@ -0,0 +1,24 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +model = dict( + type='ImageClassifier', + backbone=dict( + type='EfficientFormer', + arch='l7', + drop_path_rate=0, + init_cfg=[ + dict( + type='TruncNormal', + layer=['Conv2d', 'Linear'], + std=.02, + bias=0.), + dict(type='Constant', layer=['GroupNorm'], val=1., bias=0.), + dict(type='Constant', layer=['LayerScale'], val=1e-5) + ]), + neck=dict(type='GlobalAveragePooling', dim=1), + head=dict( + type='EfficientFormerClsHead', in_channels=768, num_classes=1000)) diff --git a/configs/efficientformer/metafile.yml b/configs/efficientformer/metafile.yml new file mode 100644 index 0000000..33c4786 --- /dev/null +++ b/configs/efficientformer/metafile.yml @@ -0,0 +1,67 @@ +Collections: + - Name: EfficientFormer + Metadata: + Training Data: ImageNet-1k + Architecture: + - Pooling + - 1x1 Convolution + - LayerScale + - MetaFormer + Paper: + URL: https://arxiv.org/pdf/2206.01191.pdf + Title: "EfficientFormer: Vision Transformers at MobileNet Speed" + README: configs/efficientformer/README.md + Code: + Version: v0.24.0 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/efficientformer.py + +Models: + - Name: efficientformer-l1_3rdparty_8xb128_in1k + Metadata: + FLOPs: 1304601088 # 1.3G + Parameters: 12278696 # 12M + In Collections: EfficientFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.46 + Top 5 Accuracy: 94.99 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth + Config: configs/efficientformer/efficientformer-l1_8xb128_in1k.py + Converted From: + Weights: https://drive.google.com/file/d/11SbX-3cfqTOc247xKYubrAjBiUmr818y/view?usp=sharing + Code: https://github.com/snap-research/EfficientFormer + - Name: efficientformer-l3_3rdparty_8xb128_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 3737045760 # 3.7G + Parameters: 31406000 # 31M + In Collections: EfficientFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.45 + Top 5 Accuracy: 96.18 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth + Config: configs/efficientformer/efficientformer-l3_8xb128_in1k.py + Converted From: + Weights: https://drive.google.com/file/d/1OyyjKKxDyMj-BcfInp4GlDdwLu3hc30m/view?usp=sharing + Code: https://github.com/snap-research/EfficientFormer + - Name: efficientformer-l7_3rdparty_8xb128_in1k + Metadata: + FLOPs: 10163951616 # 10.2G + Parameters: 82229328 # 82M + In Collections: EfficientFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.40 + Top 5 Accuracy: 96.60 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth + Config: configs/efficientformer/efficientformer-l7_8xb128_in1k.py + Converted From: + Weights: https://drive.google.com/file/d/1cVw-pctJwgvGafeouynqWWCwgkcoFMM5/view?usp=sharing + Code: https://github.com/snap-research/EfficientFormer diff --git a/configs/efficientnet/README.md b/configs/efficientnet/README.md new file mode 100644 index 0000000..832f5c6 --- /dev/null +++ b/configs/efficientnet/README.md @@ -0,0 +1,62 @@ +# EfficientNet + +> [Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946v5) + + + +## Abstract + +Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. + +
+ +
+ +## Results and models + +### ImageNet-1k + +In the result table, AA means trained with AutoAugment pre-processing, more details can be found in the [paper](https://arxiv.org/abs/1805.09501), and AdvProp is a method to train with adversarial examples, more details can be found in the [paper](https://arxiv.org/abs/1911.09665). + +Note: In MMClassification, we support training with AutoAugment, don't support AdvProp by now. + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------------------------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------: | :------------------------------------------------------------------: | +| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) | +| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) | +| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) | +| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) | +| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) | +| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) | +| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) | +| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) | +| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) | +| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) | +| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) | +| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) | +| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) | +| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) | +| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) | +| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) | +| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) | +| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) | +| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) | +| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) | +| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) | +| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) | +| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) | + +*Models with * are converted from the [official repo](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@inproceedings{tan2019efficientnet, + title={Efficientnet: Rethinking model scaling for convolutional neural networks}, + author={Tan, Mingxing and Le, Quoc}, + booktitle={International Conference on Machine Learning}, + pages={6105--6114}, + year={2019}, + organization={PMLR} +} +``` diff --git a/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py new file mode 100644 index 0000000..fbb490d --- /dev/null +++ b/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b0.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=224, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b0_8xb32_in1k.py b/configs/efficientnet/efficientnet-b0_8xb32_in1k.py new file mode 100644 index 0000000..33931e5 --- /dev/null +++ b/configs/efficientnet/efficientnet-b0_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b0.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=224, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py new file mode 100644 index 0000000..6b66395 --- /dev/null +++ b/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b1.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=240, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=240, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b1_8xb32_in1k.py b/configs/efficientnet/efficientnet-b1_8xb32_in1k.py new file mode 100644 index 0000000..d702a15 --- /dev/null +++ b/configs/efficientnet/efficientnet-b1_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b1.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=240, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=240, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py new file mode 100644 index 0000000..ae8cda8 --- /dev/null +++ b/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b2.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=260, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=260, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b2_8xb32_in1k.py b/configs/efficientnet/efficientnet-b2_8xb32_in1k.py new file mode 100644 index 0000000..53f7c84 --- /dev/null +++ b/configs/efficientnet/efficientnet-b2_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b2.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=260, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=260, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py new file mode 100644 index 0000000..dfd3f92 --- /dev/null +++ b/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b3.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=300, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=300, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b3_8xb32_in1k.py b/configs/efficientnet/efficientnet-b3_8xb32_in1k.py new file mode 100644 index 0000000..2838713 --- /dev/null +++ b/configs/efficientnet/efficientnet-b3_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b3.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=300, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=300, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py new file mode 100644 index 0000000..333a19a --- /dev/null +++ b/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b4.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=380, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=380, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b4_8xb32_in1k.py b/configs/efficientnet/efficientnet-b4_8xb32_in1k.py new file mode 100644 index 0000000..82f06cd --- /dev/null +++ b/configs/efficientnet/efficientnet-b4_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b4.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=380, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=380, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py new file mode 100644 index 0000000..f66855c --- /dev/null +++ b/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b5.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=456, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=456, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b5_8xb32_in1k.py b/configs/efficientnet/efficientnet-b5_8xb32_in1k.py new file mode 100644 index 0000000..9b0eaab --- /dev/null +++ b/configs/efficientnet/efficientnet-b5_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b5.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=456, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=456, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py new file mode 100644 index 0000000..da64e0e --- /dev/null +++ b/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b6.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=528, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=528, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b6_8xb32_in1k.py b/configs/efficientnet/efficientnet-b6_8xb32_in1k.py new file mode 100644 index 0000000..6e03bb4 --- /dev/null +++ b/configs/efficientnet/efficientnet-b6_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b6.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=528, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=528, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py new file mode 100644 index 0000000..27c19fc --- /dev/null +++ b/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b7.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=600, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=600, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b7_8xb32_in1k.py b/configs/efficientnet/efficientnet-b7_8xb32_in1k.py new file mode 100644 index 0000000..5146383 --- /dev/null +++ b/configs/efficientnet/efficientnet-b7_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b7.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=600, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=600, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py new file mode 100644 index 0000000..25540a1 --- /dev/null +++ b/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b8.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=672, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=672, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-b8_8xb32_in1k.py b/configs/efficientnet/efficientnet-b8_8xb32_in1k.py new file mode 100644 index 0000000..4ff28c0 --- /dev/null +++ b/configs/efficientnet/efficientnet-b8_8xb32_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_b8.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=672, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=672, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-em_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-em_8xb32-01norm_in1k.py new file mode 100644 index 0000000..faa5386 --- /dev/null +++ b/configs/efficientnet/efficientnet-em_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_em.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=240, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=240, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/efficientnet-es_8xb32-01norm_in1k.py b/configs/efficientnet/efficientnet-es_8xb32-01norm_in1k.py new file mode 100644 index 0000000..5f11746 --- /dev/null +++ b/configs/efficientnet/efficientnet-es_8xb32-01norm_in1k.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/efficientnet_es.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=224, + efficientnet_style=True, + interpolation='bicubic'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/efficientnet/metafile.yml b/configs/efficientnet/metafile.yml new file mode 100644 index 0000000..c8bbf0d --- /dev/null +++ b/configs/efficientnet/metafile.yml @@ -0,0 +1,391 @@ +Collections: + - Name: EfficientNet + Metadata: + Training Data: ImageNet-1k + Architecture: + - 1x1 Convolution + - Average Pooling + - Convolution + - Dense Connections + - Dropout + - Inverted Residual Block + - RMSProp + - Squeeze-and-Excitation Block + - Swish + Paper: + URL: https://arxiv.org/abs/1905.11946v5 + Title: "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks" + README: configs/efficientnet/README.md + Code: + Version: v0.20.1 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/efficientnet.py + +Models: + - Name: efficientnet-b0_3rdparty_8xb32_in1k + Metadata: + FLOPs: 16481180 + Parameters: 5288548 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 76.74 + Top 5 Accuracy: 93.17 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth + Config: configs/efficientnet/efficientnet-b0_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b0.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b0_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 16481180 + Parameters: 5288548 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.26 + Top 5 Accuracy: 93.41 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth + Config: configs/efficientnet/efficientnet-b0_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b0.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 16481180 + Parameters: 5288548 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.53 + Top 5 Accuracy: 93.61 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth + Config: configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b0.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b1_3rdparty_8xb32_in1k + Metadata: + FLOPs: 27052224 + Parameters: 7794184 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.68 + Top 5 Accuracy: 94.28 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth + Config: configs/efficientnet/efficientnet-b1_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b1.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b1_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 27052224 + Parameters: 7794184 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.20 + Top 5 Accuracy: 94.42 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth + Config: configs/efficientnet/efficientnet-b1_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b1.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 27052224 + Parameters: 7794184 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.52 + Top 5 Accuracy: 94.43 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth + Config: configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b1.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b2_3rdparty_8xb32_in1k + Metadata: + FLOPs: 34346386 + Parameters: 9109994 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.64 + Top 5 Accuracy: 94.80 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth + Config: configs/efficientnet/efficientnet-b2_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b2.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b2_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 34346386 + Parameters: 9109994 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.21 + Top 5 Accuracy: 94.96 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth + Config: configs/efficientnet/efficientnet-b2_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b2.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 34346386 + Parameters: 9109994 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.45 + Top 5 Accuracy: 95.07 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth + Config: configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b2.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b3_3rdparty_8xb32_in1k + Metadata: + FLOPs: 58641904 + Parameters: 12233232 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.01 + Top 5 Accuracy: 95.34 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth + Config: configs/efficientnet/efficientnet-b3_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b3.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b3_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 58641904 + Parameters: 12233232 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.58 + Top 5 Accuracy: 95.67 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth + Config: configs/efficientnet/efficientnet-b3_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b3.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 58641904 + Parameters: 12233232 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.81 + Top 5 Accuracy: 95.69 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth + Config: configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b3.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b4_3rdparty_8xb32_in1k + Metadata: + FLOPs: 121870624 + Parameters: 19341616 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.57 + Top 5 Accuracy: 96.09 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth + Config: configs/efficientnet/efficientnet-b4_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b4.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b4_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 121870624 + Parameters: 19341616 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.95 + Top 5 Accuracy: 96.26 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth + Config: configs/efficientnet/efficientnet-b4_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b4.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 121870624 + Parameters: 19341616 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.25 + Top 5 Accuracy: 96.44 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth + Config: configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b4.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b5_3rdparty_8xb32_in1k + Metadata: + FLOPs: 243879440 + Parameters: 30389784 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.18 + Top 5 Accuracy: 96.47 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth + Config: configs/efficientnet/efficientnet-b5_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckpts/efficientnet-b5.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b5_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 243879440 + Parameters: 30389784 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.82 + Top 5 Accuracy: 96.76 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth + Config: configs/efficientnet/efficientnet-b5_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b5.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 243879440 + Parameters: 30389784 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.21 + Top 5 Accuracy: 96.98 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth + Config: configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b5.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b6_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 412002408 + Parameters: 43040704 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.05 + Top 5 Accuracy: 96.82 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth + Config: configs/efficientnet/efficientnet-b6_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b6.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 412002408 + Parameters: 43040704 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.74 + Top 5 Accuracy: 97.14 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth + Config: configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b6.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b7_3rdparty_8xb32-aa_in1k + Metadata: + FLOPs: 715526512 + Parameters: 66347960 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.38 + Top 5 Accuracy: 96.88 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth + Config: configs/efficientnet/efficientnet-b7_8xb32_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-b7.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 715526512 + Parameters: 66347960 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 85.14 + Top 5 Accuracy: 97.23 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth + Config: configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b7.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet + - Name: efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k + Metadata: + FLOPs: 1092755326 + Parameters: 87413142 + In Collections: EfficientNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 85.38 + Top 5 Accuracy: 97.28 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth + Config: configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py + Converted From: + Weights: https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/advprop/efficientnet-b8.tar.gz + Code: https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet diff --git a/configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py b/configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py new file mode 100644 index 0000000..9075a89 --- /dev/null +++ b/configs/fp16/resnet50_b32x8_fp16_dynamic_imagenet.py @@ -0,0 +1,6 @@ +_base_ = '../resnet/resnet50_8xb32-fp16-dynamic_in1k.py' + +_deprecation_ = dict( + expected='../resnet/resnet50_8xb32-fp16-dynamic_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/fp16/resnet50_b32x8_fp16_imagenet.py b/configs/fp16/resnet50_b32x8_fp16_imagenet.py new file mode 100644 index 0000000..a73a409 --- /dev/null +++ b/configs/fp16/resnet50_b32x8_fp16_imagenet.py @@ -0,0 +1,6 @@ +_base_ = '../resnet/resnet50_8xb32-fp16_in1k.py' + +_deprecation_ = dict( + expected='../resnet/resnet50_8xb32-fp16_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/gpvit/gpvit_l1.py b/configs/gpvit/gpvit_l1.py new file mode 100644 index 0000000..56a4874 --- /dev/null +++ b/configs/gpvit/gpvit_l1.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs64_swin_224_lmdb.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='GPViT', + arch='L1', + img_size=224, + drop_path_rate=-1, # dpr is in arch config + att_with_cp=False, + group_with_cp=False), + neck=dict(type='GroupNeck', embed_dims=216), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=216, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + topk=(1, 5)), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# data settings +samples_per_gpu=128 +data = dict(samples_per_gpu=samples_per_gpu, workers_per_gpu=4) + +# opt settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + }) +world_size = 16 +optimizer = dict( + lr=5e-4 * samples_per_gpu * world_size / 512, + paramwise_cfg=paramwise_cfg) +lr_config = dict(warmup_iters=15) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# other running settings +checkpoint_config = dict(interval=5, max_keep_ckpts=5) +evaluation = dict(interval=5, metric='accuracy') +fp16 = None # make sure fp16 (mm version) is None when using AMP optimizer +runner = dict(type='AmpEpochBasedRunner') +work_dir = 'work_dirs/gpvit_l1' diff --git a/configs/gpvit/gpvit_l2.py b/configs/gpvit/gpvit_l2.py new file mode 100644 index 0000000..5261395 --- /dev/null +++ b/configs/gpvit/gpvit_l2.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs64_swin_224_lmdb.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='GPViT', + arch='L2', + img_size=224, + drop_path_rate=-1, # dpr is in arch config + att_with_cp=False, + group_with_cp=False), + neck=dict(type='GroupNeck', embed_dims=348), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=348, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + topk=(1, 5)), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# data settings +samples_per_gpu=128 +data = dict(samples_per_gpu=samples_per_gpu, workers_per_gpu=4) + +# opt settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + }) +world_size = 16 +optimizer = dict( + lr=5e-4 * samples_per_gpu * world_size / 512, + paramwise_cfg=paramwise_cfg) +lr_config = dict(warmup_iters=15) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# other running settings +checkpoint_config = dict(interval=5, max_keep_ckpts=5) +evaluation = dict(interval=5, metric='accuracy') +fp16 = None # make sure fp16 (mm version) is None when using AMP optimizer +runner = dict(type='AmpEpochBasedRunner') +work_dir = 'work_dirs/gpvit_l2' diff --git a/configs/gpvit/gpvit_l3.py b/configs/gpvit/gpvit_l3.py new file mode 100644 index 0000000..0fb1868 --- /dev/null +++ b/configs/gpvit/gpvit_l3.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs64_swin_224_lmdb.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='GPViT', + arch='L3', + img_size=224, + drop_path_rate=-1, # dpr is in arch config + att_with_cp=False, + group_with_cp=False), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=432, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + topk=(1, 5)), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# data settings +samples_per_gpu=128 +data = dict(samples_per_gpu=samples_per_gpu, workers_per_gpu=4) + +# opt settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + }) +world_size = 16 +optimizer = dict( + lr=5e-4 * samples_per_gpu * world_size / 512, + paramwise_cfg=paramwise_cfg) +lr_config = dict(warmup_iters=15) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# other running settings +checkpoint_config = dict(interval=5, max_keep_ckpts=5) +evaluation = dict(interval=5, metric='accuracy') +fp16 = None # make sure fp16 (mm version) is None when using AMP optimizer +runner = dict(type='AmpEpochBasedRunner') +work_dir = 'work_dirs/gpvit_l3' diff --git a/configs/gpvit/gpvit_l4.py b/configs/gpvit/gpvit_l4.py new file mode 100644 index 0000000..9bddb42 --- /dev/null +++ b/configs/gpvit/gpvit_l4.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/datasets/imagenet_bs64_swin_224_lmdb.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='ImageClassifier', + backbone=dict( + type='GPViT', + arch='L4', + img_size=224, + drop_path_rate=-1, # dpr is in arch config + att_with_cp=False, + group_with_cp=False), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=624, + loss=dict( + type='LabelSmoothLoss', label_smooth_val=0.1, mode='original'), + topk=(1, 5)), + init_cfg=[ + dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.), + dict(type='Constant', layer='LayerNorm', val=1., bias=0.) + ], + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# data settings +samples_per_gpu=128 +data = dict(samples_per_gpu=samples_per_gpu, workers_per_gpu=4) + +# opt settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + }) +world_size = 16 +optimizer = dict( + lr=5e-4 * samples_per_gpu * world_size / 512, + paramwise_cfg=paramwise_cfg) +lr_config = dict(warmup_iters=15) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# other running settings +checkpoint_config = dict(interval=5, max_keep_ckpts=5) +evaluation = dict(interval=5, metric='accuracy') +fp16 = None # make sure fp16 (mm version) is None when using AMP optimizer +runner = dict(type='AmpEpochBasedRunner') +work_dir = 'work_dirs/gpvit_l4' diff --git a/configs/hornet/README.md b/configs/hornet/README.md new file mode 100644 index 0000000..7c1b9a9 --- /dev/null +++ b/configs/hornet/README.md @@ -0,0 +1,51 @@ +# HorNet + +> [HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions](https://arxiv.org/pdf/2207.14284v2.pdf) + + + +## Abstract + +Recent progress in vision Transformers exhibits great success in various tasks driven by the new spatial modeling mechanism based on dot-product self-attention. In this paper, we show that the key ingredients behind the vision Transformers, namely input-adaptive, long-range and high-order spatial interactions, can also be efficiently implemented with a convolution-based framework. We present the Recursive Gated Convolution (g nConv) that performs high-order spatial interactions with gated convolutions and recursive designs. The new operation is highly flexible and customizable, which is compatible with various variants of convolution and extends the two-order interactions in self-attention to arbitrary orders without introducing significant extra computation. g nConv can serve as a plug-and-play module to improve various vision Transformers and convolution-based models. Based on the operation, we construct a new family of generic vision backbones named HorNet. Extensive experiments on ImageNet classification, COCO object detection and ADE20K semantic segmentation show HorNet outperform Swin Transformers and ConvNeXt by a significant margin with similar overall architecture and training configurations. HorNet also shows favorable scalability to more training data and a larger model size. Apart from the effectiveness in visual encoders, we also show g nConv can be applied to task-specific decoders and consistently improve dense prediction performance with less computation. Our results demonstrate that g nConv can be a new basic module for visual modeling that effectively combines the merits of both vision Transformers and CNNs. Code is available at https://github.com/raoyongming/HorNet. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------: | :----------------------------------------------------------------: | +| HorNet-T\* | From scratch | 224x224 | 22.41 | 3.98 | 82.84 | 96.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny_3rdparty_in1k_20220915-0e8eedff.pth) | +| HorNet-T-GF\* | From scratch | 224x224 | 22.99 | 3.9 | 82.98 | 96.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-tiny-gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny-gf_3rdparty_in1k_20220915-4c35a66b.pth) | +| HorNet-S\* | From scratch | 224x224 | 49.53 | 8.83 | 83.79 | 96.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-small_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small_3rdparty_in1k_20220915-5935f60f.pth) | +| HorNet-S-GF\* | From scratch | 224x224 | 50.4 | 8.71 | 83.98 | 96.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-small-gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small-gf_3rdparty_in1k_20220915-649ca492.pth) | +| HorNet-B\* | From scratch | 224x224 | 87.26 | 15.59 | 84.24 | 96.94 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-base_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base_3rdparty_in1k_20220915-a06176bb.pth) | +| HorNet-B-GF\* | From scratch | 224x224 | 88.42 | 15.42 | 84.32 | 96.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hornet/hornet-base-gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base-gf_3rdparty_in1k_20220915-82c06fa7.pth) | + +\*Models with * are converted from [the official repo](https://github.com/raoyongming/HorNet). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. + +### Pre-trained Models + +The pre-trained models on ImageNet-21k are used to fine-tune on the downstream tasks. + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Download | +| :--------------: | :----------: | :--------: | :-------: | :------: | :------------------------------------------------------------------------------------------------------------------------: | +| HorNet-L\* | ImageNet-21k | 224x224 | 194.54 | 34.83 | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large_3rdparty_in21k_20220909-9ccef421.pth) | +| HorNet-L-GF\* | ImageNet-21k | 224x224 | 196.29 | 34.58 | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large-gf_3rdparty_in21k_20220909-3aea3b61.pth) | +| HorNet-L-GF384\* | ImageNet-21k | 384x384 | 201.23 | 101.63 | [model](https://download.openmmlab.com/mmclassification/v0/hornet/hornet-large-gf384_3rdparty_in21k_20220909-80894290.pth) | + +\*Models with * are converted from [the official repo](https://github.com/raoyongming/HorNet). + +## Citation + +``` +@article{rao2022hornet, + title={HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions}, + author={Rao, Yongming and Zhao, Wenliang and Tang, Yansong and Zhou, Jie and Lim, Ser-Lam and Lu, Jiwen}, + journal={arXiv preprint arXiv:2207.14284}, + year={2022} +} +``` diff --git a/configs/hornet/hornet-base-gf_8xb64_in1k.py b/configs/hornet/hornet-base-gf_8xb64_in1k.py new file mode 100644 index 0000000..6c29de6 --- /dev/null +++ b/configs/hornet/hornet-base-gf_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-base-gf.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-base_8xb64_in1k.py b/configs/hornet/hornet-base_8xb64_in1k.py new file mode 100644 index 0000000..969d8b9 --- /dev/null +++ b/configs/hornet/hornet-base_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-base.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=5.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-small-gf_8xb64_in1k.py b/configs/hornet/hornet-small-gf_8xb64_in1k.py new file mode 100644 index 0000000..deb570e --- /dev/null +++ b/configs/hornet/hornet-small-gf_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-small-gf.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-small_8xb64_in1k.py b/configs/hornet/hornet-small_8xb64_in1k.py new file mode 100644 index 0000000..c07fa60 --- /dev/null +++ b/configs/hornet/hornet-small_8xb64_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-small.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=64) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=5.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-tiny-gf_8xb128_in1k.py b/configs/hornet/hornet-tiny-gf_8xb128_in1k.py new file mode 100644 index 0000000..3a1d1a7 --- /dev/null +++ b/configs/hornet/hornet-tiny-gf_8xb128_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-tiny-gf.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=128) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=1.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/hornet-tiny_8xb128_in1k.py b/configs/hornet/hornet-tiny_8xb128_in1k.py new file mode 100644 index 0000000..69a7cdf --- /dev/null +++ b/configs/hornet/hornet-tiny_8xb128_in1k.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/hornet/hornet-tiny.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +data = dict(samples_per_gpu=128) + +optimizer = dict(lr=4e-3) +optimizer_config = dict(grad_clip=dict(max_norm=100.0), _delete_=True) + +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] diff --git a/configs/hornet/metafile.yml b/configs/hornet/metafile.yml new file mode 100644 index 0000000..7120772 --- /dev/null +++ b/configs/hornet/metafile.yml @@ -0,0 +1,97 @@ +Collections: + - Name: HorNet + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - AdamW + - Weight Decay + Architecture: + - HorNet + - gnConv + Paper: + URL: https://arxiv.org/pdf/2207.14284v2.pdf + Title: "HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions" + README: configs/hornet/README.md + Code: + Version: v0.24.0 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/hornet.py + +Models: + - Name: hornet-tiny_3rdparty_in1k + Metadata: + FLOPs: 3980000000 # 3.98G + Parameters: 22410000 # 22.41M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.84 + Top 5 Accuracy: 96.24 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny_3rdparty_in1k_20220915-0e8eedff.pth + Config: configs/hornet/hornet-tiny_8xb128_in1k.py + - Name: hornet-tiny-gf_3rdparty_in1k + Metadata: + FLOPs: 3900000000 # 3.9G + Parameters: 22990000 # 22.99M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.98 + Top 5 Accuracy: 96.38 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-tiny-gf_3rdparty_in1k_20220915-4c35a66b.pth + Config: configs/hornet/hornet-tiny-gf_8xb128_in1k.py + - Name: hornet-small_3rdparty_in1k + Metadata: + FLOPs: 8830000000 # 8.83G + Parameters: 49530000 # 49.53M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.79 + Top 5 Accuracy: 96.75 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small_3rdparty_in1k_20220915-5935f60f.pth + Config: configs/hornet/hornet-small_8xb64_in1k.py + - Name: hornet-small-gf_3rdparty_in1k + Metadata: + FLOPs: 8710000000 # 8.71G + Parameters: 50400000 # 50.4M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.98 + Top 5 Accuracy: 96.77 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-small-gf_3rdparty_in1k_20220915-649ca492.pth + Config: configs/hornet/hornet-small-gf_8xb64_in1k.py + - Name: hornet-base_3rdparty_in1k + Metadata: + FLOPs: 15590000000 # 15.59G + Parameters: 87260000 # 87.26M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.24 + Top 5 Accuracy: 96.94 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base_3rdparty_in1k_20220915-a06176bb.pth + Config: configs/hornet/hornet-base_8xb64_in1k.py + - Name: hornet-base-gf_3rdparty_in1k + Metadata: + FLOPs: 15420000000 # 15.42G + Parameters: 88420000 # 88.42M + In Collection: HorNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.32 + Top 5 Accuracy: 96.95 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hornet/hornet-base-gf_3rdparty_in1k_20220915-82c06fa7.pth + Config: configs/hornet/hornet-base-gf_8xb64_in1k.py diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md new file mode 100644 index 0000000..0a30ccd --- /dev/null +++ b/configs/hrnet/README.md @@ -0,0 +1,44 @@ +# HRNet + +> [Deep High-Resolution Representation Learning for Visual Recognition](https://arxiv.org/abs/1908.07919v2) + + + +## Abstract + +High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions *in series* (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams *in parallel*; (ii) Repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. + +
+ +
+ +## Results and models + +## ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) | +| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) | +| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) | +| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) | +| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) | +| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) | +| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) | +| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) | +| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) | + +*Models with * are converted from the [official repo](https://github.com/HRNet/HRNet-Image-Classification). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@article{WangSCJDZLMTWLX19, + title={Deep High-Resolution Representation Learning for Visual Recognition}, + author={Jingdong Wang and Ke Sun and Tianheng Cheng and + Borui Jiang and Chaorui Deng and Yang Zhao and Dong Liu and Yadong Mu and + Mingkui Tan and Xinggang Wang and Wenyu Liu and Bin Xiao}, + journal = {TPAMI} + year={2019} +} +``` diff --git a/configs/hrnet/hrnet-w18_4xb32_in1k.py b/configs/hrnet/hrnet-w18_4xb32_in1k.py new file mode 100644 index 0000000..a84fe67 --- /dev/null +++ b/configs/hrnet/hrnet-w18_4xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/hrnet/hrnet-w18.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/hrnet/hrnet-w30_4xb32_in1k.py b/configs/hrnet/hrnet-w30_4xb32_in1k.py new file mode 100644 index 0000000..d2a9c0d --- /dev/null +++ b/configs/hrnet/hrnet-w30_4xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/hrnet/hrnet-w30.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/hrnet/hrnet-w32_4xb32_in1k.py b/configs/hrnet/hrnet-w32_4xb32_in1k.py new file mode 100644 index 0000000..91380a9 --- /dev/null +++ b/configs/hrnet/hrnet-w32_4xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/hrnet/hrnet-w32.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/hrnet/hrnet-w40_4xb32_in1k.py b/configs/hrnet/hrnet-w40_4xb32_in1k.py new file mode 100644 index 0000000..5d35cec --- /dev/null +++ b/configs/hrnet/hrnet-w40_4xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/hrnet/hrnet-w40.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/hrnet/hrnet-w44_4xb32_in1k.py b/configs/hrnet/hrnet-w44_4xb32_in1k.py new file mode 100644 index 0000000..ce6bb41 --- /dev/null +++ b/configs/hrnet/hrnet-w44_4xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/hrnet/hrnet-w44.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/hrnet/hrnet-w48_4xb32_in1k.py b/configs/hrnet/hrnet-w48_4xb32_in1k.py new file mode 100644 index 0000000..6943892 --- /dev/null +++ b/configs/hrnet/hrnet-w48_4xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/hrnet/hrnet-w48.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/hrnet/hrnet-w64_4xb32_in1k.py b/configs/hrnet/hrnet-w64_4xb32_in1k.py new file mode 100644 index 0000000..0009bc6 --- /dev/null +++ b/configs/hrnet/hrnet-w64_4xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/hrnet/hrnet-w64.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml new file mode 100644 index 0000000..64fe142 --- /dev/null +++ b/configs/hrnet/metafile.yml @@ -0,0 +1,162 @@ +Collections: + - Name: HRNet + Metadata: + Training Data: ImageNet-1k + Architecture: + - Batch Normalization + - Convolution + - ReLU + - Residual Connection + Paper: + URL: https://arxiv.org/abs/1908.07919v2 + Title: "Deep High-Resolution Representation Learning for Visual Recognition" + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/hrnet.py + Version: v0.20.1 + +Models: + - Name: hrnet-w18_3rdparty_8xb32_in1k + Metadata: + FLOPs: 4330397932 + Parameters: 21295164 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 76.75 + Top 5 Accuracy: 93.44 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth + Config: configs/hrnet/hrnet-w18_4xb32_in1k.py + Converted From: + Weights: https://1drv.ms/u/s!Aus8VCZ_C_33cMkPimlmClRvmpw + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w30_3rdparty_8xb32_in1k + Metadata: + FLOPs: 8168305684 + Parameters: 37708380 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.19 + Top 5 Accuracy: 94.22 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth + Config: configs/hrnet/hrnet-w30_4xb32_in1k.py + Converted From: + Weights: https://1drv.ms/u/s!Aus8VCZ_C_33cQoACCEfrzcSaVI + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w32_3rdparty_8xb32_in1k + Metadata: + FLOPs: 8986267584 + Parameters: 41228840 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.44 + Top 5 Accuracy: 94.19 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth + Config: configs/hrnet/hrnet-w32_4xb32_in1k.py + Converted From: + Weights: https://1drv.ms/u/s!Aus8VCZ_C_33dYBMemi9xOUFR0w + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w40_3rdparty_8xb32_in1k + Metadata: + FLOPs: 12767574064 + Parameters: 57553320 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.94 + Top 5 Accuracy: 94.47 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth + Config: configs/hrnet/hrnet-w40_4xb32_in1k.py + Converted From: + Weights: https://1drv.ms/u/s!Aus8VCZ_C_33ck0gvo5jfoWBOPo + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w44_3rdparty_8xb32_in1k + Metadata: + FLOPs: 14963902632 + Parameters: 67061144 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.88 + Top 5 Accuracy: 94.37 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth + Config: configs/hrnet/hrnet-w44_4xb32_in1k.py + Converted From: + Weights: https://1drv.ms/u/s!Aus8VCZ_C_33czZQ0woUb980gRs + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w48_3rdparty_8xb32_in1k + Metadata: + FLOPs: 17364014752 + Parameters: 77466024 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.32 + Top 5 Accuracy: 94.52 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth + Config: configs/hrnet/hrnet-w48_4xb32_in1k.py + Converted From: + Weights: https://1drv.ms/u/s!Aus8VCZ_C_33dKvqI6pBZlifgJk + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w64_3rdparty_8xb32_in1k + Metadata: + FLOPs: 29002298752 + Parameters: 128056104 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.46 + Top 5 Accuracy: 94.65 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth + Config: configs/hrnet/hrnet-w64_4xb32_in1k.py + Converted From: + Weights: https://1drv.ms/u/s!Aus8VCZ_C_33gQbJsUPTIj3rQu99 + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w18_3rdparty_8xb32-ssld_in1k + Metadata: + FLOPs: 4330397932 + Parameters: 21295164 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.06 + Top 5 Accuracy: 95.7 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth + Config: configs/hrnet/hrnet-w18_4xb32_in1k.py + Converted From: + Weights: https://github.com/HRNet/HRNet-Image-Classification/releases/download/PretrainedWeights/HRNet_W18_C_ssld_pretrained.pth + Code: https://github.com/HRNet/HRNet-Image-Classification + - Name: hrnet-w48_3rdparty_8xb32-ssld_in1k + Metadata: + FLOPs: 17364014752 + Parameters: 77466024 + In Collection: HRNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.63 + Top 5 Accuracy: 96.79 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth + Config: configs/hrnet/hrnet-w48_4xb32_in1k.py + Converted From: + Weights: https://github.com/HRNet/HRNet-Image-Classification/releases/download/PretrainedWeights/HRNet_W48_C_ssld_pretrained.pth + Code: https://github.com/HRNet/HRNet-Image-Classification diff --git a/configs/lenet/README.md b/configs/lenet/README.md new file mode 100644 index 0000000..2cd68ea --- /dev/null +++ b/configs/lenet/README.md @@ -0,0 +1,28 @@ +# LeNet + +> [Backpropagation Applied to Handwritten Zip Code Recognition](https://ieeexplore.ieee.org/document/6795724) + + + +## Abstract + +The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification. + +
+ +
+ +## Citation + +``` +@ARTICLE{6795724, + author={Y. {LeCun} and B. {Boser} and J. S. {Denker} and D. {Henderson} and R. E. {Howard} and W. {Hubbard} and L. D. {Jackel}}, + journal={Neural Computation}, + title={Backpropagation Applied to Handwritten Zip Code Recognition}, + year={1989}, + volume={1}, + number={4}, + pages={541-551}, + doi={10.1162/neco.1989.1.4.541}} +} +``` diff --git a/configs/lenet/lenet5_mnist.py b/configs/lenet/lenet5_mnist.py new file mode 100644 index 0000000..7286b79 --- /dev/null +++ b/configs/lenet/lenet5_mnist.py @@ -0,0 +1,59 @@ +# model settings +model = dict( + type='ImageClassifier', + backbone=dict(type='LeNet5', num_classes=10), + neck=None, + head=dict( + type='ClsHead', + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + )) +# dataset settings +dataset_type = 'MNIST' +img_norm_cfg = dict(mean=[33.46], std=[78.87], to_rgb=True) +train_pipeline = [ + dict(type='Resize', size=32), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']), +] +test_pipeline = [ + dict(type='Resize', size=32), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), +] +data = dict( + samples_per_gpu=128, + workers_per_gpu=2, + train=dict( + type=dataset_type, data_prefix='data/mnist', pipeline=train_pipeline), + val=dict( + type=dataset_type, data_prefix='data/mnist', pipeline=test_pipeline), + test=dict( + type=dataset_type, data_prefix='data/mnist', pipeline=test_pipeline)) +evaluation = dict( + interval=5, metric='accuracy', metric_options={'topk': (1, )}) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[15]) +# checkpoint saving +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=150, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=5) +dist_params = dict(backend='nccl') +log_level = 'INFO' +work_dir = './work_dirs/mnist/' +load_from = None +resume_from = None +workflow = [('train', 1)] diff --git a/configs/mlp_mixer/README.md b/configs/mlp_mixer/README.md new file mode 100644 index 0000000..5ec9887 --- /dev/null +++ b/configs/mlp_mixer/README.md @@ -0,0 +1,37 @@ +# Mlp-Mixer + +> [MLP-Mixer: An all-MLP Architecture for Vision](https://arxiv.org/abs/2105.01601) + + + +## Abstract + +Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary. We present MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs). MLP-Mixer contains two types of layers: one with MLPs applied independently to image patches (i.e. "mixing" the per-location features), and one with MLPs applied across patches (i.e. "mixing" spatial information). When trained on large datasets, or with modern regularization schemes, MLP-Mixer attains competitive scores on image classification benchmarks, with pre-training and inference cost comparable to state-of-the-art models. We hope that these results spark further research beyond the realms of well established CNNs and Transformers. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) | +| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) | + +*Models with * are converted from [timm](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mlp_mixer.py). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@misc{tolstikhin2021mlpmixer, + title={MLP-Mixer: An all-MLP Architecture for Vision}, + author={Ilya Tolstikhin and Neil Houlsby and Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Thomas Unterthiner and Jessica Yung and Andreas Steiner and Daniel Keysers and Jakob Uszkoreit and Mario Lucic and Alexey Dosovitskiy}, + year={2021}, + eprint={2105.01601}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/configs/mlp_mixer/metafile.yml b/configs/mlp_mixer/metafile.yml new file mode 100644 index 0000000..e8efa08 --- /dev/null +++ b/configs/mlp_mixer/metafile.yml @@ -0,0 +1,50 @@ +Collections: + - Name: MLP-Mixer + Metadata: + Training Data: ImageNet-1k + Architecture: + - MLP + - Layer Normalization + - Dropout + Paper: + URL: https://arxiv.org/abs/2105.01601 + Title: "MLP-Mixer: An all-MLP Architecture for Vision" + README: configs/mlp_mixer/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.18.0/mmcls/models/backbones/mlp_mixer.py + Version: v0.18.0 + +Models: + - Name: mlp-mixer-base-p16_3rdparty_64xb64_in1k + In Collection: MLP-Mixer + Config: configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py + Metadata: + FLOPs: 12610000000 # 12.61 G + Parameters: 59880000 # 59.88 M + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 76.68 + Top 5 Accuracy: 92.25 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224-76587d61.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mlp_mixer.py#L70 + + - Name: mlp-mixer-large-p16_3rdparty_64xb64_in1k + In Collection: MLP-Mixer + Config: configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py + Metadata: + FLOPs: 44570000000 # 44.57 G + Parameters: 208200000 # 208.2 M + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 72.34 + Top 5 Accuracy: 88.02 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_b16_224_in21k-617b3de2.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/mlp_mixer.py#L73 diff --git a/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py b/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py new file mode 100644 index 0000000..e35dae5 --- /dev/null +++ b/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/mlp_mixer_base_patch16.py', + '../_base_/datasets/imagenet_bs64_mixer_224.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py', +] diff --git a/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py b/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py new file mode 100644 index 0000000..459563c --- /dev/null +++ b/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/mlp_mixer_large_patch16.py', + '../_base_/datasets/imagenet_bs64_mixer_224.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py', +] diff --git a/configs/mobilenet_v2/README.md b/configs/mobilenet_v2/README.md new file mode 100644 index 0000000..675c8dd --- /dev/null +++ b/configs/mobilenet_v2/README.md @@ -0,0 +1,38 @@ +# MobileNet V2 + +> [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) + + + +## Abstract + +In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes. We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite. Additionally, we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3. + +The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer. Additionally, we find that it is important to remove non-linearities in the narrow layers in order to maintain representational power. We demonstrate that this improves performance and provide an intuition that led to this design. Finally, our approach allows decoupling of the input/output domains from the expressiveness of the transformation, which provides a convenient framework for further analysis. We measure our performance on Imagenet classification, COCO object detection, VOC image segmentation. We evaluate the trade-offs between accuracy, and number of operations measured by multiply-adds (MAdd), as well as the number of parameters + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) | + +## Citation + +``` +@INPROCEEDINGS{8578572, + author={M. {Sandler} and A. {Howard} and M. {Zhu} and A. {Zhmoginov} and L. {Chen}}, + booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + title={MobileNetV2: Inverted Residuals and Linear Bottlenecks}, + year={2018}, + volume={}, + number={}, + pages={4510-4520}, + doi={10.1109/CVPR.2018.00474}} +} +``` diff --git a/configs/mobilenet_v2/metafile.yml b/configs/mobilenet_v2/metafile.yml new file mode 100644 index 0000000..e16557f --- /dev/null +++ b/configs/mobilenet_v2/metafile.yml @@ -0,0 +1,34 @@ +Collections: + - Name: MobileNet V2 + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Epochs: 300 + Batch Size: 256 + Architecture: + - MobileNet V2 + Paper: + URL: https://arxiv.org/abs/1801.04381 + Title: "MobileNetV2: Inverted Residuals and Linear Bottlenecks" + README: configs/mobilenet_v2/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/mobilenet_v2.py#L101 + Version: v0.15.0 + +Models: + - Name: mobilenet-v2_8xb32_in1k + Metadata: + FLOPs: 319000000 + Parameters: 3500000 + In Collection: MobileNet V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 71.86 + Top 5 Accuracy: 90.42 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth + Config: configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py diff --git a/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py b/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py new file mode 100644 index 0000000..afd2d97 --- /dev/null +++ b/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/mobilenet_v2_1x.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_epochstep.py', + '../_base_/default_runtime.py' +] diff --git a/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py b/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py new file mode 100644 index 0000000..26c2b6d --- /dev/null +++ b/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'mobilenet-v2_8xb32_in1k.py' + +_deprecation_ = dict( + expected='mobilenet-v2_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/mobilenet_v3/README.md b/configs/mobilenet_v3/README.md new file mode 100644 index 0000000..737c4d3 --- /dev/null +++ b/configs/mobilenet_v3/README.md @@ -0,0 +1,36 @@ +# MobileNet V3 + +> [Searching for MobileNetV3](https://arxiv.org/abs/1905.02244) + + + +## Abstract + +We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone CPUs through a combination of hardware-aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances. This paper starts the exploration of how automated search algorithms and network design can work together to harness complementary approaches improving the overall state of the art. Through this process we create two new MobileNet models for release: MobileNetV3-Large and MobileNetV3-Small which are targeted for high and low resource use cases. These models are then adapted and applied to the tasks of object detection and semantic segmentation. For the task of semantic segmentation (or any dense pixel prediction), we propose a new efficient segmentation decoder Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP). We achieve new state of the art results for mobile classification, detection and segmentation. MobileNetV3-Large is 3.2% more accurate on ImageNet classification while reducing latency by 15% compared to MobileNetV2. MobileNetV3-Small is 4.6% more accurate while reducing latency by 5% compared to MobileNetV2. MobileNetV3-Large detection is 25% faster at roughly the same accuracy as MobileNetV2 on COCO detection. MobileNetV3-Large LR-ASPP is 30% faster than MobileNetV2 R-ASPP at similar accuracy for Cityscapes segmentation. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :------------------------------------------------------------------------: | +| MobileNetV3-Small\* | 2.54 | 0.06 | 67.66 | 87.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth) | +| MobileNetV3-Large\* | 5.48 | 0.23 | 74.04 | 91.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth) | + +*Models with * are converted from [torchvision](https://pytorch.org/vision/stable/_modules/torchvision/models/mobilenetv3.html). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@inproceedings{Howard_2019_ICCV, + author = {Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and Le, Quoc V. and Adam, Hartwig}, + title = {Searching for MobileNetV3}, + booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2019} +} +``` diff --git a/configs/mobilenet_v3/metafile.yml b/configs/mobilenet_v3/metafile.yml new file mode 100644 index 0000000..09c4732 --- /dev/null +++ b/configs/mobilenet_v3/metafile.yml @@ -0,0 +1,47 @@ +Collections: + - Name: MobileNet V3 + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - RMSprop with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Epochs: 600 + Batch Size: 1024 + Architecture: + - MobileNet V3 + Paper: + URL: https://arxiv.org/abs/1905.02244 + Title: Searching for MobileNetV3 + README: configs/mobilenet_v3/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/mobilenet_v3.py + Version: v0.15.0 + +Models: + - Name: mobilenet_v3_small_imagenet + Metadata: + FLOPs: 60000000 + Parameters: 2540000 + In Collection: MobileNet V3 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 67.66 + Top 5 Accuracy: 87.41 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth + Config: configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py + - Name: mobilenet_v3_large_imagenet + Metadata: + FLOPs: 230000000 + Parameters: 5480000 + In Collection: MobileNet V3 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 74.04 + Top 5 Accuracy: 91.34 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth + Config: configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py diff --git a/configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py b/configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py new file mode 100644 index 0000000..985ef52 --- /dev/null +++ b/configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py @@ -0,0 +1,158 @@ +# Refer to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification +# ---------------------------- +# -[x] auto_augment='imagenet' +# -[x] batch_size=128 (per gpu) +# -[x] epochs=600 +# -[x] opt='rmsprop' +# -[x] lr=0.064 +# -[x] eps=0.0316 +# -[x] alpha=0.9 +# -[x] weight_decay=1e-05 +# -[x] momentum=0.9 +# -[x] lr_gamma=0.973 +# -[x] lr_step_size=2 +# -[x] nproc_per_node=8 +# -[x] random_erase=0.2 +# -[x] workers=16 (workers_per_gpu) +# - modify: RandomErasing use RE-M instead of RE-0 + +_base_ = [ + '../_base_/models/mobilenet_v3_large_imagenet.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/default_runtime.py' +] + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +policies = [ + [ + dict(type='Posterize', bits=4, prob=0.4), + dict(type='Rotate', angle=30., prob=0.6) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], + [ + dict(type='Posterize', bits=5, prob=0.6), + dict(type='Posterize', bits=5, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8) + ], + [ + dict(type='Solarize', thr=256 / 9 * 6, prob=0.6), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Posterize', bits=6, prob=0.8), + dict(type='Equalize', prob=1.)], + [ + dict(type='Rotate', angle=10., prob=0.2), + dict(type='Solarize', thr=256 / 9, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.6), + dict(type='Posterize', bits=5, prob=0.4) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0., prob=0.4) + ], + [ + dict(type='Rotate', angle=30., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Equalize', prob=0.0), + dict(type='Equalize', prob=0.8)], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0.2, prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0.8, prob=0.8), + dict(type='Solarize', thr=256 / 9 * 2, prob=0.8) + ], + [ + dict(type='Sharpness', magnitude=0.7, prob=0.4), + dict(type='Invert', prob=0.6) + ], + [ + dict( + type='Shear', + magnitude=0.3 / 9 * 5, + prob=0.6, + direction='horizontal'), + dict(type='Equalize', prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], +] + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='AutoAugment', policies=policies), + dict( + type='RandomErasing', + erase_prob=0.2, + mode='const', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean']), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +data = dict( + samples_per_gpu=128, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline)) +evaluation = dict(interval=10, metric='accuracy') + +# optimizer +optimizer = dict( + type='RMSprop', + lr=0.064, + alpha=0.9, + momentum=0.9, + eps=0.0316, + weight_decay=1e-5) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=2, gamma=0.973, by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=600) diff --git a/configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py b/configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py new file mode 100644 index 0000000..06e63da --- /dev/null +++ b/configs/mobilenet_v3/mobilenet-v3-small_8xb16_cifar10.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/mobilenet-v3-small_cifar.py', + '../_base_/datasets/cifar10_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] + +lr_config = dict(policy='step', step=[120, 170]) +runner = dict(type='EpochBasedRunner', max_epochs=200) diff --git a/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py b/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py new file mode 100644 index 0000000..2612166 --- /dev/null +++ b/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py @@ -0,0 +1,158 @@ +# Refer to https://pytorch.org/blog/ml-models-torchvision-v0.9/#classification +# ---------------------------- +# -[x] auto_augment='imagenet' +# -[x] batch_size=128 (per gpu) +# -[x] epochs=600 +# -[x] opt='rmsprop' +# -[x] lr=0.064 +# -[x] eps=0.0316 +# -[x] alpha=0.9 +# -[x] weight_decay=1e-05 +# -[x] momentum=0.9 +# -[x] lr_gamma=0.973 +# -[x] lr_step_size=2 +# -[x] nproc_per_node=8 +# -[x] random_erase=0.2 +# -[x] workers=16 (workers_per_gpu) +# - modify: RandomErasing use RE-M instead of RE-0 + +_base_ = [ + '../_base_/models/mobilenet_v3_small_imagenet.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/default_runtime.py' +] + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +policies = [ + [ + dict(type='Posterize', bits=4, prob=0.4), + dict(type='Rotate', angle=30., prob=0.6) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], + [ + dict(type='Posterize', bits=5, prob=0.6), + dict(type='Posterize', bits=5, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8) + ], + [ + dict(type='Solarize', thr=256 / 9 * 6, prob=0.6), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Posterize', bits=6, prob=0.8), + dict(type='Equalize', prob=1.)], + [ + dict(type='Rotate', angle=10., prob=0.2), + dict(type='Solarize', thr=256 / 9, prob=0.6) + ], + [ + dict(type='Equalize', prob=0.6), + dict(type='Posterize', bits=5, prob=0.4) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0., prob=0.4) + ], + [ + dict(type='Rotate', angle=30., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [dict(type='Equalize', prob=0.0), + dict(type='Equalize', prob=0.8)], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [ + dict(type='Rotate', angle=30 / 9 * 8, prob=0.8), + dict(type='ColorTransform', magnitude=0.2, prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0.8, prob=0.8), + dict(type='Solarize', thr=256 / 9 * 2, prob=0.8) + ], + [ + dict(type='Sharpness', magnitude=0.7, prob=0.4), + dict(type='Invert', prob=0.6) + ], + [ + dict( + type='Shear', + magnitude=0.3 / 9 * 5, + prob=0.6, + direction='horizontal'), + dict(type='Equalize', prob=1.) + ], + [ + dict(type='ColorTransform', magnitude=0., prob=0.4), + dict(type='Equalize', prob=0.6) + ], + [ + dict(type='Equalize', prob=0.4), + dict(type='Solarize', thr=256 / 9 * 5, prob=0.2) + ], + [ + dict(type='Solarize', thr=256 / 9 * 4, prob=0.6), + dict(type='AutoContrast', prob=0.6) + ], + [dict(type='Invert', prob=0.6), + dict(type='Equalize', prob=1.)], + [ + dict(type='ColorTransform', magnitude=0.4, prob=0.6), + dict(type='Contrast', magnitude=0.8, prob=1.) + ], + [dict(type='Equalize', prob=0.8), + dict(type='Equalize', prob=0.6)], +] + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='AutoAugment', policies=policies), + dict( + type='RandomErasing', + erase_prob=0.2, + mode='const', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean']), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +data = dict( + samples_per_gpu=128, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline)) +evaluation = dict(interval=10, metric='accuracy') + +# optimizer +optimizer = dict( + type='RMSprop', + lr=0.064, + alpha=0.9, + momentum=0.9, + eps=0.0316, + weight_decay=1e-5) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=2, gamma=0.973, by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=600) diff --git a/configs/mobilenet_v3/mobilenet_v3_large_imagenet.py b/configs/mobilenet_v3/mobilenet_v3_large_imagenet.py new file mode 100644 index 0000000..93e89a4 --- /dev/null +++ b/configs/mobilenet_v3/mobilenet_v3_large_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'mobilenet-v3-large_8xb32_in1k.py' + +_deprecation_ = dict( + expected='mobilenet-v3-large_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/mobilenet_v3/mobilenet_v3_small_cifar.py b/configs/mobilenet_v3/mobilenet_v3_small_cifar.py new file mode 100644 index 0000000..c09bd1c --- /dev/null +++ b/configs/mobilenet_v3/mobilenet_v3_small_cifar.py @@ -0,0 +1,6 @@ +_base_ = 'mobilenet-v3-small_8xb16_cifar10.py' + +_deprecation_ = dict( + expected='mobilenet-v3-small_8xb16_cifar10.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/mobilenet_v3/mobilenet_v3_small_imagenet.py b/configs/mobilenet_v3/mobilenet_v3_small_imagenet.py new file mode 100644 index 0000000..15debd0 --- /dev/null +++ b/configs/mobilenet_v3/mobilenet_v3_small_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'mobilenet-v3-small_8xb32_in1k.py' + +_deprecation_ = dict( + expected='mobilenet-v3-small_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/mvit/README.md b/configs/mvit/README.md new file mode 100644 index 0000000..6f5c560 --- /dev/null +++ b/configs/mvit/README.md @@ -0,0 +1,44 @@ +# MViT V2 + +> [MViTv2: Improved Multiscale Vision Transformers for Classification and Detection](http://openaccess.thecvf.com//content/CVPR2022/papers/Li_MViTv2_Improved_Multiscale_Vision_Transformers_for_Classification_and_Detection_CVPR_2022_paper.pdf) + + + +## Abstract + +In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video +classification, as well as object detection. We present an improved version of MViT that incorporates +decomposed relative positional embeddings and residual pooling connections. We instantiate this architecture +in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where +it outperforms prior work. We further compare MViTv2s' pooling attention to window attention mechanisms where +it outperforms the latter in accuracy/compute. Without bells-and-whistles, MViTv2 has state-of-the-art +performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 boxAP on COCO object detection as +well as 86.1% on Kinetics-400 video classification. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Pretrain | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------: | :----------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :---------------------------------------------------------------------: | +| MViTv2-tiny\* | From scratch | 24.17 | 4.70 | 82.33 | 96.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-tiny_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth) | +| MViTv2-small\* | From scratch | 34.87 | 7.00 | 83.63 | 96.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) | +| MViTv2-base\* | From scratch | 51.47 | 10.20 | 84.34 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) | +| MViTv2-large\* | From scratch | 217.99 | 42.10 | 85.25 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) | + +*Models with * are converted from the [official repo](https://github.com/facebookresearch/mvit). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@inproceedings{li2021improved, + title={MViTv2: Improved multiscale vision transformers for classification and detection}, + author={Li, Yanghao and Wu, Chao-Yuan and Fan, Haoqi and Mangalam, Karttikeya and Xiong, Bo and Malik, Jitendra and Feichtenhofer, Christoph}, + booktitle={CVPR}, + year={2022} +} +``` diff --git a/configs/mvit/metafile.yml b/configs/mvit/metafile.yml new file mode 100644 index 0000000..8d46a0c --- /dev/null +++ b/configs/mvit/metafile.yml @@ -0,0 +1,95 @@ +Collections: + - Name: MViT V2 + Metadata: + Architecture: + - Attention Dropout + - Convolution + - Dense Connections + - GELU + - Layer Normalization + - Scaled Dot-Product Attention + - Attention Pooling + Paper: + URL: http://openaccess.thecvf.com//content/CVPR2022/papers/Li_MViTv2_Improved_Multiscale_Vision_Transformers_for_Classification_and_Detection_CVPR_2022_paper.pdf + Title: 'MViTv2: Improved Multiscale Vision Transformers for Classification and Detection' + README: configs/mvit/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.24.0/mmcls/models/backbones/mvit.py + Version: v0.24.0 + +Models: + - Name: mvitv2-tiny_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 4700000000 + Parameters: 24173320 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 82.33 + Top 5 Accuracy: 96.15 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_T_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-tiny_8xb256_in1k.py + + - Name: mvitv2-small_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 7000000000 + Parameters: 34870216 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 83.63 + Top 5 Accuracy: 96.51 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_S_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-small_8xb256_in1k.py + + - Name: mvitv2-base_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 10200000000 + Parameters: 51472744 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 84.34 + Top 5 Accuracy: 96.86 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_B_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-base_8xb256_in1k.py + + - Name: mvitv2-large_3rdparty_in1k + In Collection: MViT V2 + Metadata: + FLOPs: 42100000000 + Parameters: 217992952 + Training Data: + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 85.25 + Top 5 Accuracy: 97.14 + Weights: https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth + Converted From: + Weights: https://dl.fbaipublicfiles.com/mvit/mvitv2_models/MViTv2_L_in1k.pyth + Code: https://github.com/facebookresearch/mvit + Config: configs/mvit/mvitv2-large_8xb256_in1k.py diff --git a/configs/mvit/mvitv2-base_8xb256_in1k.py b/configs/mvit/mvitv2-base_8xb256_in1k.py new file mode 100644 index 0000000..ea92cf4 --- /dev/null +++ b/configs/mvit/mvitv2-base_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-base.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/configs/mvit/mvitv2-large_8xb256_in1k.py b/configs/mvit/mvitv2-large_8xb256_in1k.py new file mode 100644 index 0000000..fbb81d6 --- /dev/null +++ b/configs/mvit/mvitv2-large_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-large.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs2048_AdamW.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/configs/mvit/mvitv2-small_8xb256_in1k.py b/configs/mvit/mvitv2-small_8xb256_in1k.py new file mode 100644 index 0000000..1803859 --- /dev/null +++ b/configs/mvit/mvitv2-small_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-small.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs2048_AdamW.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/configs/mvit/mvitv2-tiny_8xb256_in1k.py b/configs/mvit/mvitv2-tiny_8xb256_in1k.py new file mode 100644 index 0000000..f4b9bc4 --- /dev/null +++ b/configs/mvit/mvitv2-tiny_8xb256_in1k.py @@ -0,0 +1,29 @@ +_base_ = [ + '../_base_/models/mvit/mvitv2-tiny.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs2048_AdamW.py', + '../_base_/default_runtime.py' +] + +# dataset settings +data = dict(samples_per_gpu=256) + +# schedule settings +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.pos_embed': dict(decay_mult=0.0), + '.rel_pos_h': dict(decay_mult=0.0), + '.rel_pos_w': dict(decay_mult=0.0) + }) + +optimizer = dict(lr=0.00025, paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=1.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=70, + warmup_by_epoch=True) diff --git a/configs/poolformer/README.md b/configs/poolformer/README.md new file mode 100644 index 0000000..cc557e1 --- /dev/null +++ b/configs/poolformer/README.md @@ -0,0 +1,38 @@ +# PoolFormer + +> [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) + + + +## Abstract + +Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in transformers with an embarrassingly simple spatial pooling operator to conduct only basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned vision transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 49%/61% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :--------------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------------: | :--------------------------------------------------------------------------: | +| PoolFormer-S12\* | 11.92 | 1.87 | 77.24 | 93.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s12_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth) | +| PoolFormer-S24\* | 21.39 | 3.51 | 80.33 | 95.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s24_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s24_3rdparty_32xb128_in1k_20220414-d7055904.pth) | +| PoolFormer-S36\* | 30.86 | 5.15 | 81.43 | 95.45 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-s36_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s36_3rdparty_32xb128_in1k_20220414-d78ff3e8.pth) | +| PoolFormer-M36\* | 56.17 | 8.96 | 82.14 | 95.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-m36_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m36_3rdparty_32xb128_in1k_20220414-c55e0949.pth) | +| PoolFormer-M48\* | 73.47 | 11.80 | 82.51 | 95.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/poolformer/poolformer-m48_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m48_3rdparty_32xb128_in1k_20220414-9378f3eb.pth) | + +*Models with * are converted from the [official repo](https://github.com/sail-sg/poolformer). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@article{yu2021metaformer, + title={MetaFormer is Actually What You Need for Vision}, + author={Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng}, + journal={arXiv preprint arXiv:2111.11418}, + year={2021} +} +``` diff --git a/configs/poolformer/metafile.yml b/configs/poolformer/metafile.yml new file mode 100644 index 0000000..d94219d --- /dev/null +++ b/configs/poolformer/metafile.yml @@ -0,0 +1,99 @@ +Collections: + - Name: PoolFormer + Metadata: + Training Data: ImageNet-1k + Architecture: + - Pooling + - 1x1 Convolution + - LayerScale + Paper: + URL: https://arxiv.org/abs/2111.11418 + Title: MetaFormer is Actually What You Need for Vision + README: configs/poolformer/README.md + Code: + Version: v0.22.1 + URL: https://github.com/open-mmlab/mmclassification/blob/v0.22.1/mmcls/models/backbones/poolformer.py + +Models: + - Name: poolformer-s12_3rdparty_32xb128_in1k + Metadata: + FLOPs: 1871399424 + Parameters: 11915176 + In Collections: PoolFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.24 + Top 5 Accuracy: 93.51 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth + Config: configs/poolformer/poolformer-s12_32xb128_in1k.py + Converted From: + Weights: https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s12.pth.tar + Code: https://github.com/sail-sg/poolformer + - Name: poolformer-s24_3rdparty_32xb128_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 3510411008 + Parameters: 21388968 + In Collections: PoolFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.33 + Top 5 Accuracy: 95.05 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s24_3rdparty_32xb128_in1k_20220414-d7055904.pth + Config: configs/poolformer/poolformer-s24_32xb128_in1k.py + Converted From: + Weights: https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s24.pth.tar + Code: https://github.com/sail-sg/poolformer + - Name: poolformer-s36_3rdparty_32xb128_in1k + Metadata: + FLOPs: 5149422592 + Parameters: 30862760 + In Collections: PoolFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.43 + Top 5 Accuracy: 95.45 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s36_3rdparty_32xb128_in1k_20220414-d78ff3e8.pth + Config: configs/poolformer/poolformer-s36_32xb128_in1k.py + Converted From: + Weights: https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_s36.pth.tar + Code: https://github.com/sail-sg/poolformer + - Name: poolformer-m36_3rdparty_32xb128_in1k + Metadata: + Training Data: ImageNet-1k + FLOPs: 8960175744 + Parameters: 56172520 + In Collections: PoolFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.14 + Top 5 Accuracy: 95.71 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m36_3rdparty_32xb128_in1k_20220414-c55e0949.pth + Config: configs/poolformer/poolformer-m36_32xb128_in1k.py + Converted From: + Weights: https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_m36.pth.tar + Code: https://github.com/sail-sg/poolformer + - Name: poolformer-m48_3rdparty_32xb128_in1k + Metadata: + FLOPs: 11801805696 + Parameters: 73473448 + In Collections: PoolFormer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.51 + Top 5 Accuracy: 95.95 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-m48_3rdparty_32xb128_in1k_20220414-9378f3eb.pth + Config: configs/poolformer/poolformer-m48_32xb128_in1k.py + Converted From: + Weights: https://github.com/sail-sg/poolformer/releases/download/v1.0/poolformer_m48.pth.tar + Code: https://github.com/sail-sg/poolformer diff --git a/configs/poolformer/poolformer-m36_32xb128_in1k.py b/configs/poolformer/poolformer-m36_32xb128_in1k.py new file mode 100644 index 0000000..1937a78 --- /dev/null +++ b/configs/poolformer/poolformer-m36_32xb128_in1k.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/poolformer/poolformer_m36.py', + '../_base_/datasets/imagenet_bs128_poolformer_medium_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=4e-3) diff --git a/configs/poolformer/poolformer-m48_32xb128_in1k.py b/configs/poolformer/poolformer-m48_32xb128_in1k.py new file mode 100644 index 0000000..a65b76a --- /dev/null +++ b/configs/poolformer/poolformer-m48_32xb128_in1k.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/poolformer/poolformer_m48.py', + '../_base_/datasets/imagenet_bs128_poolformer_medium_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=4e-3) diff --git a/configs/poolformer/poolformer-s12_32xb128_in1k.py b/configs/poolformer/poolformer-s12_32xb128_in1k.py new file mode 100644 index 0000000..98027c0 --- /dev/null +++ b/configs/poolformer/poolformer-s12_32xb128_in1k.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/poolformer/poolformer_s12.py', + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=4e-3) diff --git a/configs/poolformer/poolformer-s24_32xb128_in1k.py b/configs/poolformer/poolformer-s24_32xb128_in1k.py new file mode 100644 index 0000000..9774259 --- /dev/null +++ b/configs/poolformer/poolformer-s24_32xb128_in1k.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/poolformer/poolformer_s24.py', + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=4e-3) diff --git a/configs/poolformer/poolformer-s36_32xb128_in1k.py b/configs/poolformer/poolformer-s36_32xb128_in1k.py new file mode 100644 index 0000000..4d742d3 --- /dev/null +++ b/configs/poolformer/poolformer-s36_32xb128_in1k.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/poolformer/poolformer_s36.py', + '../_base_/datasets/imagenet_bs128_poolformer_small_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py', +] + +optimizer = dict(lr=4e-3) diff --git a/configs/regnet/README.md b/configs/regnet/README.md new file mode 100644 index 0000000..1ae074d --- /dev/null +++ b/configs/regnet/README.md @@ -0,0 +1,51 @@ +# RegNet + +> [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) + + + +## Abstract + +In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------------: | :--------------------------------------------------------------------------: | +| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) | +| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) | +| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) | +| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) | +| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) | +| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) | +| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) | +| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.log.json) | +| RegNetX-400MF\* | 5.16 | 0.41 | 72.55 | 90.91 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-400MF-0db9f35c.pth) | +| RegNetX-800MF\* | 7.26 | 0.81 | 75.21 | 92.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-800MF-4f9d1e8a.pth) | +| RegNetX-1.6GF\* | 9.19 | 1.63 | 77.04 | 93.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-1.6GF-cfb32375.pth) | +| RegNetX-3.2GF\* | 15.3 | 3.21 | 78.26 | 94.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-3.2GF-82c43fd5.pth) | +| RegNetX-4.0GF\* | 22.12 | 4.0 | 78.72 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-4.0GF-ef8bb32c.pth) | +| RegNetX-6.4GF\* | 26.21 | 6.51 | 79.22 | 94.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-6.4GF-6888c0ea.pth) | +| RegNetX-8.0GF\* | 39.57 | 8.03 | 79.31 | 94.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-8.0GF-cb4c77ec.pth) | +| RegNetX-12GF\* | 46.11 | 12.15 | 79.91 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/convert/RegNetX-12GF-0574538f.pth) | + +*Models with * are converted from [pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). The config files of these models are only for validation.* + +## Citation + +``` +@article{radosavovic2020designing, + title={Designing Network Design Spaces}, + author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, + year={2020}, + eprint={2003.13678}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/configs/regnet/metafile.yml b/configs/regnet/metafile.yml new file mode 100644 index 0000000..6b301ab --- /dev/null +++ b/configs/regnet/metafile.yml @@ -0,0 +1,122 @@ +Collections: + - Name: RegNet + Metadata: + Training Data: ImageNet-1k + Architecture: + - Neural Architecture Search + - Design Space Design + - Precise BN + - SGD with nesterov + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: Designing Network Design Spaces + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.18.0/mmcls/models/backbones/regnet.py + Version: v0.18.0 + +Models: + - Name: regnetx-400mf_8xb128_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-400mf_8xb128_in1k.py + Metadata: + FLOPs: 410000000 # 0.41G + Parameters: 5160000 # 5.16M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 72.56 + Top 5 Accuracy: 90.78 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth + - Name: regnetx-800mf_8xb128_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-800mf_8xb128_in1k.py + Metadata: + FLOPs: 810000000 # 0.81G + Parameters: 7260000 # 7.26M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 74.76 + Top 5 Accuracy: 92.32 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth + - Name: regnetx-1.6gf_8xb128_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-1.6gf_8xb128_in1k.py + Metadata: + FLOPs: 1630000000 # 1.63G + Parameters: 9190000 # 9.19M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 76.84 + Top 5 Accuracy: 93.31 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth + - Name: regnetx-3.2gf_8xb64_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-3.2gf_8xb64_in1k.py + Metadata: + FLOPs: 1530000000 # 1.53G + Parameters: 3210000 # 32.1M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 78.09 + Top 5 Accuracy: 94.08 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth + - Name: regnetx-4.0gf_8xb64_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-4.0gf_8xb64_in1k.py + Metadata: + FLOPs: 4000000000 # 4G + Parameters: 22120000 # 22.12M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 78.60 + Top 5 Accuracy: 94.17 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth + - Name: regnetx-6.4gf_8xb64_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-6.4gf_8xb64_in1k.py + Metadata: + FLOPs: 6510000000 # 6.51G + Parameters: 26210000 # 26.21M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 79.38 + Top 5 Accuracy: 94.65 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth + - Name: regnetx-8.0gf_8xb64_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-8.0gf_8xb64_in1k.py + Metadata: + FLOPs: 8030000000 # 8.03G + Parameters: 39570000 # 39.57M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 79.12 + Top 5 Accuracy: 94.51 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth + - Name: regnetx-12gf_8xb64_in1k + In Collection: RegNet + Config: configs/regnet/regnetx-12gf_8xb64_in1k.py + Metadata: + FLOPs: 12150000000 # 12.15G + Parameters: 46110000 # 46.11M + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 79.67 + Top 5 Accuracy: 95.03 + Weights: https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth diff --git a/configs/regnet/regnetx-1.6gf_8xb128_in1k.py b/configs/regnet/regnetx-1.6gf_8xb128_in1k.py new file mode 100644 index 0000000..d3e9e93 --- /dev/null +++ b/configs/regnet/regnetx-1.6gf_8xb128_in1k.py @@ -0,0 +1,6 @@ +_base_ = ['./regnetx-400mf_8xb128_in1k.py'] + +# model settings +model = dict( + backbone=dict(type='RegNet', arch='regnetx_1.6gf'), + head=dict(in_channels=912, )) diff --git a/configs/regnet/regnetx-12gf_8xb64_in1k.py b/configs/regnet/regnetx-12gf_8xb64_in1k.py new file mode 100644 index 0000000..5da0ebe --- /dev/null +++ b/configs/regnet/regnetx-12gf_8xb64_in1k.py @@ -0,0 +1,11 @@ +_base_ = ['./regnetx-400mf_8xb128_in1k.py'] + +# model settings +model = dict( + backbone=dict(type='RegNet', arch='regnetx_12gf'), + head=dict(in_channels=2240, )) + +# for batch_size 512, use lr = 0.4 +optimizer = dict(lr=0.4) + +data = dict(samples_per_gpu=64, ) diff --git a/configs/regnet/regnetx-3.2gf_8xb64_in1k.py b/configs/regnet/regnetx-3.2gf_8xb64_in1k.py new file mode 100644 index 0000000..98c4a0b --- /dev/null +++ b/configs/regnet/regnetx-3.2gf_8xb64_in1k.py @@ -0,0 +1,11 @@ +_base_ = ['./regnetx-400mf_8xb128_in1k.py'] + +# model settings +model = dict( + backbone=dict(type='RegNet', arch='regnetx_3.2gf'), + head=dict(in_channels=1008, )) + +# for batch_size 512, use lr = 0.4 +optimizer = dict(lr=0.4) + +data = dict(samples_per_gpu=64, ) diff --git a/configs/regnet/regnetx-4.0gf_8xb64_in1k.py b/configs/regnet/regnetx-4.0gf_8xb64_in1k.py new file mode 100644 index 0000000..87bc847 --- /dev/null +++ b/configs/regnet/regnetx-4.0gf_8xb64_in1k.py @@ -0,0 +1,11 @@ +_base_ = ['./regnetx-400mf_8xb128_in1k.py'] + +# model settings +model = dict( + backbone=dict(type='RegNet', arch='regnetx_4.0gf'), + head=dict(in_channels=1360, )) + +# for batch_size 512, use lr = 0.4 +optimizer = dict(lr=0.4) + +data = dict(samples_per_gpu=64, ) diff --git a/configs/regnet/regnetx-400mf_8xb128_in1k.py b/configs/regnet/regnetx-400mf_8xb128_in1k.py new file mode 100644 index 0000000..86fee90 --- /dev/null +++ b/configs/regnet/regnetx-400mf_8xb128_in1k.py @@ -0,0 +1,77 @@ +_base_ = [ + '../_base_/models/regnet/regnetx_400mf.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs1024_coslr.py', + '../_base_/default_runtime.py' +] + +# Precise BN hook will update the bn stats, so this hook should be executed +# before CheckpointHook, which has priority of 'NORMAL'. So set the +# priority of PreciseBNHook to 'ABOVE_NORMAL' here. +custom_hooks = [ + dict( + type='PreciseBNHook', + num_samples=8192, + interval=1, + priority='ABOVE_NORMAL') +] + +# sgd with nesterov, base ls is 0.8 for batch_size 1024, +# 0.4 for batch_size 512 and 0.2 for batch_size 256 when training ImageNet1k +optimizer = dict(lr=0.8, nesterov=True) + +# dataset settings +dataset_type = 'ImageNet' + +# normalization params, in order of BGR +NORM_MEAN = [103.53, 116.28, 123.675] +NORM_STD = [57.375, 57.12, 58.395] + +# lighting params, in order of RGB, from repo. pycls +EIGVAL = [0.2175, 0.0188, 0.0045] +EIGVEC = [[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.814], + [-0.5836, -0.6948, 0.4203]] + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='Lighting', + eigval=EIGVAL, + eigvec=EIGVEC, + alphastd=25.5, # because the value range of images is [0,255] + to_rgb=True + ), # BGR image from cv2 in LoadImageFromFile, convert to RGB here + dict(type='Normalize', mean=NORM_MEAN, std=NORM_STD, + to_rgb=True), # RGB2BGR + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', mean=NORM_MEAN, std=NORM_STD, to_rgb=False), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=128, + workers_per_gpu=8, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) diff --git a/configs/regnet/regnetx-6.4gf_8xb64_in1k.py b/configs/regnet/regnetx-6.4gf_8xb64_in1k.py new file mode 100644 index 0000000..02ee424 --- /dev/null +++ b/configs/regnet/regnetx-6.4gf_8xb64_in1k.py @@ -0,0 +1,11 @@ +_base_ = ['./regnetx-400mf_8xb128_in1k.py'] + +# model settings +model = dict( + backbone=dict(type='RegNet', arch='regnetx_6.4gf'), + head=dict(in_channels=1624, )) + +# for batch_size 512, use lr = 0.4 +optimizer = dict(lr=0.4) + +data = dict(samples_per_gpu=64, ) diff --git a/configs/regnet/regnetx-8.0gf_8xb64_in1k.py b/configs/regnet/regnetx-8.0gf_8xb64_in1k.py new file mode 100644 index 0000000..84ab811 --- /dev/null +++ b/configs/regnet/regnetx-8.0gf_8xb64_in1k.py @@ -0,0 +1,11 @@ +_base_ = ['./regnetx-400mf_8xb128_in1k.py'] + +# model settings +model = dict( + backbone=dict(type='RegNet', arch='regnetx_8.0gf'), + head=dict(in_channels=1920, )) + +# for batch_size 512, use lr = 0.4 +optimizer = dict(lr=0.4) + +data = dict(samples_per_gpu=64, ) diff --git a/configs/regnet/regnetx-800mf_8xb128_in1k.py b/configs/regnet/regnetx-800mf_8xb128_in1k.py new file mode 100644 index 0000000..9cd7137 --- /dev/null +++ b/configs/regnet/regnetx-800mf_8xb128_in1k.py @@ -0,0 +1,6 @@ +_base_ = ['./regnetx-400mf_8xb128_in1k.py'] + +# model settings +model = dict( + backbone=dict(type='RegNet', arch='regnetx_800mf'), + head=dict(in_channels=672, )) diff --git a/configs/repmlp/README.md b/configs/repmlp/README.md new file mode 100644 index 0000000..4533463 --- /dev/null +++ b/configs/repmlp/README.md @@ -0,0 +1,93 @@ +# RepMLP + +> [RepMLP: Re-parameterizing Convolutions into Fully-connected Layers forImage Recognition](https://arxiv.org/abs/2105.01883) + + + +## Abstract + +We propose RepMLP, a multi-layer-perceptron-style neural network building block for image recognition, which is composed of a series of fully-connected (FC) layers. Compared to convolutional layers, FC layers are more efficient, better at modeling the long-range dependencies and positional patterns, but worse at capturing the local structures, hence usually less favored for image recognition. We propose a structural re-parameterization technique that adds local prior into an FC to make it powerful for image recognition. Specifically, we construct convolutional layers inside a RepMLP during training and merge them into the FC for inference. On CIFAR, a simple pure-MLP model shows performance very close to CNN. By inserting RepMLP in traditional CNN, we improve ResNets by 1.8% accuracy on ImageNet, 2.9% for face recognition, and 2.3% mIoU on Cityscapes with lower FLOPs. Our intriguing findings highlight that combining the global representational capacity and positional perception of FC with the local prior of convolution can improve the performance of neural network with faster speed on both the tasks with translation invariance (e.g., semantic segmentation) and those with aligned images and positional patterns (e.g., face recognition). + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| RepMLP-B224\* | 68.24 | 6.71 | 80.41 | 95.12 | [train_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_8xb64_in1k.py) \| [deploy_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k_20220330-1cb1f11b.pth) | +| RepMLP-B256\* | 96.45 | 9.69 | 81.11 | 95.5 | [train_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-base_8xb64_in1k-256px.py) \| [deploy_cfg](https://github.com/open-mmlab/mmclassification/blob/master/configs/repmlp/repmlp-b256_deploy_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k-256px_20220330-7c5a91ce.pth) | + +*Models with * are converted from [the official repo.](https://github.com/DingXiaoH/RepMLP). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## How to use + +The checkpoints provided are all `training-time` models. Use the reparameterize tool to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations. + +### Use tool + +Use provided tool to reparameterize the given model and save the checkpoint: + +```bash +python tools/convert_models/reparameterize_model.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH} +``` + +`${CFG_PATH}` is the config file, `${SRC_CKPT_PATH}` is the source chenpoint file, `${TARGET_CKPT_PATH}` is the target deploy weight file path. + +To use reparameterized weights, the config file must switch to the deploy config files. + +```bash +python tools/test.py ${Deploy_CFG} ${Deploy_Checkpoint} --metrics accuracy +``` + +### In the code + +Use `backbone.switch_to_deploy()` or `classificer.backbone.switch_to_deploy()` to switch to the deploy mode. For example: + +```python +from mmcls.models import build_backbone + +backbone_cfg=dict(type='RepMLPNet', arch='B', img_size=224, reparam_conv_kernels=(1, 3), deploy=False) +backbone = build_backbone(backbone_cfg) +backbone.switch_to_deploy() +``` + +or + +```python +from mmcls.models import build_classifier + +cfg = dict( + type='ImageClassifier', + backbone=dict( + type='RepMLPNet', + arch='B', + img_size=224, + reparam_conv_kernels=(1, 3), + deploy=False), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=768, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) + +classifier = build_classifier(cfg) +classifier.backbone.switch_to_deploy() +``` + +## Citation + +``` +@article{ding2021repmlp, + title={Repmlp: Re-parameterizing convolutions into fully-connected layers for image recognition}, + author={Ding, Xiaohan and Xia, Chunlong and Zhang, Xiangyu and Chu, Xiaojie and Han, Jungong and Ding, Guiguang}, + journal={arXiv preprint arXiv:2105.01883}, + year={2021} +} +``` diff --git a/configs/repmlp/metafile.yml b/configs/repmlp/metafile.yml new file mode 100644 index 0000000..19caecb --- /dev/null +++ b/configs/repmlp/metafile.yml @@ -0,0 +1,48 @@ +Collections: + - Name: RepMLP + Metadata: + Training Data: ImageNet-1k + Architecture: + - Multi-layer Perceptron + - Re-parameterization Convolution + Paper: + URL: https://arxiv.org/abs/2105.01883 + Title: 'RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition' + README: configs/repmlp/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.21.0/mmcls/models/backbones/repmlp.py + Version: v0.21.0 + +Models: + - Name: repmlp-base_3rdparty_8xb64_in1k + In Collection: RepMLP + Config: configs/repmlp/repmlp-base_8xb64_in1k.py + Metadata: + FLOPs: 6710000000 # 6.71 G + Parameters: 68240000 # 68.24 M + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.41 + Top 5 Accuracy: 95.14 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k_20220330-1cb1f11b.pth + Converted From: + Weights: https://github.com/DingXiaoH/RepMLP + Code: https://github.com/DingXiaoH/RepMLP/blob/072d8516beba83d75dfe6ebb12f625abad4b53d5/repmlpnet.py#L274 + - Name: repmlp-base_3rdparty_8xb64_in1k-256px.py + In Collection: RepMLP + Config: configs/repmlp/repmlp-base_8xb64_in1k-256px.py + Metadata: + FLOPs: 9690000000 # 9.69 G + Parameters: 96450000 # 96.45M + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.11 + Top 5 Accuracy: 95.50 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/repmlp/repmlp-base_3rdparty_8xb64_in1k-256px_20220330-7c5a91ce.pth + Converted From: + Weights: https://github.com/DingXiaoH/RepMLP + Code: https://github.com/DingXiaoH/RepMLP/blob/072d8516beba83d75dfe6ebb12f625abad4b53d5/repmlpnet.py#L278 diff --git a/configs/repmlp/repmlp-base_8xb64_in1k-256px.py b/configs/repmlp/repmlp-base_8xb64_in1k-256px.py new file mode 100644 index 0000000..ff03c6f --- /dev/null +++ b/configs/repmlp/repmlp-base_8xb64_in1k-256px.py @@ -0,0 +1,21 @@ +_base_ = [ + '../_base_/models/repmlp-base_224.py', + '../_base_/datasets/imagenet_bs64_mixer_224.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(img_size=256)) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256 * 256 // 224, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=256), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/configs/repmlp/repmlp-base_8xb64_in1k.py b/configs/repmlp/repmlp-base_8xb64_in1k.py new file mode 100644 index 0000000..430cdc0 --- /dev/null +++ b/configs/repmlp/repmlp-base_8xb64_in1k.py @@ -0,0 +1,20 @@ +_base_ = [ + '../_base_/models/repmlp-base_224.py', + '../_base_/datasets/imagenet_bs64_pil_resize.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +test_pipeline = [ + dict(type='LoadImageFromFile'), + # resizing to (256, 256) here, different with resizing shorter edge to 256 + dict(type='Resize', size=(256, 256), backend='pillow'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py b/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py new file mode 100644 index 0000000..b5b2c88 --- /dev/null +++ b/configs/repmlp/repmlp-base_delopy_8xb64_in1k.py @@ -0,0 +1,3 @@ +_base_ = ['./repmlp-base_8xb64_in1k.py'] + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repmlp/repmlp-base_deploy_8xb64_in1k-256px.py b/configs/repmlp/repmlp-base_deploy_8xb64_in1k-256px.py new file mode 100644 index 0000000..27ff50a --- /dev/null +++ b/configs/repmlp/repmlp-base_deploy_8xb64_in1k-256px.py @@ -0,0 +1,3 @@ +_base_ = ['./repmlp-base_8xb64_in1k-256px.py'] + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/README.md b/configs/repvgg/README.md new file mode 100644 index 0000000..b934132 --- /dev/null +++ b/configs/repvgg/README.md @@ -0,0 +1,101 @@ +# RepVGG + +> [Repvgg: Making vgg-style convnets great again](https://arxiv.org/abs/2101.03697) + + + +## Abstract + +We present a simple but powerful architecture of convolutional neural network, which has a VGG-like inference-time body composed of nothing but a stack of 3x3 convolution and ReLU, while the training-time model has a multi-branch topology. Such decoupling of the training-time and inference-time architecture is realized by a structural re-parameterization technique so that the model is named RepVGG. On ImageNet, RepVGG reaches over 80% top-1 accuracy, which is the first time for a plain model, to the best of our knowledge. On NVIDIA 1080Ti GPU, RepVGG models run 83% faster than ResNet-50 or 101% faster than ResNet-101 with higher accuracy and show favorable accuracy-speed trade-off compared to the state-of-the-art models like EfficientNet and RegNet. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Epochs | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :----: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :----------------------------------------------: | :-------------------------------------------------: | +| RepVGG-A0\* | 120 | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) | +| RepVGG-A1\* | 120 | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) | +| RepVGG-A2\* | 120 | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) | +| RepVGG-B0\* | 120 | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) | +| RepVGG-B1\* | 120 | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) | +| RepVGG-B1g2\* | 120 | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) | +| RepVGG-B1g4\* | 120 | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) | +| RepVGG-B2\* | 120 | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) | +| RepVGG-B2g4\* | 200 | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) | +| RepVGG-B3\* | 200 | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) | +| RepVGG-B3g4\* | 200 | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) | +| RepVGG-D2se\* | 200 | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \|[config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) | + +*Models with * are converted from the [official repo](https://github.com/DingXiaoH/RepVGG). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## How to use + +The checkpoints provided are all `training-time` models. Use the reparameterize tool to switch them to more efficient `inference-time` architecture, which not only has fewer parameters but also less calculations. + +### Use tool + +Use provided tool to reparameterize the given model and save the checkpoint: + +```bash +python tools/convert_models/reparameterize_model.py ${CFG_PATH} ${SRC_CKPT_PATH} ${TARGET_CKPT_PATH} +``` + +`${CFG_PATH}` is the config file, `${SRC_CKPT_PATH}` is the source chenpoint file, `${TARGET_CKPT_PATH}` is the target deploy weight file path. + +To use reparameterized weights, the config file must switch to the deploy config files. + +```bash +python tools/test.py ${Deploy_CFG} ${Deploy_Checkpoint} --metrics accuracy +``` + +### In the code + +Use `backbone.switch_to_deploy()` or `classificer.backbone.switch_to_deploy()` to switch to the deploy mode. For example: + +```python +from mmcls.models import build_backbone + +backbone_cfg=dict(type='RepVGG',arch='A0'), +backbone = build_backbone(backbone_cfg) +backbone.switch_to_deploy() +``` + +or + +```python +from mmcls.models import build_classifier + +cfg = dict( + type='ImageClassifier', + backbone=dict( + type='RepVGG', + arch='A0'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=1280, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) + +classifier = build_classifier(cfg) +classifier.backbone.switch_to_deploy() +``` + +## Citation + +``` +@inproceedings{ding2021repvgg, + title={Repvgg: Making vgg-style convnets great again}, + author={Ding, Xiaohan and Zhang, Xiangyu and Ma, Ningning and Han, Jungong and Ding, Guiguang and Sun, Jian}, + booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, + pages={13733--13742}, + year={2021} +} +``` diff --git a/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..20787f2 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..eea0da9 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-A1_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..7b0cea7 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-A2_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..23a2898 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..24355ed --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B1_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..579fcc4 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B1g2_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..eab5d44 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B1g4_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py b/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..0681f14 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B2_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..8f18401 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..e60b067 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..46f1877 --- /dev/null +++ b/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..66dff3b --- /dev/null +++ b/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = '../repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(deploy=True)) diff --git a/configs/repvgg/metafile.yml b/configs/repvgg/metafile.yml new file mode 100644 index 0000000..84fee59 --- /dev/null +++ b/configs/repvgg/metafile.yml @@ -0,0 +1,208 @@ +Collections: + - Name: RepVGG + Metadata: + Training Data: ImageNet-1k + Architecture: + - re-parameterization Convolution + - VGG-style Neural Network + Paper: + URL: https://arxiv.org/abs/2101.03697 + Title: 'RepVGG: Making VGG-style ConvNets Great Again' + README: configs/repvgg/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.16.0/mmcls/models/backbones/repvgg.py#L257 + Version: v0.16.0 + +Models: + - Name: repvgg-A0_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 1520000000 + Parameters: 9110000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 72.41 + Top 5 Accuracy: 90.50 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L196 + - Name: repvgg-A1_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 2640000000 + Parameters: 14090000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 74.47 + Top 5 Accuracy: 91.85 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L200 + - Name: repvgg-A2_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 28210000000 + Parameters: 5700000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 76.48 + Top 5 Accuracy: 93.01 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L204 + - Name: repvgg-B0_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 15820000000 + Parameters: 3420000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 75.14 + Top 5 Accuracy: 92.42 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L208 + - Name: repvgg-B1_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 57420000000 + Parameters: 13160000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 78.37 + Top 5 Accuracy: 94.11 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L212 + - Name: repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 45780000000 + Parameters: 9820000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 77.79 + Top 5 Accuracy: 93.88 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L216 + - Name: repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 39970000000 + Parameters: 8150000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 77.58 + Top 5 Accuracy: 93.84 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L220 + - Name: repvgg-B2_3rdparty_4xb64-coslr-120e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py + Metadata: + FLOPs: 89020000000 + Parameters: 20420000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 78.78 + Top 5 Accuracy: 94.42 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L225 + - Name: repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 61760000000 + Parameters: 12630000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 79.38 + Top 5 Accuracy: 94.68 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L229 + - Name: repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 123090000000 + Parameters: 29170000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 80.52 + Top 5 Accuracy: 95.26 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L238 + - Name: repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 83830000000 + Parameters: 17900000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 80.22 + Top 5 Accuracy: 95.10 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L238 + - Name: repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k + In Collection: RepVGG + Config: configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py + Metadata: + FLOPs: 133330000000 + Parameters: 36560000 + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 81.81 + Top 5 Accuracy: 95.94 + Weights: https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth + Converted From: + Weights: https://drive.google.com/drive/folders/1Avome4KvNp0Lqh2QwhXO6L5URQjzCjUq + Code: https://github.com/DingXiaoH/RepVGG/blob/9f272318abfc47a2b702cd0e916fca8d25d683e7/repvgg.py#L250 diff --git a/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..a7fd3bb --- /dev/null +++ b/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/repvgg-A0_in1k.py', + '../_base_/datasets/imagenet_bs64_pil_resize.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] + +runner = dict(max_epochs=120) diff --git a/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..649020f --- /dev/null +++ b/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='A1')) diff --git a/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..eedaf2d --- /dev/null +++ b/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='A2'), head=dict(in_channels=1408)) diff --git a/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..b3ce7ea --- /dev/null +++ b/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B0'), head=dict(in_channels=1280)) diff --git a/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..30adea3 --- /dev/null +++ b/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B1'), head=dict(in_channels=2048)) diff --git a/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..2749db8 --- /dev/null +++ b/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B1g2'), head=dict(in_channels=2048)) diff --git a/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..2647690 --- /dev/null +++ b/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B1g4'), head=dict(in_channels=2048)) diff --git a/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py b/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py new file mode 100644 index 0000000..4d21556 --- /dev/null +++ b/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-A0_4xb64-coslr-120e_in1k.py' + +model = dict(backbone=dict(arch='B2'), head=dict(in_channels=2560)) diff --git a/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..11331cf --- /dev/null +++ b/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(arch='B2g4')) diff --git a/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..7b6dc50 --- /dev/null +++ b/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/repvgg-B3_lbs-mixup_in1k.py', + '../_base_/datasets/imagenet_bs64_pil_resize.py', + '../_base_/schedules/imagenet_bs256_200e_coslr_warmup.py', + '../_base_/default_runtime.py' +] diff --git a/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..67e3688 --- /dev/null +++ b/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(arch='B3g4')) diff --git a/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py b/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py new file mode 100644 index 0000000..d235610 --- /dev/null +++ b/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py @@ -0,0 +1,3 @@ +_base_ = './repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py' + +model = dict(backbone=dict(arch='D2se')) diff --git a/configs/res2net/README.md b/configs/res2net/README.md new file mode 100644 index 0000000..6119009 --- /dev/null +++ b/configs/res2net/README.md @@ -0,0 +1,37 @@ +# Res2Net + +> [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/pdf/1904.01169.pdf) + + + +## Abstract + +Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------------------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :-------------------------------------------------------------------: | +| Res2Net-50-14w-8s\* | 224x224 | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) \| [log](<>) | +| Res2Net-50-26w-8s\* | 224x224 | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) \| [log](<>) | +| Res2Net-101-26w-4s\* | 224x224 | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) \| [log](<>) | + +*Models with * are converted from the [official repo](https://github.com/Res2Net/Res2Net-PretrainedModels). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@article{gao2019res2net, + title={Res2Net: A New Multi-scale Backbone Architecture}, + author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}, + journal={IEEE TPAMI}, + year={2021}, + doi={10.1109/TPAMI.2019.2938758}, +} +``` diff --git a/configs/res2net/metafile.yml b/configs/res2net/metafile.yml new file mode 100644 index 0000000..d76f898 --- /dev/null +++ b/configs/res2net/metafile.yml @@ -0,0 +1,70 @@ +Collections: + - Name: Res2Net + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - Batch Normalization + - Convolution + - Global Average Pooling + - ReLU + - Res2Net Block + Paper: + Title: 'Res2Net: A New Multi-scale Backbone Architecture' + URL: https://arxiv.org/pdf/1904.01169.pdf + README: configs/res2net/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.17.0/mmcls/models/backbones/res2net.py + Version: v0.17.0 + +Models: + - Name: res2net50-w14-s8_3rdparty_8xb32_in1k + Metadata: + FLOPs: 4220000000 + Parameters: 25060000 + In Collection: Res2Net + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.14 + Top 5 Accuracy: 93.85 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth + Converted From: + Weights: https://1drv.ms/u/s!AkxDDnOtroRPdOTqhF8ne_aakDI?e=EVb8Ri + Code: https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net.py#L221 + Config: configs/res2net/res2net50-w14-s8_8xb32_in1k.py + - Name: res2net50-w26-s8_3rdparty_8xb32_in1k + Metadata: + FLOPs: 8390000000 + Parameters: 48400000 + In Collection: Res2Net + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.20 + Top 5 Accuracy: 94.36 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth + Converted From: + Weights: https://1drv.ms/u/s!AkxDDnOtroRPdTrAd_Afzc26Z7Q?e=slYqsR + Code: https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net.py#L201 + Config: configs/res2net/res2net50-w26-s8_8xb32_in1k.py + - Name: res2net101-w26-s4_3rdparty_8xb32_in1k + Metadata: + FLOPs: 8120000000 + Parameters: 45210000 + In Collection: Res2Net + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.19 + Top 5 Accuracy: 94.44 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth + Converted From: + Weights: https://1drv.ms/u/s!AkxDDnOtroRPcJRgTLkahL0cFYw?e=nwbnic + Code: https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net.py#L181 + Config: configs/res2net/res2net101-w26-s4_8xb32_in1k.py diff --git a/configs/res2net/res2net101-w26-s4_8xb32_in1k.py b/configs/res2net/res2net101-w26-s4_8xb32_in1k.py new file mode 100644 index 0000000..7ebe9e9 --- /dev/null +++ b/configs/res2net/res2net101-w26-s4_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/res2net101-w26-s4.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/res2net/res2net50-w14-s8_8xb32_in1k.py b/configs/res2net/res2net50-w14-s8_8xb32_in1k.py new file mode 100644 index 0000000..56cc02e --- /dev/null +++ b/configs/res2net/res2net50-w14-s8_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/res2net50-w14-s8.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/res2net/res2net50-w26-s8_8xb32_in1k.py b/configs/res2net/res2net50-w26-s8_8xb32_in1k.py new file mode 100644 index 0000000..d7dcbeb --- /dev/null +++ b/configs/res2net/res2net50-w26-s8_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/res2net50-w26-s8.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnest/README.md b/configs/resnest/README.md new file mode 100644 index 0000000..eb6c5fd --- /dev/null +++ b/configs/resnest/README.md @@ -0,0 +1,26 @@ +# ResNeSt + +> [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955) + + + +## Abstract + +It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge. The source code for complete system and pretrained models are publicly available. + +
+ +
+ +## Citation + +``` +@misc{zhang2020resnest, + title={ResNeSt: Split-Attention Networks}, + author={Hang Zhang and Chongruo Wu and Zhongyue Zhang and Yi Zhu and Haibin Lin and Zhi Zhang and Yue Sun and Tong He and Jonas Mueller and R. Manmatha and Mu Li and Alexander Smola}, + year={2020}, + eprint={2004.08955}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/configs/resnest/resnest101_32xb64_in1k.py b/configs/resnest/resnest101_32xb64_in1k.py new file mode 100644 index 0000000..27b1882 --- /dev/null +++ b/configs/resnest/resnest101_32xb64_in1k.py @@ -0,0 +1,181 @@ +_base_ = ['../_base_/models/resnest101.py', '../_base_/default_runtime.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_lighting_cfg = dict( + eigval=[55.4625, 4.7940, 1.1475], + eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], + [-0.5836, -0.6948, 0.4203]], + alphastd=0.1, + to_rgb=True) +policies = [ + dict(type='AutoContrast', prob=0.5), + dict(type='Equalize', prob=0.5), + dict(type='Invert', prob=0.5), + dict( + type='Rotate', + magnitude_key='angle', + magnitude_range=(0, 30), + pad_val=0, + prob=0.5, + random_negative_prob=0.5), + dict( + type='Posterize', + magnitude_key='bits', + magnitude_range=(0, 4), + prob=0.5), + dict( + type='Solarize', + magnitude_key='thr', + magnitude_range=(0, 256), + prob=0.5), + dict( + type='SolarizeAdd', + magnitude_key='magnitude', + magnitude_range=(0, 110), + thr=128, + prob=0.5), + dict( + type='ColorTransform', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Contrast', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Brightness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Sharpness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5), + dict( + type='Cutout', + magnitude_key='shape', + magnitude_range=(1, 41), + pad_val=0, + prob=0.5), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5, + interpolation='bicubic'), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5, + interpolation='bicubic') +] +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandAugment', + policies=policies, + num_policies=2, + magnitude_level=12), + dict( + type='RandomResizedCrop', + size=256, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict(type='Lighting', **img_lighting_cfg), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=False), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=256, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') + +# optimizer +optimizer = dict( + type='SGD', + lr=0.8, + momentum=0.9, + weight_decay=1e-4, + paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.)) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=5, + warmup_ratio=1e-6, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=270) diff --git a/configs/resnest/resnest101_b64x32_imagenet.py b/configs/resnest/resnest101_b64x32_imagenet.py new file mode 100644 index 0000000..31c3647 --- /dev/null +++ b/configs/resnest/resnest101_b64x32_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnest101_32xb64_in1k.py' + +_deprecation_ = dict( + expected='resnest101_32xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnest/resnest200_64xb32_in1k.py b/configs/resnest/resnest200_64xb32_in1k.py new file mode 100644 index 0000000..3b166a2 --- /dev/null +++ b/configs/resnest/resnest200_64xb32_in1k.py @@ -0,0 +1,181 @@ +_base_ = ['../_base_/models/resnest200.py', '../_base_/default_runtime.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_lighting_cfg = dict( + eigval=[55.4625, 4.7940, 1.1475], + eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], + [-0.5836, -0.6948, 0.4203]], + alphastd=0.1, + to_rgb=True) +policies = [ + dict(type='AutoContrast', prob=0.5), + dict(type='Equalize', prob=0.5), + dict(type='Invert', prob=0.5), + dict( + type='Rotate', + magnitude_key='angle', + magnitude_range=(0, 30), + pad_val=0, + prob=0.5, + random_negative_prob=0.5), + dict( + type='Posterize', + magnitude_key='bits', + magnitude_range=(0, 4), + prob=0.5), + dict( + type='Solarize', + magnitude_key='thr', + magnitude_range=(0, 256), + prob=0.5), + dict( + type='SolarizeAdd', + magnitude_key='magnitude', + magnitude_range=(0, 110), + thr=128, + prob=0.5), + dict( + type='ColorTransform', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Contrast', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Brightness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Sharpness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5), + dict( + type='Cutout', + magnitude_key='shape', + magnitude_range=(1, 41), + pad_val=0, + prob=0.5), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5, + interpolation='bicubic'), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5, + interpolation='bicubic') +] +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandAugment', + policies=policies, + num_policies=2, + magnitude_level=12), + dict( + type='RandomResizedCrop', + size=320, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict(type='Lighting', **img_lighting_cfg), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=False), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=320, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') + +# optimizer +optimizer = dict( + type='SGD', + lr=0.8, + momentum=0.9, + weight_decay=1e-4, + paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.)) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=5, + warmup_ratio=1e-6, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=270) diff --git a/configs/resnest/resnest200_b32x64_imagenet.py b/configs/resnest/resnest200_b32x64_imagenet.py new file mode 100644 index 0000000..8e62865 --- /dev/null +++ b/configs/resnest/resnest200_b32x64_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnest200_64xb32_in1k.py' + +_deprecation_ = dict( + expected='resnest200_64xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnest/resnest269_64xb32_in1k.py b/configs/resnest/resnest269_64xb32_in1k.py new file mode 100644 index 0000000..7a4db09 --- /dev/null +++ b/configs/resnest/resnest269_64xb32_in1k.py @@ -0,0 +1,181 @@ +_base_ = ['../_base_/models/resnest269.py', '../_base_/default_runtime.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_lighting_cfg = dict( + eigval=[55.4625, 4.7940, 1.1475], + eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], + [-0.5836, -0.6948, 0.4203]], + alphastd=0.1, + to_rgb=True) +policies = [ + dict(type='AutoContrast', prob=0.5), + dict(type='Equalize', prob=0.5), + dict(type='Invert', prob=0.5), + dict( + type='Rotate', + magnitude_key='angle', + magnitude_range=(0, 30), + pad_val=0, + prob=0.5, + random_negative_prob=0.5), + dict( + type='Posterize', + magnitude_key='bits', + magnitude_range=(0, 4), + prob=0.5), + dict( + type='Solarize', + magnitude_key='thr', + magnitude_range=(0, 256), + prob=0.5), + dict( + type='SolarizeAdd', + magnitude_key='magnitude', + magnitude_range=(0, 110), + thr=128, + prob=0.5), + dict( + type='ColorTransform', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Contrast', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Brightness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Sharpness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5), + dict( + type='Cutout', + magnitude_key='shape', + magnitude_range=(1, 41), + pad_val=0, + prob=0.5), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5, + interpolation='bicubic'), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5, + interpolation='bicubic') +] +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandAugment', + policies=policies, + num_policies=2, + magnitude_level=12), + dict( + type='RandomResizedCrop', + size=416, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict(type='Lighting', **img_lighting_cfg), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=False), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=416, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=32, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') + +# optimizer +optimizer = dict( + type='SGD', + lr=0.8, + momentum=0.9, + weight_decay=1e-4, + paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.)) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=5, + warmup_ratio=1e-6, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=270) diff --git a/configs/resnest/resnest269_b32x64_imagenet.py b/configs/resnest/resnest269_b32x64_imagenet.py new file mode 100644 index 0000000..0f8b76c --- /dev/null +++ b/configs/resnest/resnest269_b32x64_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnest269_64xb32_in1k.py' + +_deprecation_ = dict( + expected='resnest269_64xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnest/resnest50_32xb64_in1k.py b/configs/resnest/resnest50_32xb64_in1k.py new file mode 100644 index 0000000..812a3be --- /dev/null +++ b/configs/resnest/resnest50_32xb64_in1k.py @@ -0,0 +1,181 @@ +_base_ = ['../_base_/models/resnest50.py', '../_base_/default_runtime.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_lighting_cfg = dict( + eigval=[55.4625, 4.7940, 1.1475], + eigvec=[[-0.5675, 0.7192, 0.4009], [-0.5808, -0.0045, -0.8140], + [-0.5836, -0.6948, 0.4203]], + alphastd=0.1, + to_rgb=True) +policies = [ + dict(type='AutoContrast', prob=0.5), + dict(type='Equalize', prob=0.5), + dict(type='Invert', prob=0.5), + dict( + type='Rotate', + magnitude_key='angle', + magnitude_range=(0, 30), + pad_val=0, + prob=0.5, + random_negative_prob=0.5), + dict( + type='Posterize', + magnitude_key='bits', + magnitude_range=(0, 4), + prob=0.5), + dict( + type='Solarize', + magnitude_key='thr', + magnitude_range=(0, 256), + prob=0.5), + dict( + type='SolarizeAdd', + magnitude_key='magnitude', + magnitude_range=(0, 110), + thr=128, + prob=0.5), + dict( + type='ColorTransform', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Contrast', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Brightness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Sharpness', + magnitude_key='magnitude', + magnitude_range=(-0.9, 0.9), + prob=0.5, + random_negative_prob=0.), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5), + dict( + type='Shear', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5), + dict( + type='Cutout', + magnitude_key='shape', + magnitude_range=(1, 41), + pad_val=0, + prob=0.5), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='horizontal', + random_negative_prob=0.5, + interpolation='bicubic'), + dict( + type='Translate', + magnitude_key='magnitude', + magnitude_range=(0, 0.3), + pad_val=0, + prob=0.5, + direction='vertical', + random_negative_prob=0.5, + interpolation='bicubic') +] +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandAugment', + policies=policies, + num_policies=2, + magnitude_level=12), + dict( + type='RandomResizedCrop', + size=224, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict(type='Lighting', **img_lighting_cfg), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=False), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='CenterCrop', + crop_size=224, + efficientnet_style=True, + interpolation='bicubic', + backend='pillow'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict( + type=dataset_type, + data_prefix='data/imagenet/train', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline), + test=dict( + # replace `data/val` with `data/test` for standard test + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') + +# optimizer +optimizer = dict( + type='SGD', + lr=0.8, + momentum=0.9, + weight_decay=1e-4, + paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.)) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=5, + warmup_ratio=1e-6, + warmup_by_epoch=True) +runner = dict(type='EpochBasedRunner', max_epochs=270) diff --git a/configs/resnest/resnest50_b64x32_imagenet.py b/configs/resnest/resnest50_b64x32_imagenet.py new file mode 100644 index 0000000..c0da422 --- /dev/null +++ b/configs/resnest/resnest50_b64x32_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnest50_32xb64_in1k.py' + +_deprecation_ = dict( + expected='resnest50_32xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/README.md b/configs/resnet/README.md new file mode 100644 index 0000000..d32fcd6 --- /dev/null +++ b/configs/resnet/README.md @@ -0,0 +1,91 @@ +# ResNet + +> [Deep Residual Learning for Image Recognition](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html) + + + +## Abstract + +Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. + +The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation. + +
+ +
+ +## Results and models + +The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. + +| Model | resolution | Params(M) | Flops(G) | Download | +| :------------: | :--------: | :-------: | :------: | :-------------------------------------------------------------------------------------------------------------------: | +| ResNet-50-mill | 224x224 | 86.74 | 15.14 | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | + +*The "mill" means using the mutil-label pretrain weight from [ImageNet-21K Pretraining for the Masses](https://github.com/Alibaba-MIIL/ImageNet21K).* + +### Cifar10 + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | +| ResNet-18 | 11.17 | 0.56 | 94.82 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.log.json) | +| ResNet-34 | 21.28 | 1.16 | 95.34 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.log.json) | +| ResNet-50 | 23.52 | 1.31 | 95.55 | 99.91 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.log.json) | +| ResNet-101 | 42.51 | 2.52 | 95.58 | 99.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.log.json) | +| ResNet-152 | 58.16 | 3.74 | 95.76 | 99.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.log.json) | + +### Cifar100 + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | +| ResNet-50 | 23.71 | 1.31 | 79.90 | 95.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar100.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.log.json) | + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| ResNet-18 | 11.69 | 1.82 | 69.90 | 89.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.log.json) | +| ResNet-34 | 21.8 | 3.68 | 73.62 | 91.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.log.json) | +| ResNet-50 | 25.56 | 4.12 | 76.55 | 93.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.log.json) | +| ResNet-101 | 44.55 | 7.85 | 77.97 | 94.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.log.json) | +| ResNet-152 | 60.19 | 11.58 | 78.48 | 94.13 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.log.json) | +| ResNetV1C-50 | 25.58 | 4.36 | 77.01 | 93.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.log.json) | +| ResNetV1C-101 | 44.57 | 8.09 | 78.30 | 94.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.log.json) | +| ResNetV1C-152 | 60.21 | 11.82 | 78.76 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1c152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.log.json) | +| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) | +| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) | +| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) | +| ResNet-50 (fp16) | 25.56 | 4.12 | 76.30 | 93.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32-fp16_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.log.json) | +| Wide-ResNet-50\* | 68.88 | 11.44 | 78.48 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/wide-resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth) | +| Wide-ResNet-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | +| ResNet-50 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) | +| ResNet-50 (rsb-a2) | 25.56 | 4.12 | 79.55 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.log.json) | +| ResNet-50 (rsb-a3) | 25.56 | 4.12 | 78.30 | 93.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.log.json) | + +*The "rsb" means using the training settings from [ResNet strikes back: An improved training procedure in timm](https://arxiv.org/abs/2110.00476).* + +*Models with * are converted from the [official repo](https://github.com/pytorch/vision). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +### CUB-200-2011 + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: | +| ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | 448x448 | 23.92 | 16.48 | 88.45 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cub.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.log.json) | + +### Stanford-Cars + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :-------: | :--------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :------------------------------------------------: | :---------------------------------------------------: | +| ResNet-50 | [ImageNet-21k-mill](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth) | 448x448 | 23.92 | 16.48 | 92.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb8_cars.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.log.json) | + +## Citation + +``` +@inproceedings{he2016deep, + title={Deep residual learning for image recognition}, + author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={770--778}, + year={2016} +} +``` diff --git a/configs/resnet/metafile.yml b/configs/resnet/metafile.yml new file mode 100644 index 0000000..4be4bf9 --- /dev/null +++ b/configs/resnet/metafile.yml @@ -0,0 +1,365 @@ +Collections: + - Name: ResNet + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Epochs: 100 + Batch Size: 256 + Architecture: + - ResNet + Paper: + URL: https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html + Title: "Deep Residual Learning for Image Recognition" + README: configs/resnet/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/resnet.py#L383 + Version: v0.15.0 + +Models: + - Name: resnet18_8xb16_cifar10 + Metadata: + Training Data: CIFAR-10 + Epochs: 200 + Batch Size: 128 + FLOPs: 560000000 + Parameters: 11170000 + In Collection: ResNet + Results: + - Dataset: CIFAR-10 + Metrics: + Top 1 Accuracy: 94.82 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20210528-bd6371c8.pth + Config: configs/resnet/resnet18_8xb16_cifar10.py + - Name: resnet34_8xb16_cifar10 + Metadata: + Training Data: CIFAR-10 + Epochs: 200 + Batch Size: 128 + FLOPs: 1160000000 + Parameters: 21280000 + In Collection: ResNet + Results: + - Dataset: CIFAR-10 + Metrics: + Top 1 Accuracy: 95.34 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20210528-a8aa36a6.pth + Config: configs/resnet/resnet34_8xb16_cifar10.py + - Name: resnet50_8xb16_cifar10 + Metadata: + Training Data: CIFAR-10 + Epochs: 200 + Batch Size: 128 + FLOPs: 1310000000 + Parameters: 23520000 + In Collection: ResNet + Results: + - Dataset: CIFAR-10 + Metrics: + Top 1 Accuracy: 95.55 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth + Config: configs/resnet/resnet50_8xb16_cifar10.py + - Name: resnet101_8xb16_cifar10 + Metadata: + Training Data: CIFAR-10 + Epochs: 200 + Batch Size: 128 + FLOPs: 2520000000 + Parameters: 42510000 + In Collection: ResNet + Results: + - Dataset: CIFAR-10 + Metrics: + Top 1 Accuracy: 95.58 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20210528-2d29e936.pth + Config: configs/resnet/resnet101_8xb16_cifar10.py + - Name: resnet152_8xb16_cifar10 + Metadata: + Training Data: CIFAR-10 + Epochs: 200 + Batch Size: 128 + FLOPs: 3740000000 + Parameters: 58160000 + In Collection: ResNet + Results: + - Dataset: CIFAR-10 + Metrics: + Top 1 Accuracy: 95.76 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20210528-3e8e9178.pth + Config: configs/resnet/resnet152_8xb16_cifar10.py + - Name: resnet50_8xb16_cifar100 + Metadata: + Training Data: CIFAR-100 + Epochs: 200 + Batch Size: 128 + FLOPs: 1310000000 + Parameters: 23710000 + In Collection: ResNet + Results: + - Dataset: CIFAR-100 + Metrics: + Top 1 Accuracy: 79.90 + Top 5 Accuracy: 95.19 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar100_20210528-67b58a1b.pth + Config: configs/resnet/resnet50_8xb16_cifar100.py + - Name: resnet18_8xb32_in1k + Metadata: + FLOPs: 1820000000 + Parameters: 11690000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 69.90 + Top 5 Accuracy: 89.43 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth + Config: configs/resnet/resnet18_8xb32_in1k.py + - Name: resnet34_8xb32_in1k + Metadata: + FLOPs: 3680000000 + Parameters: 2180000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 73.62 + Top 5 Accuracy: 91.59 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_8xb32_in1k_20210831-f257d4e6.pth + Config: configs/resnet/resnet34_8xb32_in1k.py + - Name: resnet50_8xb32_in1k + Metadata: + FLOPs: 4120000000 + Parameters: 25560000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 76.55 + Top 5 Accuracy: 93.06 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth + Config: configs/resnet/resnet50_8xb32_in1k.py + - Name: resnet101_8xb32_in1k + Metadata: + FLOPs: 7850000000 + Parameters: 44550000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.97 + Top 5 Accuracy: 94.06 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_8xb32_in1k_20210831-539c63f8.pth + Config: configs/resnet/resnet101_8xb32_in1k.py + - Name: resnet152_8xb32_in1k + Metadata: + FLOPs: 11580000000 + Parameters: 60190000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.48 + Top 5 Accuracy: 94.13 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_8xb32_in1k_20210901-4d7582fa.pth + Config: configs/resnet/resnet152_8xb32_in1k.py + - Name: resnetv1d50_8xb32_in1k + Metadata: + FLOPs: 4360000000 + Parameters: 25580000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.54 + Top 5 Accuracy: 93.57 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth + Config: configs/resnet/resnetv1d50_8xb32_in1k.py + - Name: resnetv1d101_8xb32_in1k + Metadata: + FLOPs: 8090000000 + Parameters: 44570000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.93 + Top 5 Accuracy: 94.48 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth + Config: configs/resnet/resnetv1d101_8xb32_in1k.py + - Name: resnetv1d152_8xb32_in1k + Metadata: + FLOPs: 11820000000 + Parameters: 60210000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.41 + Top 5 Accuracy: 94.70 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth + Config: configs/resnet/resnetv1d152_8xb32_in1k.py + - Name: resnet50_8xb32-fp16_in1k + Metadata: + FLOPs: 4120000000 + Parameters: 25560000 + Training Techniques: + - SGD with Momentum + - Weight Decay + - Mixed Precision Training + In Collection: ResNet + Results: + - Task: Image Classification + Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 76.30 + Top 5 Accuracy: 93.07 + Weights: https://download.openmmlab.com/mmclassification/v0/fp16/resnet50_batch256_fp16_imagenet_20210320-b3964210.pth + Config: configs/resnet/resnet50_8xb32-fp16_in1k.py + - Name: resnet50_8xb256-rsb-a1-600e_in1k + Metadata: + FLOPs: 4120000000 + Parameters: 25560000 + Training Techniques: + - LAMB + - Weight Decay + - Cosine Annealing + - Mixup + - CutMix + - RepeatAugSampler + - RandAugment + Epochs: 600 + Batch Size: 2048 + In Collection: ResNet + Results: + - Task: Image Classification + Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 80.12 + Top 5 Accuracy: 94.78 + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth + Config: configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py + - Name: resnet50_8xb256-rsb-a2-300e_in1k + Metadata: + FLOPs: 4120000000 + Parameters: 25560000 + Training Techniques: + - LAMB + - Weight Decay + - Cosine Annealing + - Mixup + - CutMix + - RepeatAugSampler + - RandAugment + Epochs: 300 + Batch Size: 2048 + In Collection: ResNet + Results: + - Task: Image Classification + Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.55 + Top 5 Accuracy: 94.37 + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a2-300e_in1k_20211228-0fd8be6e.pth + Config: configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py + - Name: resnet50_8xb256-rsb-a3-100e_in1k + Metadata: + FLOPs: 4120000000 + Parameters: 25560000 + Training Techniques: + - LAMB + - Weight Decay + - Cosine Annealing + - Mixup + - CutMix + - RandAugment + Batch Size: 2048 + In Collection: ResNet + Results: + - Task: Image Classification + Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.30 + Top 5 Accuracy: 93.80 + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a3-100e_in1k_20211228-3493673c.pth + Config: configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py + - Name: resnetv1c50_8xb32_in1k + Metadata: + FLOPs: 4360000000 + Parameters: 25580000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.01 + Top 5 Accuracy: 93.58 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c50_8xb32_in1k_20220214-3343eccd.pth + Config: configs/resnet/resnetv1c50_8xb32_in1k.py + - Name: resnetv1c101_8xb32_in1k + Metadata: + FLOPs: 8090000000 + Parameters: 44570000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.30 + Top 5 Accuracy: 94.27 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c101_8xb32_in1k_20220214-434fe45f.pth + Config: configs/resnet/resnetv1c101_8xb32_in1k.py + - Name: resnetv1c152_8xb32_in1k + Metadata: + FLOPs: 11820000000 + Parameters: 60210000 + In Collection: ResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.76 + Top 5 Accuracy: 94.41 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1c152_8xb32_in1k_20220214-c013291f.pth + Config: configs/resnet/resnetv1c152_8xb32_in1k.py + - Name: resnet50_8xb8_cub + Metadata: + FLOPs: 16480000000 + Parameters: 23920000 + In Collection: ResNet + Results: + - Dataset: CUB-200-2011 + Metrics: + Top 1 Accuracy: 88.45 + Task: Image Classification + Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cub_20220307-57840e60.pth + Config: configs/resnet/resnet50_8xb8_cub.py + - Name: resnet50_8xb8_cars + Metadata: + FLOPs: 16480000000 + Parameters: 23920000 + In Collection: ResNet + Results: + - Dataset: StanfordCars + Metrics: + Top 1 Accuracy: 92.82 + Task: Image Classification + Pretrain: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth + Weights: https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb8_cars_20220812-9d85901a.pth + Config: configs/resnet/resnet50_8xb8_cars.py diff --git a/configs/resnet/resnet101_8xb16_cifar10.py b/configs/resnet/resnet101_8xb16_cifar10.py new file mode 100644 index 0000000..166a174 --- /dev/null +++ b/configs/resnet/resnet101_8xb16_cifar10.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet101_cifar.py', + '../_base_/datasets/cifar10_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet101_8xb32_in1k.py b/configs/resnet/resnet101_8xb32_in1k.py new file mode 100644 index 0000000..388d2cd --- /dev/null +++ b/configs/resnet/resnet101_8xb32_in1k.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet101.py', '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet101_b16x8_cifar10.py b/configs/resnet/resnet101_b16x8_cifar10.py new file mode 100644 index 0000000..57758f2 --- /dev/null +++ b/configs/resnet/resnet101_b16x8_cifar10.py @@ -0,0 +1,6 @@ +_base_ = 'resnet101_8xb16_cifar10.py' + +_deprecation_ = dict( + expected='resnet101_8xb16_cifar10.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet101_b32x8_imagenet.py b/configs/resnet/resnet101_b32x8_imagenet.py new file mode 100644 index 0000000..8d45adc --- /dev/null +++ b/configs/resnet/resnet101_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet101_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnet101_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet152_8xb16_cifar10.py b/configs/resnet/resnet152_8xb16_cifar10.py new file mode 100644 index 0000000..3f307b6 --- /dev/null +++ b/configs/resnet/resnet152_8xb16_cifar10.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet152_cifar.py', + '../_base_/datasets/cifar10_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet152_8xb32_in1k.py b/configs/resnet/resnet152_8xb32_in1k.py new file mode 100644 index 0000000..cc9dc2c --- /dev/null +++ b/configs/resnet/resnet152_8xb32_in1k.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet152.py', '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet152_b16x8_cifar10.py b/configs/resnet/resnet152_b16x8_cifar10.py new file mode 100644 index 0000000..5c76cac --- /dev/null +++ b/configs/resnet/resnet152_b16x8_cifar10.py @@ -0,0 +1,6 @@ +_base_ = 'resnet152_8xb16_cifar10.py' + +_deprecation_ = dict( + expected='resnet152_8xb16_cifar10.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet152_b32x8_imagenet.py b/configs/resnet/resnet152_b32x8_imagenet.py new file mode 100644 index 0000000..133638a --- /dev/null +++ b/configs/resnet/resnet152_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet152_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnet152_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet18_8xb16_cifar10.py b/configs/resnet/resnet18_8xb16_cifar10.py new file mode 100644 index 0000000..c7afa39 --- /dev/null +++ b/configs/resnet/resnet18_8xb16_cifar10.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet18_cifar.py', '../_base_/datasets/cifar10_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet18_8xb32_in1k.py b/configs/resnet/resnet18_8xb32_in1k.py new file mode 100644 index 0000000..ac452ff --- /dev/null +++ b/configs/resnet/resnet18_8xb32_in1k.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet18.py', '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet18_b16x8_cifar10.py b/configs/resnet/resnet18_b16x8_cifar10.py new file mode 100644 index 0000000..5a25a0e --- /dev/null +++ b/configs/resnet/resnet18_b16x8_cifar10.py @@ -0,0 +1,6 @@ +_base_ = 'resnet18_8xb16_cifar10.py' + +_deprecation_ = dict( + expected='resnet18_8xb16_cifar10.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet18_b32x8_imagenet.py b/configs/resnet/resnet18_b32x8_imagenet.py new file mode 100644 index 0000000..e6d08f6 --- /dev/null +++ b/configs/resnet/resnet18_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet18_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnet18_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet34_8xb16_cifar10.py b/configs/resnet/resnet34_8xb16_cifar10.py new file mode 100644 index 0000000..7f5cd51 --- /dev/null +++ b/configs/resnet/resnet34_8xb16_cifar10.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet34_cifar.py', '../_base_/datasets/cifar10_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet34_8xb32_in1k.py b/configs/resnet/resnet34_8xb32_in1k.py new file mode 100644 index 0000000..7749261 --- /dev/null +++ b/configs/resnet/resnet34_8xb32_in1k.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet34.py', '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet34_b16x8_cifar10.py b/configs/resnet/resnet34_b16x8_cifar10.py new file mode 100644 index 0000000..eec98b2 --- /dev/null +++ b/configs/resnet/resnet34_b16x8_cifar10.py @@ -0,0 +1,6 @@ +_base_ = 'resnet34_8xb16_cifar10.py' + +_deprecation_ = dict( + expected='resnet34_8xb16_cifar10.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet34_b32x8_imagenet.py b/configs/resnet/resnet34_b32x8_imagenet.py new file mode 100644 index 0000000..144613a --- /dev/null +++ b/configs/resnet/resnet34_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet34_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnet34_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_32xb64-warmup-coslr_in1k.py b/configs/resnet/resnet50_32xb64-warmup-coslr_in1k.py new file mode 100644 index 0000000..c26245e --- /dev/null +++ b/configs/resnet/resnet50_32xb64-warmup-coslr_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py', + '../_base_/schedules/imagenet_bs2048_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_32xb64-warmup-lbs_in1k.py b/configs/resnet/resnet50_32xb64-warmup-lbs_in1k.py new file mode 100644 index 0000000..2f24f9a --- /dev/null +++ b/configs/resnet/resnet50_32xb64-warmup-lbs_in1k.py @@ -0,0 +1,12 @@ +_base_ = ['./resnet50_32xb64-warmup_in1k.py'] +model = dict( + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict( + type='LabelSmoothLoss', + loss_weight=1.0, + label_smooth_val=0.1, + num_classes=1000), + )) diff --git a/configs/resnet/resnet50_32xb64-warmup_in1k.py b/configs/resnet/resnet50_32xb64-warmup_in1k.py new file mode 100644 index 0000000..34d5288 --- /dev/null +++ b/configs/resnet/resnet50_32xb64-warmup_in1k.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs64.py', + '../_base_/schedules/imagenet_bs2048.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb128_coslr-90e_in21k.py b/configs/resnet/resnet50_8xb128_coslr-90e_in21k.py new file mode 100644 index 0000000..8cc7921 --- /dev/null +++ b/configs/resnet/resnet50_8xb128_coslr-90e_in21k.py @@ -0,0 +1,11 @@ +_base_ = [ + '../_base_/models/resnet50.py', '../_base_/datasets/imagenet21k_bs128.py', + '../_base_/schedules/imagenet_bs1024_coslr.py', + '../_base_/default_runtime.py' +] + +# model settings +model = dict(head=dict(num_classes=21843)) + +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=90) diff --git a/configs/resnet/resnet50_8xb16-mixup_cifar10.py b/configs/resnet/resnet50_8xb16-mixup_cifar10.py new file mode 100644 index 0000000..2420ebf --- /dev/null +++ b/configs/resnet/resnet50_8xb16-mixup_cifar10.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet50_cifar_mixup.py', + '../_base_/datasets/cifar10_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb16_cifar10.py b/configs/resnet/resnet50_8xb16_cifar10.py new file mode 100644 index 0000000..669e5de --- /dev/null +++ b/configs/resnet/resnet50_8xb16_cifar10.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet50_cifar.py', '../_base_/datasets/cifar10_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb16_cifar100.py b/configs/resnet/resnet50_8xb16_cifar100.py new file mode 100644 index 0000000..39bd90f --- /dev/null +++ b/configs/resnet/resnet50_8xb16_cifar100.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/resnet50_cifar.py', + '../_base_/datasets/cifar100_bs16.py', + '../_base_/schedules/cifar10_bs128.py', '../_base_/default_runtime.py' +] + +model = dict(head=dict(num_classes=100)) + +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0005) +lr_config = dict(policy='step', step=[60, 120, 160], gamma=0.2) diff --git a/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py b/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py new file mode 100644 index 0000000..192776f --- /dev/null +++ b/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py @@ -0,0 +1,33 @@ +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/imagenet_bs256_rsb_a12.py', + '../_base_/schedules/imagenet_bs2048_rsb.py', + '../_base_/default_runtime.py' +] + +# Model settings +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + drop_path_rate=0.05, + ), + head=dict( + loss=dict( + type='LabelSmoothLoss', + label_smooth_val=0.1, + mode='original', + )), + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.2, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# Dataset settings +sampler = dict(type='RepeatAugSampler') + +# Schedule settings +runner = dict(max_epochs=600) +optimizer = dict( + weight_decay=0.01, + paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.), +) diff --git a/configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py b/configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py new file mode 100644 index 0000000..fcdc880 --- /dev/null +++ b/configs/resnet/resnet50_8xb256-rsb-a2-300e_in1k.py @@ -0,0 +1,25 @@ +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/imagenet_bs256_rsb_a12.py', + '../_base_/schedules/imagenet_bs2048_rsb.py', + '../_base_/default_runtime.py' +] + +# Model settings +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + drop_path_rate=0.05, + ), + head=dict(loss=dict(use_sigmoid=True)), + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.1, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# Dataset settings +sampler = dict(type='RepeatAugSampler') + +# Schedule settings +runner = dict(max_epochs=300) +optimizer = dict(paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.)) diff --git a/configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py b/configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py new file mode 100644 index 0000000..4ff52ac --- /dev/null +++ b/configs/resnet/resnet50_8xb256-rsb-a3-100e_in1k.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/imagenet_bs256_rsb_a3.py', + '../_base_/schedules/imagenet_bs2048_rsb.py', + '../_base_/default_runtime.py' +] + +# Model settings +model = dict( + backbone=dict(norm_cfg=dict(type='SyncBN', requires_grad=True)), + head=dict(loss=dict(use_sigmoid=True)), + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.1, num_classes=1000, prob=0.5), + dict(type='BatchCutMix', alpha=1.0, num_classes=1000, prob=0.5) + ])) + +# Schedule settings +optimizer = dict( + lr=0.008, paramwise_cfg=dict(bias_decay_mult=0., norm_decay_mult=0.)) diff --git a/configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py b/configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py new file mode 100644 index 0000000..dab82c6 --- /dev/null +++ b/configs/resnet/resnet50_8xb32-coslr-preciseBN_in1k.py @@ -0,0 +1,12 @@ +_base_ = 'resnet50_8xb32-coslr_in1k.py' + +# Precise BN hook will update the bn stats, so this hook should be executed +# before CheckpointHook, which has priority of 'NORMAL'. So set the +# priority of PreciseBNHook to 'ABOVE_NORMAL' here. +custom_hooks = [ + dict( + type='PreciseBNHook', + num_samples=8192, + interval=1, + priority='ABOVE_NORMAL') +] diff --git a/configs/resnet/resnet50_8xb32-coslr_in1k.py b/configs/resnet/resnet50_8xb32-coslr_in1k.py new file mode 100644 index 0000000..938a114 --- /dev/null +++ b/configs/resnet/resnet50_8xb32-coslr_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256_coslr.py', + '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb32-cutmix_in1k.py b/configs/resnet/resnet50_8xb32-cutmix_in1k.py new file mode 100644 index 0000000..2f8d0ca --- /dev/null +++ b/configs/resnet/resnet50_8xb32-cutmix_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet50_cutmix.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py b/configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py new file mode 100644 index 0000000..7a6c93c --- /dev/null +++ b/configs/resnet/resnet50_8xb32-fp16-dynamic_in1k.py @@ -0,0 +1,4 @@ +_base_ = ['./resnet50_8xb32_in1k.py'] + +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/resnet/resnet50_8xb32-fp16_in1k.py b/configs/resnet/resnet50_8xb32-fp16_in1k.py new file mode 100644 index 0000000..4245d19 --- /dev/null +++ b/configs/resnet/resnet50_8xb32-fp16_in1k.py @@ -0,0 +1,4 @@ +_base_ = ['./resnet50_8xb32_in1k.py'] + +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/configs/resnet/resnet50_8xb32-lbs_in1k.py b/configs/resnet/resnet50_8xb32-lbs_in1k.py new file mode 100644 index 0000000..1c1aa5a --- /dev/null +++ b/configs/resnet/resnet50_8xb32-lbs_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet50_label_smooth.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb32-mixup_in1k.py b/configs/resnet/resnet50_8xb32-mixup_in1k.py new file mode 100644 index 0000000..2a153d0 --- /dev/null +++ b/configs/resnet/resnet50_8xb32-mixup_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnet50_mixup.py', + '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb32_in1k.py b/configs/resnet/resnet50_8xb32_in1k.py new file mode 100644 index 0000000..c32f333 --- /dev/null +++ b/configs/resnet/resnet50_8xb32_in1k.py @@ -0,0 +1,4 @@ +_base_ = [ + '../_base_/models/resnet50.py', '../_base_/datasets/imagenet_bs32.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnet50_8xb8_cars.py b/configs/resnet/resnet50_8xb8_cars.py new file mode 100644 index 0000000..2d2db45 --- /dev/null +++ b/configs/resnet/resnet50_8xb8_cars.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/stanford_cars_bs8_448.py', + '../_base_/schedules/stanford_cars_bs8.py', '../_base_/default_runtime.py' +] + +# use pre-train weight converted from https://github.com/Alibaba-MIIL/ImageNet21K # noqa +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth' # noqa + +model = dict( + type='ImageClassifier', + backbone=dict( + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint, prefix='backbone')), + head=dict(num_classes=196, )) + +log_config = dict(interval=50) +checkpoint_config = dict( + interval=1, max_keep_ckpts=3) # save last three checkpoints diff --git a/configs/resnet/resnet50_8xb8_cub.py b/configs/resnet/resnet50_8xb8_cub.py new file mode 100644 index 0000000..dffb076 --- /dev/null +++ b/configs/resnet/resnet50_8xb8_cub.py @@ -0,0 +1,19 @@ +_base_ = [ + '../_base_/models/resnet50.py', '../_base_/datasets/cub_bs8_448.py', + '../_base_/schedules/cub_bs64.py', '../_base_/default_runtime.py' +] + +# use pre-train weight converted from https://github.com/Alibaba-MIIL/ImageNet21K # noqa +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_3rdparty-mill_in21k_20220331-faac000b.pth' # noqa + +model = dict( + type='ImageClassifier', + backbone=dict( + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint, prefix='backbone')), + head=dict(num_classes=200, )) + +log_config = dict(interval=20) # log every 20 intervals + +checkpoint_config = dict( + interval=1, max_keep_ckpts=3) # save last three checkpoints diff --git a/configs/resnet/resnet50_b16x8_cifar10.py b/configs/resnet/resnet50_b16x8_cifar10.py new file mode 100644 index 0000000..e40d1ee --- /dev/null +++ b/configs/resnet/resnet50_b16x8_cifar10.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb16_cifar10.py' + +_deprecation_ = dict( + expected='resnet50_8xb16_cifar10.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b16x8_cifar100.py b/configs/resnet/resnet50_b16x8_cifar100.py new file mode 100644 index 0000000..b49b6f4 --- /dev/null +++ b/configs/resnet/resnet50_b16x8_cifar100.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb16_cifar100.py' + +_deprecation_ = dict( + expected='resnet50_8xb16_cifar100.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b16x8_cifar10_mixup.py b/configs/resnet/resnet50_b16x8_cifar10_mixup.py new file mode 100644 index 0000000..409a40e --- /dev/null +++ b/configs/resnet/resnet50_b16x8_cifar10_mixup.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb16-mixup_cifar10.py' + +_deprecation_ = dict( + expected='resnet50_8xb16-mixup_cifar10.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b32x8_coslr_imagenet.py b/configs/resnet/resnet50_b32x8_coslr_imagenet.py new file mode 100644 index 0000000..647153b --- /dev/null +++ b/configs/resnet/resnet50_b32x8_coslr_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb32-coslr_in1k.py' + +_deprecation_ = dict( + expected='resnet50_8xb32-coslr_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b32x8_cutmix_imagenet.py b/configs/resnet/resnet50_b32x8_cutmix_imagenet.py new file mode 100644 index 0000000..87b27d5 --- /dev/null +++ b/configs/resnet/resnet50_b32x8_cutmix_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb32-cutmix_in1k.py' + +_deprecation_ = dict( + expected='resnet50_8xb32-cutmix_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b32x8_imagenet.py b/configs/resnet/resnet50_b32x8_imagenet.py new file mode 100644 index 0000000..7d7f69e --- /dev/null +++ b/configs/resnet/resnet50_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnet50_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b32x8_label_smooth_imagenet.py b/configs/resnet/resnet50_b32x8_label_smooth_imagenet.py new file mode 100644 index 0000000..6e87415 --- /dev/null +++ b/configs/resnet/resnet50_b32x8_label_smooth_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb32-lbs_in1k.py' + +_deprecation_ = dict( + expected='resnet50_8xb32-lbs_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b32x8_mixup_imagenet.py b/configs/resnet/resnet50_b32x8_mixup_imagenet.py new file mode 100644 index 0000000..3405319 --- /dev/null +++ b/configs/resnet/resnet50_b32x8_mixup_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_8xb32-mixup_in1k.py' + +_deprecation_ = dict( + expected='resnet50_8xb32-mixup_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b64x32_warmup_coslr_imagenet.py b/configs/resnet/resnet50_b64x32_warmup_coslr_imagenet.py new file mode 100644 index 0000000..4724616 --- /dev/null +++ b/configs/resnet/resnet50_b64x32_warmup_coslr_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_32xb64-warmup-coslr_in1k.py' + +_deprecation_ = dict( + expected='resnet50_32xb64-warmup-coslr_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b64x32_warmup_imagenet.py b/configs/resnet/resnet50_b64x32_warmup_imagenet.py new file mode 100644 index 0000000..3e35054 --- /dev/null +++ b/configs/resnet/resnet50_b64x32_warmup_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_32xb64-warmup_in1k.py' + +_deprecation_ = dict( + expected='resnet50_32xb64-warmup_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnet50_b64x32_warmup_label_smooth_imagenet.py b/configs/resnet/resnet50_b64x32_warmup_label_smooth_imagenet.py new file mode 100644 index 0000000..2544e33 --- /dev/null +++ b/configs/resnet/resnet50_b64x32_warmup_label_smooth_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnet50_32xb64-warmup-lbs_in1k.py' + +_deprecation_ = dict( + expected='resnet50_32xb64-warmup-lbs_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnetv1c101_8xb32_in1k.py b/configs/resnet/resnetv1c101_8xb32_in1k.py new file mode 100644 index 0000000..441aff5 --- /dev/null +++ b/configs/resnet/resnetv1c101_8xb32_in1k.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/resnetv1c50.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(depth=101)) diff --git a/configs/resnet/resnetv1c152_8xb32_in1k.py b/configs/resnet/resnetv1c152_8xb32_in1k.py new file mode 100644 index 0000000..b9f466f --- /dev/null +++ b/configs/resnet/resnetv1c152_8xb32_in1k.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/resnetv1c50.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(depth=152)) diff --git a/configs/resnet/resnetv1c50_8xb32_in1k.py b/configs/resnet/resnetv1c50_8xb32_in1k.py new file mode 100644 index 0000000..aa1c8b6 --- /dev/null +++ b/configs/resnet/resnetv1c50_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnetv1c50.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnetv1d101_8xb32_in1k.py b/configs/resnet/resnetv1d101_8xb32_in1k.py new file mode 100644 index 0000000..b16ca86 --- /dev/null +++ b/configs/resnet/resnetv1d101_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnetv1d101.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnetv1d101_b32x8_imagenet.py b/configs/resnet/resnetv1d101_b32x8_imagenet.py new file mode 100644 index 0000000..e736937 --- /dev/null +++ b/configs/resnet/resnetv1d101_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnetv1d101_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnetv1d101_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnetv1d152_8xb32_in1k.py b/configs/resnet/resnetv1d152_8xb32_in1k.py new file mode 100644 index 0000000..76926dd --- /dev/null +++ b/configs/resnet/resnetv1d152_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnetv1d152.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnetv1d152_b32x8_imagenet.py b/configs/resnet/resnetv1d152_b32x8_imagenet.py new file mode 100644 index 0000000..88e5b9f --- /dev/null +++ b/configs/resnet/resnetv1d152_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnetv1d152_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnetv1d152_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnet/resnetv1d50_8xb32_in1k.py b/configs/resnet/resnetv1d50_8xb32_in1k.py new file mode 100644 index 0000000..208bde4 --- /dev/null +++ b/configs/resnet/resnetv1d50_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnetv1d50.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnet/resnetv1d50_b32x8_imagenet.py b/configs/resnet/resnetv1d50_b32x8_imagenet.py new file mode 100644 index 0000000..5455e05 --- /dev/null +++ b/configs/resnet/resnetv1d50_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnetv1d50_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnetv1d50_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnext/README.md b/configs/resnext/README.md new file mode 100644 index 0000000..56df277 --- /dev/null +++ b/configs/resnext/README.md @@ -0,0 +1,36 @@ +# ResNeXt + +> [Aggregated Residual Transformations for Deep Neural Networks](https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html) + + + +## Abstract + +We present a simple, highly modularized network architecture for image classification. Our network is constructed by repeating a building block that aggregates a set of transformations with the same topology. Our simple design results in a homogeneous, multi-branch architecture that has only a few hyper-parameters to set. This strategy exposes a new dimension, which we call "cardinality" (the size of the set of transformations), as an essential factor in addition to the dimensions of depth and width. On the ImageNet-1K dataset, we empirically show that even under the restricted condition of maintaining complexity, increasing cardinality is able to improve classification accuracy. Moreover, increasing cardinality is more effective than going deeper or wider when we increase the capacity. Our models, named ResNeXt, are the foundations of our entry to the ILSVRC 2016 classification task in which we secured 2nd place. We further investigate ResNeXt on an ImageNet-5K set and the COCO detection set, also showing better results than its ResNet counterpart. The code and models are publicly available online. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) | +| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.61 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) | +| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.27 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) | +| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.88 | 94.33 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) | + +## Citation + +``` +@inproceedings{xie2017aggregated, + title={Aggregated residual transformations for deep neural networks}, + author={Xie, Saining and Girshick, Ross and Doll{\'a}r, Piotr and Tu, Zhuowen and He, Kaiming}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={1492--1500}, + year={2017} +} +``` diff --git a/configs/resnext/metafile.yml b/configs/resnext/metafile.yml new file mode 100644 index 0000000..c68e7f9 --- /dev/null +++ b/configs/resnext/metafile.yml @@ -0,0 +1,73 @@ +Collections: + - Name: ResNeXt + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Epochs: 100 + Batch Size: 256 + Architecture: + - ResNeXt + Paper: + URL: https://openaccess.thecvf.com/content_cvpr_2017/html/Xie_Aggregated_Residual_Transformations_CVPR_2017_paper.html + Title: "Aggregated Residual Transformations for Deep Neural Networks" + README: configs/resnext/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/resnext.py#L90 + Version: v0.15.0 + +Models: + - Name: resnext50-32x4d_8xb32_in1k + Metadata: + FLOPs: 4270000000 + Parameters: 25030000 + In Collection: ResNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.90 + Top 5 Accuracy: 93.66 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth + Config: configs/resnext/resnext50-32x4d_8xb32_in1k.py + - Name: resnext101-32x4d_8xb32_in1k + Metadata: + FLOPs: 8030000000 + Parameters: 44180000 + In Collection: ResNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.61 + Top 5 Accuracy: 94.17 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth + Config: configs/resnext/resnext101-32x4d_8xb32_in1k.py + - Name: resnext101-32x8d_8xb32_in1k + Metadata: + FLOPs: 16500000000 + Parameters: 88790000 + In Collection: ResNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 79.27 + Top 5 Accuracy: 94.58 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth + Config: configs/resnext/resnext101-32x8d_8xb32_in1k.py + - Name: resnext152-32x4d_8xb32_in1k + Metadata: + FLOPs: 11800000000 + Parameters: 59950000 + In Collection: ResNeXt + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.88 + Top 5 Accuracy: 94.33 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth + Config: configs/resnext/resnext152-32x4d_8xb32_in1k.py diff --git a/configs/resnext/resnext101-32x4d_8xb32_in1k.py b/configs/resnext/resnext101-32x4d_8xb32_in1k.py new file mode 100644 index 0000000..970aa60 --- /dev/null +++ b/configs/resnext/resnext101-32x4d_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnext101_32x4d.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnext/resnext101-32x8d_8xb32_in1k.py b/configs/resnext/resnext101-32x8d_8xb32_in1k.py new file mode 100644 index 0000000..315d05f --- /dev/null +++ b/configs/resnext/resnext101-32x8d_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnext101_32x8d.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnext/resnext101_32x4d_b32x8_imagenet.py b/configs/resnext/resnext101_32x4d_b32x8_imagenet.py new file mode 100644 index 0000000..07d66c3 --- /dev/null +++ b/configs/resnext/resnext101_32x4d_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnext101-32x4d_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnext101-32x4d_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnext/resnext101_32x8d_b32x8_imagenet.py b/configs/resnext/resnext101_32x8d_b32x8_imagenet.py new file mode 100644 index 0000000..071ca60 --- /dev/null +++ b/configs/resnext/resnext101_32x8d_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnext101-32x8d_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnext101-32x8d_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnext/resnext152-32x4d_8xb32_in1k.py b/configs/resnext/resnext152-32x4d_8xb32_in1k.py new file mode 100644 index 0000000..9c13731 --- /dev/null +++ b/configs/resnext/resnext152-32x4d_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnext152_32x4d.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnext/resnext152_32x4d_b32x8_imagenet.py b/configs/resnext/resnext152_32x4d_b32x8_imagenet.py new file mode 100644 index 0000000..6d05c8b --- /dev/null +++ b/configs/resnext/resnext152_32x4d_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnext152-32x4d_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnext152-32x4d_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/resnext/resnext50-32x4d_8xb32_in1k.py b/configs/resnext/resnext50-32x4d_8xb32_in1k.py new file mode 100644 index 0000000..bd9c9fc --- /dev/null +++ b/configs/resnext/resnext50-32x4d_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/resnext50_32x4d.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/resnext/resnext50_32x4d_b32x8_imagenet.py b/configs/resnext/resnext50_32x4d_b32x8_imagenet.py new file mode 100644 index 0000000..92ae063 --- /dev/null +++ b/configs/resnext/resnext50_32x4d_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'resnext50-32x4d_8xb32_in1k.py' + +_deprecation_ = dict( + expected='resnext50-32x4d_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/seresnet/README.md b/configs/seresnet/README.md new file mode 100644 index 0000000..ccfd1d1 --- /dev/null +++ b/configs/seresnet/README.md @@ -0,0 +1,34 @@ +# SE-ResNet + +> [Squeeze-and-Excitation Networks](https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html) + + + +## Abstract + +The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251%, surpassing the winning entry of 2016 by a relative improvement of ~25%. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) | +| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) | + +## Citation + +``` +@inproceedings{hu2018squeeze, + title={Squeeze-and-excitation networks}, + author={Hu, Jie and Shen, Li and Sun, Gang}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={7132--7141}, + year={2018} +} +``` diff --git a/configs/seresnet/metafile.yml b/configs/seresnet/metafile.yml new file mode 100644 index 0000000..7d2a381 --- /dev/null +++ b/configs/seresnet/metafile.yml @@ -0,0 +1,47 @@ +Collections: + - Name: SEResNet + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Epochs: 140 + Batch Size: 256 + Architecture: + - ResNet + Paper: + URL: https://openaccess.thecvf.com/content_cvpr_2018/html/Hu_Squeeze-and-Excitation_Networks_CVPR_2018_paper.html + Title: "Squeeze-and-Excitation Networks" + README: configs/seresnet/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/seresnet.py#L58 + Version: v0.15.0 + +Models: + - Name: seresnet50_8xb32_in1k + Metadata: + FLOPs: 4130000000 + Parameters: 28090000 + In Collection: SEResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 77.74 + Top 5 Accuracy: 93.84 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth + Config: configs/seresnet/seresnet50_8xb32_in1k.py + - Name: seresnet101_8xb32_in1k + Metadata: + FLOPs: 7860000000 + Parameters: 49330000 + In Collection: SEResNet + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.26 + Top 5 Accuracy: 94.07 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth + Config: configs/seresnet/seresnet101_8xb32_in1k.py diff --git a/configs/seresnet/seresnet101_8xb32_in1k.py b/configs/seresnet/seresnet101_8xb32_in1k.py new file mode 100644 index 0000000..8be39e7 --- /dev/null +++ b/configs/seresnet/seresnet101_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/seresnet101.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/seresnet/seresnet101_b32x8_imagenet.py b/configs/seresnet/seresnet101_b32x8_imagenet.py new file mode 100644 index 0000000..46daa09 --- /dev/null +++ b/configs/seresnet/seresnet101_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'seresnet101_8xb32_in1k.py' + +_deprecation_ = dict( + expected='seresnet101_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/seresnet/seresnet50_8xb32_in1k.py b/configs/seresnet/seresnet50_8xb32_in1k.py new file mode 100644 index 0000000..19082bd --- /dev/null +++ b/configs/seresnet/seresnet50_8xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/seresnet50.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256_140e.py', + '../_base_/default_runtime.py' +] diff --git a/configs/seresnet/seresnet50_b32x8_imagenet.py b/configs/seresnet/seresnet50_b32x8_imagenet.py new file mode 100644 index 0000000..0fb9df3 --- /dev/null +++ b/configs/seresnet/seresnet50_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'seresnet50_8xb32_in1k.py' + +_deprecation_ = dict( + expected='seresnet50_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/seresnet/seresnext101-32x4d_8xb32_in1k.py b/configs/seresnet/seresnext101-32x4d_8xb32_in1k.py new file mode 100644 index 0000000..0177830 --- /dev/null +++ b/configs/seresnet/seresnext101-32x4d_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/seresnext101_32x4d.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/seresnet/seresnext101_32x4d_b32x8_imagenet.py b/configs/seresnet/seresnext101_32x4d_b32x8_imagenet.py new file mode 100644 index 0000000..cb99ec6 --- /dev/null +++ b/configs/seresnet/seresnext101_32x4d_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'seresnext101-32x4d_8xb32_in1k.py' + +_deprecation_ = dict( + expected='seresnext101-32x4d_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/seresnet/seresnext50-32x4d_8xb32_in1k.py b/configs/seresnet/seresnext50-32x4d_8xb32_in1k.py new file mode 100644 index 0000000..4d593e4 --- /dev/null +++ b/configs/seresnet/seresnext50-32x4d_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/seresnext50_32x4d.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/seresnet/seresnext50_32x4d_b32x8_imagenet.py b/configs/seresnet/seresnext50_32x4d_b32x8_imagenet.py new file mode 100644 index 0000000..4922960 --- /dev/null +++ b/configs/seresnet/seresnext50_32x4d_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'seresnext50-32x4d_8xb32_in1k.py' + +_deprecation_ = dict( + expected='seresnext50-32x4d_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/shufflenet_v1/README.md b/configs/shufflenet_v1/README.md new file mode 100644 index 0000000..fd13127 --- /dev/null +++ b/configs/shufflenet_v1/README.md @@ -0,0 +1,33 @@ +# ShuffleNet V1 + +> [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_ShuffleNet_An_Extremely_CVPR_2018_paper.html) + + + +## Abstract + +We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------------------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------: | :--------------------------------------------------------------------: | +| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) | + +## Citation + +``` +@inproceedings{zhang2018shufflenet, + title={Shufflenet: An extremely efficient convolutional neural network for mobile devices}, + author={Zhang, Xiangyu and Zhou, Xinyu and Lin, Mengxiao and Sun, Jian}, + booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, + pages={6848--6856}, + year={2018} +} +``` diff --git a/configs/shufflenet_v1/metafile.yml b/configs/shufflenet_v1/metafile.yml new file mode 100644 index 0000000..2cfffa1 --- /dev/null +++ b/configs/shufflenet_v1/metafile.yml @@ -0,0 +1,35 @@ +Collections: + - Name: Shufflenet V1 + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + - No BN decay + Training Resources: 8x 1080 GPUs + Epochs: 300 + Batch Size: 1024 + Architecture: + - Shufflenet V1 + Paper: + URL: https://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_ShuffleNet_An_Extremely_CVPR_2018_paper.html + Title: "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" + README: configs/shufflenet_v1/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/shufflenet_v1.py#L152 + Version: v0.15.0 + +Models: + - Name: shufflenet-v1-1x_16xb64_in1k + Metadata: + FLOPs: 146000000 + Parameters: 1870000 + In Collection: Shufflenet V1 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 68.13 + Top 5 Accuracy: 87.81 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth + Config: configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py diff --git a/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py b/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py new file mode 100644 index 0000000..58e45f1 --- /dev/null +++ b/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/shufflenet_v1_1x.py', + '../_base_/datasets/imagenet_bs64_pil_resize.py', + '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py', + '../_base_/default_runtime.py' +] diff --git a/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py b/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py new file mode 100644 index 0000000..0312197 --- /dev/null +++ b/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'shufflenet-v1-1x_16xb64_in1k.py' + +_deprecation_ = dict( + expected='shufflenet-v1-1x_16xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/shufflenet_v2/README.md b/configs/shufflenet_v2/README.md new file mode 100644 index 0000000..7827154 --- /dev/null +++ b/configs/shufflenet_v2/README.md @@ -0,0 +1,33 @@ +# ShuffleNet V2 + +> [Shufflenet v2: Practical guidelines for efficient cnn architecture design](https://openaccess.thecvf.com/content_ECCV_2018/papers/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf) + + + +## Abstract + +Currently, the neural network architecture design is mostly guided by the *indirect* metric of computation complexity, i.e., FLOPs. However, the *direct* metric, e.g., speed, also depends on the other factors such as memory access cost and platform characterics. Thus, this work proposes to evaluate the direct metric on the target platform, beyond only considering FLOPs. Based on a series of controlled experiments, this work derives several practical *guidelines* for efficient network design. Accordingly, a new architecture is presented, called *ShuffleNet V2*. Comprehensive ablation experiments verify that our model is the state-of-the-art in terms of speed and accuracy tradeoff. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------------: | :-------: | :------: | :-------: | :-------: | :-----------------------------------------------------------------------: | :-------------------------------------------------------------------------: | +| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) | + +## Citation + +``` +@inproceedings{ma2018shufflenet, + title={Shufflenet v2: Practical guidelines for efficient cnn architecture design}, + author={Ma, Ningning and Zhang, Xiangyu and Zheng, Hai-Tao and Sun, Jian}, + booktitle={Proceedings of the European conference on computer vision (ECCV)}, + pages={116--131}, + year={2018} +} +``` diff --git a/configs/shufflenet_v2/metafile.yml b/configs/shufflenet_v2/metafile.yml new file mode 100644 index 0000000..a06322d --- /dev/null +++ b/configs/shufflenet_v2/metafile.yml @@ -0,0 +1,35 @@ +Collections: + - Name: Shufflenet V2 + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + - No BN decay + Training Resources: 8x 1080 GPUs + Epochs: 300 + Batch Size: 1024 + Architecture: + - Shufflenet V2 + Paper: + URL: https://openaccess.thecvf.com/content_ECCV_2018/papers/Ningning_Light-weight_CNN_Architecture_ECCV_2018_paper.pdf + Title: "ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" + README: configs/shufflenet_v2/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/shufflenet_v2.py#L134 + Version: v0.15.0 + +Models: + - Name: shufflenet-v2-1x_16xb64_in1k + Metadata: + FLOPs: 149000000 + Parameters: 2280000 + In Collection: Shufflenet V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 69.55 + Top 5 Accuracy: 88.92 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth + Config: configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py diff --git a/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py b/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py new file mode 100644 index 0000000..a106ab8 --- /dev/null +++ b/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/shufflenet_v2_1x.py', + '../_base_/datasets/imagenet_bs64_pil_resize.py', + '../_base_/schedules/imagenet_bs1024_linearlr_bn_nowd.py', + '../_base_/default_runtime.py' +] diff --git a/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py b/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py new file mode 100644 index 0000000..c0938b0 --- /dev/null +++ b/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'shufflenet-v2-1x_16xb64_in1k.py' + +_deprecation_ = dict( + expected='shufflenet-v2-1x_16xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/swin_transformer/README.md b/configs/swin_transformer/README.md new file mode 100644 index 0000000..86975ec --- /dev/null +++ b/configs/swin_transformer/README.md @@ -0,0 +1,60 @@ +# Swin Transformer + +> [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/pdf/2103.14030.pdf) + + + +## Abstract + +This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with **S**hifted **win**dows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures. + +
+ +
+ +## Results and models + +### ImageNet-21k + +The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. + +| Model | resolution | Params(M) | Flops(G) | Download | +| :----: | :--------: | :-------: | :------: | :---------------------------------------------------------------------------------------------------------------------: | +| Swin-B | 224x224 | 86.74 | 15.14 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k.pth) | +| Swin-B | 384x384 | 86.88 | 44.49 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth) | +| Swin-L | 224x224 | 195.00 | 34.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k.pth) | +| Swin-L | 384x384 | 195.20 | 100.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth) | + +### ImageNet-1k + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :-------------------------------------------------------------------: | +| Swin-T | From scratch | 224x224 | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json) | +| Swin-S | From scratch | 224x224 | 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json) | +| Swin-B | From scratch | 224x224 | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json) | +| Swin-S\* | From scratch | 224x224 | 49.61 | 8.52 | 83.21 | 96.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_small_patch4_window7_224-cc7a01c9.pth) | +| Swin-B\* | From scratch | 224x224 | 87.77 | 15.14 | 83.42 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224-4670dd19.pth) | +| Swin-B\* | From scratch | 384x384 | 87.90 | 44.49 | 84.49 | 96.95 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384-02c598a4.pth) | +| Swin-B\* | ImageNet-21k | 224x224 | 87.77 | 15.14 | 85.16 | 97.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224_22kto1k-f967f799.pth) | +| Swin-B\* | ImageNet-21k | 384x384 | 87.90 | 44.49 | 86.44 | 98.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-base_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384_22kto1k-d59b0d1d.pth) | +| Swin-L\* | ImageNet-21k | 224x224 | 196.53 | 34.04 | 86.24 | 97.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window7_224_22kto1k-5f0996db.pth) | +| Swin-L\* | ImageNet-21k | 384x384 | 196.74 | 100.04 | 87.25 | 98.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window12_384_22kto1k-0a40944b.pth) | + +*Models with * are converted from the [official repo](https://github.com/microsoft/Swin-Transformer#main-results-on-imagenet-with-pretrained-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +### CUB-200-2011 + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :----: | :---------------------------------------------------: | :--------: | :-------: | :------: | :-------: | :-------------------------------------------------: | :----------------------------------------------------: | +| Swin-L | [ImageNet-21k](https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-base_3rdparty_in21k-384px.pth) | 384x384 | 195.51 | 100.04 | 91.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-large_8xb8_cub_384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin-large_8xb8_cub_384px_20220307-1bbaee6a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin-large_8xb8_cub_384px_20220307-1bbaee6a.log.json) | + +## Citation + +``` +@article{liu2021Swin, + title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows}, + author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining}, + journal={arXiv preprint arXiv:2103.14030}, + year={2021} +} +``` diff --git a/configs/swin_transformer/metafile.yml b/configs/swin_transformer/metafile.yml new file mode 100644 index 0000000..b44c1ba --- /dev/null +++ b/configs/swin_transformer/metafile.yml @@ -0,0 +1,201 @@ +Collections: + - Name: Swin-Transformer + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - AdamW + - Weight Decay + Training Resources: 16x V100 GPUs + Epochs: 300 + Batch Size: 1024 + Architecture: + - Shift Window Multihead Self Attention + Paper: + URL: https://arxiv.org/pdf/2103.14030.pdf + Title: "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" + README: configs/swin_transformer/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/swin_transformer.py#L176 + Version: v0.15.0 + +Models: + - Name: swin-tiny_16xb64_in1k + Metadata: + FLOPs: 4360000000 + Parameters: 28290000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.18 + Top 5 Accuracy: 95.61 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth + Config: configs/swin_transformer/swin-tiny_16xb64_in1k.py + - Name: swin-small_16xb64_in1k + Metadata: + FLOPs: 8520000000 + Parameters: 49610000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.02 + Top 5 Accuracy: 96.29 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth + Config: configs/swin_transformer/swin-small_16xb64_in1k.py + - Name: swin-base_16xb64_in1k + Metadata: + FLOPs: 15140000000 + Parameters: 87770000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.36 + Top 5 Accuracy: 96.44 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth + Config: configs/swin_transformer/swin-base_16xb64_in1k.py + - Name: swin-tiny_3rdparty_in1k + Metadata: + FLOPs: 4360000000 + Parameters: 28290000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.18 + Top 5 Accuracy: 95.52 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_tiny_patch4_window7_224-160bb0a5.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-tiny_16xb64_in1k.py + - Name: swin-small_3rdparty_in1k + Metadata: + FLOPs: 8520000000 + Parameters: 49610000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.21 + Top 5 Accuracy: 96.25 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_small_patch4_window7_224-cc7a01c9.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-small_16xb64_in1k.py + - Name: swin-base_3rdparty_in1k + Metadata: + FLOPs: 15140000000 + Parameters: 87770000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.42 + Top 5 Accuracy: 96.44 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224-4670dd19.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-base_16xb64_in1k.py + - Name: swin-base_3rdparty_in1k-384 + Metadata: + FLOPs: 44490000000 + Parameters: 87900000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.49 + Top 5 Accuracy: 96.95 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384-02c598a4.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-base_16xb64_in1k-384px.py + - Name: swin-base_in21k-pre-3rdparty_in1k + Metadata: + FLOPs: 15140000000 + Parameters: 87770000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 85.16 + Top 5 Accuracy: 97.50 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window7_224_22kto1k-f967f799.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window7_224_22kto1k.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-base_16xb64_in1k.py + - Name: swin-base_in21k-pre-3rdparty_in1k-384 + Metadata: + FLOPs: 44490000000 + Parameters: 87900000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.44 + Top 5 Accuracy: 98.05 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_base_patch4_window12_384_22kto1k-d59b0d1d.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22kto1k.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-base_16xb64_in1k-384px.py + - Name: swin-large_in21k-pre-3rdparty_in1k + Metadata: + FLOPs: 34040000000 + Parameters: 196530000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.24 + Top 5 Accuracy: 97.88 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window7_224_22kto1k-5f0996db.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22kto1k.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-large_16xb64_in1k.py + - Name: swin-large_in21k-pre-3rdparty_in1k-384 + Metadata: + FLOPs: 100040000000 + Parameters: 196740000 + In Collection: Swin-Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 87.25 + Top 5 Accuracy: 98.25 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin_large_patch4_window12_384_22kto1k-0a40944b.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22kto1k.pth + Code: https://github.com/microsoft/Swin-Transformer/blob/777f6c66604bb5579086c4447efe3620344d95a9/models/swin_transformer.py#L458 + Config: configs/swin_transformer/swin-large_16xb64_in1k-384px.py + - Name: swin-large_8xb8_cub_384px + Metadata: + FLOPs: 100040000000 + Parameters: 195510000 + In Collection: Swin-Transformer + Results: + - Dataset: CUB-200-2011 + Metrics: + Top 1 Accuracy: 91.87 + Task: Image Classification + Pretrain: https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k-384px.pth + Weights: https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin-large_8xb8_cub_384px_20220307-1bbaee6a.pth + Config: configs/swin_transformer/swin-large_8xb8_cub_384px.py diff --git a/configs/swin_transformer/swin-base_16xb64_in1k-384px.py b/configs/swin_transformer/swin-base_16xb64_in1k-384px.py new file mode 100644 index 0000000..711a0d6 --- /dev/null +++ b/configs/swin_transformer/swin-base_16xb64_in1k-384px.py @@ -0,0 +1,7 @@ +# Only for evaluation +_base_ = [ + '../_base_/models/swin_transformer/base_384.py', + '../_base_/datasets/imagenet_bs64_swin_384.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer/swin-base_16xb64_in1k.py b/configs/swin_transformer/swin-base_16xb64_in1k.py new file mode 100644 index 0000000..2a4548a --- /dev/null +++ b/configs/swin_transformer/swin-base_16xb64_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer/base_224.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer/swin-large_16xb64_in1k-384px.py b/configs/swin_transformer/swin-large_16xb64_in1k-384px.py new file mode 100644 index 0000000..a7f0ad2 --- /dev/null +++ b/configs/swin_transformer/swin-large_16xb64_in1k-384px.py @@ -0,0 +1,7 @@ +# Only for evaluation +_base_ = [ + '../_base_/models/swin_transformer/large_384.py', + '../_base_/datasets/imagenet_bs64_swin_384.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer/swin-large_16xb64_in1k.py b/configs/swin_transformer/swin-large_16xb64_in1k.py new file mode 100644 index 0000000..4e875c5 --- /dev/null +++ b/configs/swin_transformer/swin-large_16xb64_in1k.py @@ -0,0 +1,7 @@ +# Only for evaluation +_base_ = [ + '../_base_/models/swin_transformer/large_224.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer/swin-large_8xb8_cub_384px.py b/configs/swin_transformer/swin-large_8xb8_cub_384px.py new file mode 100644 index 0000000..d113716 --- /dev/null +++ b/configs/swin_transformer/swin-large_8xb8_cub_384px.py @@ -0,0 +1,37 @@ +_base_ = [ + '../_base_/models/swin_transformer/large_384.py', + '../_base_/datasets/cub_bs8_384.py', '../_base_/schedules/cub_bs64.py', + '../_base_/default_runtime.py' +] + +# model settings +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/convert/swin-large_3rdparty_in21k-384px.pth' # noqa +model = dict( + type='ImageClassifier', + backbone=dict( + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint, prefix='backbone')), + head=dict(num_classes=200, )) + +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={ + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0) + }) + +optimizer = dict( + _delete_=True, + type='AdamW', + lr=5e-6, + weight_decay=0.0005, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=paramwise_cfg) +optimizer_config = dict(grad_clip=dict(max_norm=5.0), _delete_=True) + +log_config = dict(interval=20) # log every 20 intervals + +checkpoint_config = dict( + interval=1, max_keep_ckpts=3) # save last three checkpoints diff --git a/configs/swin_transformer/swin-small_16xb64_in1k.py b/configs/swin_transformer/swin-small_16xb64_in1k.py new file mode 100644 index 0000000..aa1fa21 --- /dev/null +++ b/configs/swin_transformer/swin-small_16xb64_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer/small_224.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer/swin-tiny_16xb64_in1k.py b/configs/swin_transformer/swin-tiny_16xb64_in1k.py new file mode 100644 index 0000000..e1ed022 --- /dev/null +++ b/configs/swin_transformer/swin-tiny_16xb64_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer/tiny_224.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py b/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py new file mode 100644 index 0000000..912c379 --- /dev/null +++ b/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'swin-base_16xb64_in1k.py' + +_deprecation_ = dict( + expected='swin-base_16xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/swin_transformer/swin_base_384_evalonly_imagenet.py b/configs/swin_transformer/swin_base_384_evalonly_imagenet.py new file mode 100644 index 0000000..9ed5888 --- /dev/null +++ b/configs/swin_transformer/swin_base_384_evalonly_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'swin-base_16xb64_in1k-384px.py' + +_deprecation_ = dict( + expected='swin-base_16xb64_in1k-384px.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/swin_transformer/swin_large_224_evalonly_imagenet.py b/configs/swin_transformer/swin_large_224_evalonly_imagenet.py new file mode 100644 index 0000000..5ebb54a --- /dev/null +++ b/configs/swin_transformer/swin_large_224_evalonly_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'swin-large_16xb64_in1k.py' + +_deprecation_ = dict( + expected='swin-large_16xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/swin_transformer/swin_large_384_evalonly_imagenet.py b/configs/swin_transformer/swin_large_384_evalonly_imagenet.py new file mode 100644 index 0000000..9a59f5b --- /dev/null +++ b/configs/swin_transformer/swin_large_384_evalonly_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'swin-large_16xb64_in1k-384px.py' + +_deprecation_ = dict( + expected='swin-large_16xb64_in1k-384px.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/swin_transformer/swin_small_224_b16x64_300e_imagenet.py b/configs/swin_transformer/swin_small_224_b16x64_300e_imagenet.py new file mode 100644 index 0000000..a747aa4 --- /dev/null +++ b/configs/swin_transformer/swin_small_224_b16x64_300e_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'swin-small_16xb64_in1k.py' + +_deprecation_ = dict( + expected='swin-small_16xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/swin_transformer/swin_tiny_224_b16x64_300e_imagenet.py b/configs/swin_transformer/swin_tiny_224_b16x64_300e_imagenet.py new file mode 100644 index 0000000..2160eb9 --- /dev/null +++ b/configs/swin_transformer/swin_tiny_224_b16x64_300e_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'swin-tiny_16xb64_in1k.py' + +_deprecation_ = dict( + expected='swin-tiny_16xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/swin_transformer_v2/README.md b/configs/swin_transformer_v2/README.md new file mode 100644 index 0000000..31d1aff --- /dev/null +++ b/configs/swin_transformer_v2/README.md @@ -0,0 +1,58 @@ +# Swin Transformer V2 + +> [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883.pdf) + + + +## Abstract + +Large-scale NLP models have been shown to significantly improve the performance on language tasks with no signs of saturation. They also demonstrate amazing few-shot capabilities like that of human beings. This paper aims to explore large-scale models in computer vision. We tackle three major issues in training and application of large vision models, including training instability, resolution gaps between pre-training and fine-tuning, and hunger on labelled data. Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images. Through these techniques, this paper successfully trained a 3 billion-parameter Swin Transformer V2 model, which is the largest dense vision model to date, and makes it capable of training with images of up to 1,536×1,536 resolution. It set new performance records on 4 representative vision tasks, including ImageNet-V2 image classification, COCO object detection, ADE20K semantic segmentation, and Kinetics-400 video action classification. Also note our training is much more efficient than that in Google's billion-level visual models, which consumes 40 times less labelled data and 40 times less training time. + +
+ +
+ +## Results and models + +### ImageNet-21k + +The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. + +| Model | resolution | Params(M) | Flops(G) | Download | +| :------: | :--------: | :-------: | :------: | :--------------------------------------------------------------------------------------------------------------------------------------: | +| Swin-B\* | 192x192 | 87.92 | 8.51 | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth) | +| Swin-L\* | 192x192 | 196.74 | 19.04 | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-large-w12_3rdparty_in21k-192px_20220803-d9073fee.pth) | + +### ImageNet-1k + +| Model | Pretrain | resolution | window | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------: | :----------: | :--------: | :----: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------: | :----------------------------------------------------------------: | +| Swin-T\* | From scratch | 256x256 | 8x8 | 28.35 | 4.35 | 81.76 | 95.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w8_3rdparty_in1k-256px_20220803-e318968f.pth) | +| Swin-T\* | From scratch | 256x256 | 16x16 | 28.35 | 4.4 | 82.81 | 96.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w16_3rdparty_in1k-256px_20220803-9651cdd7.pth) | +| Swin-S\* | From scratch | 256x256 | 8x8 | 49.73 | 8.45 | 83.74 | 96.6 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w8_3rdparty_in1k-256px_20220803-b01a4332.pth) | +| Swin-S\* | From scratch | 256x256 | 16x16 | 49.73 | 8.57 | 84.13 | 96.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w16_3rdparty_in1k-256px_20220803-b707d206.pth) | +| Swin-B\* | From scratch | 256x256 | 8x8 | 87.92 | 14.99 | 84.2 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w8_3rdparty_in1k-256px_20220803-8ff28f2b.pth) | +| Swin-B\* | From scratch | 256x256 | 16x16 | 87.92 | 15.14 | 84.6 | 97.05 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_3rdparty_in1k-256px_20220803-5a1886b7.pth) | +| Swin-B\* | ImageNet-21k | 256x256 | 16x16 | 87.92 | 15.14 | 86.17 | 97.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_in21k-pre_3rdparty_in1k-256px_20220803-8d7aa8ad.pth) | +| Swin-B\* | ImageNet-21k | 384x384 | 24x24 | 87.92 | 34.07 | 87.14 | 98.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w24_in21k-pre_3rdparty_in1k-384px_20220803-44eb70f8.pth) | +| Swin-L\* | ImageNet-21k | 256X256 | 16x16 | 196.75 | 33.86 | 86.93 | 98.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w16_in21k-pre_3rdparty_in1k-256px_20220803-c40cbed7.pth) | +| Swin-L\* | ImageNet-21k | 384x384 | 24x24 | 196.75 | 76.2 | 87.59 | 98.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w24_in21k-pre_3rdparty_in1k-384px_20220803-3b36c165.pth) | + +*Models with * are converted from the [official repo](https://github.com/microsoft/Swin-Transformer#main-results-on-imagenet-with-pretrained-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +*ImageNet-21k pretrained models with input resolution of 256x256 and 384x384 both fine-tuned from the same pre-training model using a smaller input resolution of 192x192.* + +## Citation + +``` +@article{https://doi.org/10.48550/arxiv.2111.09883, + doi = {10.48550/ARXIV.2111.09883}, + url = {https://arxiv.org/abs/2111.09883}, + author = {Liu, Ze and Hu, Han and Lin, Yutong and Yao, Zhuliang and Xie, Zhenda and Wei, Yixuan and Ning, Jia and Cao, Yue and Zhang, Zheng and Dong, Li and Wei, Furu and Guo, Baining}, + keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, + title = {Swin Transformer V2: Scaling Up Capacity and Resolution}, + publisher = {arXiv}, + year = {2021}, + copyright = {Creative Commons Attribution 4.0 International} +} +``` diff --git a/configs/swin_transformer_v2/metafile.yml b/configs/swin_transformer_v2/metafile.yml new file mode 100644 index 0000000..cef8392 --- /dev/null +++ b/configs/swin_transformer_v2/metafile.yml @@ -0,0 +1,204 @@ +Collections: + - Name: Swin-Transformer-V2 + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - AdamW + - Weight Decay + Training Resources: 16x V100 GPUs + Epochs: 300 + Batch Size: 1024 + Architecture: + - Shift Window Multihead Self Attention + Paper: + URL: https://arxiv.org/abs/2111.09883.pdf + Title: "Swin Transformer V2: Scaling Up Capacity and Resolution" + README: configs/swin_transformer_v2/README.md + +Models: + - Name: swinv2-tiny-w8_3rdparty_in1k-256px + Metadata: + FLOPs: 4350000000 + Parameters: 28350000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.76 + Top 5 Accuracy: 95.87 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w8_3rdparty_in1k-256px_20220803-e318968f.pth + Config: configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window8_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-tiny-w16_3rdparty_in1k-256px + Metadata: + FLOPs: 4400000000 + Parameters: 28350000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.81 + Top 5 Accuracy: 96.23 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-tiny-w16_3rdparty_in1k-256px_20220803-9651cdd7.pth + Config: configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_tiny_patch4_window16_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-small-w8_3rdparty_in1k-256px + Metadata: + FLOPs: 8450000000 + Parameters: 49730000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.74 + Top 5 Accuracy: 96.6 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w8_3rdparty_in1k-256px_20220803-b01a4332.pth + Config: configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window8_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-small-w16_3rdparty_in1k-256px + Metadata: + FLOPs: 8570000000 + Parameters: 49730000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.13 + Top 5 Accuracy: 96.83 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-small-w16_3rdparty_in1k-256px_20220803-b707d206.pth + Config: configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_small_patch4_window16_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w8_3rdparty_in1k-256px + Metadata: + FLOPs: 14990000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.2 + Top 5 Accuracy: 96.86 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w8_3rdparty_in1k-256px_20220803-8ff28f2b.pth + Config: configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window8_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w16_3rdparty_in1k-256px + Metadata: + FLOPs: 15140000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.6 + Top 5 Accuracy: 97.05 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_3rdparty_in1k-256px_20220803-5a1886b7.pth + Config: configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window16_256.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w16_in21k-pre_3rdparty_in1k-256px + Metadata: + Training Data: ImageNet-21k + FLOPs: 15140000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.17 + Top 5 Accuracy: 97.88 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w16_in21k-pre_3rdparty_in1k-256px_20220803-8d7aa8ad.pth + Config: configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to16_192to256_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w24_in21k-pre_3rdparty_in1k-384px + Metadata: + Training Data: ImageNet-21k + FLOPs: 34070000000 + Parameters: 87920000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 87.14 + Top 5 Accuracy: 98.23 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-base-w24_in21k-pre_3rdparty_in1k-384px_20220803-44eb70f8.pth + Config: configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12to24_192to384_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-large-w16_in21k-pre_3rdparty_in1k-256px + Metadata: + Training Data: ImageNet-21k + FLOPs: 33860000000 + Parameters: 196750000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 86.93 + Top 5 Accuracy: 98.06 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w16_in21k-pre_3rdparty_in1k-256px_20220803-c40cbed7.pth + Config: configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to16_192to256_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-large-w24_in21k-pre_3rdparty_in1k-384px + Metadata: + Training Data: ImageNet-21k + FLOPs: 76200000000 + Parameters: 196750000 + In Collection: Swin-Transformer-V2 + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 87.59 + Top 5 Accuracy: 98.27 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/swinv2-large-w24_in21k-pre_3rdparty_in1k-384px_20220803-3b36c165.pth + Config: configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12to24_192to384_22kto1k_ft.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-base-w12_3rdparty_in21k-192px + Metadata: + Training Data: ImageNet-21k + FLOPs: 8510000000 + Parameters: 87920000 + In Collections: Swin-Transformer-V2 + Results: null + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-base-w12_3rdparty_in21k-192px_20220803-f7dc9763.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_base_patch4_window12_192_22k.pth + Code: https://github.com/microsoft/Swin-Transformer + - Name: swinv2-large-w12_3rdparty_in21k-192px + Metadata: + Training Data: ImageNet-21k + FLOPs: 19040000000 + Parameters: 196740000 + In Collections: Swin-Transformer-V2 + Results: null + Weights: https://download.openmmlab.com/mmclassification/v0/swin-v2/pretrain/swinv2-large-w12_3rdparty_in21k-192px_20220803-d9073fee.pth + Converted From: + Weights: https://github.com/SwinTransformer/storage/releases/download/v2.0.0/swinv2_large_patch4_window12_192_22k.pth + Code: https://github.com/microsoft/Swin-Transformer diff --git a/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py new file mode 100644 index 0000000..5f375ee --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w16_16xb64_in1k-256px.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py new file mode 100644 index 0000000..0725f9e --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w16_in21k-pre_16xb64_in1k-256px.py @@ -0,0 +1,13 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + window_size=[16, 16, 16, 8], + drop_path_rate=0.2, + pretrained_window_sizes=[12, 12, 12, 6])) diff --git a/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py b/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py new file mode 100644 index 0000000..3dd4e5f --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w24_in21k-pre_16xb64_in1k-384px.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_384.py', + '../_base_/datasets/imagenet_bs64_swin_384.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + img_size=384, + window_size=[24, 24, 24, 12], + drop_path_rate=0.2, + pretrained_window_sizes=[12, 12, 12, 6])) diff --git a/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py new file mode 100644 index 0000000..23fc407 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-base-w8_16xb64_in1k-256px.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/base_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py new file mode 100644 index 0000000..62a2a29 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-large-w16_in21k-pre_16xb64_in1k-256px.py @@ -0,0 +1,13 @@ +# Only for evaluation +_base_ = [ + '../_base_/models/swin_transformer_v2/large_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + window_size=[16, 16, 16, 8], pretrained_window_sizes=[12, 12, 12, 6]), +) diff --git a/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py b/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py new file mode 100644 index 0000000..d97d9b2 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-large-w24_in21k-pre_16xb64_in1k-384px.py @@ -0,0 +1,15 @@ +# Only for evaluation +_base_ = [ + '../_base_/models/swin_transformer_v2/large_384.py', + '../_base_/datasets/imagenet_bs64_swin_384.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict( + type='ImageClassifier', + backbone=dict( + img_size=384, + window_size=[24, 24, 24, 12], + pretrained_window_sizes=[12, 12, 12, 6]), +) diff --git a/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py new file mode 100644 index 0000000..f87265d --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-small-w16_16xb64_in1k-256px.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/small_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py new file mode 100644 index 0000000..f1001f1 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-small-w8_16xb64_in1k-256px.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/small_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py new file mode 100644 index 0000000..7e1f290 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-tiny-w16_16xb64_in1k-256px.py @@ -0,0 +1,8 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/tiny_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(window_size=[16, 16, 16, 8])) diff --git a/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py b/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py new file mode 100644 index 0000000..2cdc9a2 --- /dev/null +++ b/configs/swin_transformer_v2/swinv2-tiny-w8_16xb64_in1k-256px.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/swin_transformer_v2/tiny_256.py', + '../_base_/datasets/imagenet_bs64_swin_256.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] diff --git a/configs/t2t_vit/README.md b/configs/t2t_vit/README.md new file mode 100644 index 0000000..1f80c25 --- /dev/null +++ b/configs/t2t_vit/README.md @@ -0,0 +1,36 @@ +# Tokens-to-Token ViT + +> [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://arxiv.org/abs/2101.11986) + + + +## Abstract + +Transformers, which are popular for language modeling, have been explored for solving vision tasks recently, e.g., the Vision Transformer (ViT) for image classification. The ViT model splits each image into a sequence of tokens with fixed length and then applies multiple Transformer layers to model their global relation for classification. However, ViT achieves inferior performance to CNNs when trained from scratch on a midsize dataset like ImageNet. We find it is because: 1) the simple tokenization of input images fails to model the important local structure such as edges and lines among neighboring pixels, leading to low training sample efficiency; 2) the redundant attention backbone design of ViT leads to limited feature richness for fixed computation budgets and limited training samples. To overcome such limitations, we propose a new Tokens-To-Token Vision Transformer (T2T-ViT), which incorporates 1) a layer-wise Tokens-to-Token (T2T) transformation to progressively structurize the image to tokens by recursively aggregating neighboring Tokens into one Token (Tokens-to-Token), such that local structure represented by surrounding tokens can be modeled and tokens length can be reduced; 2) an efficient backbone with a deep-narrow structure for vision transformer motivated by CNN architecture design after empirical study. Notably, T2T-ViT reduces the parameter count and MACs of vanilla ViT by half, while achieving more than 3.0% improvement when trained from scratch on ImageNet. It also outperforms ResNets and achieves comparable performance with MobileNets by directly training on ImageNet. For example, T2T-ViT with comparable size to ResNet50 (21.5M parameters) can achieve 83.3% top1 accuracy in image resolution 384×384 on ImageNet. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json) | +| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json) | +| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json) | + +*In consistent with the [official repo](https://github.com/yitu-opensource/T2T-ViT), we adopt the best checkpoints during training.* + +## Citation + +``` +@article{yuan2021tokens, + title={Tokens-to-token vit: Training vision transformers from scratch on imagenet}, + author={Yuan, Li and Chen, Yunpeng and Wang, Tao and Yu, Weihao and Shi, Yujun and Tay, Francis EH and Feng, Jiashi and Yan, Shuicheng}, + journal={arXiv preprint arXiv:2101.11986}, + year={2021} +} +``` diff --git a/configs/t2t_vit/metafile.yml b/configs/t2t_vit/metafile.yml new file mode 100644 index 0000000..f212542 --- /dev/null +++ b/configs/t2t_vit/metafile.yml @@ -0,0 +1,58 @@ +Collections: + - Name: Tokens-to-Token ViT + Metadata: + Training Data: ImageNet-1k + Architecture: + - Layer Normalization + - Scaled Dot-Product Attention + - Attention Dropout + - Dropout + - Tokens to Token + Paper: + URL: https://arxiv.org/abs/2101.11986 + Title: "Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet" + README: configs/t2t_vit/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.17.0/mmcls/models/backbones/t2t_vit.py + Version: v0.17.0 + +Models: + - Name: t2t-vit-t-14_8xb64_in1k + Metadata: + FLOPs: 4340000000 + Parameters: 21470000 + In Collection: Tokens-to-Token ViT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.83 + Top 5 Accuracy: 95.84 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth + Config: configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py + - Name: t2t-vit-t-19_8xb64_in1k + Metadata: + FLOPs: 7800000000 + Parameters: 39080000 + In Collection: Tokens-to-Token ViT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.63 + Top 5 Accuracy: 96.18 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth + Config: configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py + - Name: t2t-vit-t-24_8xb64_in1k + Metadata: + FLOPs: 12690000000 + Parameters: 64000000 + In Collection: Tokens-to-Token ViT + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.71 + Top 5 Accuracy: 96.09 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth + Config: configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py diff --git a/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py b/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py new file mode 100644 index 0000000..a391df4 --- /dev/null +++ b/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py @@ -0,0 +1,35 @@ +_base_ = [ + '../_base_/models/t2t-vit-t-14.py', + '../_base_/datasets/imagenet_bs64_t2t_224.py', + '../_base_/default_runtime.py', +] + +# optimizer +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={'cls_token': dict(decay_mult=0.0)}, +) +optimizer = dict( + type='AdamW', + lr=5e-4, + weight_decay=0.05, + paramwise_cfg=paramwise_cfg, +) +optimizer_config = dict(grad_clip=None) + +# learning policy +# FIXME: lr in the first 300 epochs conforms to the CosineAnnealing and +# the lr in the last 10 epoch equals to min_lr +lr_config = dict( + policy='CosineAnnealingCooldown', + min_lr=1e-5, + cool_down_time=10, + cool_down_ratio=0.1, + by_epoch=True, + warmup_by_epoch=True, + warmup='linear', + warmup_iters=10, + warmup_ratio=1e-6) +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] +runner = dict(type='EpochBasedRunner', max_epochs=310) diff --git a/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py b/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py new file mode 100644 index 0000000..e1157f8 --- /dev/null +++ b/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py @@ -0,0 +1,35 @@ +_base_ = [ + '../_base_/models/t2t-vit-t-19.py', + '../_base_/datasets/imagenet_bs64_t2t_224.py', + '../_base_/default_runtime.py', +] + +# optimizer +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={'cls_token': dict(decay_mult=0.0)}, +) +optimizer = dict( + type='AdamW', + lr=5e-4, + weight_decay=0.065, + paramwise_cfg=paramwise_cfg, +) +optimizer_config = dict(grad_clip=None) + +# learning policy +# FIXME: lr in the first 300 epochs conforms to the CosineAnnealing and +# the lr in the last 10 epoch equals to min_lr +lr_config = dict( + policy='CosineAnnealingCooldown', + min_lr=1e-5, + cool_down_time=10, + cool_down_ratio=0.1, + by_epoch=True, + warmup_by_epoch=True, + warmup='linear', + warmup_iters=10, + warmup_ratio=1e-6) +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] +runner = dict(type='EpochBasedRunner', max_epochs=310) diff --git a/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py b/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py new file mode 100644 index 0000000..815f2f1 --- /dev/null +++ b/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py @@ -0,0 +1,35 @@ +_base_ = [ + '../_base_/models/t2t-vit-t-24.py', + '../_base_/datasets/imagenet_bs64_t2t_224.py', + '../_base_/default_runtime.py', +] + +# optimizer +paramwise_cfg = dict( + norm_decay_mult=0.0, + bias_decay_mult=0.0, + custom_keys={'cls_token': dict(decay_mult=0.0)}, +) +optimizer = dict( + type='AdamW', + lr=5e-4, + weight_decay=0.065, + paramwise_cfg=paramwise_cfg, +) +optimizer_config = dict(grad_clip=None) + +# learning policy +# FIXME: lr in the first 300 epochs conforms to the CosineAnnealing and +# the lr in the last 10 epoch equals to min_lr +lr_config = dict( + policy='CosineAnnealingCooldown', + min_lr=1e-5, + cool_down_time=10, + cool_down_ratio=0.1, + by_epoch=True, + warmup_by_epoch=True, + warmup='linear', + warmup_iters=10, + warmup_ratio=1e-6) +custom_hooks = [dict(type='EMAHook', momentum=4e-5, priority='ABOVE_NORMAL')] +runner = dict(type='EpochBasedRunner', max_epochs=310) diff --git a/configs/tnt/README.md b/configs/tnt/README.md new file mode 100644 index 0000000..948eef7 --- /dev/null +++ b/configs/tnt/README.md @@ -0,0 +1,36 @@ +# TNT + +> [Transformer in Transformer](https://arxiv.org/abs/2103.00112) + + + +## Abstract + +Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations. In this paper, we point out that the attention inside these local patches are also essential for building visual transformers with high performance and we explore a new architecture, namely, Transformer iN Transformer (TNT). Specifically, we regard the local patches (e.g., 16×16) as "visual sentences" and present to further divide them into smaller patches (e.g., 4×4) as "visual words". The attention of each word will be calculated with other words in the given visual sentence with negligible computational costs. Features of both words and sentences will be aggregated to enhance the representation ability. Experiments on several benchmarks demonstrate the effectiveness of the proposed TNT architecture, e.g., we achieve an 81.5% top-1 accuracy on the ImageNet, which is about 1.7% higher than that of the state-of-the-art visual transformer with similar computational cost. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | +| TNT-small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) | + +*Models with * are converted from [timm](https://github.com/rwightman/pytorch-image-models/). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@misc{han2021transformer, + title={Transformer in Transformer}, + author={Kai Han and An Xiao and Enhua Wu and Jianyuan Guo and Chunjing Xu and Yunhe Wang}, + year={2021}, + eprint={2103.00112}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/configs/tnt/metafile.yml b/configs/tnt/metafile.yml new file mode 100644 index 0000000..67f3c78 --- /dev/null +++ b/configs/tnt/metafile.yml @@ -0,0 +1,29 @@ +Collections: + - Name: Transformer in Transformer + Metadata: + Training Data: ImageNet-1k + Paper: + URL: https://arxiv.org/abs/2103.00112 + Title: "Transformer in Transformer" + README: configs/tnt/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/tnt.py#L203 + Version: v0.15.0 + +Models: + - Name: tnt-small-p16_3rdparty_in1k + Metadata: + FLOPs: 3360000000 + Parameters: 23760000 + In Collection: Transformer in Transformer + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.52 + Top 5 Accuracy: 95.73 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth + Config: configs/tnt/tnt-s-p16_16xb64_in1k.py + Converted From: + Weights: https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar + Code: https://github.com/contrastive/pytorch-image-models/blob/809271b0f3e5d9be4e11c0c5cec1dbba8b5e2c60/timm/models/tnt.py#L144 diff --git a/configs/tnt/tnt-s-p16_16xb64_in1k.py b/configs/tnt/tnt-s-p16_16xb64_in1k.py new file mode 100644 index 0000000..3669368 --- /dev/null +++ b/configs/tnt/tnt-s-p16_16xb64_in1k.py @@ -0,0 +1,39 @@ +# accuracy_top-1 : 81.52 accuracy_top-5 : 95.73 +_base_ = [ + '../_base_/models/tnt_s_patch16_224.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/default_runtime.py' +] + +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + interpolation='bicubic', + backend='pillow'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +dataset_type = 'ImageNet' +data = dict( + samples_per_gpu=64, workers_per_gpu=4, test=dict(pipeline=test_pipeline)) + +# optimizer +optimizer = dict(type='AdamW', lr=1e-3, weight_decay=0.05) +optimizer_config = dict(grad_clip=None) + +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup_by_epoch=True, + warmup='linear', + warmup_iters=5, + warmup_ratio=1e-3) +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/configs/tnt/tnt_s_patch16_224_evalonly_imagenet.py b/configs/tnt/tnt_s_patch16_224_evalonly_imagenet.py new file mode 100644 index 0000000..3c054d4 --- /dev/null +++ b/configs/tnt/tnt_s_patch16_224_evalonly_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'tnt-s-p16_16xb64_in1k.py' + +_deprecation_ = dict( + expected='tnt-s-p16_16xb64_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/twins/README.md b/configs/twins/README.md new file mode 100644 index 0000000..87e7294 --- /dev/null +++ b/configs/twins/README.md @@ -0,0 +1,39 @@ +# Twins + +> [Twins: Revisiting the Design of Spatial Attention in Vision Transformers](http://arxiv-export-lb.library.cornell.edu/abs/2104.13840) + + + +## Abstract + +Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their success in these tasks. In this work, we revisit the design of the spatial attention and demonstrate that a carefully-devised yet simple spatial attention mechanism performs favourably against the state-of-the-art schemes. As a result, we propose two vision transformer architectures, namely, Twins-PCPVT and Twins-SVT. Our proposed architectures are highly-efficient and easy to implement, only involving matrix multiplications that are highly optimized in modern deep learning frameworks. More importantly, the proposed architectures achieve excellent performance on a wide range of visual tasks, including image level classification as well as dense detection and segmentation. The simplicity and strong performance suggest that our proposed architectures may serve as stronger backbones for many vision tasks. Our code is released at [this https URL](https://github.com/Meituan-AutoML/Twins). + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :-------: | :------: | :-------: | :-------: | :-------------------------------------------------------------------------: | :---------------------------------------------------------------------------: | +| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) | +| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) | +| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) | +| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) | +| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) | +| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) | + +*Models with * are converted from [the official repo](https://github.com/Meituan-AutoML/Twins). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. The validation accuracy is a little different from the official paper because of the PyTorch version. This result is get in PyTorch=1.9 while the official result is get in PyTorch=1.7* + +## Citation + +``` +@article{chu2021twins, + title={Twins: Revisiting spatial attention design in vision transformers}, + author={Chu, Xiangxiang and Tian, Zhi and Wang, Yuqing and Zhang, Bo and Ren, Haibing and Wei, Xiaolin and Xia, Huaxia and Shen, Chunhua}, + journal={arXiv preprint arXiv:2104.13840}, + year={2021}altgvt +} +``` diff --git a/configs/twins/metafile.yml b/configs/twins/metafile.yml new file mode 100644 index 0000000..f8a7d81 --- /dev/null +++ b/configs/twins/metafile.yml @@ -0,0 +1,114 @@ +Collections: + - Name: Twins + Metadata: + Training Data: ImageNet-1k + Architecture: + - Global Subsampled Attention + - Locally Grouped SelfAttention + - Conditional Position Encoding + - Pyramid Vision Transformer + Paper: + URL: http://arxiv-export-lb.library.cornell.edu/abs/2104.13840 + Title: "Twins: Revisiting the Design of Spatial Attention in Vision Transformers" + README: configs/twins/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/twins.py + Version: v0.20.1 + +Models: + - Name: twins-pcpvt-small_3rdparty_8xb128_in1k + Metadata: + FLOPs: 3670000000 # 3.67G + Parameters: 24110000 # 24.11M + In Collection: Twins + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.14 + Top 5 Accuracy: 95.69 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth + Config: configs/twins/twins-pcpvt-small_8xb128_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/twins.py + - Name: twins-pcpvt-base_3rdparty_8xb128_in1k + Metadata: + FLOPs: 6450000000 # 6.45G + Parameters: 43830000 # 43.83M + In Collection: Twins + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.66 + Top 5 Accuracy: 96.26 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth + Config: configs/twins/twins-pcpvt-base_8xb128_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/twins.py + - Name: twins-pcpvt-large_3rdparty_16xb64_in1k + Metadata: + FLOPs: 9510000000 # 9.51G + Parameters: 60990000 # 60.99M + In Collection: Twins + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.09 + Top 5 Accuracy: 96.59 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth + Config: configs/twins/twins-pcpvt-large_16xb64_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/twins.py + - Name: twins-svt-small_3rdparty_8xb128_in1k + Metadata: + FLOPs: 2820000000 # 2.82G + Parameters: 24060000 # 24.06M + In Collection: Twins + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.77 + Top 5 Accuracy: 95.57 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth + Config: configs/twins/twins-svt-small_8xb128_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/twins.py + - Name: twins-svt-base_8xb128_3rdparty_in1k + Metadata: + FLOPs: 8350000000 # 8.35G + Parameters: 56070000 # 56.07M + In Collection: Twins + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.13 + Top 5 Accuracy: 96.29 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth + Config: configs/twins/twins-svt-base_8xb128_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/twins.py + - Name: twins-svt-large_3rdparty_16xb64_in1k + Metadata: + FLOPs: 14820000000 # 14.82G + Parameters: 99270000 # 99.27M + In Collection: Twins + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.60 + Top 5 Accuracy: 96.50 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth + Config: configs/twins/twins-svt-large_16xb64_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vt3p-weights/twins_pcpvt_small-e70e7e7a.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/twins.py diff --git a/configs/twins/twins-pcpvt-base_8xb128_in1k.py b/configs/twins/twins-pcpvt-base_8xb128_in1k.py new file mode 100644 index 0000000..8ea9adc --- /dev/null +++ b/configs/twins/twins-pcpvt-base_8xb128_in1k.py @@ -0,0 +1,33 @@ +_base_ = [ + '../_base_/models/twins_pcpvt_base.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +data = dict(samples_per_gpu=128) + +paramwise_cfg = dict(_delete=True, norm_decay_mult=0.0, bias_decay_mult=0.0) + +# for batch in each gpu is 128, 8 gpu +# lr = 5e-4 * 128 * 8 / 512 = 0.001 +optimizer = dict( + type='AdamW', + lr=5e-4 * 128 * 8 / 512, + weight_decay=0.05, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=paramwise_cfg) +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=5.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + by_epoch=True, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=5, + warmup_by_epoch=True) + +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/twins/twins-pcpvt-large_16xb64_in1k.py b/configs/twins/twins-pcpvt-large_16xb64_in1k.py new file mode 100644 index 0000000..e9c9a35 --- /dev/null +++ b/configs/twins/twins-pcpvt-large_16xb64_in1k.py @@ -0,0 +1,5 @@ +_base_ = ['twins-pcpvt-base_8xb128_in1k.py'] + +model = dict(backbone=dict(arch='large'), head=dict(in_channels=512)) + +data = dict(samples_per_gpu=64) diff --git a/configs/twins/twins-pcpvt-small_8xb128_in1k.py b/configs/twins/twins-pcpvt-small_8xb128_in1k.py new file mode 100644 index 0000000..cb8bdc3 --- /dev/null +++ b/configs/twins/twins-pcpvt-small_8xb128_in1k.py @@ -0,0 +1,3 @@ +_base_ = ['twins-pcpvt-base_8xb128_in1k.py'] + +model = dict(backbone=dict(arch='small'), head=dict(in_channels=512)) diff --git a/configs/twins/twins-svt-base_8xb128_in1k.py b/configs/twins/twins-svt-base_8xb128_in1k.py new file mode 100644 index 0000000..e2db230 --- /dev/null +++ b/configs/twins/twins-svt-base_8xb128_in1k.py @@ -0,0 +1,33 @@ +_base_ = [ + '../_base_/models/twins_svt_base.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +data = dict(samples_per_gpu=128) + +paramwise_cfg = dict(_delete=True, norm_decay_mult=0.0, bias_decay_mult=0.0) + +# for batch in each gpu is 128, 8 gpu +# lr = 5e-4 * 128 * 8 / 512 = 0.001 +optimizer = dict( + type='AdamW', + lr=5e-4 * 128 * 8 / 512, + weight_decay=0.05, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=paramwise_cfg) +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=5.0)) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + by_epoch=True, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=5, + warmup_by_epoch=True) + +evaluation = dict(interval=1, metric='accuracy') diff --git a/configs/twins/twins-svt-large_16xb64_in1k.py b/configs/twins/twins-svt-large_16xb64_in1k.py new file mode 100644 index 0000000..9288a70 --- /dev/null +++ b/configs/twins/twins-svt-large_16xb64_in1k.py @@ -0,0 +1,5 @@ +_base_ = ['twins-svt-base_8xb128_in1k.py'] + +data = dict(samples_per_gpu=64) + +model = dict(backbone=dict(arch='large'), head=dict(in_channels=1024)) diff --git a/configs/twins/twins-svt-small_8xb128_in1k.py b/configs/twins/twins-svt-small_8xb128_in1k.py new file mode 100644 index 0000000..b92f1d3 --- /dev/null +++ b/configs/twins/twins-svt-small_8xb128_in1k.py @@ -0,0 +1,3 @@ +_base_ = ['twins-svt-base_8xb128_in1k.py'] + +model = dict(backbone=dict(arch='small'), head=dict(in_channels=512)) diff --git a/configs/van/README.md b/configs/van/README.md new file mode 100644 index 0000000..a84cf32 --- /dev/null +++ b/configs/van/README.md @@ -0,0 +1,50 @@ +# Visual Attention Network + +> [Visual Attention Network](https://arxiv.org/pdf/2202.09741v2.pdf) + + + +## Abstract + +While originally designed for natural language processing (NLP) tasks, the self-attention mechanism has recently taken various computer vision areas by storm. However, the 2D nature of images brings three challenges for applying self-attention in computer vision. (1) Treating images as 1D sequences neglects their 2D structures. (2) The quadratic complexity is too expensive for high-resolution images. (3) It only captures spatial adaptability but ignores channel adaptability. In this paper, we propose a novel large kernel attention (LKA) module to enable self-adaptive and long-range correlations in self-attention while avoiding the above issues. We further introduce a novel neural network based on LKA, namely Visual Attention Network (VAN). While extremely simple and efficient, VAN outperforms the state-of-the-art vision transformers and convolutional neural networks with a large margin in extensive experiments, including image classification, object detection, semantic segmentation, instance segmentation, etc. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :----------------------------------------------------------------: | :-------------------------------------------------------------------: | +| VAN-B0\* | From scratch | 224x224 | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b0_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | +| VAN-B1\* | From scratch | 224x224 | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | +| VAN-B2\* | From scratch | 224x224 | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b2_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | +| VAN-B3\* | From scratch | 224x224 | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | +| VAN-B4\* | From scratch | 224x224 | 60.28 | 12.22 | 84.13 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-b4_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in1k_20220909-f4665b92.pth) | + +\*Models with * are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results. + +### Pre-trained Models + +The pre-trained models on ImageNet-21k are used to fine-tune on the downstream tasks. + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Download | +| :------: | :----------: | :--------: | :-------: | :------: | :---------------------------------------------------------------------------------------------------------: | +| VAN-B4\* | ImageNet-21k | 224x224 | 60.28 | 12.22 | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in21k_20220909-db926b18.pth) | +| VAN-B5\* | ImageNet-21k | 224x224 | 89.97 | 17.21 | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b5_3rdparty_in21k_20220909-18e904e3.pth) | +| VAN-B6\* | ImageNet-21k | 224x224 | 283.9 | 55.28 | [model](https://download.openmmlab.com/mmclassification/v0/van/van-b6_3rdparty_in21k_20220909-96c2cb3a.pth) | + +\*Models with * are converted from [the official repo](https://github.com/Visual-Attention-Network/VAN-Classification). + +## Citation + +``` +@article{guo2022visual, + title={Visual Attention Network}, + author={Guo, Meng-Hao and Lu, Cheng-Ze and Liu, Zheng-Ning and Cheng, Ming-Ming and Hu, Shi-Min}, + journal={arXiv preprint arXiv:2202.09741}, + year={2022} +} +``` diff --git a/configs/van/metafile.yml b/configs/van/metafile.yml new file mode 100644 index 0000000..c32df84 --- /dev/null +++ b/configs/van/metafile.yml @@ -0,0 +1,84 @@ +Collections: + - Name: Visual-Attention-Network + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - AdamW + - Weight Decay + Architecture: + - Visual Attention Network + - LKA + Paper: + URL: https://arxiv.org/pdf/2202.09741v2.pdf + Title: "Visual Attention Network" + README: configs/van/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.23.0/mmcls/models/backbones/van.py + Version: v0.23.0 + +Models: + - Name: van-b0_3rdparty_in1k + Metadata: + FLOPs: 880000000 # 0.88G + Parameters: 4110000 # 4.11M + In Collection: Visual-Attention-Network + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 75.41 + Top 5 Accuracy: 93.02 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth + Config: configs/van/van-b0_8xb128_in1k.py + - Name: van-b1_3rdparty_in1k + Metadata: + FLOPs: 2520000000 # 2.52G + Parameters: 13860000 # 13.86M + In Collection: Visual-Attention-Network + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.01 + Top 5 Accuracy: 95.63 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth + Config: configs/van/van-b1_8xb128_in1k.py + - Name: van-b2_3rdparty_in1k + Metadata: + FLOPs: 5030000000 # 5.03G + Parameters: 26580000 # 26.58M + In Collection: Visual-Attention-Network + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 82.80 + Top 5 Accuracy: 96.21 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth + Config: configs/van/van-b2_8xb128_in1k.py + - Name: van-b3_3rdparty_in1k + Metadata: + FLOPs: 8990000000 # 8.99G + Parameters: 44770000 # 44.77M + In Collection: Visual-Attention-Network + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 83.86 + Top 5 Accuracy: 96.73 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth + Config: configs/van/van-b3_8xb128_in1k.py + - Name: van-b4_3rdparty_in1k + Metadata: + FLOPs: 12220000000 # 12.22G + Parameters: 60280000 # 60.28M + In Collection: Visual-Attention-Network + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 84.13 + Top 5 Accuracy: 96.86 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/van/van-b4_3rdparty_in1k_20220909-f4665b92.pth + Config: configs/van/van-b4_8xb128_in1k.py diff --git a/configs/van/van-b0_8xb128_in1k.py b/configs/van/van-b0_8xb128_in1k.py new file mode 100644 index 0000000..1acb7af --- /dev/null +++ b/configs/van/van-b0_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b0.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b1_8xb128_in1k.py b/configs/van/van-b1_8xb128_in1k.py new file mode 100644 index 0000000..64483db --- /dev/null +++ b/configs/van/van-b1_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b1.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b2_8xb128_in1k.py b/configs/van/van-b2_8xb128_in1k.py new file mode 100644 index 0000000..88493dc --- /dev/null +++ b/configs/van/van-b2_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b2.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b3_8xb128_in1k.py b/configs/van/van-b3_8xb128_in1k.py new file mode 100644 index 0000000..6b415f6 --- /dev/null +++ b/configs/van/van-b3_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b3.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-b4_8xb128_in1k.py b/configs/van/van-b4_8xb128_in1k.py new file mode 100644 index 0000000..ba8914f --- /dev/null +++ b/configs/van/van-b4_8xb128_in1k.py @@ -0,0 +1,61 @@ +_base_ = [ + '../_base_/models/van/van_b4.py', + '../_base_/datasets/imagenet_bs64_swin_224.py', + '../_base_/schedules/imagenet_bs1024_adamw_swin.py', + '../_base_/default_runtime.py' +] + +# Note that the mean and variance used here are different from other configs +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='RandomResizedCrop', + size=224, + backend='pillow', + interpolation='bicubic'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict( + type='RandAugment', + policies={{_base_.rand_increasing_policies}}, + num_policies=2, + total_level=10, + magnitude_level=9, + magnitude_std=0.5, + hparams=dict( + pad_val=[round(x) for x in img_norm_cfg['mean'][::-1]], + interpolation='bicubic')), + dict(type='ColorJitter', brightness=0.4, contrast=0.4, saturation=0.4), + dict( + type='RandomErasing', + erase_prob=0.25, + mode='rand', + min_area_ratio=0.02, + max_area_ratio=1 / 3, + fill_color=img_norm_cfg['mean'][::-1], + fill_std=img_norm_cfg['std'][::-1]), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='Resize', + size=(248, -1), + backend='pillow', + interpolation='bicubic'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/van/van-base_8xb128_in1k.py b/configs/van/van-base_8xb128_in1k.py new file mode 100644 index 0000000..e331980 --- /dev/null +++ b/configs/van/van-base_8xb128_in1k.py @@ -0,0 +1,6 @@ +_base_ = ['./van-b2_8xb128_in1k.py'] + +_deprecation_ = dict( + expected='van-b2_8xb128_in1k.p', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/configs/van/van-large_8xb128_in1k.py b/configs/van/van-large_8xb128_in1k.py new file mode 100644 index 0000000..84f8c7e --- /dev/null +++ b/configs/van/van-large_8xb128_in1k.py @@ -0,0 +1,6 @@ +_base_ = ['./van-b3_8xb128_in1k.py'] + +_deprecation_ = dict( + expected='van-b3_8xb128_in1k.p', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/configs/van/van-small_8xb128_in1k.py b/configs/van/van-small_8xb128_in1k.py new file mode 100644 index 0000000..75d3220 --- /dev/null +++ b/configs/van/van-small_8xb128_in1k.py @@ -0,0 +1,6 @@ +_base_ = ['./van-b1_8xb128_in1k.py'] + +_deprecation_ = dict( + expected='van-b1_8xb128_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/configs/van/van-tiny_8xb128_in1k.py b/configs/van/van-tiny_8xb128_in1k.py new file mode 100644 index 0000000..9f83e77 --- /dev/null +++ b/configs/van/van-tiny_8xb128_in1k.py @@ -0,0 +1,6 @@ +_base_ = ['./van-b0_8xb128_in1k.py'] + +_deprecation_ = dict( + expected='van-b0_8xb128_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/1017', +) diff --git a/configs/vgg/README.md b/configs/vgg/README.md new file mode 100644 index 0000000..454489f --- /dev/null +++ b/configs/vgg/README.md @@ -0,0 +1,39 @@ +# VGG + +> [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556) + + + +## Abstract + +In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------: | :-------: | :------: | :-------: | :-------: | :---------------------------------------------------------------------------: | :-----------------------------------------------------------------------------: | +| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) | +| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) | +| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) | +| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.log.json) | +| VGG-11-BN | 132.87 | 7.64 | 70.67 | 90.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) | +| VGG-13-BN | 133.05 | 11.36 | 72.12 | 90.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) | +| VGG-16-BN | 138.37 | 15.53 | 73.74 | 91.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) | +| VGG-19-BN | 143.68 | 19.7 | 74.68 | 92.27 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json) | + +## Citation + +``` +@article{simonyan2014very, + title={Very deep convolutional networks for large-scale image recognition}, + author={Simonyan, Karen and Zisserman, Andrew}, + journal={arXiv preprint arXiv:1409.1556}, + year={2014} +} +``` diff --git a/configs/vgg/metafile.yml b/configs/vgg/metafile.yml new file mode 100644 index 0000000..4410c95 --- /dev/null +++ b/configs/vgg/metafile.yml @@ -0,0 +1,125 @@ +Collections: + - Name: VGG + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x Xp GPUs + Epochs: 100 + Batch Size: 256 + Architecture: + - VGG + Paper: + URL: https://arxiv.org/abs/1409.1556 + Title: "Very Deep Convolutional Networks for Large-Scale Image" + README: configs/vgg/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.15.0/mmcls/models/backbones/vgg.py#L39 + Version: v0.15.0 + +Models: + - Name: vgg11_8xb32_in1k + Metadata: + FLOPs: 7630000000 + Parameters: 132860000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 68.75 + Top 5 Accuracy: 88.87 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth + Config: configs/vgg/vgg11_8xb32_in1k.py + - Name: vgg13_8xb32_in1k + Metadata: + FLOPs: 11340000000 + Parameters: 133050000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 70.02 + Top 5 Accuracy: 89.46 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth + Config: configs/vgg/vgg13_8xb32_in1k.py + - Name: vgg16_8xb32_in1k + Metadata: + FLOPs: 15500000000 + Parameters: 138360000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 71.62 + Top 5 Accuracy: 90.49 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth + Config: configs/vgg/vgg16_8xb32_in1k.py + - Name: vgg19_8xb32_in1k + Metadata: + FLOPs: 19670000000 + Parameters: 143670000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 72.41 + Top 5 Accuracy: 90.8 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth + Config: configs/vgg/vgg19_8xb32_in1k.py + - Name: vgg11bn_8xb32_in1k + Metadata: + FLOPs: 7640000000 + Parameters: 132870000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 70.67 + Top 5 Accuracy: 90.16 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth + Config: configs/vgg/vgg11bn_8xb32_in1k.py + - Name: vgg13bn_8xb32_in1k + Metadata: + FLOPs: 11360000000 + Parameters: 133050000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 72.12 + Top 5 Accuracy: 90.66 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth + Config: configs/vgg/vgg13bn_8xb32_in1k.py + - Name: vgg16bn_8xb32_in1k + Metadata: + FLOPs: 15530000000 + Parameters: 138370000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 73.74 + Top 5 Accuracy: 91.66 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth + Config: configs/vgg/vgg16bn_8xb32_in1k.py + - Name: vgg19bn_8xb32_in1k + Metadata: + FLOPs: 19700000000 + Parameters: 143680000 + In Collection: VGG + Results: + - Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 74.68 + Top 5 Accuracy: 92.27 + Task: Image Classification + Weights: https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth + Config: configs/vgg/vgg19bn_8xb32_in1k.py diff --git a/configs/vgg/vgg11_8xb32_in1k.py b/configs/vgg/vgg11_8xb32_in1k.py new file mode 100644 index 0000000..c5742bc --- /dev/null +++ b/configs/vgg/vgg11_8xb32_in1k.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/vgg11.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', + '../_base_/default_runtime.py', +] +optimizer = dict(lr=0.01) diff --git a/configs/vgg/vgg11_b32x8_imagenet.py b/configs/vgg/vgg11_b32x8_imagenet.py new file mode 100644 index 0000000..b15396b --- /dev/null +++ b/configs/vgg/vgg11_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg11_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg11_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg11bn_8xb32_in1k.py b/configs/vgg/vgg11bn_8xb32_in1k.py new file mode 100644 index 0000000..4ead074 --- /dev/null +++ b/configs/vgg/vgg11bn_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/vgg11bn.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/vgg/vgg11bn_b32x8_imagenet.py b/configs/vgg/vgg11bn_b32x8_imagenet.py new file mode 100644 index 0000000..350c9be --- /dev/null +++ b/configs/vgg/vgg11bn_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg11bn_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg11bn_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg13_8xb32_in1k.py b/configs/vgg/vgg13_8xb32_in1k.py new file mode 100644 index 0000000..50d26f3 --- /dev/null +++ b/configs/vgg/vgg13_8xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/vgg13.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] +optimizer = dict(lr=0.01) diff --git a/configs/vgg/vgg13_b32x8_imagenet.py b/configs/vgg/vgg13_b32x8_imagenet.py new file mode 100644 index 0000000..6198ca2 --- /dev/null +++ b/configs/vgg/vgg13_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg13_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg13_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg13bn_8xb32_in1k.py b/configs/vgg/vgg13bn_8xb32_in1k.py new file mode 100644 index 0000000..8d22a81 --- /dev/null +++ b/configs/vgg/vgg13bn_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/vgg13bn.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/vgg/vgg13bn_b32x8_imagenet.py b/configs/vgg/vgg13bn_b32x8_imagenet.py new file mode 100644 index 0000000..0a715d7 --- /dev/null +++ b/configs/vgg/vgg13bn_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg13bn_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg13bn_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg16_8xb16_voc.py b/configs/vgg/vgg16_8xb16_voc.py new file mode 100644 index 0000000..d096959 --- /dev/null +++ b/configs/vgg/vgg16_8xb16_voc.py @@ -0,0 +1,25 @@ +_base_ = ['../_base_/datasets/voc_bs16.py', '../_base_/default_runtime.py'] + +# use different head for multilabel task +model = dict( + type='ImageClassifier', + backbone=dict(type='VGG', depth=16, num_classes=20), + neck=None, + head=dict( + type='MultiLabelClsHead', + loss=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))) + +# load model pretrained on imagenet +load_from = 'https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth' # noqa + +# optimizer +optimizer = dict( + type='SGD', + lr=0.001, + momentum=0.9, + weight_decay=0, + paramwise_cfg=dict(custom_keys={'.backbone.classifier': dict(lr_mult=10)})) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=20, gamma=0.1) +runner = dict(type='EpochBasedRunner', max_epochs=40) diff --git a/configs/vgg/vgg16_8xb32_in1k.py b/configs/vgg/vgg16_8xb32_in1k.py new file mode 100644 index 0000000..55cd9fc --- /dev/null +++ b/configs/vgg/vgg16_8xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/vgg16.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] +optimizer = dict(lr=0.01) diff --git a/configs/vgg/vgg16_b16x8_voc.py b/configs/vgg/vgg16_b16x8_voc.py new file mode 100644 index 0000000..06225e7 --- /dev/null +++ b/configs/vgg/vgg16_b16x8_voc.py @@ -0,0 +1,6 @@ +_base_ = 'vgg16_8xb16_voc.py' + +_deprecation_ = dict( + expected='vgg16_8xb16_voc.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg16_b32x8_imagenet.py b/configs/vgg/vgg16_b32x8_imagenet.py new file mode 100644 index 0000000..2fefb94 --- /dev/null +++ b/configs/vgg/vgg16_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg16_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg16_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg16bn_8xb32_in1k.py b/configs/vgg/vgg16bn_8xb32_in1k.py new file mode 100644 index 0000000..60674c7 --- /dev/null +++ b/configs/vgg/vgg16bn_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/vgg16bn.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/vgg/vgg16bn_b32x8_imagenet.py b/configs/vgg/vgg16bn_b32x8_imagenet.py new file mode 100644 index 0000000..cb21917 --- /dev/null +++ b/configs/vgg/vgg16bn_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg16bn_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg16bn_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg19_8xb32_in1k.py b/configs/vgg/vgg19_8xb32_in1k.py new file mode 100644 index 0000000..6b033c9 --- /dev/null +++ b/configs/vgg/vgg19_8xb32_in1k.py @@ -0,0 +1,6 @@ +_base_ = [ + '../_base_/models/vgg19.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] +optimizer = dict(lr=0.01) diff --git a/configs/vgg/vgg19_b32x8_imagenet.py b/configs/vgg/vgg19_b32x8_imagenet.py new file mode 100644 index 0000000..e8b8b25 --- /dev/null +++ b/configs/vgg/vgg19_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg19_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg19_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vgg/vgg19bn_8xb32_in1k.py b/configs/vgg/vgg19bn_8xb32_in1k.py new file mode 100644 index 0000000..18a1897 --- /dev/null +++ b/configs/vgg/vgg19bn_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/vgg19bn.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/vgg/vgg19bn_b32x8_imagenet.py b/configs/vgg/vgg19bn_b32x8_imagenet.py new file mode 100644 index 0000000..f615496 --- /dev/null +++ b/configs/vgg/vgg19bn_b32x8_imagenet.py @@ -0,0 +1,6 @@ +_base_ = 'vgg19bn_8xb32_in1k.py' + +_deprecation_ = dict( + expected='vgg19bn_8xb32_in1k.py', + reference='https://github.com/open-mmlab/mmclassification/pull/508', +) diff --git a/configs/vision_transformer/README.md b/configs/vision_transformer/README.md new file mode 100644 index 0000000..c35c242 --- /dev/null +++ b/configs/vision_transformer/README.md @@ -0,0 +1,57 @@ +# Vision Transformer + +> [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/pdf/2010.11929.pdf) + + + +## Abstract + +While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train. + +
+ +
+ +## Results and models + +The training step of Vision Transformers is divided into two steps. The first +step is training the model on a large dataset, like ImageNet-21k, and get the +pre-trained model. And the second step is training the model on the target +dataset, like ImageNet-1k, and get the fine-tuned model. Here, we provide both +pre-trained models and fine-tuned models. + +### ImageNet-21k + +The pre-trained models on ImageNet-21k are used to fine-tune, and therefore don't have evaluation results. + +| Model | resolution | Params(M) | Flops(G) | Download | +| :-------: | :--------: | :-------: | :------: | :--------------------------------------------------------------------------------------------------------------------------------------: | +| ViT-B16\* | 224x224 | 86.86 | 33.03 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p16_3rdparty_pt-64xb64_in1k-224_20210928-02284250.pth) | +| ViT-B32\* | 224x224 | 88.30 | 8.56 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p32_3rdparty_pt-64xb64_in1k-224_20210928-eee25dd4.pth) | +| ViT-L16\* | 224x224 | 304.72 | 116.68 | [model](https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-large-p16_3rdparty_pt-64xb64_in1k-224_20210928-0001f9a1.pth) | + +*Models with * are converted from the [official repo](https://github.com/google-research/vision_transformer#available-vit-models).* + +### ImageNet-1k + +| Model | Pretrain | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-----------: | :----------: | :--------: | :-------: | :------: | :-------: | :-------: | :--------------------------------------------------------------: | :----------------------------------------------------------------: | +| ViT-B16\* | ImageNet-21k | 384x384 | 86.86 | 33.03 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) | +| ViT-B32\* | ImageNet-21k | 384x384 | 88.30 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) | +| ViT-L16\* | ImageNet-21k | 384x384 | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) | +| ViT-B16 (IPU) | ImageNet-21k | 224x224 | 86.86 | 33.03 | 81.22 | 95.56 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-4xb544-ipu_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/vit-base-p16_ft-4xb544-ipu_in1k_20220603-c215811a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vit/vit-base-p16_ft-4xb544-ipu_in1k.log) | + +*Models with * are converted from the [official repo](https://github.com/google-research/vision_transformer#available-vit-models). The config files of these models are only for validation. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +``` +@inproceedings{ + dosovitskiy2021an, + title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, + author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby}, + booktitle={International Conference on Learning Representations}, + year={2021}, + url={https://openreview.net/forum?id=YicbFdNTTy} +} +``` diff --git a/configs/vision_transformer/metafile.yml b/configs/vision_transformer/metafile.yml new file mode 100644 index 0000000..9ac8046 --- /dev/null +++ b/configs/vision_transformer/metafile.yml @@ -0,0 +1,79 @@ +Collections: + - Name: Vision Transformer + Metadata: + Architecture: + - Attention Dropout + - Convolution + - Dense Connections + - Dropout + - GELU + - Layer Normalization + - Multi-Head Attention + - Scaled Dot-Product Attention + - Tanh Activation + Paper: + URL: https://arxiv.org/pdf/2010.11929.pdf + Title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale' + README: configs/vision_transformer/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.17.0/mmcls/models/backbones/vision_transformer.py + Version: v0.17.0 + +Models: + - Name: vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384 + In Collection: Vision Transformer + Metadata: + FLOPs: 33030000000 + Parameters: 86860000 + Training Data: + - ImageNet-21k + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 85.43 + Top 5 Accuracy: 97.77 + Weights: https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth + Converted From: + Weights: https://console.cloud.google.com/storage/browser/_details/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz + Code: https://github.com/google-research/vision_transformer/blob/88a52f8892c80c10de99194990a517b4d80485fd/vit_jax/models.py#L208 + Config: configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py + - Name: vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384 + In Collection: Vision Transformer + Metadata: + FLOPs: 8560000000 + Parameters: 88300000 + Training Data: + - ImageNet-21k + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 84.01 + Top 5 Accuracy: 97.08 + Weights: https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth + Converted From: + Weights: https://console.cloud.google.com/storage/browser/_details/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz + Code: https://github.com/google-research/vision_transformer/blob/88a52f8892c80c10de99194990a517b4d80485fd/vit_jax/models.py#L208 + Config: configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py + - Name: vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384 + In Collection: Vision Transformer + Metadata: + FLOPs: 116680000000 + Parameters: 304720000 + Training Data: + - ImageNet-21k + - ImageNet-1k + Results: + - Dataset: ImageNet-1k + Task: Image Classification + Metrics: + Top 1 Accuracy: 85.63 + Top 5 Accuracy: 97.63 + Weights: https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth + Converted From: + Weights: https://console.cloud.google.com/storage/browser/_details/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_strong1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz + Code: https://github.com/google-research/vision_transformer/blob/88a52f8892c80c10de99194990a517b4d80485fd/vit_jax/models.py#L208 + Config: configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py diff --git a/configs/vision_transformer/vit-base-p16_ft-4xb544-ipu_in1k.py b/configs/vision_transformer/vit-base-p16_ft-4xb544-ipu_in1k.py new file mode 100644 index 0000000..097d8d6 --- /dev/null +++ b/configs/vision_transformer/vit-base-p16_ft-4xb544-ipu_in1k.py @@ -0,0 +1,115 @@ +_base_ = [ + '../_base_/models/vit-base-p16.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/default_runtime.py' +] + +# specific to vit pretrain +paramwise_cfg = dict(custom_keys={ + '.cls_token': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0) +}) + +pretrained = 'https://download.openmmlab.com/mmclassification/v0/vit/pretrain/vit-base-p16_3rdparty_pt-64xb64_in1k-224_20210928-02284250.pth' # noqa + +model = dict( + head=dict( + loss=dict(type='CrossEntropyLoss', loss_weight=1.0, _delete_=True), ), + backbone=dict( + img_size=224, + init_cfg=dict( + type='Pretrained', + checkpoint=pretrained, + _delete_=True, + prefix='backbone'))) + +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='ToHalf', keys=['img']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(224, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToHalf', keys=['img']), + dict(type='Collect', keys=['img']) +] + +# change batch size +data = dict( + samples_per_gpu=17, + workers_per_gpu=16, + drop_last=True, + train=dict(pipeline=train_pipeline), + train_dataloader=dict(mode='async'), + val=dict(pipeline=test_pipeline, ), + val_dataloader=dict(samples_per_gpu=4, workers_per_gpu=1), + test=dict(pipeline=test_pipeline), + test_dataloader=dict(samples_per_gpu=4, workers_per_gpu=1)) + +# remove clip-norm +optimizer_config = dict() + +# optimizer +optimizer = dict( + type='SGD', + lr=0.08, + weight_decay=1e-5, + momentum=0.9, + paramwise_cfg=paramwise_cfg, +) + +# learning policy +lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + warmup_iters=800, + warmup_ratio=0.02, +) + +# ipu cfg +# model partition config +ipu_model_cfg = dict( + train_split_edges=[ + dict(layer_to_call='backbone.patch_embed', ipu_id=0), + dict(layer_to_call='backbone.layers.3', ipu_id=1), + dict(layer_to_call='backbone.layers.6', ipu_id=2), + dict(layer_to_call='backbone.layers.9', ipu_id=3) + ], + train_ckpt_nodes=['backbone.layers.{}'.format(i) for i in range(12)]) + +# device config +options_cfg = dict( + randomSeed=42, + partialsType='half', + train_cfg=dict( + executionStrategy='SameAsIpu', + Training=dict(gradientAccumulation=32), + availableMemoryProportion=[0.3, 0.3, 0.3, 0.3], + ), + eval_cfg=dict(deviceIterations=1, ), +) + +# add model partition config and device config to runner +runner = dict( + type='IterBasedRunner', + ipu_model_cfg=ipu_model_cfg, + options_cfg=options_cfg, + max_iters=5000) + +checkpoint_config = dict(interval=1000) + +fp16 = dict(loss_scale=256.0, velocity_accum_type='half', accum_type='half') diff --git a/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py b/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py new file mode 100644 index 0000000..cb42d0d --- /dev/null +++ b/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py @@ -0,0 +1,36 @@ +_base_ = [ + '../_base_/models/vit-base-p16.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(img_size=384)) + +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=384, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(384, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=384), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) diff --git a/configs/vision_transformer/vit-base-p16_pt-64xb64_in1k-224.py b/configs/vision_transformer/vit-base-p16_pt-64xb64_in1k-224.py new file mode 100644 index 0000000..79c323b --- /dev/null +++ b/configs/vision_transformer/vit-base-p16_pt-64xb64_in1k-224.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/vit-base-p16.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict( + head=dict(hidden_dim=3072), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000, + prob=1.))) diff --git a/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py b/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py new file mode 100644 index 0000000..0386fef --- /dev/null +++ b/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py @@ -0,0 +1,36 @@ +_base_ = [ + '../_base_/models/vit-base-p32.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(img_size=384)) + +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=384, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(384, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=384), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) diff --git a/configs/vision_transformer/vit-base-p32_pt-64xb64_in1k-224.py b/configs/vision_transformer/vit-base-p32_pt-64xb64_in1k-224.py new file mode 100644 index 0000000..a477e21 --- /dev/null +++ b/configs/vision_transformer/vit-base-p32_pt-64xb64_in1k-224.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/vit-base-p32.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict( + head=dict(hidden_dim=3072), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000, + prob=1.))) diff --git a/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py b/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py new file mode 100644 index 0000000..5be9918 --- /dev/null +++ b/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py @@ -0,0 +1,36 @@ +_base_ = [ + '../_base_/models/vit-large-p16.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(img_size=384)) + +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=384, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(384, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=384), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) diff --git a/configs/vision_transformer/vit-large-p16_pt-64xb64_in1k-224.py b/configs/vision_transformer/vit-large-p16_pt-64xb64_in1k-224.py new file mode 100644 index 0000000..5cf7a7d --- /dev/null +++ b/configs/vision_transformer/vit-large-p16_pt-64xb64_in1k-224.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/vit-large-p16.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict( + head=dict(hidden_dim=3072), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000, + prob=1.))) diff --git a/configs/vision_transformer/vit-large-p32_ft-64xb64_in1k-384.py b/configs/vision_transformer/vit-large-p32_ft-64xb64_in1k-384.py new file mode 100644 index 0000000..60506b0 --- /dev/null +++ b/configs/vision_transformer/vit-large-p32_ft-64xb64_in1k-384.py @@ -0,0 +1,37 @@ +# Refer to pytorch-image-models +_base_ = [ + '../_base_/models/vit-large-p32.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(img_size=384)) + +img_norm_cfg = dict( + mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=384, backend='pillow'), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(384, -1), backend='pillow'), + dict(type='CenterCrop', crop_size=384), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] + +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) diff --git a/configs/vision_transformer/vit-large-p32_pt-64xb64_in1k-224.py b/configs/vision_transformer/vit-large-p32_pt-64xb64_in1k-224.py new file mode 100644 index 0000000..773ade8 --- /dev/null +++ b/configs/vision_transformer/vit-large-p32_pt-64xb64_in1k-224.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/vit-large-p32.py', + '../_base_/datasets/imagenet_bs64_pil_resize_autoaug.py', + '../_base_/schedules/imagenet_bs4096_AdamW.py', + '../_base_/default_runtime.py' +] + +model = dict( + head=dict(hidden_dim=3072), + train_cfg=dict( + augments=dict(type='BatchMixup', alpha=0.2, num_classes=1000, + prob=1.))) diff --git a/configs/wrn/README.md b/configs/wrn/README.md new file mode 100644 index 0000000..b036caa --- /dev/null +++ b/configs/wrn/README.md @@ -0,0 +1,35 @@ +# Wide-ResNet + +> [Wide Residual Networks](https://arxiv.org/abs/1605.07146) + + + +## Abstract + +Deep residual networks were shown to be able to scale up to thousands of layers and still have improving performance. However, each fraction of a percent of improved accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel architecture where we decrease depth and increase width of residual networks. We call the resulting network structures wide residual networks (WRNs) and show that these are far superior over their commonly used thin and very deep counterparts. For example, we demonstrate that even a simple 16-layer-deep wide residual network outperforms in accuracy and efficiency all previous deep residual networks, including thousand-layer-deep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and significant improvements on ImageNet. + +
+ +
+ +## Results and models + +### ImageNet-1k + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :-------------: | :-------: | :------: | :-------: | :-------: | :------------------------------------------------------------------------: | :--------------------------------------------------------------------------: | +| WRN-50\* | 68.88 | 11.44 | 78.48 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth) | +| WRN-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | +| WRN-50 (timm)\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) | + +*Models with * are converted from the [TorchVision](https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py) and [TIMM](https://github.com/rwightman/pytorch-image-models/blob/master). The config files of these models are only for inference. We don't ensure these config files' training accuracy and welcome you to contribute your reproduction results.* + +## Citation + +```bibtex +@INPROCEEDINGS{Zagoruyko2016WRN, + author = {Sergey Zagoruyko and Nikos Komodakis}, + title = {Wide Residual Networks}, + booktitle = {BMVC}, + year = {2016}} +``` diff --git a/configs/wrn/metafile.yml b/configs/wrn/metafile.yml new file mode 100644 index 0000000..cc37eef --- /dev/null +++ b/configs/wrn/metafile.yml @@ -0,0 +1,77 @@ +Collections: + - Name: Wide-ResNet + Metadata: + Training Data: ImageNet-1k + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Epochs: 100 + Batch Size: 256 + Architecture: + - 1x1 Convolution + - Batch Normalization + - Convolution + - Global Average Pooling + - Max Pooling + - ReLU + - Residual Connection + - Softmax + - Wide Residual Block + Paper: + URL: https://arxiv.org/abs/1605.07146 + Title: "Wide Residual Networks" + README: configs/wrn/README.md + Code: + URL: https://github.com/open-mmlab/mmclassification/blob/v0.20.1/mmcls/models/backbones/resnet.py#L383 + Version: v0.20.1 + +Models: + - Name: wide-resnet50_3rdparty_8xb32_in1k + Metadata: + FLOPs: 11440000000 # 11.44G + Parameters: 68880000 # 68.88M + In Collection: Wide-ResNet + Results: + - Task: Image Classification + Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.48 + Top 5 Accuracy: 94.08 + Weights: https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty_8xb32_in1k_20220304-66678344.pth + Config: configs/wrn/wide-resnet50_8xb32_in1k.py + Converted From: + Weights: https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth + Code: https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py + - Name: wide-resnet101_3rdparty_8xb32_in1k + Metadata: + FLOPs: 22810000000 # 22.81G + Parameters: 126890000 # 126.89M + In Collection: Wide-ResNet + Results: + - Task: Image Classification + Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 78.84 + Top 5 Accuracy: 94.28 + Weights: https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth + Config: configs/wrn/wide-resnet101_8xb32_in1k.py + Converted From: + Weights: https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth + Code: https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py + - Name: wide-resnet50_3rdparty-timm_8xb32_in1k + Metadata: + FLOPs: 11440000000 # 11.44G + Parameters: 68880000 # 68.88M + In Collection: Wide-ResNet + Results: + - Task: Image Classification + Dataset: ImageNet-1k + Metrics: + Top 1 Accuracy: 81.45 + Top 5 Accuracy: 95.53 + Weights: https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth + Config: configs/wrn/wide-resnet50_timm_8xb32_in1k.py + Converted From: + Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/wide_resnet50_racm-8234f177.pth + Code: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/resnet.py diff --git a/configs/wrn/wide-resnet101_8xb32_in1k.py b/configs/wrn/wide-resnet101_8xb32_in1k.py new file mode 100644 index 0000000..d1bf5e5 --- /dev/null +++ b/configs/wrn/wide-resnet101_8xb32_in1k.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/wide-resnet50.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] + +model = dict(backbone=dict(depth=101)) diff --git a/configs/wrn/wide-resnet50_8xb32_in1k.py b/configs/wrn/wide-resnet50_8xb32_in1k.py new file mode 100644 index 0000000..edf6a05 --- /dev/null +++ b/configs/wrn/wide-resnet50_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/wide-resnet50.py', + '../_base_/datasets/imagenet_bs32_pil_resize.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/configs/wrn/wide-resnet50_timm_8xb32_in1k.py b/configs/wrn/wide-resnet50_timm_8xb32_in1k.py new file mode 100644 index 0000000..8dca8f3 --- /dev/null +++ b/configs/wrn/wide-resnet50_timm_8xb32_in1k.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/wide-resnet50.py', + '../_base_/datasets/imagenet_bs32_pil_bicubic.py', + '../_base_/schedules/imagenet_bs256.py', '../_base_/default_runtime.py' +] diff --git a/docs/en/Makefile b/docs/en/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/en/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/en/_static/css/readthedocs.css b/docs/en/_static/css/readthedocs.css new file mode 100644 index 0000000..577a67a --- /dev/null +++ b/docs/en/_static/css/readthedocs.css @@ -0,0 +1,27 @@ +.header-logo { + background-image: url("../image/mmcls-logo.png"); + background-size: 204px 40px; + height: 40px; + width: 204px; +} + +pre { + white-space: pre; +} + +article.pytorch-article section code { + padding: .2em .4em; + background-color: #f3f4f7; + border-radius: 5px; +} + +/* Disable the change in tables */ +article.pytorch-article section table code { + padding: unset; + background-color: unset; + border-radius: unset; +} + +table.autosummary td { + width: 50% +} diff --git a/docs/en/_static/image/mmcls-logo.png b/docs/en/_static/image/mmcls-logo.png new file mode 100644 index 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JE;56U{Rxs2h5P^j literal 0 HcmV?d00001 diff --git a/docs/en/_static/js/custom.js b/docs/en/_static/js/custom.js new file mode 100644 index 0000000..44a4057 --- /dev/null +++ b/docs/en/_static/js/custom.js @@ -0,0 +1 @@ +var collapsedSections = ['Model zoo']; diff --git a/docs/en/_templates/classtemplate.rst b/docs/en/_templates/classtemplate.rst new file mode 100644 index 0000000..4f74842 --- /dev/null +++ b/docs/en/_templates/classtemplate.rst @@ -0,0 +1,14 @@ +.. role:: hidden + :class: hidden-section +.. currentmodule:: {{ module }} + + +{{ name | underline}} + +.. autoclass:: {{ name }} + :members: + + +.. + autogenerated from source/_templates/classtemplate.rst + note it does not have :inherited-members: diff --git a/docs/en/api/apis.rst b/docs/en/api/apis.rst new file mode 100644 index 0000000..67e05b9 --- /dev/null +++ b/docs/en/api/apis.rst @@ -0,0 +1,45 @@ +.. role:: hidden + :class: hidden-section + +mmcls.apis +=================================== + +These are some high-level APIs for classification tasks. + +.. contents:: mmcls.apis + :depth: 2 + :local: + :backlinks: top + +.. currentmodule:: mmcls.apis + +Train +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + + init_random_seed + set_random_seed + train_model + +Test +------------------ +.. autosummary:: + :toctree: generated + :nosignatures: + + single_gpu_test + multi_gpu_test + +Inference +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + + init_model + inference_model + show_result_pyplot diff --git a/docs/en/api/core.rst b/docs/en/api/core.rst new file mode 100644 index 0000000..83e1dbf --- /dev/null +++ b/docs/en/api/core.rst @@ -0,0 +1,62 @@ +.. role:: hidden + :class: hidden-section + +mmcls.core +=================================== + +This package includes some runtime components. These components are useful in +classification tasks but not supported by MMCV yet. + +.. note:: + + Some components may be moved to MMCV in the future. + +.. contents:: mmcls.core + :depth: 2 + :local: + :backlinks: top + +.. currentmodule:: mmcls.core + +Evaluation +------------------ + +Evaluation metrics calculation functions + +.. autosummary:: + :toctree: generated + :nosignatures: + + precision + recall + f1_score + precision_recall_f1 + average_precision + mAP + support + average_performance + calculate_confusion_matrix + +Hook +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + ClassNumCheckHook + PreciseBNHook + CosineAnnealingCooldownLrUpdaterHook + MMClsWandbHook + + +Optimizers +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + Lamb diff --git a/docs/en/api/datasets.rst b/docs/en/api/datasets.rst new file mode 100644 index 0000000..640ce1a --- /dev/null +++ b/docs/en/api/datasets.rst @@ -0,0 +1,61 @@ +.. role:: hidden + :class: hidden-section + +mmcls.datasets +=================================== + +The ``datasets`` package contains several usual datasets for image classification tasks and some dataset wrappers. + +.. currentmodule:: mmcls.datasets + +Custom Dataset +-------------- + +.. autoclass:: CustomDataset + +ImageNet +-------- + +.. autoclass:: ImageNet + +.. autoclass:: ImageNet21k + +CIFAR +----- + +.. autoclass:: CIFAR10 + +.. autoclass:: CIFAR100 + +MNIST +----- + +.. autoclass:: MNIST + +.. autoclass:: FashionMNIST + +VOC +--- + +.. autoclass:: VOC + +StanfordCars Cars +----------------- + +.. autoclass:: StanfordCars + +Base classes +------------ + +.. autoclass:: BaseDataset + +.. autoclass:: MultiLabelDataset + +Dataset Wrappers +---------------- + +.. autoclass:: ConcatDataset + +.. autoclass:: RepeatDataset + +.. autoclass:: ClassBalancedDataset diff --git a/docs/en/api/models.rst b/docs/en/api/models.rst new file mode 100644 index 0000000..0c31791 --- /dev/null +++ b/docs/en/api/models.rst @@ -0,0 +1,141 @@ +.. role:: hidden + :class: hidden-section + +mmcls.models +=================================== + +The ``models`` package contains several sub-packages for addressing the different components of a model. + +- :ref:`classifiers`: The top-level module which defines the whole process of a classification model. +- :ref:`backbones`: Usually a feature extraction network, e.g., ResNet, MobileNet. +- :ref:`necks`: The component between backbones and heads, e.g., GlobalAveragePooling. +- :ref:`heads`: The component for specific tasks. In MMClassification, we provides heads for classification. +- :ref:`losses`: Loss functions. + +.. currentmodule:: mmcls.models + +.. autosummary:: + :toctree: generated + :nosignatures: + + build_classifier + build_backbone + build_neck + build_head + build_loss + +.. _classifiers: + +Classifier +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + BaseClassifier + ImageClassifier + +.. _backbones: + +Backbones +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + AlexNet + CSPDarkNet + CSPNet + CSPResNeXt + CSPResNet + Conformer + ConvMixer + ConvNeXt + DenseNet + DistilledVisionTransformer + EfficientNet + HRNet + LeNet5 + MlpMixer + MobileNetV2 + MobileNetV3 + PCPVT + PoolFormer + RegNet + RepMLPNet + RepVGG + Res2Net + ResNeSt + ResNeXt + ResNet + ResNetV1c + ResNetV1d + ResNet_CIFAR + SEResNeXt + SEResNet + SVT + ShuffleNetV1 + ShuffleNetV2 + SwinTransformer + T2T_ViT + TIMMBackbone + TNT + VAN + VGG + VisionTransformer + EfficientFormer + HorNet + +.. _necks: + +Necks +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + GlobalAveragePooling + GeneralizedMeanPooling + HRFuseScales + +.. _heads: + +Heads +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + ClsHead + LinearClsHead + StackedLinearClsHead + MultiLabelClsHead + MultiLabelLinearClsHead + VisionTransformerClsHead + DeiTClsHead + ConformerHead + +.. _losses: + +Losses +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + Accuracy + AsymmetricLoss + CrossEntropyLoss + LabelSmoothLoss + FocalLoss + SeesawLoss diff --git a/docs/en/api/models.utils.augment.rst b/docs/en/api/models.utils.augment.rst new file mode 100644 index 0000000..54442f7 --- /dev/null +++ b/docs/en/api/models.utils.augment.rst @@ -0,0 +1,35 @@ +.. role:: hidden + :class: hidden-section + +Batch Augmentation +=================================== + +Batch augmentation is the augmentation which involve multiple samples, such as Mixup and CutMix. + +In MMClassification, these batch augmentation is used as a part of :ref:`classifiers`. A typical usage is as below: + +.. code-block:: python + + model = dict( + backbone = ..., + neck = ..., + head = ..., + train_cfg=dict(augments=[ + dict(type='BatchMixup', alpha=0.8, prob=0.5, num_classes=num_classes), + dict(type='BatchCutMix', alpha=1.0, prob=0.5, num_classes=num_classes), + ])) + ) + +.. currentmodule:: mmcls.models.utils.augment + +Mixup +----- +.. autoclass:: BatchMixupLayer + +CutMix +------ +.. autoclass:: BatchCutMixLayer + +ResizeMix +--------- +.. autoclass:: BatchResizeMixLayer diff --git a/docs/en/api/models.utils.rst b/docs/en/api/models.utils.rst new file mode 100644 index 0000000..c9687a7 --- /dev/null +++ b/docs/en/api/models.utils.rst @@ -0,0 +1,50 @@ +.. role:: hidden + :class: hidden-section + +mmcls.models.utils +=================================== + +This package includes some helper functions and common components used in various networks. + +.. contents:: mmcls.models.utils + :depth: 2 + :local: + :backlinks: top + +.. currentmodule:: mmcls.models.utils + +Common Components +------------------ + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + InvertedResidual + SELayer + ShiftWindowMSA + MultiheadAttention + ConditionalPositionEncoding + +Helper Functions +------------------ + +channel_shuffle +^^^^^^^^^^^^^^^ +.. autofunction:: channel_shuffle + +make_divisible +^^^^^^^^^^^^^^ +.. autofunction:: make_divisible + +to_ntuple +^^^^^^^^^^^^^^ +.. autofunction:: to_ntuple +.. autofunction:: to_2tuple +.. autofunction:: to_3tuple +.. autofunction:: to_4tuple + +is_tracing +^^^^^^^^^^^^^^ +.. autofunction:: is_tracing diff --git a/docs/en/api/transforms.rst b/docs/en/api/transforms.rst new file mode 100644 index 0000000..4a39f08 --- /dev/null +++ b/docs/en/api/transforms.rst @@ -0,0 +1,171 @@ +.. role:: hidden + :class: hidden-section + +Data Transformations +*********************************** + +In MMClassification, the data preparation and the dataset is decomposed. The +datasets only define how to get samples' basic information from the file +system. These basic information includes the ground-truth label and raw images +data / the paths of images. + +To prepare the inputs data, we need to do some transformations on these basic +information. These transformations includes loading, preprocessing and +formatting. And a series of data transformations makes up a data pipeline. +Therefore, you can find the a ``pipeline`` argument in the configs of dataset, +for example: + +.. code:: python + + img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) + ] + test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=256), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ] + + data = dict( + train=dict(..., pipeline=train_pipeline), + val=dict(..., pipeline=test_pipeline), + test=dict(..., pipeline=test_pipeline), + ) + +Every item of a pipeline list is one of the following data transformations class. And if you want to add a custom data transformation class, the tutorial :doc:`Custom Data Pipelines ` will help you. + +.. contents:: mmcls.datasets.pipelines + :depth: 2 + :local: + :backlinks: top + +.. currentmodule:: mmcls.datasets.pipelines + +Loading +======= + +LoadImageFromFile +--------------------- +.. autoclass:: LoadImageFromFile + +Preprocessing and Augmentation +============================== + +CenterCrop +--------------------- +.. autoclass:: CenterCrop + +Lighting +--------------------- +.. autoclass:: Lighting + +Normalize +--------------------- +.. autoclass:: Normalize + +Pad +--------------------- +.. autoclass:: Pad + +Resize +--------------------- +.. autoclass:: Resize + +RandomCrop +--------------------- +.. autoclass:: RandomCrop + +RandomErasing +--------------------- +.. autoclass:: RandomErasing + +RandomFlip +--------------------- +.. autoclass:: RandomFlip + +RandomGrayscale +--------------------- +.. autoclass:: RandomGrayscale + +RandomResizedCrop +--------------------- +.. autoclass:: RandomResizedCrop + +ColorJitter +--------------------- +.. autoclass:: ColorJitter + + +Composed Augmentation +--------------------- +Composed augmentation is a kind of methods which compose a series of data +augmentation transformations, such as ``AutoAugment`` and ``RandAugment``. + +.. autoclass:: AutoAugment + +.. autoclass:: RandAugment + +In composed augmentation, we need to specify several data transformations or +several groups of data transformations (The ``policies`` argument) as the +random sampling space. These data transformations are chosen from the below +table. In addition, we provide some preset policies in `this folder`_. + +.. _this folder: https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/datasets/pipelines + +.. autosummary:: + :toctree: generated + :nosignatures: + :template: classtemplate.rst + + AutoContrast + Brightness + ColorTransform + Contrast + Cutout + Equalize + Invert + Posterize + Rotate + Sharpness + Shear + Solarize + SolarizeAdd + Translate + +Formatting +========== + +Collect +--------------------- +.. autoclass:: Collect + +ImageToTensor +--------------------- +.. autoclass:: ImageToTensor + +ToNumpy +--------------------- +.. autoclass:: ToNumpy + +ToPIL +--------------------- +.. autoclass:: ToPIL + +ToTensor +--------------------- +.. autoclass:: ToTensor + +Transpose +--------------------- +.. autoclass:: Transpose diff --git a/docs/en/api/utils.rst b/docs/en/api/utils.rst new file mode 100644 index 0000000..206fc82 --- /dev/null +++ b/docs/en/api/utils.rst @@ -0,0 +1,23 @@ +.. role:: hidden + :class: hidden-section + +mmcls.utils +=================================== + +These are some useful help function in the ``utils`` package. + +.. contents:: mmcls.utils + :depth: 1 + :local: + :backlinks: top + +.. currentmodule:: mmcls.utils + +.. autosummary:: + :toctree: generated + :nosignatures: + + collect_env + get_root_logger + load_json_log + setup_multi_processes diff --git a/docs/en/changelog.md b/docs/en/changelog.md new file mode 100644 index 0000000..7928a13 --- /dev/null +++ b/docs/en/changelog.md @@ -0,0 +1,746 @@ +# Changelog + +## v0.25.0(06/12/2022) + +### Highlights + +- Support MLU backend. + +### New Features + +- Support MLU backend. ([#1159](https://github.com/open-mmlab/mmclassification/pull/1159)) +- Support Activation Checkpointing for ConvNeXt. ([#1152](https://github.com/open-mmlab/mmclassification/pull/1152)) + +### Improvements + +- Add `dist_train_arm.sh` for ARM device and update NPU results. ([#1218](https://github.com/open-mmlab/mmclassification/pull/1218)) + +### Bug Fixes + +- Fix a bug caused `MMClsWandbHook` stuck. ([#1242](https://github.com/open-mmlab/mmclassification/pull/1242)) +- Fix the redundant `device_ids` in `tools/test.py`. ([#1215](https://github.com/open-mmlab/mmclassification/pull/1215)) + +### Docs Update + +- Add version banner and version warning in master docs. ([#1216](https://github.com/open-mmlab/mmclassification/pull/1216)) +- Update NPU support doc. ([#1198](https://github.com/open-mmlab/mmclassification/pull/1198)) +- Fixed typo in `pytorch2torchscript.md`. ([#1173](https://github.com/open-mmlab/mmclassification/pull/1173)) +- Fix typo in `miscellaneous.md`. ([#1137](https://github.com/open-mmlab/mmclassification/pull/1137)) +- further detail for the doc for `ClassBalancedDataset`. ([#901](https://github.com/open-mmlab/mmclassification/pull/901)) + +## v0.24.1(31/10/2022) + +### New Features + +- Support mmcls with NPU backend. ([#1072](https://github.com/open-mmlab/mmclassification/pull/1072)) + +### Bug Fixes + +- Fix performance issue in convnext DDP train. ([#1098](https://github.com/open-mmlab/mmclassification/pull/1098)) + +## v0.24.0(30/9/2022) + +### Highlights + +- Support HorNet, EfficientFormerm, SwinTransformer V2 and MViT backbones. +- Support Standford Cars dataset. + +### New Features + +- Support HorNet Backbone. ([#1013](https://github.com/open-mmlab/mmclassification/pull/1013)) +- Support EfficientFormer. ([#954](https://github.com/open-mmlab/mmclassification/pull/954)) +- Support Stanford Cars dataset. ([#893](https://github.com/open-mmlab/mmclassification/pull/893)) +- Support CSRA head. ([#881](https://github.com/open-mmlab/mmclassification/pull/881)) +- Support Swin Transform V2. ([#799](https://github.com/open-mmlab/mmclassification/pull/799)) +- Support MViT and add checkpoints. ([#924](https://github.com/open-mmlab/mmclassification/pull/924)) + +### Improvements + +- [Improve] replace loop of progressbar in api/test. ([#878](https://github.com/open-mmlab/mmclassification/pull/878)) +- [Enhance] RepVGG for YOLOX-PAI. ([#1025](https://github.com/open-mmlab/mmclassification/pull/1025)) +- [Enhancement] Update VAN. ([#1017](https://github.com/open-mmlab/mmclassification/pull/1017)) +- [Refactor] Re-write `get_sinusoid_encoding` from third-party implementation. ([#965](https://github.com/open-mmlab/mmclassification/pull/965)) +- [Improve] Upgrade onnxsim to v0.4.0. ([#915](https://github.com/open-mmlab/mmclassification/pull/915)) +- [Improve] Fixed typo in `RepVGG`. ([#985](https://github.com/open-mmlab/mmclassification/pull/985)) +- [Improve] Using `train_step` instead of `forward` in PreciseBNHook ([#964](https://github.com/open-mmlab/mmclassification/pull/964)) +- [Improve] Use `forward_dummy` to calculate FLOPS. ([#953](https://github.com/open-mmlab/mmclassification/pull/953)) + +### Bug Fixes + +- Fix warning with `torch.meshgrid`. ([#860](https://github.com/open-mmlab/mmclassification/pull/860)) +- Add matplotlib minimum version requriments. ([#909](https://github.com/open-mmlab/mmclassification/pull/909)) +- val loader should not drop last by default. ([#857](https://github.com/open-mmlab/mmclassification/pull/857)) +- Fix config.device bug in toturial. ([#1059](https://github.com/open-mmlab/mmclassification/pull/1059)) +- Fix attenstion clamp max params ([#1034](https://github.com/open-mmlab/mmclassification/pull/1034)) +- Fix device mismatch in Swin-v2. ([#976](https://github.com/open-mmlab/mmclassification/pull/976)) +- Fix the output position of Swin-Transformer. ([#947](https://github.com/open-mmlab/mmclassification/pull/947)) + +### Docs Update + +- Fix typo in config.md. ([#827](https://github.com/open-mmlab/mmclassification/pull/827)) +- Add version for torchvision to avoide error. ([#903](https://github.com/open-mmlab/mmclassification/pull/903)) +- Fixed typo for `--out-dir` option of analyze_results.py. ([#898](https://github.com/open-mmlab/mmclassification/pull/898)) +- Refine the docstring of RegNet ([#935](https://github.com/open-mmlab/mmclassification/pull/935)) + +## v0.23.2(28/7/2022) + +### New Features + +- Support MPS device. ([#894](https://github.com/open-mmlab/mmclassification/pull/894)) + +### Bug Fixes + +- Fix a bug in Albu which caused crashing. ([#918](https://github.com/open-mmlab/mmclassification/pull/918)) + +## v0.23.1(2/6/2022) + +### New Features + +- Dedicated MMClsWandbHook for MMClassification (Weights and Biases Integration) ([#764](https://github.com/open-mmlab/mmclassification/pull/764)) + +### Improvements + +- Use mdformat instead of markdownlint to format markdown. ([#844](https://github.com/open-mmlab/mmclassification/pull/844)) + +### Bug Fixes + +- Fix wrong `--local_rank`. + +### Docs Update + +- Update install tutorials. ([#854](https://github.com/open-mmlab/mmclassification/pull/854)) +- Fix wrong link in README. ([#835](https://github.com/open-mmlab/mmclassification/pull/835)) + +## v0.23.0(1/5/2022) + +### New Features + +- Support DenseNet. ([#750](https://github.com/open-mmlab/mmclassification/pull/750)) +- Support VAN. ([#739](https://github.com/open-mmlab/mmclassification/pull/739)) + +### Improvements + +- Support training on IPU and add fine-tuning configs of ViT. ([#723](https://github.com/open-mmlab/mmclassification/pull/723)) + +### Docs Update + +- New style API reference, and easier to use! Welcome [view it](https://mmclassification.readthedocs.io/en/master/api/models.html). ([#774](https://github.com/open-mmlab/mmclassification/pull/774)) + +## v0.22.1(15/4/2022) + +### New Features + +- [Feature] Support resize relative position embedding in `SwinTransformer`. ([#749](https://github.com/open-mmlab/mmclassification/pull/749)) +- [Feature] Add PoolFormer backbone and checkpoints. ([#746](https://github.com/open-mmlab/mmclassification/pull/746)) + +### Improvements + +- [Enhance] Improve CPE performance by reduce memory copy. ([#762](https://github.com/open-mmlab/mmclassification/pull/762)) +- [Enhance] Add extra dataloader settings in configs. ([#752](https://github.com/open-mmlab/mmclassification/pull/752)) + +## v0.22.0(30/3/2022) + +### Highlights + +- Support a series of CSP Network, such as CSP-ResNet, CSP-ResNeXt and CSP-DarkNet. +- A new `CustomDataset` class to help you build dataset of yourself! +- Support ConvMixer, RepMLP and new dataset - CUB dataset. + +### New Features + +- [Feature] Add CSPNet and backbone and checkpoints ([#735](https://github.com/open-mmlab/mmclassification/pull/735)) +- [Feature] Add `CustomDataset`. ([#738](https://github.com/open-mmlab/mmclassification/pull/738)) +- [Feature] Add diff seeds to diff ranks. ([#744](https://github.com/open-mmlab/mmclassification/pull/744)) +- [Feature] Support ConvMixer. ([#716](https://github.com/open-mmlab/mmclassification/pull/716)) +- [Feature] Our `dist_train` & `dist_test` tools support distributed training on multiple machines. ([#734](https://github.com/open-mmlab/mmclassification/pull/734)) +- [Feature] Add RepMLP backbone and checkpoints. ([#709](https://github.com/open-mmlab/mmclassification/pull/709)) +- [Feature] Support CUB dataset. ([#703](https://github.com/open-mmlab/mmclassification/pull/703)) +- [Feature] Support ResizeMix. ([#676](https://github.com/open-mmlab/mmclassification/pull/676)) + +### Improvements + +- [Enhance] Use `--a-b` instead of `--a_b` in arguments. ([#754](https://github.com/open-mmlab/mmclassification/pull/754)) +- [Enhance] Add `get_cat_ids` and `get_gt_labels` to KFoldDataset. ([#721](https://github.com/open-mmlab/mmclassification/pull/721)) +- [Enhance] Set torch seed in `worker_init_fn`. ([#733](https://github.com/open-mmlab/mmclassification/pull/733)) + +### Bug Fixes + +- [Fix] Fix the discontiguous output feature map of ConvNeXt. ([#743](https://github.com/open-mmlab/mmclassification/pull/743)) + +### Docs Update + +- [Docs] Add brief installation steps in README for copy&paste. ([#755](https://github.com/open-mmlab/mmclassification/pull/755)) +- [Docs] fix logo url link from mmocr to mmcls. ([#732](https://github.com/open-mmlab/mmclassification/pull/732)) + +## v0.21.0(04/03/2022) + +### Highlights + +- Support ResNetV1c and Wide-ResNet, and provide pre-trained models. +- Support dynamic input shape for ViT-based algorithms. Now our ViT, DeiT, Swin-Transformer and T2T-ViT support forwarding with any input shape. +- Reproduce training results of DeiT. And our DeiT-T and DeiT-S have higher accuracy comparing with the official weights. + +### New Features + +- Add ResNetV1c. ([#692](https://github.com/open-mmlab/mmclassification/pull/692)) +- Support Wide-ResNet. ([#715](https://github.com/open-mmlab/mmclassification/pull/715)) +- Support gem pooling ([#677](https://github.com/open-mmlab/mmclassification/pull/677)) + +### Improvements + +- Reproduce training results of DeiT. ([#711](https://github.com/open-mmlab/mmclassification/pull/711)) +- Add ConvNeXt pretrain models on ImageNet-1k. ([#707](https://github.com/open-mmlab/mmclassification/pull/707)) +- Support dynamic input shape for ViT-based algorithms. ([#706](https://github.com/open-mmlab/mmclassification/pull/706)) +- Add `evaluate` function for ConcatDataset. ([#650](https://github.com/open-mmlab/mmclassification/pull/650)) +- Enhance vis-pipeline tool. ([#604](https://github.com/open-mmlab/mmclassification/pull/604)) +- Return code 1 if scripts runs failed. ([#694](https://github.com/open-mmlab/mmclassification/pull/694)) +- Use PyTorch official `one_hot` to implement `convert_to_one_hot`. ([#696](https://github.com/open-mmlab/mmclassification/pull/696)) +- Add a new pre-commit-hook to automatically add a copyright. ([#710](https://github.com/open-mmlab/mmclassification/pull/710)) +- Add deprecation message for deploy tools. ([#697](https://github.com/open-mmlab/mmclassification/pull/697)) +- Upgrade isort pre-commit hooks. ([#687](https://github.com/open-mmlab/mmclassification/pull/687)) +- Use `--gpu-id` instead of `--gpu-ids` in non-distributed multi-gpu training/testing. ([#688](https://github.com/open-mmlab/mmclassification/pull/688)) +- Remove deprecation. ([#633](https://github.com/open-mmlab/mmclassification/pull/633)) + +### Bug Fixes + +- Fix Conformer forward with irregular input size. ([#686](https://github.com/open-mmlab/mmclassification/pull/686)) +- Add `dist.barrier` to fix a bug in directory checking. ([#666](https://github.com/open-mmlab/mmclassification/pull/666)) + +## v0.20.1(07/02/2022) + +### Bug Fixes + +- Fix the MMCV dependency version. + +## v0.20.0(30/01/2022) + +### Highlights + +- Support K-fold cross-validation. The tutorial will be released later. +- Support HRNet, ConvNeXt, Twins and EfficientNet. +- Support model conversion from PyTorch to Core-ML by a tool. + +### New Features + +- Support K-fold cross-validation. ([#563](https://github.com/open-mmlab/mmclassification/pull/563)) +- Support HRNet and add pre-trained models. ([#660](https://github.com/open-mmlab/mmclassification/pull/660)) +- Support ConvNeXt and add pre-trained models. ([#670](https://github.com/open-mmlab/mmclassification/pull/670)) +- Support Twins and add pre-trained models. ([#642](https://github.com/open-mmlab/mmclassification/pull/642)) +- Support EfficientNet and add pre-trained models.([#649](https://github.com/open-mmlab/mmclassification/pull/649)) +- Support `features_only` option in `TIMMBackbone`. ([#668](https://github.com/open-mmlab/mmclassification/pull/668)) +- Add conversion script from pytorch to Core-ML model. ([#597](https://github.com/open-mmlab/mmclassification/pull/597)) + +### Improvements + +- New-style CPU training and inference. ([#674](https://github.com/open-mmlab/mmclassification/pull/674)) +- Add setup multi-processing both in train and test. ([#671](https://github.com/open-mmlab/mmclassification/pull/671)) +- Rewrite channel split operation in ShufflenetV2. ([#632](https://github.com/open-mmlab/mmclassification/pull/632)) +- Deprecate the support for "python setup.py test". ([#646](https://github.com/open-mmlab/mmclassification/pull/646)) +- Support single-label, softmax, custom eps by asymmetric loss. ([#609](https://github.com/open-mmlab/mmclassification/pull/609)) +- Save class names in best checkpoint created by evaluation hook. ([#641](https://github.com/open-mmlab/mmclassification/pull/641)) + +### Bug Fixes + +- Fix potential unexcepted behaviors if `metric_options` is not specified in multi-label evaluation. ([#647](https://github.com/open-mmlab/mmclassification/pull/647)) +- Fix API changes in `pytorch-grad-cam>=1.3.7`. ([#656](https://github.com/open-mmlab/mmclassification/pull/656)) +- Fix bug which breaks `cal_train_time` in `analyze_logs.py`. ([#662](https://github.com/open-mmlab/mmclassification/pull/662)) + +### Docs Update + +- Update README in configs according to OpenMMLab standard. ([#672](https://github.com/open-mmlab/mmclassification/pull/672)) +- Update installation guide and README. ([#624](https://github.com/open-mmlab/mmclassification/pull/624)) + +## v0.19.0(31/12/2021) + +### Highlights + +- The feature extraction function has been enhanced. See [#593](https://github.com/open-mmlab/mmclassification/pull/593) for more details. +- Provide the high-acc ResNet-50 training settings from [*ResNet strikes back*](https://arxiv.org/abs/2110.00476). +- Reproduce the training accuracy of T2T-ViT & RegNetX, and provide self-training checkpoints. +- Support DeiT & Conformer backbone and checkpoints. +- Provide a CAM visualization tool based on [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam), and detailed [user guide](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#class-activation-map-visualization)! + +### New Features + +- Support Precise BN. ([#401](https://github.com/open-mmlab/mmclassification/pull/401)) +- Add CAM visualization tool. ([#577](https://github.com/open-mmlab/mmclassification/pull/577)) +- Repeated Aug and Sampler Registry. ([#588](https://github.com/open-mmlab/mmclassification/pull/588)) +- Add DeiT backbone and checkpoints. ([#576](https://github.com/open-mmlab/mmclassification/pull/576)) +- Support LAMB optimizer. ([#591](https://github.com/open-mmlab/mmclassification/pull/591)) +- Implement the conformer backbone. ([#494](https://github.com/open-mmlab/mmclassification/pull/494)) +- Add the frozen function for Swin Transformer model. ([#574](https://github.com/open-mmlab/mmclassification/pull/574)) +- Support using checkpoint in Swin Transformer to save memory. ([#557](https://github.com/open-mmlab/mmclassification/pull/557)) + +### Improvements + +- [Reproduction] Reproduce RegNetX training accuracy. ([#587](https://github.com/open-mmlab/mmclassification/pull/587)) +- [Reproduction] Reproduce training results of T2T-ViT. ([#610](https://github.com/open-mmlab/mmclassification/pull/610)) +- [Enhance] Provide high-acc training settings of ResNet. ([#572](https://github.com/open-mmlab/mmclassification/pull/572)) +- [Enhance] Set a random seed when the user does not set a seed. ([#554](https://github.com/open-mmlab/mmclassification/pull/554)) +- [Enhance] Added `NumClassCheckHook` and unit tests. ([#559](https://github.com/open-mmlab/mmclassification/pull/559)) +- [Enhance] Enhance feature extraction function. ([#593](https://github.com/open-mmlab/mmclassification/pull/593)) +- [Enhance] Improve efficiency of precision, recall, f1_score and support. ([#595](https://github.com/open-mmlab/mmclassification/pull/595)) +- [Enhance] Improve accuracy calculation performance. ([#592](https://github.com/open-mmlab/mmclassification/pull/592)) +- [Refactor] Refactor `analysis_log.py`. ([#529](https://github.com/open-mmlab/mmclassification/pull/529)) +- [Refactor] Use new API of matplotlib to handle blocking input in visualization. ([#568](https://github.com/open-mmlab/mmclassification/pull/568)) +- [CI] Cancel previous runs that are not completed. ([#583](https://github.com/open-mmlab/mmclassification/pull/583)) +- [CI] Skip build CI if only configs or docs modification. ([#575](https://github.com/open-mmlab/mmclassification/pull/575)) + +### Bug Fixes + +- Fix test sampler bug. ([#611](https://github.com/open-mmlab/mmclassification/pull/611)) +- Try to create a symbolic link, otherwise copy. ([#580](https://github.com/open-mmlab/mmclassification/pull/580)) +- Fix a bug for multiple output in swin transformer. ([#571](https://github.com/open-mmlab/mmclassification/pull/571)) + +### Docs Update + +- Update mmcv, torch, cuda version in Dockerfile and docs. ([#594](https://github.com/open-mmlab/mmclassification/pull/594)) +- Add analysis&misc docs. ([#525](https://github.com/open-mmlab/mmclassification/pull/525)) +- Fix docs build dependency. ([#584](https://github.com/open-mmlab/mmclassification/pull/584)) + +## v0.18.0(30/11/2021) + +### Highlights + +- Support MLP-Mixer backbone and provide pre-trained checkpoints. +- Add a tool to visualize the learning rate curve of the training phase. Welcome to use with the [tutorial](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#learning-rate-schedule-visualization)! + +### New Features + +- Add MLP Mixer Backbone. ([#528](https://github.com/open-mmlab/mmclassification/pull/528), [#539](https://github.com/open-mmlab/mmclassification/pull/539)) +- Support positive weights in BCE. ([#516](https://github.com/open-mmlab/mmclassification/pull/516)) +- Add a tool to visualize learning rate in each iterations. ([#498](https://github.com/open-mmlab/mmclassification/pull/498)) + +### Improvements + +- Use CircleCI to do unit tests. ([#567](https://github.com/open-mmlab/mmclassification/pull/567)) +- Focal loss for single label tasks. ([#548](https://github.com/open-mmlab/mmclassification/pull/548)) +- Remove useless `import_modules_from_string`. ([#544](https://github.com/open-mmlab/mmclassification/pull/544)) +- Rename config files according to the config name standard. ([#508](https://github.com/open-mmlab/mmclassification/pull/508)) +- Use `reset_classifier` to remove head of timm backbones. ([#534](https://github.com/open-mmlab/mmclassification/pull/534)) +- Support passing arguments to loss from head. ([#523](https://github.com/open-mmlab/mmclassification/pull/523)) +- Refactor `Resize` transform and add `Pad` transform. ([#506](https://github.com/open-mmlab/mmclassification/pull/506)) +- Update mmcv dependency version. ([#509](https://github.com/open-mmlab/mmclassification/pull/509)) + +### Bug Fixes + +- Fix bug when using `ClassBalancedDataset`. ([#555](https://github.com/open-mmlab/mmclassification/pull/555)) +- Fix a bug when using iter-based runner with 'val' workflow. ([#542](https://github.com/open-mmlab/mmclassification/pull/542)) +- Fix interpolation method checking in `Resize`. ([#547](https://github.com/open-mmlab/mmclassification/pull/547)) +- Fix a bug when load checkpoints in mulit-GPUs environment. ([#527](https://github.com/open-mmlab/mmclassification/pull/527)) +- Fix an error on indexing scalar metrics in `analyze_result.py`. ([#518](https://github.com/open-mmlab/mmclassification/pull/518)) +- Fix wrong condition judgment in `analyze_logs.py` and prevent empty curve. ([#510](https://github.com/open-mmlab/mmclassification/pull/510)) + +### Docs Update + +- Fix vit config and model broken links. ([#564](https://github.com/open-mmlab/mmclassification/pull/564)) +- Add abstract and image for every paper. ([#546](https://github.com/open-mmlab/mmclassification/pull/546)) +- Add mmflow and mim in banner and readme. ([#543](https://github.com/open-mmlab/mmclassification/pull/543)) +- Add schedule and runtime tutorial docs. ([#499](https://github.com/open-mmlab/mmclassification/pull/499)) +- Add the top-5 acc in ResNet-CIFAR README. ([#531](https://github.com/open-mmlab/mmclassification/pull/531)) +- Fix TOC of `visualization.md` and add example images. ([#513](https://github.com/open-mmlab/mmclassification/pull/513)) +- Use docs link of other projects and add MMCV docs. ([#511](https://github.com/open-mmlab/mmclassification/pull/511)) + +## v0.17.0(29/10/2021) + +### Highlights + +- Support Tokens-to-Token ViT backbone and Res2Net backbone. Welcome to use! +- Support ImageNet21k dataset. +- Add a pipeline visualization tool. Try it with the [tutorials](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#pipeline-visualization)! + +### New Features + +- Add Tokens-to-Token ViT backbone and converted checkpoints. ([#467](https://github.com/open-mmlab/mmclassification/pull/467)) +- Add Res2Net backbone and converted weights. ([#465](https://github.com/open-mmlab/mmclassification/pull/465)) +- Support ImageNet21k dataset. ([#461](https://github.com/open-mmlab/mmclassification/pull/461)) +- Support seesaw loss. ([#500](https://github.com/open-mmlab/mmclassification/pull/500)) +- Add a pipeline visualization tool. ([#406](https://github.com/open-mmlab/mmclassification/pull/406)) +- Add a tool to find broken files. ([#482](https://github.com/open-mmlab/mmclassification/pull/482)) +- Add a tool to test TorchServe. ([#468](https://github.com/open-mmlab/mmclassification/pull/468)) + +### Improvements + +- Refator Vision Transformer. ([#395](https://github.com/open-mmlab/mmclassification/pull/395)) +- Use context manager to reuse matplotlib figures. ([#432](https://github.com/open-mmlab/mmclassification/pull/432)) + +### Bug Fixes + +- Remove `DistSamplerSeedHook` if use `IterBasedRunner`. ([#501](https://github.com/open-mmlab/mmclassification/pull/501)) +- Set the priority of `EvalHook` to "LOW" to avoid a bug when using `IterBasedRunner`. ([#488](https://github.com/open-mmlab/mmclassification/pull/488)) +- Fix a wrong parameter of `get_root_logger` in `apis/train.py`. ([#486](https://github.com/open-mmlab/mmclassification/pull/486)) +- Fix version check in dataset builder. ([#474](https://github.com/open-mmlab/mmclassification/pull/474)) + +### Docs Update + +- Add English Colab tutorials and update Chinese Colab tutorials. ([#483](https://github.com/open-mmlab/mmclassification/pull/483), [#497](https://github.com/open-mmlab/mmclassification/pull/497)) +- Add tutuorial for config files. ([#487](https://github.com/open-mmlab/mmclassification/pull/487)) +- Add model-pages in Model Zoo. ([#480](https://github.com/open-mmlab/mmclassification/pull/480)) +- Add code-spell pre-commit hook and fix a large mount of typos. ([#470](https://github.com/open-mmlab/mmclassification/pull/470)) + +## v0.16.0(30/9/2021) + +### Highlights + +- We have improved compatibility with downstream repositories like MMDetection and MMSegmentation. We will add some examples about how to use our backbones in MMDetection. +- Add RepVGG backbone and checkpoints. Welcome to use it! +- Add timm backbones wrapper, now you can simply use backbones of pytorch-image-models in MMClassification! + +### New Features + +- Add RepVGG backbone and checkpoints. ([#414](https://github.com/open-mmlab/mmclassification/pull/414)) +- Add timm backbones wrapper. ([#427](https://github.com/open-mmlab/mmclassification/pull/427)) + +### Improvements + +- Fix TnT compatibility and verbose warning. ([#436](https://github.com/open-mmlab/mmclassification/pull/436)) +- Support setting `--out-items` in `tools/test.py`. ([#437](https://github.com/open-mmlab/mmclassification/pull/437)) +- Add datetime info and saving model using torch\<1.6 format. ([#439](https://github.com/open-mmlab/mmclassification/pull/439)) +- Improve downstream repositories compatibility. ([#421](https://github.com/open-mmlab/mmclassification/pull/421)) +- Rename the option `--options` to `--cfg-options` in some tools. ([#425](https://github.com/open-mmlab/mmclassification/pull/425)) +- Add PyTorch 1.9 and Python 3.9 build workflow, and remove some CI. ([#422](https://github.com/open-mmlab/mmclassification/pull/422)) + +### Bug Fixes + +- Fix format error in `test.py` when metric returns `np.ndarray`. ([#441](https://github.com/open-mmlab/mmclassification/pull/441)) +- Fix `publish_model` bug if no parent of `out_file`. ([#463](https://github.com/open-mmlab/mmclassification/pull/463)) +- Fix num_classes bug in pytorch2onnx.py. ([#458](https://github.com/open-mmlab/mmclassification/pull/458)) +- Fix missing runtime requirement `packaging`. ([#459](https://github.com/open-mmlab/mmclassification/pull/459)) +- Fix saving simplified model bug in ONNX export tool. ([#438](https://github.com/open-mmlab/mmclassification/pull/438)) + +### Docs Update + +- Update `getting_started.md` and `install.md`. And rewrite `finetune.md`. ([#466](https://github.com/open-mmlab/mmclassification/pull/466)) +- Use PyTorch style docs theme. ([#457](https://github.com/open-mmlab/mmclassification/pull/457)) +- Update metafile and Readme. ([#435](https://github.com/open-mmlab/mmclassification/pull/435)) +- Add `CITATION.cff`. ([#428](https://github.com/open-mmlab/mmclassification/pull/428)) + +## v0.15.0(31/8/2021) + +### Highlights + +- Support `hparams` argument in `AutoAugment` and `RandAugment` to provide hyperparameters for sub-policies. +- Support custom squeeze channels in `SELayer`. +- Support classwise weight in losses. + +### New Features + +- Add `hparams` argument in `AutoAugment` and `RandAugment` and some other improvement. ([#398](https://github.com/open-mmlab/mmclassification/pull/398)) +- Support classwise weight in losses. ([#388](https://github.com/open-mmlab/mmclassification/pull/388)) +- Enhance `SELayer` to support custom squeeze channels. ([#417](https://github.com/open-mmlab/mmclassification/pull/417)) + +### Code Refactor + +- Better result visualization. ([#419](https://github.com/open-mmlab/mmclassification/pull/419)) +- Use `post_process` function to handle pred result processing. ([#390](https://github.com/open-mmlab/mmclassification/pull/390)) +- Update `digit_version` function. ([#402](https://github.com/open-mmlab/mmclassification/pull/402)) +- Avoid albumentations to install both opencv and opencv-headless. ([#397](https://github.com/open-mmlab/mmclassification/pull/397)) +- Avoid unnecessary listdir when building ImageNet. ([#396](https://github.com/open-mmlab/mmclassification/pull/396)) +- Use dynamic mmcv download link in TorchServe dockerfile. ([#387](https://github.com/open-mmlab/mmclassification/pull/387)) + +### Docs Improvement + +- Add readme of some algorithms and update meta yml. ([#418](https://github.com/open-mmlab/mmclassification/pull/418)) +- Add Copyright information. ([#413](https://github.com/open-mmlab/mmclassification/pull/413)) +- Fix typo 'metirc'. ([#411](https://github.com/open-mmlab/mmclassification/pull/411)) +- Update QQ group QR code. ([#393](https://github.com/open-mmlab/mmclassification/pull/393)) +- Add PR template and modify issue template. ([#380](https://github.com/open-mmlab/mmclassification/pull/380)) + +## v0.14.0(4/8/2021) + +### Highlights + +- Add transformer-in-transformer backbone and pretrain checkpoints, refers to [the paper](https://arxiv.org/abs/2103.00112). +- Add Chinese colab tutorial. +- Provide dockerfile to build mmcls dev docker image. + +### New Features + +- Add transformer in transformer backbone and pretrain checkpoints. ([#339](https://github.com/open-mmlab/mmclassification/pull/339)) +- Support mim, welcome to use mim to manage your mmcls project. ([#376](https://github.com/open-mmlab/mmclassification/pull/376)) +- Add Dockerfile. ([#365](https://github.com/open-mmlab/mmclassification/pull/365)) +- Add ResNeSt configs. ([#332](https://github.com/open-mmlab/mmclassification/pull/332)) + +### Improvements + +- Use the `presistent_works` option if available, to accelerate training. ([#349](https://github.com/open-mmlab/mmclassification/pull/349)) +- Add Chinese ipynb tutorial. ([#306](https://github.com/open-mmlab/mmclassification/pull/306)) +- Refactor unit tests. ([#321](https://github.com/open-mmlab/mmclassification/pull/321)) +- Support to test mmdet inference with mmcls backbone. ([#343](https://github.com/open-mmlab/mmclassification/pull/343)) +- Use zero as default value of `thrs` in metrics. ([#341](https://github.com/open-mmlab/mmclassification/pull/341)) + +### Bug Fixes + +- Fix ImageNet dataset annotation file parse bug. ([#370](https://github.com/open-mmlab/mmclassification/pull/370)) +- Fix docstring typo and init bug in ShuffleNetV1. ([#374](https://github.com/open-mmlab/mmclassification/pull/374)) +- Use local ATTENTION registry to avoid conflict with other repositories. ([#376](https://github.com/open-mmlab/mmclassification/pull/375)) +- Fix swin transformer config bug. ([#355](https://github.com/open-mmlab/mmclassification/pull/355)) +- Fix `patch_cfg` argument bug in SwinTransformer. ([#368](https://github.com/open-mmlab/mmclassification/pull/368)) +- Fix duplicate `init_weights` call in ViT init function. ([#373](https://github.com/open-mmlab/mmclassification/pull/373)) +- Fix broken `_base_` link in a resnet config. ([#361](https://github.com/open-mmlab/mmclassification/pull/361)) +- Fix vgg-19 model link missing. ([#363](https://github.com/open-mmlab/mmclassification/pull/363)) + +## v0.13.0(3/7/2021) + +- Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet. + +### New Features + +- Support Swin-Transformer backbone and add training configs for Swin-Transformer on ImageNet. (#271) +- Add pretained model of RegNetX. (#269) +- Support adding custom hooks in config file. (#305) +- Improve and add Chinese translation of `CONTRIBUTING.md` and all tools tutorials. (#320) +- Dump config before training. (#282) +- Add torchscript and torchserve deployment tools. (#279, #284) + +### Improvements + +- Improve test tools and add some new tools. (#322) +- Correct MobilenetV3 backbone structure and add pretained models. (#291) +- Refactor `PatchEmbed` and `HybridEmbed` as independent components. (#330) +- Refactor mixup and cutmix as `Augments` to support more functions. (#278) +- Refactor weights initialization method. (#270, #318, #319) +- Refactor `LabelSmoothLoss` to support multiple calculation formulas. (#285) + +### Bug Fixes + +- Fix bug for CPU training. (#286) +- Fix missing test data when `num_imgs` can not be evenly divided by `num_gpus`. (#299) +- Fix build compatible with pytorch v1.3-1.5. (#301) +- Fix `magnitude_std` bug in `RandAugment`. (#309) +- Fix bug when `samples_per_gpu` is 1. (#311) + +## v0.12.0(3/6/2021) + +- Finish adding Chinese tutorials and build Chinese documentation on readthedocs. +- Update ResNeXt checkpoints and ResNet checkpoints on CIFAR. + +### New Features + +- Improve and add Chinese translation of `data_pipeline.md` and `new_modules.md`. (#265) +- Build Chinese translation on readthedocs. (#267) +- Add an argument efficientnet_style to `RandomResizedCrop` and `CenterCrop`. (#268) + +### Improvements + +- Only allow directory operation when rank==0 when testing. (#258) +- Fix typo in `base_head`. (#274) +- Update ResNeXt checkpoints. (#283) + +### Bug Fixes + +- Add attribute `data.test` in MNIST configs. (#264) +- Download CIFAR/MNIST dataset only on rank 0. (#273) +- Fix MMCV version compatibility. (#276) +- Fix CIFAR color channels bug and update checkpoints in model zoo. (#280) + +## v0.11.1(21/5/2021) + +- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. + +### New Features + +- Add `dim` argument for `GlobalAveragePooling`. (#236) +- Add random noise to `RandAugment` magnitude. (#240) +- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. (#243) + +### Improvements + +- Refactor arguments passing for Heads. (#239) +- Allow more flexible `magnitude_range` in `RandAugment`. (#249) +- Inherits MMCV registry so that in the future OpenMMLab repos like MMDet and MMSeg could directly use the backbones supported in MMCls. (#252) + +### Bug Fixes + +- Fix typo in `analyze_results.py`. (#237) +- Fix typo in unittests. (#238) +- Check if specified tmpdir exists when testing to avoid deleting existing data. (#242 & #258) +- Add missing config files in `MANIFEST.in`. (#250 & #255) +- Use temporary directory under shared directory to collect results to avoid unavailability of temporary directory for multi-node testing. (#251) + +## v0.11.0(1/5/2021) + +- Support cutmix trick. +- Support random augmentation. +- Add `tools/deployment/test.py` as a ONNX runtime test tool. +- Support ViT backbone and add training configs for ViT on ImageNet. +- Add Chinese `README.md` and some Chinese tutorials. + +### New Features + +- Support cutmix trick. (#198) +- Add `simplify` option in `pytorch2onnx.py`. (#200) +- Support random augmentation. (#201) +- Add config and checkpoint for training ResNet on CIFAR-100. (#208) +- Add `tools/deployment/test.py` as a ONNX runtime test tool. (#212) +- Support ViT backbone and add training configs for ViT on ImageNet. (#214) +- Add finetuning configs for ViT on ImageNet. (#217) +- Add `device` option to support training on CPU. (#219) +- Add Chinese `README.md` and some Chinese tutorials. (#221) +- Add `metafile.yml` in configs to support interaction with paper with code(PWC) and MMCLI. (#225) +- Upload configs and converted checkpoints for ViT fintuning on ImageNet. (#230) + +### Improvements + +- Fix `LabelSmoothLoss` so that label smoothing and mixup could be enabled at the same time. (#203) +- Add `cal_acc` option in `ClsHead`. (#206) +- Check `CLASSES` in checkpoint to avoid unexpected key error. (#207) +- Check mmcv version when importing mmcls to ensure compatibility. (#209) +- Update `CONTRIBUTING.md` to align with that in MMCV. (#210) +- Change tags to html comments in configs README.md. (#226) +- Clean codes in ViT backbone. (#227) +- Reformat `pytorch2onnx.md` tutorial. (#229) +- Update `setup.py` to support MMCLI. (#232) + +### Bug Fixes + +- Fix missing `cutmix_prob` in ViT configs. (#220) +- Fix backend for resize in ResNeXt configs. (#222) + +## v0.10.0(1/4/2021) + +- Support AutoAugmentation +- Add tutorials for installation and usage. + +### New Features + +- Add `Rotate` pipeline for data augmentation. (#167) +- Add `Invert` pipeline for data augmentation. (#168) +- Add `Color` pipeline for data augmentation. (#171) +- Add `Solarize` and `Posterize` pipeline for data augmentation. (#172) +- Support fp16 training. (#178) +- Add tutorials for installation and basic usage of MMClassification.(#176) +- Support `AutoAugmentation`, `AutoContrast`, `Equalize`, `Contrast`, `Brightness` and `Sharpness` pipelines for data augmentation. (#179) + +### Improvements + +- Support dynamic shape export to onnx. (#175) +- Release training configs and update model zoo for fp16 (#184) +- Use MMCV's EvalHook in MMClassification (#182) + +### Bug Fixes + +- Fix wrong naming in vgg config (#181) + +## v0.9.0(1/3/2021) + +- Implement mixup trick. +- Add a new tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. + +### New Features + +- Implement mixup and provide configs of training ResNet50 using mixup. (#160) +- Add `Shear` pipeline for data augmentation. (#163) +- Add `Translate` pipeline for data augmentation. (#165) +- Add `tools/onnx2tensorrt.py` as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (#153) + +### Improvements + +- Add `--eval-options` in `tools/test.py` to support eval options override, matching the behavior of other open-mmlab projects. (#158) +- Support showing and saving painted results in `mmcls.apis.test` and `tools/test.py`, matching the behavior of other open-mmlab projects. (#162) + +### Bug Fixes + +- Fix configs for VGG, replace checkpoints converted from other repos with the ones trained by ourselves and upload the missing logs in the model zoo. (#161) + +## v0.8.0(31/1/2021) + +- Support multi-label task. +- Support more flexible metrics settings. +- Fix bugs. + +### New Features + +- Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (#123) +- Add BCE loss for multi-label task. (#130) +- Add focal loss for multi-label task. (#131) +- Support PASCAL VOC 2007 dataset for multi-label task. (#134) +- Add asymmetric loss for multi-label task. (#132) +- Add analyze_results.py to select images for success/fail demonstration. (#142) +- Support new metric that calculates the total number of occurrences of each label. (#143) +- Support class-wise evaluation results. (#143) +- Add thresholds in eval_metrics. (#146) +- Add heads and a baseline config for multilabel task. (#145) + +### Improvements + +- Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (#135) +- Add tags in README.md. (#137) +- Fix optional issues in docstring. (#138) +- Update stat.py to classify papers. (#139) +- Fix mismatched columns in README.md. (#150) +- Fix test.py to support more evaluation metrics. (#155) + +### Bug Fixes + +- Fix bug in VGG weight_init. (#140) +- Fix bug in 2 ResNet configs in which outdated heads were used. (#147) +- Fix bug of misordered height and width in `RandomCrop` and `RandomResizedCrop`. (#151) +- Fix missing `meta_keys` in `Collect`. (#149 & #152) + +## v0.7.0(31/12/2020) + +- Add more evaluation metrics. +- Fix bugs. + +### New Features + +- Remove installation of MMCV from requirements. (#90) +- Add 3 evaluation metrics: precision, recall and F-1 score. (#93) +- Allow config override during testing and inference with `--options`. (#91 & #96) + +### Improvements + +- Use `build_runner` to make runners more flexible. (#54) +- Support to get category ids in `BaseDataset`. (#72) +- Allow `CLASSES` override during `BaseDateset` initialization. (#85) +- Allow input image as ndarray during inference. (#87) +- Optimize MNIST config. (#98) +- Add config links in model zoo documentation. (#99) +- Use functions from MMCV to collect environment. (#103) +- Refactor config files so that they are now categorized by methods. (#116) +- Add README in config directory. (#117) +- Add model statistics. (#119) +- Refactor documentation in consistency with other MM repositories. (#126) + +### Bug Fixes + +- Add missing `CLASSES` argument to dataset wrappers. (#66) +- Fix slurm evaluation error during training. (#69) +- Resolve error caused by shape in `Accuracy`. (#104) +- Fix bug caused by extremely insufficient data in distributed sampler.(#108) +- Fix bug in `gpu_ids` in distributed training. (#107) +- Fix bug caused by extremely insufficient data in collect results during testing (#114) + +## v0.6.0(11/10/2020) + +- Support new method: ResNeSt and VGG. +- Support new dataset: CIFAR10. +- Provide new tools to do model inference, model conversion from pytorch to onnx. + +### New Features + +- Add model inference. (#16) +- Add pytorch2onnx. (#20) +- Add PIL backend for transform `Resize`. (#21) +- Add ResNeSt. (#25) +- Add VGG and its pretained models. (#27) +- Add CIFAR10 configs and models. (#38) +- Add albumentations transforms. (#45) +- Visualize results on image demo. (#58) + +### Improvements + +- Replace urlretrieve with urlopen in dataset.utils. (#13) +- Resize image according to its short edge. (#22) +- Update ShuffleNet config. (#31) +- Update pre-trained models for shufflenet_v2, shufflenet_v1, se-resnet50, se-resnet101. (#33) + +### Bug Fixes + +- Fix init_weights in `shufflenet_v2.py`. (#29) +- Fix the parameter `size` in test_pipeline. (#30) +- Fix the parameter in cosine lr schedule. (#32) +- Fix the convert tools for mobilenet_v2. (#34) +- Fix crash in CenterCrop transform when image is greyscale (#40) +- Fix outdated configs. (#53) diff --git a/docs/en/community/CONTRIBUTING.md b/docs/en/community/CONTRIBUTING.md new file mode 120000 index 0000000..c97564d --- /dev/null +++ b/docs/en/community/CONTRIBUTING.md @@ -0,0 +1 @@ +../../../CONTRIBUTING.md \ No newline at end of file diff --git a/docs/en/compatibility.md b/docs/en/compatibility.md new file mode 100644 index 0000000..1affb8e --- /dev/null +++ b/docs/en/compatibility.md @@ -0,0 +1,8 @@ +# Compatibility of MMClassification 0.x + +## MMClassification 0.20.1 + +### MMCV compatibility + +In Twins backbone, we use the `PatchEmbed` module of MMCV, and this module is added after MMCV 1.4.2. +Therefore, we need to update the mmcv version to 1.4.2. diff --git a/docs/en/conf.py b/docs/en/conf.py new file mode 100644 index 0000000..aa1d9f4 --- /dev/null +++ b/docs/en/conf.py @@ -0,0 +1,256 @@ +# flake8: noqa +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import subprocess +import sys + +import pytorch_sphinx_theme +from sphinx.builders.html import StandaloneHTMLBuilder + +sys.path.insert(0, os.path.abspath('../../')) + +# -- Project information ----------------------------------------------------- + +project = 'MMClassification' +copyright = '2020, OpenMMLab' +author = 'MMClassification Authors' + +# The full version, including alpha/beta/rc tags +version_file = '../../mmcls/version.py' + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +release = get_version() + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', + 'sphinx.ext.napoleon', + 'sphinx.ext.viewcode', + 'myst_parser', + 'sphinx_copybutton', +] + +autodoc_mock_imports = ['mmcv._ext', 'matplotlib'] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = { + '.rst': 'restructuredtext', + '.md': 'markdown', +} + +language = 'en' + +# The master toctree document. +master_doc = 'index' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'pytorch_sphinx_theme' +html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()] + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# yapf: disable +html_theme_options = { + 'logo_url': 'https://mmclassification.readthedocs.io/en/latest/', + 'menu': [ + { + 'name': 'GitHub', + 'url': 'https://github.com/open-mmlab/mmclassification' + }, + { + 'name': 'Colab Tutorials', + 'children': [ + { + 'name': 'Train and inference with shell commands', + 'url': 'https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_tools.ipynb', + }, + { + 'name': 'Train and inference with Python APIs', + 'url': 'https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/en/tutorials/MMClassification_python.ipynb', + }, + ] + }, + { + 'name': 'Version', + 'children': [ + { + 'name': 'MMClassification 0.x', + 'url': 'https://mmclassification.readthedocs.io/en/latest/', + 'description': 'master branch' + }, + { + 'name': 'MMClassification 1.x', + 'url': 'https://mmclassification.readthedocs.io/en/dev-1.x/', + 'description': '1.x branch' + }, + ], + } + ], + # Specify the language of shared menu + 'menu_lang': 'en', + 'header_note': { + 'content': + 'You are reading the documentation for MMClassification 0.x, which ' + 'will soon be deprecated at the end of 2022. We recommend you upgrade ' + 'to MMClassification 1.0 to enjoy fruitful new features and better ' + 'performance brought by OpenMMLab 2.0. Check the ' + 'installation tutorial, ' + 'migration tutorial ' + 'and changelog ' + 'for more details.', + } +} +# yapf: enable + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] +html_css_files = ['css/readthedocs.css'] +html_js_files = ['js/custom.js'] + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'mmclsdoc' + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + 'preamble': + r''' +\hypersetup{unicode=true} +\usepackage{CJKutf8} +\DeclareUnicodeCharacter{00A0}{\nobreakspace} +\DeclareUnicodeCharacter{2203}{\ensuremath{\exists}} +\DeclareUnicodeCharacter{2200}{\ensuremath{\forall}} +\DeclareUnicodeCharacter{2286}{\ensuremath{\subseteq}} +\DeclareUnicodeCharacter{2713}{x} +\DeclareUnicodeCharacter{27FA}{\ensuremath{\Longleftrightarrow}} +\DeclareUnicodeCharacter{221A}{\ensuremath{\sqrt{}}} +\DeclareUnicodeCharacter{221B}{\ensuremath{\sqrt[3]{}}} +\DeclareUnicodeCharacter{2295}{\ensuremath{\oplus}} +\DeclareUnicodeCharacter{2297}{\ensuremath{\otimes}} +\begin{CJK}{UTF8}{gbsn} +\AtEndDocument{\end{CJK}} +''', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'mmcls.tex', 'MMClassification Documentation', author, + 'manual'), +] + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [(master_doc, 'mmcls', 'MMClassification Documentation', [author], + 1)] + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'mmcls', 'MMClassification Documentation', author, 'mmcls', + 'OpenMMLab image classification toolbox and benchmark.', 'Miscellaneous'), +] + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ['search.html'] + +# set priority when building html +StandaloneHTMLBuilder.supported_image_types = [ + 'image/svg+xml', 'image/gif', 'image/png', 'image/jpeg' +] + +# -- Extension configuration ------------------------------------------------- +# Ignore >>> when copying code +copybutton_prompt_text = r'>>> |\.\.\. ' +copybutton_prompt_is_regexp = True +# Auto-generated header anchors +myst_heading_anchors = 3 +# Configuration for intersphinx +intersphinx_mapping = { + 'python': ('https://docs.python.org/3', None), + 'numpy': ('https://numpy.org/doc/stable', None), + 'torch': ('https://pytorch.org/docs/stable/', None), + 'mmcv': ('https://mmcv.readthedocs.io/en/master/', None), +} + + +def builder_inited_handler(app): + subprocess.run(['./stat.py']) + + +def setup(app): + app.connect('builder-inited', builder_inited_handler) diff --git a/docs/en/device/npu.md b/docs/en/device/npu.md new file mode 100644 index 0000000..857a3a4 --- /dev/null +++ b/docs/en/device/npu.md @@ -0,0 +1,64 @@ +# NPU (HUAWEI Ascend) + +## Usage + +### General Usage + +Please install MMCV with NPU device support according to {external+mmcv:doc}`the tutorial `. + +Here we use 8 NPUs on your computer to train the model with the following command: + +```shell +bash ./tools/dist_train.sh configs/resnet/resnet50_8xb32_in1k.py 8 --device npu +``` + +Also, you can use only one NPU to train the model with the following command: + +```shell +python ./tools/train.py configs/resnet/resnet50_8xb32_in1k.py --device npu +``` + +### High-performance Usage on ARM server + +Since the scheduling ability of ARM CPUs when processing resource preemption is not as good as that of X86 CPUs during multi-card training, we provide a high-performance startup script to accelerate training with the following command: + +```shell +# The script under the 8 cards of a single machine is shown here +bash tools/dist_train_arm.sh configs/resnet/resnet50_8xb32_in1k.py 8 --device npu --cfg-options data.workers_per_gpu=$(($(nproc)/8)) +``` + +For resnet50 8 NPUs training with batch_size(data.samples_per_gpu)=512, the performance data is shown below: + +| CPU | Start Script | IterTime(s) | +| :------------------ | :------------------------ | :--------------: | +| ARM(Kunpeng920 \*4) | ./tools/dist_train.sh | ~0.9(0.85-1.0) | +| ARM(Kunpeng920 \*4) | ./tools/dist_train_arm.sh | ~0.8(0.78s-0.85) | + +## Models Results + +| Model | Top-1 (%) | Top-5 (%) | Config | Download | +| :---------------------------------------------------------: | :-------: | :-------: | :----------------------------------------------------------: | :-------------------------------------------------------------: | +| [ResNet-50](../papers/resnet.md) | 76.38 | 93.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnet50_8xb32_in1k.log) | +| [ResNetXt-32x4d-50](../papers/resnext.md) | 77.55 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) | +| [HRNet-W18](../papers/hrnet.md) | 77.01 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) | +| [ResNetV1D-152](../papers/resnet.md) | 79.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnetv1d152_8xb32_in1k.log.json) | +| [SE-ResNet-50](../papers/seresnet.md) | 77.64 | 93.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) | +| [VGG-11](../papers/vgg.md) | 68.92 | 88.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) | +| [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md) | 69.53 | 88.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/shufflenet-v2-1x_16xb64_in1k.json) | +| [MobileNetV2](../papers/mobilenet_v2.md) | 71.758 | 90.394 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/mobilenet-v2_8xb32_in1k.json) | +| [MobileNetV3-Small](../papers/mobilenet_v3.md) | 67.522 | 87.316 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v3/mobilenet-v3-small_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/mobilenet-v3-small_8xb32_in1k.json) | +| [\*CSPResNeXt50](../papers/cspnet.md) | 77.10 | 93.55 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) | +| [\*EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) | 75.55 | 92.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) | +| [\*\*DenseNet121](../papers/densenet.md) | 72.62 | 91.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) | + +**Notes:** + +- If not specially marked, the results are almost same between results on the NPU and results on the GPU with FP32. +- (\*) The training results of these models are lower than the results on the readme in the corresponding model, mainly + because the results on the readme are directly the weight of the timm of the eval, and the results on this side are + retrained according to the config with mmcls. The results of the config training on the GPU are consistent with the + results of the NPU. +- (\*\*) The accuracy of this model is slightly lower because config is a 4-card config, we use 8 cards to run, and users + can adjust hyperparameters to get the best accuracy results. + +**All above models are provided by Huawei Ascend group.** diff --git a/docs/en/docutils.conf b/docs/en/docutils.conf new file mode 100644 index 0000000..0c00c84 --- /dev/null +++ b/docs/en/docutils.conf @@ -0,0 +1,2 @@ +[html writers] +table_style: colwidths-auto diff --git a/docs/en/faq.md b/docs/en/faq.md new file mode 100644 index 0000000..6bd9822 --- /dev/null +++ b/docs/en/faq.md @@ -0,0 +1,84 @@ +# Frequently Asked Questions + +We list some common troubles faced by many users and their corresponding +solutions here. Feel free to enrich the list if you find any frequent issues +and have ways to help others to solve them. If the contents here do not cover +your issue, please create an issue using the +[provided templates](https://github.com/open-mmlab/mmclassification/issues/new/choose) +and make sure you fill in all required information in the template. + +## Installation + +- Compatibility issue between MMCV and MMClassification; "AssertionError: + MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx." + + Compatible MMClassification and MMCV versions are shown as below. Please + choose the correct version of MMCV to avoid installation issues. + + | MMClassification version | MMCV version | + | :----------------------: | :--------------------: | + | dev | mmcv>=1.7.0, \<1.9.0 | + | 0.25.0 (master) | mmcv>=1.4.2, \<1.9.0 | + | 0.24.1 | mmcv>=1.4.2, \<1.9.0 | + | 0.23.2 | mmcv>=1.4.2, \<1.7.0 | + | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | + | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | + | 0.20.1 | mmcv>=1.4.2, \<=1.5.0 | + | 0.19.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.18.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.17.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.16.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.14.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.13.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.12.0 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.1 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.0 | mmcv>=1.3.0 | + | 0.10.0 | mmcv>=1.3.0 | + | 0.9.0 | mmcv>=1.1.4 | + | 0.8.0 | mmcv>=1.1.4 | + | 0.7.0 | mmcv>=1.1.4 | + | 0.6.0 | mmcv>=1.1.4 | + + ```{note} + Since the `dev` branch is under frequent development, the MMCV + version dependency may be inaccurate. If you encounter problems when using + the `dev` branch, please try to update MMCV to the latest version. + ``` + +- Using Albumentations + + If you would like to use `albumentations`, we suggest using `pip install -r requirements/albu.txt` or + `pip install -U albumentations --no-binary qudida,albumentations`. + + If you simply use `pip install albumentations>=0.3.2`, it will install `opencv-python-headless` simultaneously + (even though you have already installed `opencv-python`). Please refer to the + [official documentation](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies) + for details. + +## Coding + +- Do I need to reinstall mmcls after some code modifications? + + If you follow [the best practice](install.md) and install mmcls from source, + any local modifications made to the code will take effect without + reinstallation. + +- How to develop with multiple MMClassification versions? + + Generally speaking, we recommend to use different virtual environments to + manage MMClassification in different working directories. However, you + can also use the same environment to develop MMClassification in different + folders, like mmcls-0.21, mmcls-0.23. When you run the train or test shell script, + it will adopt the mmcls package in the current folder. And when you run other Python + script, you can also add `` PYTHONPATH=`pwd` `` at the beginning of your command + to use the package in the current folder. + + Conversely, to use the default MMClassification installed in the environment + rather than the one you are working with, you can remove the following line + in those shell scripts: + + ```shell + PYTHONPATH="$(dirname $0)/..":$PYTHONPATH + ``` diff --git a/docs/en/getting_started.md b/docs/en/getting_started.md new file mode 100644 index 0000000..4e8a9fc --- /dev/null +++ b/docs/en/getting_started.md @@ -0,0 +1,275 @@ +# Getting Started + +This page provides basic tutorials about the usage of MMClassification. + +## Prepare datasets + +It is recommended to symlink the dataset root to `$MMCLASSIFICATION/data`. +If your folder structure is different, you may need to change the corresponding paths in config files. + +``` +mmclassification +├── mmcls +├── tools +├── configs +├── docs +├── data +│ ├── imagenet +│ │ ├── meta +│ │ ├── train +│ │ ├── val +│ ├── cifar +│ │ ├── cifar-10-batches-py +│ ├── mnist +│ │ ├── train-images-idx3-ubyte +│ │ ├── train-labels-idx1-ubyte +│ │ ├── t10k-images-idx3-ubyte +│ │ ├── t10k-labels-idx1-ubyte + +``` + +For ImageNet, it has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). It can be accessed with the following steps. + +1. Register an account and login to the [download page](http://www.image-net.org/download-images). +2. Find download links for ILSVRC2012 and download the following two files + - ILSVRC2012_img_train.tar (~138GB) + - ILSVRC2012_img_val.tar (~6.3GB) +3. Untar the downloaded files +4. Download meta data using this [script](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) + +For MNIST, CIFAR10 and CIFAR100, the datasets will be downloaded and unzipped automatically if they are not found. + +For using custom datasets, please refer to [Tutorial 3: Customize Dataset](tutorials/new_dataset.md). + +## Inference with pretrained models + +We provide scripts to inference a single image, inference a dataset and test a dataset (e.g., ImageNet). + +### Inference a single image + +```shell +python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} + +# Example +python demo/image_demo.py demo/demo.JPEG configs/resnet/resnet50_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth +``` + +### Inference and test a dataset + +- single GPU +- CPU +- single node multiple GPU +- multiple node + +You can use the following commands to infer a dataset. + +```shell +# single-gpu +python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] + +# CPU: disable GPUs and run single-gpu testing script +export CUDA_VISIBLE_DEVICES=-1 +python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] + +# multi-gpu +./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--metrics ${METRICS}] [--out ${RESULT_FILE}] + +# multi-node in slurm environment +python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] --launcher slurm +``` + +Optional arguments: + +- `RESULT_FILE`: Filename of the output results. If not specified, the results will not be saved to a file. Support formats include json, yaml and pickle. +- `METRICS`：Items to be evaluated on the results, like accuracy, precision, recall, etc. + +Examples: + +Infer ResNet-50 on ImageNet validation set to get predicted labels and their corresponding predicted scores. + +```shell +python tools/test.py configs/resnet/resnet50_8xb16_cifar10.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth \ + --out result.pkl +``` + +## Train a model + +MMClassification implements distributed training and non-distributed training, +which uses `MMDistributedDataParallel` and `MMDataParallel` respectively. + +All outputs (log files and checkpoints) will be saved to the working directory, +which is specified by `work_dir` in the config file. + +By default we evaluate the model on the validation set after each epoch, you can change the evaluation interval by adding the interval argument in the training config. + +```python +evaluation = dict(interval=12) # Evaluate the model per 12 epochs. +``` + +### Train with a single GPU + +```shell +python tools/train.py ${CONFIG_FILE} [optional arguments] +``` + +If you want to specify the working directory in the command, you can add an argument `--work_dir ${YOUR_WORK_DIR}`. + +### Train with CPU + +The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process. + +```shell +export CUDA_VISIBLE_DEVICES=-1 +``` + +And then run the script [above](#train-with-a-single-gpu). + +```{warning} +The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process. +``` + +### Train with multiple GPUs in single machine + +```shell +./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments] +``` + +Optional arguments are: + +- `--no-validate` (**not suggested**): By default, the codebase will perform evaluation at every k (default value is 1) epochs during the training. To disable this behavior, use `--no-validate`. +- `--work-dir ${WORK_DIR}`: Override the working directory specified in the config file. +- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file. + +Difference between `resume-from` and `load-from`: +`resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. +`load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning. + +### Train with multiple machines + +If you launch with multiple machines simply connected with ethernet, you can simply run following commands: + +On the first machine: + +```shell +NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS +``` + +On the second machine: + +```shell +NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS +``` + +Usually it is slow if you do not have high speed networking like InfiniBand. + +If you run MMClassification on a cluster managed with [slurm](https://slurm.schedmd.com/), you can use the script `slurm_train.sh`. (This script also supports single machine training.) + +```shell +[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} +``` + +You can check [slurm_train.sh](https://github.com/open-mmlab/mmclassification/blob/master/tools/slurm_train.sh) for full arguments and environment variables. + +If you have just multiple machines connected with ethernet, you can refer to +PyTorch [launch utility](https://pytorch.org/docs/stable/distributed_deprecated.html#launch-utility). +Usually it is slow if you do not have high speed networking like InfiniBand. + +### Launch multiple jobs on a single machine + +If you launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, +you need to specify different ports (29500 by default) for each job to avoid communication conflict. + +If you use `dist_train.sh` to launch training jobs, you can set the port in commands. + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4 +CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4 +``` + +If you use launch training jobs with Slurm, you need to modify the config files (usually the 6th line from the bottom in config files) to set different communication ports. + +In `config1.py`, + +```python +dist_params = dict(backend='nccl', port=29500) +``` + +In `config2.py`, + +```python +dist_params = dict(backend='nccl', port=29501) +``` + +Then you can launch two jobs with `config1.py` ang `config2.py`. + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} +CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} +``` + +### Train with IPU + +The process of training on the IPU is consistent with single GPU training. We just need to have IPU machine and environment +and add an extra argument `--ipu-replicas ${IPU_NUM}` + +## Useful tools + +We provide lots of useful tools under `tools/` directory. + +### Get the FLOPs and params (experimental) + +We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model. + +```shell +python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] +``` + +You will get the result like this. + +``` +============================== +Input shape: (3, 224, 224) +Flops: 4.12 GFLOPs +Params: 25.56 M +============================== +``` + +```{warning} +This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double check it before you adopt it in technical reports or papers. +- FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224). +- Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details. +``` + +### Publish a model + +Before you publish a model, you may want to + +1. Convert model weights to CPU tensors. +2. Delete the optimizer states. +3. Compute the hash of the checkpoint file and append the hash id to the filename. + +```shell +python tools/convert_models/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME} +``` + +E.g., + +```shell +python tools/convert_models/publish_model.py work_dirs/resnet50/latest.pth imagenet_resnet50.pth +``` + +The final output filename will be `imagenet_resnet50_{date}-{hash id}.pth`. + +## Tutorials + +Currently, we provide five tutorials for users. + +- [learn about config](tutorials/config.md) +- [finetune models](tutorials/finetune.md) +- [add new dataset](tutorials/new_dataset.md) +- [design data pipeline](tutorials/data_pipeline.md) +- [add new modules](tutorials/new_modules.md) +- [customize schedule](tutorials/schedule.md) +- [customize runtime settings](tutorials/runtime.md). diff --git a/docs/en/index.rst b/docs/en/index.rst new file mode 100644 index 0000000..d0a15b1 --- /dev/null +++ b/docs/en/index.rst @@ -0,0 +1,99 @@ +Welcome to MMClassification's documentation! +============================================ + +You can switch between Chinese and English documentation in the lower-left corner of the layout. + +您可以在页面左下角切换中英文文档。 + +.. toctree:: + :maxdepth: 1 + :caption: Get Started + + install.md + getting_started.md + + +.. toctree:: + :maxdepth: 1 + :caption: Tutorials + + tutorials/config.md + tutorials/finetune.md + tutorials/new_dataset.md + tutorials/data_pipeline.md + tutorials/new_modules.md + tutorials/schedule.md + tutorials/runtime.md + + +.. toctree:: + :maxdepth: 1 + :caption: Model zoo + :glob: + + modelzoo_statistics.md + model_zoo.md + papers/* + + +.. toctree:: + :maxdepth: 1 + :caption: Useful Tools and Scripts + + tools/pytorch2onnx.md + tools/onnx2tensorrt.md + tools/pytorch2torchscript.md + tools/model_serving.md + tools/visualization.md + tools/analysis.md + tools/miscellaneous.md + + +.. toctree:: + :maxdepth: 1 + :caption: Community + + community/CONTRIBUTING.md + + +.. toctree:: + :maxdepth: 1 + :caption: API Reference + + mmcls.apis + mmcls.core + mmcls.models + mmcls.models.utils + mmcls.datasets + Data Transformations + Batch Augmentation + mmcls.utils + + +.. toctree:: + :maxdepth: 1 + :caption: Notes + + changelog.md + compatibility.md + faq.md + + +.. toctree:: + :maxdepth: 1 + :caption: Device Support + + device/npu.md + +.. toctree:: + :caption: Language Switch + + English + 简体中文 + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/docs/en/install.md b/docs/en/install.md new file mode 100644 index 0000000..bde1a81 --- /dev/null +++ b/docs/en/install.md @@ -0,0 +1,219 @@ +# Prerequisites + +In this section we demonstrate how to prepare an environment with PyTorch. + +MMClassification works on Linux, Windows and macOS. It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.5+. + +```{note} +If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation. +``` + +**Step 1.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html). + +**Step 2.** Create a conda environment and activate it. + +```shell +conda create --name openmmlab python=3.8 -y +conda activate openmmlab +``` + +**Step 3.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g. + +On GPU platforms: + +```shell +conda install pytorch torchvision -c pytorch +``` + +```{warning} +This command will automatically install the latest version PyTorch and cudatoolkit, please check whether they matches your environment. +``` + +On CPU platforms: + +```shell +conda install pytorch torchvision cpuonly -c pytorch +``` + +# Installation + +We recommend that users follow our best practices to install MMClassification. However, the whole process is highly customizable. See [Customize Installation](#customize-installation) section for more information. + +## Best Practices + +**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim). + +```shell +pip install -U openmim +mim install mmcv-full +``` + +**Step 1.** Install MMClassification. + +According to your needs, we support two install modes: + +- [Install from source (Recommended)](#install-from-source): You want to develop your own image classification task or new features based on MMClassification framework. For example, you want to add new dataset or new models. And you can use all tools we provided. +- [Install as a Python package](#install-as-a-python-package): You just want to call MMClassification's APIs or import MMClassification's modules in your project. + +### Install from source + +In this case, install mmcls from source: + +```shell +git clone https://github.com/open-mmlab/mmclassification.git +cd mmclassification +pip install -v -e . +# "-v" means verbose, or more output +# "-e" means installing a project in editable mode, +# thus any local modifications made to the code will take effect without reinstallation. +``` + +Optionally, if you want to contribute to MMClassification or experience experimental functions, please checkout to the dev branch: + +```shell +git checkout dev +``` + +### Install as a Python package + +Just install with pip. + +```shell +pip install mmcls +``` + +## Verify the installation + +To verify whether MMClassification is installed correctly, we provide some sample codes to run an inference demo. + +**Step 1.** We need to download config and checkpoint files. + +```shell +mim download mmcls --config resnet50_8xb32_in1k --dest . +``` + +**Step 2.** Verify the inference demo. + +Option (a). If you install mmcls from source, just run the following command: + +```shell +python demo/image_demo.py demo/demo.JPEG resnet50_8xb32_in1k.py resnet50_8xb32_in1k_20210831-ea4938fc.pth --device cpu +``` + +You will see the output result dict including `pred_label`, `pred_score` and `pred_class` in your terminal. +And if you have graphical interface (instead of remote terminal etc.), you can enable `--show` option to show +the demo image with these predictions in a window. + +Option (b). If you install mmcls as a python package, open you python interpreter and copy&paste the following codes. + +```python +from mmcls.apis import init_model, inference_model + +config_file = 'resnet50_8xb32_in1k.py' +checkpoint_file = 'resnet50_8xb32_in1k_20210831-ea4938fc.pth' +model = init_model(config_file, checkpoint_file, device='cpu') # or device='cuda:0' +inference_model(model, 'demo/demo.JPEG') +``` + +You will see a dict printed, including the predicted label, score and category name. + +## Customize Installation + +### CUDA versions + +When installing PyTorch, you need to specify the version of CUDA. If you are +not clear on which to choose, follow our recommendations: + +- For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must. +- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight. + +Please make sure the GPU driver satisfies the minimum version requirements. See [this table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions) for more information. + +```{note} +Installing CUDA runtime libraries is enough if you follow our best practices, +because no CUDA code will be compiled locally. However if you hope to compile +MMCV from source or develop other CUDA operators, you need to install the +complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads), +and its version should match the CUDA version of PyTorch. i.e., the specified +version of cudatoolkit in `conda install` command. +``` + +### Install MMCV without MIM + +MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex +way. MIM solves such dependencies automatically and makes the installation +easier. However, it is not a must. + +To install MMCV with pip instead of MIM, please follow +[MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). +This requires manually specifying a find-url based on PyTorch version and its CUDA version. + +For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3. + +```shell +pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html +``` + +### Install on CPU-only platforms + +MMClassification can be built for CPU only environment. In CPU mode you can +train (requires MMCV version >= 1.4.4), test or inference a model. + +Some functionalities are gone in this mode, usually GPU-compiled ops. But don't +worry, almost all models in MMClassification don't depends on these ops. + +### Install on Google Colab + +[Google Colab](https://research.google.com/) usually has PyTorch installed, +thus we only need to install MMCV and MMClassification with the following +commands. + +**Step 1.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim). + +```shell +!pip3 install openmim +!mim install mmcv-full +``` + +**Step 2.** Install MMClassification from the source. + +```shell +!git clone https://github.com/open-mmlab/mmclassification.git +%cd mmclassification +!pip install -e . +``` + +**Step 3.** Verification. + +```python +import mmcls +print(mmcls.__version__) +# Example output: 0.23.0 or newer +``` + +```{note} +Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python. +``` + +### Using MMClassification with Docker + +We provide a [Dockerfile](https://github.com/open-mmlab/mmclassification/blob/master/docker/Dockerfile) +to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03. + +```shell +# build an image with PyTorch 1.8.1, CUDA 10.2 +# If you prefer other versions, just modified the Dockerfile +docker build -t mmclassification docker/ +``` + +Run it with + +```shell +docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmclassification/data mmclassification +``` + +## Trouble shooting + +If you have some issues during the installation, please first view the [FAQ](faq.md) page. +You may [open an issue](https://github.com/open-mmlab/mmclassification/issues/new/choose) +on GitHub if no solution is found. diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md new file mode 100644 index 0000000..46b42a9 --- /dev/null +++ b/docs/en/model_zoo.md @@ -0,0 +1,162 @@ +# Model Zoo + +## ImageNet + +ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). +The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize. + +| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download | +| :--------------------------------: | :-------------------------------: | :-----------------------------: | :-------: | :-------: | :---------------------------------------: | :-----------------------------------------: | +| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) | +| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) | +| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) | +| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_batch256_imagenet_20210208-e6920e4a.log.json) | +| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) | +| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) | +| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) | +| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json) | +| RepVGG-A0\* | 9.11（train) \| 8.31 (deploy) | 1.52 (train) \| 1.36 (deploy) | 72.41 | 90.50 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A0_3rdparty_4xb64-coslr-120e_in1k_20210909-883ab98c.pth) | +| RepVGG-A1\* | 14.09 (train) \| 12.79 (deploy) | 2.64 (train) \| 2.37 (deploy) | 74.47 | 91.85 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A1_3rdparty_4xb64-coslr-120e_in1k_20210909-24003a24.pth) | +| RepVGG-A2\* | 28.21 (train) \| 25.5 (deploy) | 5.7 (train) \| 5.12 (deploy) | 76.48 | 93.01 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-A2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-A2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-A2_3rdparty_4xb64-coslr-120e_in1k_20210909-97d7695a.pth) | +| RepVGG-B0\* | 15.82 (train) \| 14.34 (deploy) | 3.42 (train) \| 3.06 (deploy) | 75.14 | 92.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B0_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B0_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B0_3rdparty_4xb64-coslr-120e_in1k_20210909-446375f4.pth) | +| RepVGG-B1\* | 57.42 (train) \| 51.83 (deploy) | 13.16 (train) \| 11.82 (deploy) | 78.37 | 94.11 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1_3rdparty_4xb64-coslr-120e_in1k_20210909-750cdf67.pth) | +| RepVGG-B1g2\* | 45.78 (train) \| 41.36 (deploy) | 9.82 (train) \| 8.82 (deploy) | 77.79 | 93.88 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g2_3rdparty_4xb64-coslr-120e_in1k_20210909-344f6422.pth) | +| RepVGG-B1g4\* | 39.97 (train) \| 36.13 (deploy) | 8.15 (train) \| 7.32 (deploy) | 77.58 | 93.84 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B1g4_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B1g4_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B1g4_3rdparty_4xb64-coslr-120e_in1k_20210909-d4c1a642.pth) | +| RepVGG-B2\* | 89.02 (train) \| 80.32 (deploy) | 20.46 (train) \| 18.39 (deploy) | 78.78 | 94.42 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2_4xb64-coslr-120e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2_deploy_4xb64-coslr-120e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2_3rdparty_4xb64-coslr-120e_in1k_20210909-bd6b937c.pth) | +| RepVGG-B2g4\* | 61.76 (train) \| 55.78 (deploy) | 12.63 (train) \| 11.34 (deploy) | 79.38 | 94.68 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B2g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B2g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B2g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-7b7955f0.pth) | +| RepVGG-B3\* | 123.09 (train) \| 110.96 (deploy) | 29.17 (train) \| 26.22 (deploy) | 80.52 | 95.26 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-dda968bf.pth) | +| RepVGG-B3g4\* | 83.83 (train) \| 75.63 (deploy) | 17.9 (train) \| 16.08 (deploy) | 80.22 | 95.10 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-B3g4_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-B3g4_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-4e54846a.pth) | +| RepVGG-D2se\* | 133.33 (train) \| 120.39 (deploy) | 36.56 (train) \| 32.85 (deploy) | 81.81 | 95.94 | [config (train)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/repvgg-D2se_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) \| [config (deploy)](https://github.com/open-mmlab/mmclassification/blob/master/configs/repvgg/deploy/repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/repvgg/repvgg-D2se_3rdparty_4xb64-autoaug-lbs-mixup-coslr-200e_in1k_20210909-cf3139b7.pth) | +| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.log.json) | +| ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.log.json) | +| ResNet-50 (rsb-a1) | 25.56 | 4.12 | 80.12 | 94.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb256-rsb-a1-600e_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.log.json) | +| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) | +| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) | +| Res2Net-50-14w-8s\* | 25.06 | 4.22 | 78.14 | 93.85 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w14-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth) | +| Res2Net-50-26w-8s\* | 48.40 | 8.39 | 79.20 | 94.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net50-w26-s8_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth) | +| Res2Net-101-26w-4s\* | 45.21 | 8.12 | 79.19 | 94.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/res2net/res2net101-w26-s4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth) | +| ResNeSt-50\* | 27.48 | 5.41 | 81.13 | 95.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest50_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) | +| ResNeSt-101\* | 48.28 | 10.27 | 82.32 | 96.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest101_32xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) | +| ResNeSt-200\* | 70.2 | 17.53 | 82.41 | 96.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest200_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) | +| ResNeSt-269\* | 110.93 | 22.58 | 82.70 | 96.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnest/resnest269_64xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) | +| ResNetV1D-50 | 25.58 | 4.36 | 77.54 | 93.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_b32x8_imagenet_20210531-db14775a.log.json) | +| ResNetV1D-101 | 44.57 | 8.09 | 78.93 | 94.48 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_b32x8_imagenet_20210531-6e13bcd3.log.json) | +| ResNetV1D-152 | 60.21 | 11.82 | 79.41 | 94.7 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_b32x8_imagenet_20210531-278cf22a.log.json) | +| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.90 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_b32x8_imagenet_20210429-56066e27.log.json) | +| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.71 | 94.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_b32x8_imagenet_20210506-e0fa3dd5.log.json) | +| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.23 | 94.58 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101-32x8d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_b32x8_imagenet_20210506-23a247d5.log.json) | +| ResNeXt-32x4d-152 | 59.95 | 11.8 | 78.93 | 94.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152-32x4d_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_b32x8_imagenet_20210524-927787be.log.json) | +| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) | +| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) | +| RegNetX-400MF | 5.16 | 0.41 | 72.56 | 90.78 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-400mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211213-89bfc226.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-400mf_8xb128_in1k_20211208_143316.log.json) | +| RegNetX-800MF | 7.26 | 0.81 | 74.76 | 92.32 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-800mf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211213-222b0f11.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-800mf_8xb128_in1k_20211207_143037.log.json) | +| RegNetX-1.6GF | 9.19 | 1.63 | 76.84 | 93.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-1.6gf_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211213-d1b89758.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-1.6gf_8xb128_in1k_20211208_143018.log.json) | +| RegNetX-3.2GF | 15.3 | 3.21 | 78.09 | 94.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-3.2gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211213-1fdd82ae.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-3.2gf_8xb64_in1k_20211208_142720.log.json) | +| RegNetX-4.0GF | 22.12 | 4.0 | 78.60 | 94.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-4.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211213-efed675c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-4.0gf_8xb64_in1k_20211207_150431.log.json) | +| RegNetX-6.4GF | 26.21 | 6.51 | 79.38 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-6.4gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211215-5c6089da.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-6.4gf_8xb64_in1k_20211213_172748.log.json) | +| RegNetX-8.0GF | 39.57 | 8.03 | 79.12 | 94.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-8.0gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211213-9a9fcc76.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-8.0gf_8xb64_in1k_20211208_103250.log.json) | +| RegNetX-12GF | 46.11 | 12.15 | 79.67 | 95.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/regnet/regnetx-12gf_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211213-5df8c2f8.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/regnet/regnetx-12gf_8xb64_in1k_20211208_143713.log.json) | +| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) | +| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) | +| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) | +| ViT-B/16\* | 86.86 | 33.03 | 85.43 | 97.77 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth) | +| ViT-B/32\* | 88.3 | 8.56 | 84.01 | 97.08 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-base-p32_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-9cea8599.pth) | +| ViT-L/16\* | 304.72 | 116.68 | 85.63 | 97.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vision_transformer/vit-large-p16_ft-64xb64_in1k-384.py) | [model](https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-b20ba619.pth) | +| Swin-Transformer tiny | 28.29 | 4.36 | 81.18 | 95.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-tiny_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925.log.json) | +| Swin-Transformer small | 49.61 | 8.52 | 83.02 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin-small_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219-7f9d988b.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_small_224_b16x64_300e_imagenet_20210615_110219.log.json) | +| Swin-Transformer base | 87.77 | 15.14 | 83.36 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742-93230b0d.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_base_224_b16x64_300e_imagenet_20210616_190742.log.json) | +| Transformer in Transformer small\* | 23.76 | 3.36 | 81.52 | 95.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/tnt/tnt-s-p16_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/tnt/tnt-small-p16_3rdparty_in1k_20210903-c56ee7df.pth) | +| T2T-ViT_t-14 | 21.47 | 4.34 | 81.83 | 95.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_8xb64_in1k_20211220-f7378dd5.log.json) | +| T2T-ViT_t-19 | 39.08 | 7.80 | 82.63 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-19_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-19_8xb64_in1k_20211214-7f5e3aaf.log.json) | +| T2T-ViT_t-24 | 64.00 | 12.69 | 82.71 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/t2t_vit/t2t-vit-t-24_8xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-24_8xb64_in1k_20211214-b2a68ae3.log.json) | +| Mixer-B/16\* | 59.88 | 12.61 | 76.68 | 92.25 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-base-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-base-p16_3rdparty_64xb64_in1k_20211124-1377e3e0.pth) | +| Mixer-L/16\* | 208.2 | 44.57 | 72.34 | 88.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mlp_mixer/mlp-mixer-large-p16_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mlp-mixer/mixer-large-p16_3rdparty_64xb64_in1k_20211124-5a2519d2.pth) | +| DeiT-tiny | 5.72 | 1.08 | 74.50 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny_pt-4xb256_in1k_20220218-13b382a0.log.json) | +| DeiT-tiny distilled\* | 5.72 | 1.08 | 74.51 | 91.90 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-tiny-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-tiny-distilled_3rdparty_pt-4xb256_in1k_20211216-c429839a.pth) | +| DeiT-small | 22.05 | 4.24 | 80.69 | 95.06 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-small_pt-4xb256_in1k_20220218-9425b9bb.log.json) | +| DeiT-small distilled\* | 22.05 | 4.24 | 81.17 | 95.40 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-small-distilled_pt-4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-small-distilled_3rdparty_pt-4xb256_in1k_20211216-4de1d725.pth) | +| DeiT-base | 86.57 | 16.86 | 81.76 | 95.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.pth) \| [log](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_pt-16xb64_in1k_20220216-db63c16c.log.json) | +| DeiT-base distilled\* | 86.57 | 16.86 | 83.33 | 96.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_pt-16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_pt-16xb64_in1k_20211216-42891296.pth) | +| DeiT-base 384px\* | 86.86 | 49.37 | 83.04 | 96.31 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base_3rdparty_ft-16xb32_in1k-384px_20211124-822d02f2.pth) | +| DeiT-base distilled 384px\* | 86.86 | 49.37 | 85.55 | 97.35 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/deit/deit-base-distilled_ft-16xb32_in1k-384px.py) | [model](https://download.openmmlab.com/mmclassification/v0/deit/deit-base-distilled_3rdparty_ft-16xb32_in1k-384px_20211216-e48d6000.pth) | +| Conformer-tiny-p16\* | 23.52 | 4.90 | 81.31 | 95.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-tiny-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-tiny-p16_3rdparty_8xb128_in1k_20211206-f6860372.pth) | +| Conformer-small-p32\* | 38.85 | 7.09 | 81.96 | 96.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p32_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p32_8xb128_in1k_20211206-947a0816.pth) | +| Conformer-small-p16\* | 37.67 | 10.31 | 83.32 | 96.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-small-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-small-p16_3rdparty_8xb128_in1k_20211206-3065dcf5.pth) | +| Conformer-base-p16\* | 83.29 | 22.89 | 83.82 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/conformer/conformer-base-p16_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/conformer/conformer-base-p16_3rdparty_8xb128_in1k_20211206-bfdf8637.pth) | +| PCPVT-small\* | 24.11 | 3.67 | 81.14 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-small_3rdparty_8xb128_in1k_20220126-ef23c132.pth) | +| PCPVT-base\* | 43.83 | 6.45 | 82.66 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-base_3rdparty_8xb128_in1k_20220126-f8c4b0d5.pth) | +| PCPVT-large\* | 60.99 | 9.51 | 83.09 | 96.59 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-pcpvt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-pcpvt-large_3rdparty_16xb64_in1k_20220126-c1ef8d80.pth) | +| SVT-small\* | 24.06 | 2.82 | 81.77 | 95.57 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-small_3rdparty_8xb128_in1k_20220126-8fe5205b.pth) | +| SVT-base\* | 56.07 | 8.35 | 83.13 | 96.29 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-base_3rdparty_8xb128_in1k_20220126-e31cc8e9.pth) | +| SVT-large\* | 99.27 | 14.82 | 83.60 | 96.50 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/twins/twins-svt-large_16xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/twins/twins-svt-large_3rdparty_16xb64_in1k_20220126-4817645f.pth) | +| EfficientNet-B0\* | 5.29 | 0.02 | 76.74 | 93.17 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32_in1k_20220119-a7e2a0b1.pth) | +| EfficientNet-B0 (AA)\* | 5.29 | 0.02 | 77.26 | 93.41 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa_in1k_20220119-8d939117.pth) | +| EfficientNet-B0 (AA + AdvProp)\* | 5.29 | 0.02 | 77.53 | 93.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b0_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k_20220119-26434485.pth) | +| EfficientNet-B1\* | 7.79 | 0.03 | 78.68 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32_in1k_20220119-002556d9.pth) | +| EfficientNet-B1 (AA)\* | 7.79 | 0.03 | 79.20 | 94.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa_in1k_20220119-619d8ae3.pth) | +| EfficientNet-B1 (AA + AdvProp)\* | 7.79 | 0.03 | 79.52 | 94.43 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b1_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k_20220119-5715267d.pth) | +| EfficientNet-B2\* | 9.11 | 0.03 | 79.64 | 94.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32_in1k_20220119-ea374a30.pth) | +| EfficientNet-B2 (AA)\* | 9.11 | 0.03 | 80.21 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa_in1k_20220119-dd61e80b.pth) | +| EfficientNet-B2 (AA + AdvProp)\* | 9.11 | 0.03 | 80.45 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b2_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k_20220119-1655338a.pth) | +| EfficientNet-B3\* | 12.23 | 0.06 | 81.01 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32_in1k_20220119-4b4d7487.pth) | +| EfficientNet-B3 (AA)\* | 12.23 | 0.06 | 81.58 | 95.67 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth) | +| EfficientNet-B3 (AA + AdvProp)\* | 12.23 | 0.06 | 81.81 | 95.69 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b3_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k_20220119-53b41118.pth) | +| EfficientNet-B4\* | 19.34 | 0.12 | 82.57 | 96.09 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32_in1k_20220119-81fd4077.pth) | +| EfficientNet-B4 (AA)\* | 19.34 | 0.12 | 82.95 | 96.26 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa_in1k_20220119-45b8bd2b.pth) | +| EfficientNet-B4 (AA + AdvProp)\* | 19.34 | 0.12 | 83.25 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k_20220119-38c2238c.pth) | +| EfficientNet-B5\* | 30.39 | 0.24 | 83.18 | 96.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32_in1k_20220119-e9814430.pth) | +| EfficientNet-B5 (AA)\* | 30.39 | 0.24 | 83.82 | 96.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa_in1k_20220119-2cab8b78.pth) | +| EfficientNet-B5 (AA + AdvProp)\* | 30.39 | 0.24 | 84.21 | 96.98 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b5_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k_20220119-f57a895a.pth) | +| EfficientNet-B6 (AA)\* | 43.04 | 0.41 | 84.05 | 96.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa_in1k_20220119-45b03310.pth) | +| EfficientNet-B6 (AA + AdvProp)\* | 43.04 | 0.41 | 84.74 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b6_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k_20220119-bfe3485e.pth) | +| EfficientNet-B7 (AA)\* | 66.35 | 0.72 | 84.38 | 96.88 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa_in1k_20220119-bf03951c.pth) | +| EfficientNet-B7 (AA + AdvProp)\* | 66.35 | 0.72 | 85.14 | 97.23 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b7_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k_20220119-c6dbff10.pth) | +| EfficientNet-B8 (AA + AdvProp)\* | 87.41 | 1.09 | 85.38 | 97.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b8_8xb32-01norm_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k_20220119-297ce1b7.pth) | +| ConvNeXt-T\* | 28.59 | 4.46 | 82.05 | 95.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-tiny_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-tiny_3rdparty_32xb128_in1k_20220124-18abde00.pth) | +| ConvNeXt-S\* | 50.22 | 8.69 | 83.13 | 96.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-small_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_3rdparty_32xb128_in1k_20220124-d39b5192.pth) | +| ConvNeXt-B\* | 88.59 | 15.36 | 83.85 | 96.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_3rdparty_32xb128_in1k_20220124-d0915162.pth) | +| ConvNeXt-B\* | 88.59 | 15.36 | 85.81 | 97.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-base_32xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-base_in21k-pre-3rdparty_32xb128_in1k_20220124-eb2d6ada.pth) | +| ConvNeXt-L\* | 197.77 | 34.37 | 84.30 | 96.89 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_3rdparty_64xb64_in1k_20220124-f8a0ded0.pth) | +| ConvNeXt-L\* | 197.77 | 34.37 | 86.61 | 98.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-large_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-large_in21k-pre-3rdparty_64xb64_in1k_20220124-2412403d.pth) | +| ConvNeXt-XL\* | 350.20 | 60.93 | 86.97 | 98.20 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/convnext/convnext-xlarge_64xb64_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/convnext/convnext-xlarge_in21k-pre-3rdparty_64xb64_in1k_20220124-76b6863d.pth) | +| HRNet-W18\* | 21.30 | 4.33 | 76.75 | 93.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32_in1k_20220120-0c10b180.pth) | +| HRNet-W30\* | 37.71 | 8.17 | 78.19 | 94.22 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w30_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w30_3rdparty_8xb32_in1k_20220120-8aa3832f.pth) | +| HRNet-W32\* | 41.23 | 8.99 | 78.44 | 94.19 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w32_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w32_3rdparty_8xb32_in1k_20220120-c394f1ab.pth) | +| HRNet-W40\* | 57.55 | 12.77 | 78.94 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w40_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w40_3rdparty_8xb32_in1k_20220120-9a2dbfc5.pth) | +| HRNet-W44\* | 67.06 | 14.96 | 78.88 | 94.37 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w44_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w44_3rdparty_8xb32_in1k_20220120-35d07f73.pth) | +| HRNet-W48\* | 77.47 | 17.36 | 79.32 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32_in1k_20220120-e555ef50.pth) | +| HRNet-W64\* | 128.06 | 29.00 | 79.46 | 94.65 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w64_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w64_3rdparty_8xb32_in1k_20220120-19126642.pth) | +| HRNet-W18 (ssld)\* | 21.30 | 4.33 | 81.06 | 95.70 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w18_3rdparty_8xb32-ssld_in1k_20220120-455f69ea.pth) | +| HRNet-W48 (ssld)\* | 77.47 | 17.36 | 83.63 | 96.79 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w48_4xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/hrnet/hrnet-w48_3rdparty_8xb32-ssld_in1k_20220120-d0459c38.pth) | +| WRN-50\* | 68.88 | 11.44 | 81.45 | 95.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet50_timm_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet50_3rdparty-timm_8xb32_in1k_20220304-83ae4399.pth) | +| WRN-101\* | 126.89 | 22.81 | 78.84 | 94.28 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/wrn/wide-resnet101_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/wrn/wide-resnet101_3rdparty_8xb32_in1k_20220304-8d5f9d61.pth) | +| CSPDarkNet50\* | 27.64 | 5.04 | 80.05 | 95.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspdarknet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspdarknet50_3rdparty_8xb32_in1k_20220329-bd275287.pth) | +| CSPResNet50\* | 21.62 | 3.48 | 79.55 | 94.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnet50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnet50_3rdparty_8xb32_in1k_20220329-dd6dddfb.pth) | +| CSPResNeXt50\* | 20.57 | 3.11 | 79.96 | 94.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/cspnet/cspresnext50_3rdparty_8xb32_in1k_20220329-2cc84d21.pth) | +| DenseNet121\* | 7.98 | 2.88 | 74.96 | 92.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet121_4xb256_in1k_20220426-07450f99.pth) | +| DenseNet169\* | 14.15 | 3.42 | 76.08 | 93.11 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet169_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet169_4xb256_in1k_20220426-a2889902.pth) | +| DenseNet201\* | 20.01 | 4.37 | 77.32 | 93.64 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet201_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet201_4xb256_in1k_20220426-05cae4ef.pth) | +| DenseNet161\* | 28.68 | 7.82 | 77.61 | 93.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet161_4xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/densenet/densenet161_4xb256_in1k_20220426-ee6a80a9.pth) | +| VAN-T\* | 4.11 | 0.88 | 75.41 | 93.02 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-tiny_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-tiny_8xb128_in1k_20220501-385941af.pth) | +| VAN-S\* | 13.86 | 2.52 | 81.01 | 95.63 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-small_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-small_8xb128_in1k_20220501-17bc91aa.pth) | +| VAN-B\* | 26.58 | 5.03 | 82.80 | 96.21 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-base_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-base_8xb128_in1k_20220501-6a4cc31b.pth) | +| VAN-L\* | 44.77 | 8.99 | 83.86 | 96.73 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/van/van-large_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/van/van-large_8xb128_in1k_20220501-f212ba21.pth) | +| MViTv2-tiny\* | 24.17 | 4.70 | 82.33 | 96.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-tiny_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-tiny_3rdparty_in1k_20220722-db7beeef.pth) | +| MViTv2-small\* | 34.87 | 7.00 | 83.63 | 96.51 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-small_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-small_3rdparty_in1k_20220722-986bd741.pth) | +| MViTv2-base\* | 51.47 | 10.20 | 84.34 | 96.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-base_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-base_3rdparty_in1k_20220722-9c4f0a17.pth) | +| MViTv2-large\* | 217.99 | 42.10 | 85.25 | 97.14 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mvit/mvitv2-large_8xb256_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/mvit/mvitv2-large_3rdparty_in1k_20220722-2b57b983.pth) | +| EfficientFormer-l1\* | 12.19 | 1.30 | 80.46 | 94.99 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l1_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l1_3rdparty_in1k_20220803-d66e61df.pth) | +| EfficientFormer-l3\* | 31.41 | 3.93 | 82.45 | 96.18 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l3_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l3_3rdparty_in1k_20220803-dde1c8c5.pth) | +| EfficientFormer-l7\* | 82.23 | 10.16 | 83.40 | 96.60 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientformer/efficientformer-l7_8xb128_in1k.py) | [model](https://download.openmmlab.com/mmclassification/v0/efficientformer/efficientformer-l7_3rdparty_in1k_20220803-41a552bb.pth) | + +*Models with * are converted from other repos, others are trained by ourselves.* + +## CIFAR10 + +| Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download | +| :--------------: | :-------: | :------: | :-------: | :----: | :------------------------------------------------------------------------------------------------------------: | +| ResNet-18-b16x8 | 11.17 | 0.56 | 94.82 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_8xb16_cifar10.py) | +| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_8xb16_cifar10.py) | +| ResNet-50-b16x8 | 23.52 | 1.31 | 95.55 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb16_cifar10.py) | +| ResNet-101-b16x8 | 42.51 | 2.52 | 95.58 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_8xb16_cifar10.py) | +| ResNet-152-b16x8 | 58.16 | 3.74 | 95.76 | | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_8xb16_cifar10.py) | diff --git a/docs/en/stat.py b/docs/en/stat.py new file mode 100755 index 0000000..8f1e5b2 --- /dev/null +++ b/docs/en/stat.py @@ -0,0 +1,100 @@ +#!/usr/bin/env python +import functools as func +import glob +import os +import re +from pathlib import Path + +import numpy as np + +MMCLS_ROOT = Path(__file__).absolute().parents[1] +url_prefix = 'https://github.com/open-mmlab/mmclassification/blob/master/' + +papers_root = Path('papers') +papers_root.mkdir(exist_ok=True) +files = [Path(f) for f in sorted(glob.glob('../../configs/*/README.md'))] + +stats = [] +titles = [] +num_ckpts = 0 +num_configs = 0 + +for f in files: + with open(f, 'r') as content_file: + content = content_file.read() + + # Extract checkpoints + ckpts = set(x.lower().strip() + for x in re.findall(r'\[model\]\((https?.*)\)', content)) + if len(ckpts) == 0: + continue + num_ckpts += len(ckpts) + + # Extract paper title + match_res = list(re.finditer(r'> \[(.*)\]\((.*)\)', content)) + if len(match_res) > 0: + title, paperlink = match_res[0].groups() + else: + title = content.split('\n')[0].replace('# ', '').strip() + paperlink = None + titles.append(title) + + # Replace paper link to a button + if paperlink is not None: + start = match_res[0].start() + end = match_res[0].end() + # link_button = f'{title}' + link_button = f'[{title}]({paperlink})' + content = content[:start] + link_button + content[end:] + + # Extract paper type + _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)] + assert len(_papertype) > 0 + papertype = _papertype[0] + paper = set([(papertype, title)]) + + # Write a copy of README + copy = papers_root / (f.parent.name + '.md') + if copy.exists(): + os.remove(copy) + + def replace_link(matchobj): + # Replace relative link to GitHub link. + name = matchobj.group(1) + link = matchobj.group(2) + if not link.startswith('http') and (f.parent / link).exists(): + rel_link = (f.parent / link).absolute().relative_to(MMCLS_ROOT) + link = url_prefix + str(rel_link) + return f'[{name}]({link})' + + content = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', replace_link, content) + + with open(copy, 'w') as copy_file: + copy_file.write(content) + + statsmsg = f""" +\t* [{papertype}] [{title}]({copy}) ({len(ckpts)} ckpts) +""" + stats.append(dict(paper=paper, ckpts=ckpts, statsmsg=statsmsg, copy=copy)) + +allpapers = func.reduce(lambda a, b: a.union(b), + [stat['paper'] for stat in stats]) +msglist = '\n'.join(stat['statsmsg'] for stat in stats) + +papertypes, papercounts = np.unique([t for t, _ in allpapers], + return_counts=True) +countstr = '\n'.join( + [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)]) + +modelzoo = f""" +# Model Zoo Summary + +* Number of papers: {len(set(titles))} +{countstr} + +* Number of checkpoints: {num_ckpts} +{msglist} +""" + +with open('modelzoo_statistics.md', 'w') as f: + f.write(modelzoo) diff --git a/docs/en/tools/analysis.md b/docs/en/tools/analysis.md new file mode 100644 index 0000000..0e583b0 --- /dev/null +++ b/docs/en/tools/analysis.md @@ -0,0 +1,211 @@ +# Analysis + + + +- [Log Analysis](#log-analysis) + - [Plot Curves](#plot-curves) + - [Calculate Training Time](#calculate-training-time) +- [Result Analysis](#result-analysis) + - [Evaluate Results](#evaluate-results) + - [View Typical Results](#view-typical-results) +- [Model Complexity](#model-complexity) +- [FAQs](#faqs) + + + +## Log Analysis + +### Plot Curves + +`tools/analysis_tools/analyze_logs.py` plots curves of given keys according to the log files. + +

+ +```shell +python tools/analysis_tools/analyze_logs.py plot_curve \ + ${JSON_LOGS} \ + [--keys ${KEYS}] \ + [--title ${TITLE}] \ + [--legend ${LEGEND}] \ + [--backend ${BACKEND}] \ + [--style ${STYLE}] \ + [--out ${OUT_FILE}] \ + [--window-size ${WINDOW_SIZE}] +``` + +**Description of all arguments**： + +- `json_logs` : The paths of the log files, separate multiple files by spaces. +- `--keys` : The fields of the logs to analyze, separate multiple keys by spaces. Defaults to 'loss'. +- `--title` : The title of the figure. Defaults to use the filename. +- `--legend` : The names of legend, the number of which must be equal to `len(${JSON_LOGS}) * len(${KEYS})`. Defaults to use `"${JSON_LOG}-${KEYS}"`. +- `--backend` : The backend of matplotlib. Defaults to auto selected by matplotlib. +- `--style` : The style of the figure. Default to `whitegrid`. +- `--out` : The path of the output picture. If not set, the figure won't be saved. +- `--window-size`: The shape of the display window. The format should be `'W*H'`. Defaults to `'12*7'`. + +```{note} +The `--style` option depends on `seaborn` package, please install it before setting it. +``` + +Examples: + +- Plot the loss curve in training. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss + ``` + +- Plot the top-1 accuracy and top-5 accuracy curves, and save the figure to results.jpg. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg + ``` + +- Compare the top-1 accuracy of two log files in the same figure. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend exp1 exp2 + ``` + +```{note} +The tool will automatically select to find keys in training logs or validation logs according to the keys. +Therefore, if you add a custom evaluation metric, please also add the key to `TEST_METRICS` in this tool. +``` + +### Calculate Training Time + +`tools/analysis_tools/analyze_logs.py` can also calculate the training time according to the log files. + +```shell +python tools/analysis_tools/analyze_logs.py cal_train_time \ + ${JSON_LOGS} + [--include-outliers] +``` + +**Description of all arguments**: + +- `json_logs` : The paths of the log files, separate multiple files by spaces. +- `--include-outliers` : If set, include the first iteration in each epoch (Sometimes the time of first iterations is longer). + +Example: + +```shell +python tools/analysis_tools/analyze_logs.py cal_train_time work_dirs/some_exp/20200422_153324.log.json +``` + +The output is expected to be like the below. + +```text +-----Analyze train time of work_dirs/some_exp/20200422_153324.log.json----- +slowest epoch 68, average time is 0.3818 +fastest epoch 1, average time is 0.3694 +time std over epochs is 0.0020 +average iter time: 0.3777 s/iter +``` + +## Result Analysis + +With the `--out` argument in `tools/test.py`, we can save the inference results of all samples as a file. +And with this result file, we can do further analysis. + +### Evaluate Results + +`tools/analysis_tools/eval_metric.py` can evaluate metrics again. + +```shell +python tools/analysis_tools/eval_metric.py \ + ${CONFIG} \ + ${RESULT} \ + [--metrics ${METRICS}] \ + [--cfg-options ${CFG_OPTIONS}] \ + [--metric-options ${METRIC_OPTIONS}] +``` + +Description of all arguments: + +- `config` : The path of the model config file. +- `result`: The Output result file in json/pickle format from `tools/test.py`. +- `--metrics` : Evaluation metrics, the acceptable values depend on the dataset. +- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md) +- `--metric-options`: If specified, the key-value pair arguments will be passed to the `metric_options` argument of dataset's `evaluate` function. + +```{note} +In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "class_scores" to use this tool. +``` + +**Examples**: + +```shell +python tools/analysis_tools/eval_metric.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py your_result.pkl --metrics accuracy --metric-options "topk=(1,5)" +``` + +### View Typical Results + +`tools/analysis_tools/analyze_results.py` can save the images with the highest scores in successful or failed prediction. + +```shell +python tools/analysis_tools/analyze_results.py \ + ${CONFIG} \ + ${RESULT} \ + [--out-dir ${OUT_DIR}] \ + [--topk ${TOPK}] \ + [--cfg-options ${CFG_OPTIONS}] +``` + +**Description of all arguments**: + +- `config` : The path of the model config file. +- `result`: Output result file in json/pickle format from `tools/test.py`. +- `--out-dir`: Directory to store output files. +- `--topk`: The number of images in successful or failed prediction with the highest `topk` scores to save. If not specified, it will be set to 20. +- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md) + +```{note} +In `tools/test.py`, we support using `--out-items` option to select which kind of results will be saved. Please ensure the result file includes "pred_score", "pred_label" and "pred_class" to use this tool. +``` + +**Examples**: + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/resnet/resnet50_b32x8_imagenet.py \ + result.pkl \ + --out-dir results \ + --topk 50 +``` + +## Model Complexity + +### Get the FLOPs and params (experimental) + +We provide a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model. + +```shell +python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] +``` + +Description of all arguments: + +- `config` : The path of the model config file. +- `--shape`: Input size, support single value or double value parameter, such as `--shape 256` or `--shape 224 256`. If not set, default to be `224 224`. + +You will get a result like this. + +```text +============================== +Input shape: (3, 224, 224) +Flops: 4.12 GFLOPs +Params: 25.56 M +============================== +``` + +```{warning} +This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double-check it before you adopt it in technical reports or papers. +- FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224). +- Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details. +``` + +## FAQs + +- None diff --git a/docs/en/tools/miscellaneous.md b/docs/en/tools/miscellaneous.md new file mode 100644 index 0000000..4e2d5d6 --- /dev/null +++ b/docs/en/tools/miscellaneous.md @@ -0,0 +1,59 @@ +# Miscellaneous + + + +- [Print the entire config](#print-the-entire-config) +- [Verify Dataset](#verify-dataset) +- [FAQs](#faqs) + + + +## Print the entire config + +`tools/misc/print_config.py` prints the whole config verbatim, expanding all its imports. + +```shell +python tools/misc/print_config.py ${CONFIG} [--cfg-options ${CFG_OPTIONS}] +``` + +Description of all arguments: + +- `config` : The path of the model config file. +- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md) + +**Examples**: + +```shell +python tools/misc/print_config.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py +``` + +## Verify Dataset + +`tools/misc/verify_dataset.py` can verify dataset, check whether there are broken pictures in the given dataset. + +```shell +python tools/misc/verify_dataset.py \ + ${CONFIG} \ + [--out-path ${OUT-PATH}] \ + [--phase ${PHASE}] \ + [--num-process ${NUM-PROCESS}] + [--cfg-options ${CFG_OPTIONS}] +``` + +**Description of all arguments**: + +- `config` : The path of the model config file. +- `--out-path` : The path to save the verification result, if not set, defaults to 'brokenfiles.log'. +- `--phase` : Phase of dataset to verify, accept "train" "test" and "val", if not set, defaults to "train". +- `--num-process` : number of process to use, if not set, defaults to 1. +- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Tutorial 1: Learn about Configs](../tutorials/config.md) + +**Examples**: + +```shell +python tools/misc/verify_dataset.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py --out-path broken_imgs.log --phase val --num-process 8 +``` + +## FAQs + +- None diff --git a/docs/en/tools/model_serving.md b/docs/en/tools/model_serving.md new file mode 100644 index 0000000..d633a0f --- /dev/null +++ b/docs/en/tools/model_serving.md @@ -0,0 +1,87 @@ +# Model Serving + +In order to serve an `MMClassification` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps: + +## 1. Convert model from MMClassification to TorchServe + +```shell +python tools/deployment/mmcls2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \ +--output-folder ${MODEL_STORE} \ +--model-name ${MODEL_NAME} +``` + +```{note} +${MODEL_STORE} needs to be an absolute path to a folder. +``` + +Example: + +```shell +python tools/deployment/mmcls2torchserve.py \ + configs/resnet/resnet18_8xb32_in1k.py \ + checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \ + --output-folder ./checkpoints \ + --model-name resnet18_in1k +``` + +## 2. Build `mmcls-serve` docker image + +```shell +docker build -t mmcls-serve:latest docker/serve/ +``` + +## 3. Run `mmcls-serve` + +Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment). + +In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in GPU. + +Example: + +```shell +docker run --rm \ +--cpus 8 \ +--gpus device=0 \ +-p8080:8080 -p8081:8081 -p8082:8082 \ +--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \ +mmcls-serve:latest +``` + +```{note} +`realpath ./checkpoints` points to the absolute path of "./checkpoints", and you can replace it with the absolute path where you store torchserve models. +``` + +[Read the docs](https://github.com/pytorch/serve/blob/master/docs/rest_api.md) about the Inference (8080), Management (8081) and Metrics (8082) APis + +## 4. Test deployment + +```shell +curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.JPEG +``` + +You should obtain a response similar to: + +```json +{ + "pred_label": 58, + "pred_score": 0.38102269172668457, + "pred_class": "water snake" +} +``` + +And you can use `test_torchserver.py` to compare result of TorchServe and PyTorch, and visualize them. + +```shell +python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME} +[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}] +``` + +Example: + +```shell +python tools/deployment/test_torchserver.py \ + demo/demo.JPEG \ + configs/resnet/resnet18_8xb32_in1k.py \ + checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \ + resnet18_in1k +``` diff --git a/docs/en/tools/onnx2tensorrt.md b/docs/en/tools/onnx2tensorrt.md new file mode 100644 index 0000000..ea0f148 --- /dev/null +++ b/docs/en/tools/onnx2tensorrt.md @@ -0,0 +1,80 @@ +# ONNX to TensorRT (Experimental) + + + +- [ONNX to TensorRT (Experimental)](#onnx-to-tensorrt-experimental) + - [How to convert models from ONNX to TensorRT](#how-to-convert-models-from-onnx-to-tensorrt) + - [Prerequisite](#prerequisite) + - [Usage](#usage) + - [List of supported models convertible to TensorRT](#list-of-supported-models-convertible-to-tensorrt) + - [Reminders](#reminders) + - [FAQs](#faqs) + + + +## How to convert models from ONNX to TensorRT + +### Prerequisite + +1. Please refer to [install.md](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification from source. +2. Use our tool [pytorch2onnx.md](./pytorch2onnx.md) to convert the model from PyTorch to ONNX. + +### Usage + +```bash +python tools/deployment/onnx2tensorrt.py \ + ${MODEL} \ + --trt-file ${TRT_FILE} \ + --shape ${IMAGE_SHAPE} \ + --max-batch-size ${MAX_BATCH_SIZE} \ + --workspace-size ${WORKSPACE_SIZE} \ + --fp16 \ + --show \ + --verify \ +``` + +Description of all arguments: + +- `model` : The path of an ONNX model file. +- `--trt-file`: The Path of output TensorRT engine file. If not specified, it will be set to `tmp.trt`. +- `--shape`: The height and width of model input. If not specified, it will be set to `224 224`. +- `--max-batch-size`: The max batch size of TensorRT model, should not be less than 1. +- `--fp16`: Enable fp16 mode. +- `--workspace-size` : The required GPU workspace size in GiB to build TensorRT engine. If not specified, it will be set to `1` GiB. +- `--show`: Determines whether to show the outputs of the model. If not specified, it will be set to `False`. +- `--verify`: Determines whether to verify the correctness of models between ONNXRuntime and TensorRT. If not specified, it will be set to `False`. + +Example: + +```bash +python tools/deployment/onnx2tensorrt.py \ + checkpoints/resnet/resnet18_b16x8_cifar10.onnx \ + --trt-file checkpoints/resnet/resnet18_b16x8_cifar10.trt \ + --shape 224 224 \ + --show \ + --verify \ +``` + +## List of supported models convertible to TensorRT + +The table below lists the models that are guaranteed to be convertible to TensorRT. + +| Model | Config | Status | +| :----------: | :-----------------------------------------------------: | :----: | +| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | +| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | +| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | +| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | +| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | + +Notes: + +- *All models above are tested with Pytorch==1.6.0 and TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0* + +## Reminders + +- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, we may not provide much help here due to the limited resources. Please try to dig a little deeper and debug by yourself. + +## FAQs + +- None diff --git a/docs/en/tools/pytorch2onnx.md b/docs/en/tools/pytorch2onnx.md new file mode 100644 index 0000000..7352d45 --- /dev/null +++ b/docs/en/tools/pytorch2onnx.md @@ -0,0 +1,204 @@ +# Pytorch to ONNX (Experimental) + + + +- [Pytorch to ONNX (Experimental)](#pytorch-to-onnx-experimental) + - [How to convert models from Pytorch to ONNX](#how-to-convert-models-from-pytorch-to-onnx) + - [Prerequisite](#prerequisite) + - [Usage](#usage) + - [Description of all arguments:](#description-of-all-arguments) + - [How to evaluate ONNX models with ONNX Runtime](#how-to-evaluate-onnx-models-with-onnx-runtime) + - [Prerequisite](#prerequisite-1) + - [Usage](#usage-1) + - [Description of all arguments](#description-of-all-arguments-1) + - [Results and Models](#results-and-models) + - [List of supported models exportable to ONNX](#list-of-supported-models-exportable-to-onnx) + - [Reminders](#reminders) + - [FAQs](#faqs) + + + +## How to convert models from Pytorch to ONNX + +### Prerequisite + +1. Please refer to [install](https://mmclassification.readthedocs.io/en/latest/install.html#install-mmclassification) for installation of MMClassification. +2. Install onnx and onnxruntime + +```shell +pip install onnx onnxruntime==1.5.1 +``` + +### Usage + +```bash +python tools/deployment/pytorch2onnx.py \ + ${CONFIG_FILE} \ + --checkpoint ${CHECKPOINT_FILE} \ + --output-file ${OUTPUT_FILE} \ + --shape ${IMAGE_SHAPE} \ + --opset-version ${OPSET_VERSION} \ + --dynamic-export \ + --show \ + --simplify \ + --verify \ +``` + +### Description of all arguments: + +- `config` : The path of a model config file. +- `--checkpoint` : The path of a model checkpoint file. +- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`. +- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `224 224`. +- `--opset-version` : The opset version of ONNX. If not specified, it will be set to `11`. +- `--dynamic-export` : Determines whether to export ONNX with dynamic input shape and output shapes. If not specified, it will be set to `False`. +- `--show`: Determines whether to print the architecture of the exported model. If not specified, it will be set to `False`. +- `--simplify`: Determines whether to simplify the exported ONNX model. If not specified, it will be set to `False`. +- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`. + +Example: + +```bash +python tools/deployment/pytorch2onnx.py \ + configs/resnet/resnet18_8xb16_cifar10.py \ + --checkpoint checkpoints/resnet/resnet18_8xb16_cifar10.pth \ + --output-file checkpoints/resnet/resnet18_8xb16_cifar10.onnx \ + --dynamic-export \ + --show \ + --simplify \ + --verify \ +``` + +## How to evaluate ONNX models with ONNX Runtime + +We prepare a tool `tools/deployment/test.py` to evaluate ONNX models with ONNXRuntime or TensorRT. + +### Prerequisite + +- Install onnx and onnxruntime-gpu + + ```shell + pip install onnx onnxruntime-gpu + ``` + +### Usage + +```bash +python tools/deployment/test.py \ + ${CONFIG_FILE} \ + ${ONNX_FILE} \ + --backend ${BACKEND} \ + --out ${OUTPUT_FILE} \ + --metrics ${EVALUATION_METRICS} \ + --metric-options ${EVALUATION_OPTIONS} \ + --show + --show-dir ${SHOW_DIRECTORY} \ + --cfg-options ${CFG_OPTIONS} \ +``` + +### Description of all arguments + +- `config`: The path of a model config file. +- `model`: The path of a ONNX model file. +- `--backend`: Backend for input model to run and should be `onnxruntime` or `tensorrt`. +- `--out`: The path of output result file in pickle format. +- `--metrics`: Evaluation metrics, which depends on the dataset, e.g., "accuracy", "precision", "recall", "f1_score", "support" for single label dataset, and "mAP", "CP", "CR", "CF1", "OP", "OR", "OF1" for multi-label dataset. +- `--show`: Determines whether to show classifier outputs. If not specified, it will be set to `False`. +- `--show-dir`: Directory where painted images will be saved +- `--metrics-options`: Custom options for evaluation, the key-value pair in `xxx=yyy` format will be kwargs for `dataset.evaluate()` function +- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file. + +### Results and Models + +This part selects ImageNet for onnxruntime verification. ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelConfigMetricPyTorchONNXRuntimeTensorRT-fp32TensorRT-fp16
ResNetresnet50_8xb32_in1k.pyTop 1 / 576.55 / 93.1576.49 / 93.2276.49 / 93.2276.50 / 93.20
ResNeXtresnext50-32x4d_8xb32_in1k.pyTop 1 / 577.90 / 93.6677.90 / 93.6677.90 / 93.6677.89 / 93.65
SE-ResNetseresnet50_8xb32_in1k.pyTop 1 / 577.74 / 93.8477.74 / 93.8477.74 / 93.8477.74 / 93.85
ShuffleNetV1shufflenet-v1-1x_16xb64_in1k.pyTop 1 / 568.13 / 87.8168.13 / 87.8168.13 / 87.8168.10 / 87.80
ShuffleNetV2shufflenet-v2-1x_16xb64_in1k.pyTop 1 / 569.55 / 88.9269.55 / 88.9269.55 / 88.9269.55 / 88.92
MobileNetV2mobilenet-v2_8xb32_in1k.pyTop 1 / 571.86 / 90.4271.86 / 90.4271.86 / 90.4271.88 / 90.40
+ +## List of supported models exportable to ONNX + +The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime. + +| Model | Config | Batch Inference | Dynamic Shape | Note | +| :----------: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :-------------: | :-----------: | ---- | +| MobileNetV2 | [mobilenet-v2_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py) | Y | Y | | +| ResNet | [resnet18_8xb16_cifar10.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnet/resnet18_8xb16_cifar10.py) | Y | Y | | +| ResNeXt | [resnext50-32x4d_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | Y | Y | | +| SE-ResNet | [seresnet50_8xb32_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/seresnet/seresnet50_8xb32_in1k.py) | Y | Y | | +| ShuffleNetV1 | [shufflenet-v1-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py) | Y | Y | | +| ShuffleNetV2 | [shufflenet-v2-1x_16xb64_in1k.py](https://github.com/open-mmlab/mmclassification/tree/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | Y | Y | | + +Notes: + +- *All models above are tested with Pytorch==1.6.0* + +## Reminders + +- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to dig a little deeper and debug a little bit more and hopefully solve them by yourself. + +## FAQs + +- None diff --git a/docs/en/tools/pytorch2torchscript.md b/docs/en/tools/pytorch2torchscript.md new file mode 100644 index 0000000..fca0856 --- /dev/null +++ b/docs/en/tools/pytorch2torchscript.md @@ -0,0 +1,56 @@ +# Pytorch to TorchScript (Experimental) + + + +- [Pytorch to TorchScript (Experimental)](#pytorch-to-torchscript-experimental) + - [How to convert models from Pytorch to TorchScript](#how-to-convert-models-from-pytorch-to-torchscript) + - [Usage](#usage) + - [Description of all arguments](#description-of-all-arguments) + - [Reminders](#reminders) + - [FAQs](#faqs) + + + +## How to convert models from Pytorch to TorchScript + +### Usage + +```bash +python tools/deployment/pytorch2torchscript.py \ + ${CONFIG_FILE} \ + --checkpoint ${CHECKPOINT_FILE} \ + --output-file ${OUTPUT_FILE} \ + --shape ${IMAGE_SHAPE} \ + --verify \ +``` + +### Description of all arguments + +- `config` : The path of a model config file. +- `--checkpoint` : The path of a model checkpoint file. +- `--output-file`: The path of output TorchScript model. If not specified, it will be set to `tmp.pt`. +- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `224 224`. +- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`. + +Example: + +```bash +python tools/deployment/pytorch2torchscript.py \ + configs/resnet/resnet18_8xb16_cifar10.py \ + --checkpoint checkpoints/resnet/resnet18_8xb16_cifar10.pth \ + --output-file checkpoints/resnet/resnet18_8xb16_cifar10.pt \ + --verify \ +``` + +Notes: + +- *All models are tested with Pytorch==1.8.1* + +## Reminders + +- For torch.jit.is_tracing() is only supported after v1.6. For users with pytorch v1.3-v1.5, we suggest early returning tensors manually. +- If you meet any problem with the models in this repo, please create an issue and it would be taken care of soon. + +## FAQs + +- None diff --git a/docs/en/tools/visualization.md b/docs/en/tools/visualization.md new file mode 100644 index 0000000..0128245 --- /dev/null +++ b/docs/en/tools/visualization.md @@ -0,0 +1,302 @@ +# Visualization + + + +- [Pipeline Visualization](#pipeline-visualization) +- [Learning Rate Schedule Visualization](#learning-rate-schedule-visualization) +- [Class Activation Map Visualization](#class-activation-map-visualization) +- [FAQs](#faqs) + + + +## Pipeline Visualization + +```bash +python tools/visualizations/vis_pipeline.py \ + ${CONFIG_FILE} \ + [--output-dir ${OUTPUT_DIR}] \ + [--phase ${DATASET_PHASE}] \ + [--number ${BUNBER_IMAGES_DISPLAY}] \ + [--skip-type ${SKIP_TRANSFORM_TYPE}] \ + [--mode ${DISPLAY_MODE}] \ + [--show] \ + [--adaptive] \ + [--min-edge-length ${MIN_EDGE_LENGTH}] \ + [--max-edge-length ${MAX_EDGE_LENGTH}] \ + [--bgr2rgb] \ + [--window-size ${WINDOW_SIZE}] \ + [--cfg-options ${CFG_OPTIONS}] +``` + +**Description of all arguments**： + +- `config` : The path of a model config file. +- `--output-dir`: The output path for visualized images. If not specified, it will be set to `''`, which means not to save. +- `--phase`: Phase of visualizing dataset，must be one of `[train, val, test]`. If not specified, it will be set to `train`. +- `--number`: The number of samples to visualized. If not specified, display all images in the dataset. +- `--skip-type`: The pipelines to be skipped. If not specified, it will be set to `['ToTensor', 'Normalize', 'ImageToTensor', 'Collect']`. +- `--mode`: The display mode, can be one of `[original, pipeline, concat]`. If not specified, it will be set to `concat`. +- `--show`: If set, display pictures in pop-up windows. +- `--adaptive`: If set, adaptively resize images for better visualization. +- `--min-edge-length`: The minimum edge length, used when `--adaptive` is set. When any side of the picture is smaller than `${MIN_EDGE_LENGTH}`, the picture will be enlarged while keeping the aspect ratio unchanged, and the short side will be aligned to `${MIN_EDGE_LENGTH}`. If not specified, it will be set to 200. +- `--max-edge-length`: The maximum edge length, used when `--adaptive` is set. When any side of the picture is larger than `${MAX_EDGE_LENGTH}`, the picture will be reduced while keeping the aspect ratio unchanged, and the long side will be aligned to `${MAX_EDGE_LENGTH}`. If not specified, it will be set to 1000. +- `--bgr2rgb`: If set, flip the color channel order of images. +- `--window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`. +- `--cfg-options` : Modifications to the configuration file, refer to [Tutorial 1: Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html). + +```{note} + +1. If the `--mode` is not specified, it will be set to `concat` as default, get the pictures stitched together by original pictures and transformed pictures; if the `--mode` is set to `original`, get the original pictures; if the `--mode` is set to `transformed`, get the transformed pictures; if the `--mode` is set to `pipeline`, get all the intermediate images through the pipeline. + +2. When `--adaptive` option is set, images that are too large or too small will be automatically adjusted, you can use `--min-edge-length` and `--max-edge-length` to set the adjust size. +``` + +**Examples**： + +1. In **'original'** mode, visualize 100 original pictures in the `CIFAR100` validation set, then display and save them in the `./tmp` folder： + +```shell +python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb +``` + +
+ +2. In **'transformed'** mode, visualize all the transformed pictures of the `ImageNet` training set and display them in pop-up windows： + +```shell +python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed +``` + +
+ +3. In **'concat'** mode, visualize 10 pairs of origin and transformed images for comparison in the `ImageNet` train set and save them in the `./tmp` folder： + +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive +``` + +
+ +4. In **'pipeline'** mode, visualize all the intermediate pictures in the `ImageNet` train set through the pipeline： + +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show +``` + +
+ +## Learning Rate Schedule Visualization + +```bash +python tools/visualizations/vis_lr.py \ + ${CONFIG_FILE} \ + --dataset-size ${DATASET_SIZE} \ + --ngpus ${NUM_GPUs} + --save-path ${SAVE_PATH} \ + --title ${TITLE} \ + --style ${STYLE} \ + --window-size ${WINDOW_SIZE} + --cfg-options +``` + +**Description of all arguments**： + +- `config` : The path of a model config file. +- `dataset-size` : The size of the datasets. If set，`build_dataset` will be skipped and `${DATASET_SIZE}` will be used as the size. Default to use the function `build_dataset`. +- `ngpus` : The number of GPUs used in training, default to be 1. +- `save-path` : The learning rate curve plot save path, default not to save. +- `title` : Title of figure. If not set, default to be config file name. +- `style` : Style of plt. If not set, default to be `whitegrid`. +- `window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`. +- `cfg-options` : Modifications to the configuration file, refer to [Tutorial 1: Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html). + +```{note} +Loading annotations maybe consume much time, you can directly specify the size of the dataset with `dataset-size` to save time. +``` + +**Examples**： + +```bash +python tools/visualizations/vis_lr.py configs/resnet/resnet50_b16x8_cifar100.py +``` + +
+ +When using ImageNet, directly specify the size of ImageNet, as below: + +```bash +python tools/visualizations/vis_lr.py configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py --dataset-size 1281167 --ngpus 4 --save-path ./repvgg-B3g4_4xb64-lr.jpg +``` + +
+ +## Class Activation Map Visualization + +MMClassification provides `tools\visualizations\vis_cam.py` tool to visualize class activation map. Please use `pip install "grad-cam>=1.3.6"` command to install [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam). + +The supported methods are as follows: + +| Method | What it does | +| ------------ | ---------------------------------------------------------------------------------------------------------------------------- | +| GradCAM | Weight the 2D activations by the average gradient | +| GradCAM++ | Like GradCAM but uses second order gradients | +| XGradCAM | Like GradCAM but scale the gradients by the normalized activations | +| EigenCAM | Takes the first principle component of the 2D Activations (no class discrimination, but seems to give great results) | +| EigenGradCAM | Like EigenCAM but with class discrimination: First principle component of Activations\*Grad. Looks like GradCAM, but cleaner | +| LayerCAM | Spatially weight the activations by positive gradients. Works better especially in lower layers | + +**Command**： + +```bash +python tools/visualizations/vis_cam.py \ + ${IMG} \ + ${CONFIG_FILE} \ + ${CHECKPOINT} \ + [--target-layers ${TARGET-LAYERS}] \ + [--preview-model] \ + [--method ${METHOD}] \ + [--target-category ${TARGET-CATEGORY}] \ + [--save-path ${SAVE_PATH}] \ + [--vit-like] \ + [--num-extra-tokens ${NUM-EXTRA-TOKENS}] + [--aug_smooth] \ + [--eigen_smooth] \ + [--device ${DEVICE}] \ + [--cfg-options ${CFG-OPTIONS}] +``` + +**Description of all arguments**： + +- `img` : The target picture path. +- `config` : The path of the model config file. +- `checkpoint` : The path of the checkpoint. +- `--target-layers` : The target layers to get activation maps, one or more network layers can be specified. If not set, use the norm layer of the last block. +- `--preview-model` : Whether to print all network layer names in the model. +- `--method` : Visualization method, supports `GradCAM`, `GradCAM++`, `XGradCAM`, `EigenCAM`, `EigenGradCAM`, `LayerCAM`, which is case insensitive. Defaults to `GradCAM`. +- `--target-category` : Target category, if not set, use the category detected by the given model. +- `--save-path` : The path to save the CAM visualization image. If not set, the CAM image will not be saved. +- `--vit-like` : Whether the network is ViT-like network. +- `--num-extra-tokens` : The number of extra tokens in ViT-like backbones. If not set, use num_extra_tokens the backbone. +- `--aug_smooth` : Whether to use TTA(Test Time Augment) to get CAM. +- `--eigen_smooth` : Whether to use the principal component to reduce noise. +- `--device` : The computing device used. Default to 'cpu'. +- `--cfg-options` : Modifications to the configuration file, refer to [Tutorial 1: Learn about Configs](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html). + +```{note} +The argument `--preview-model` can view all network layers names in the given model. It will be helpful if you know nothing about the model layers when setting `--target-layers`. +``` + +**Examples(CNN)**： + +Here are some examples of `target-layers` in ResNet-50, which can be any module or layer: + +- `'backbone.layer4'` means the output of the forth ResLayer. +- `'backbone.layer4.2'` means the output of the third BottleNeck block in the forth ResLayer. +- `'backbone.layer4.2.conv1'` means the output of the `conv1` layer in above BottleNeck block. + +```{note} +For `ModuleList` or `Sequential`, you can also use the index to specify which sub-module is the target layer. + +For example, the `backbone.layer4[-1]` is the same as `backbone.layer4.2` since `layer4` is a `Sequential` with three sub-modules. +``` + +1. Use different methods to visualize CAM for `ResNet50`, the `target-category` is the predicted result by the given checkpoint, using the default `target-layers`. + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/resnet/resnet50_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \ + --method GradCAM + # GradCAM++, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM + ``` + + | Image | GradCAM | GradCAM++ | EigenGradCAM | LayerCAM | + | ------------------------------------ | --------------------------------------- | ----------------------------------------- | -------------------------------------------- | ---------------------------------------- | + |
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| + +2. Use different `target-category` to get CAM from the same picture. In `ImageNet` dataset, the category 238 is 'Greater Swiss Mountain dog', the category 281 is 'tabby, tabby cat'. + + ```shell + python tools/visualizations/vis_cam.py \ + demo/cat-dog.png configs/resnet/resnet50_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \ + --target-layers 'backbone.layer4.2' \ + --method GradCAM \ + --target-category 238 + # --target-category 281 + ``` + + | Category | Image | GradCAM | XGradCAM | LayerCAM | + | -------- | ---------------------------------------------- | ------------------------------------------------ | ------------------------------------------------- | ------------------------------------------------- | + | Dog |
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| + +3. Use `--eigen-smooth` and `--aug-smooth` to improve visual effects. + + ```shell + python tools/visualizations/vis_cam.py \ + demo/dog.jpg \ + configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth \ + --target-layers 'backbone.layer16' \ + --method LayerCAM \ + --eigen-smooth --aug-smooth + ``` + + | Image | LayerCAM | eigen-smooth | aug-smooth | eigen&aug | + | ------------------------------------ | --------------------------------------- | ------------------------------------------- | ----------------------------------------- | ----------------------------------------- | + |
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| + +**Examples(Transformer)**： + +Here are some examples: + +- `'backbone.norm3'` for Swin-Transformer; +- `'backbone.layers[-1].ln1'` for ViT; + +For ViT-like networks, such as ViT, T2T-ViT and Swin-Transformer, the features are flattened. And for drawing the CAM, we need to specify the `--vit-like` argument to reshape the features into square feature maps. + +Besides the flattened features, some ViT-like networks also add extra tokens like the class token in ViT and T2T-ViT, and the distillation token in DeiT. In these networks, the final classification is done on the tokens computed in the last attention block, and therefore, the classification score will not be affected by other features and the gradient of the classification score with respect to them, will be zero. Therefore, you shouldn't use the output of the last attention block as the target layer in these networks. + +To exclude these extra tokens, we need know the number of extra tokens. Almost all transformer-based backbones in MMClassification have the `num_extra_tokens` attribute. If you want to use this tool in a new or third-party network that don't have the `num_extra_tokens` attribute, please specify it the `--num-extra-tokens` argument. + +1. Visualize CAM for `Swin Transformer`, using default `target-layers`: + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/swin_transformer/swin-tiny_16xb64_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth \ + --vit-like + ``` + +2. Visualize CAM for `Vision Transformer(ViT)`: + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py \ + https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth \ + --vit-like \ + --target-layers 'backbone.layers[-1].ln1' + ``` + +3. Visualize CAM for `T2T-ViT`: + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_3rdparty_8xb64_in1k_20210928-b7c09b62.pth \ + --vit-like \ + --target-layers 'backbone.encoder[-1].ln1' + ``` + +| Image | ResNet50 | ViT | Swin | T2T-ViT | +| --------------------------------------- | ------------------------------------------ | -------------------------------------- | --------------------------------------- | ------------------------------------------ | +|
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| + +## FAQs + +- None diff --git a/docs/en/tutorials/MMClassification_python.ipynb b/docs/en/tutorials/MMClassification_python.ipynb new file mode 100755 index 0000000..e046666 --- /dev/null +++ b/docs/en/tutorials/MMClassification_python.ipynb @@ -0,0 +1,2040 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XjQxmm04iTx4" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UdMfIsMpiODD" + }, + "source": [ + "# MMClassification Python API tutorial on Colab\n", + "\n", + "In this tutorial, we will introduce the following content:\n", + "\n", + "* How to install MMCls\n", + "* Inference a model with Python API\n", + "* Fine-tune a model with Python API" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iOl0X9UEiRvE" + }, + "source": [ + "## Install MMClassification\n", + "\n", + "Before using MMClassification, we need to prepare the environment with the following steps:\n", + "\n", + "1. Install Python, CUDA, C/C++ compiler and git\n", + "2. Install PyTorch (CUDA version)\n", + "3. Install mmcv\n", + "4. Clone mmcls source code from GitHub and install it\n", + "\n", + "Because this tutorial is on Google Colab, and the basic environment has been completed, we can skip the first two steps." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_i7cjqS_LtoP" + }, + "source": [ + "### Check environment" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c6MbAw10iUJI", + "outputId": "dd37cdf5-7bcf-4a03-f5b5-4b17c3ca16de" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content\n" + ] + } + ], + "source": [ + "%cd /content" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4IyFL3MaiYRu", + "outputId": "5008efdf-0356-4d93-ba9d-e51787036213" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DMw7QwvpiiUO", + "outputId": "33fa5eb8-d083-4a1f-d094-ab0f59e2818e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2020 NVIDIA Corporation\n", + "Built on Mon_Oct_12_20:09:46_PDT_2020\n", + "Cuda compilation tools, release 11.1, V11.1.105\n", + "Build cuda_11.1.TC455_06.29190527_0\n" + ] + } + ], + "source": [ + "# Check nvcc version\n", + "!nvcc -V" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4VIBU7Fain4D", + "outputId": "ec20652d-ca24-4b82-b407-e90354d728f8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ], + "source": [ + "# Check GCC version\n", + "!gcc --version" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24lDLCqFisZ9", + "outputId": "30ec9a1c-cdb3-436c-cdc8-f2a22afe254f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.9.0+cu111\n", + "True\n" + ] + } + ], + "source": [ + "# Check PyTorch installation\n", + "import torch, torchvision\n", + "print(torch.__version__)\n", + "print(torch.cuda.is_available())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R2aZNLUwizBs" + }, + "source": [ + "### Install MMCV\n", + "\n", + "MMCV is the basic package of all OpenMMLab packages. We have pre-built wheels on Linux, so we can download and install them directly.\n", + "\n", + "Please pay attention to PyTorch and CUDA versions to match the wheel.\n", + "\n", + "In the above steps, we have checked the version of PyTorch and CUDA, and they are 1.9.0 and 11.1 respectively, so we need to choose the corresponding wheel.\n", + "\n", + "In addition, we can also install the full version of mmcv (mmcv-full). It includes full features and various CUDA ops out of the box, but needs a longer time to build." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nla40LrLi7oo", + "outputId": "162bf14d-0d3e-4540-e85e-a46084a786b1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "Collecting mmcv\n", + " Downloading mmcv-1.3.15.tar.gz (352 kB)\n", + "\u001b[K |████████████████████████████████| 352 kB 5.2 MB/s \n", + "\u001b[?25hCollecting addict\n", + " Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv) (1.19.5)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv) (21.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv) (7.1.2)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv) (3.13)\n", + "Collecting yapf\n", + " Downloading yapf-0.31.0-py2.py3-none-any.whl (185 kB)\n", + "\u001b[K |████████████████████████████████| 185 kB 49.9 MB/s \n", + "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv) (2.4.7)\n", + "Building wheels for collected packages: mmcv\n", + " Building wheel for mmcv (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for mmcv: filename=mmcv-1.3.15-py2.py3-none-any.whl size=509835 sha256=793fe3796421336ca7a7740a1397a54016ba71ce95fd80cb80a116644adb4070\n", + " Stored in directory: /root/.cache/pip/wheels/b2/f4/4e/8f6d2dd2bef6b7eb8c89aa0e5d61acd7bff60aaf3d4d4b29b0\n", + "Successfully built mmcv\n", + "Installing collected packages: yapf, addict, mmcv\n", + "Successfully installed addict-2.4.0 mmcv-1.3.15 yapf-0.31.0\n" + ] + } + ], + "source": [ + "# Install mmcv\n", + "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.9.0/index.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GDTUrYvXjlRb" + }, + "source": [ + "### Clone and install MMClassification\n", + "\n", + "Next, we clone the latest mmcls repository from GitHub and install it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Bwme6tWHjl5s", + "outputId": "eae20624-4695-4cd9-c3e5-9c59596d150a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'mmclassification'...\n", + "remote: Enumerating objects: 4152, done.\u001b[K\n", + "remote: Counting objects: 100% (994/994), done.\u001b[K\n", + "remote: Compressing objects: 100% (576/576), done.\u001b[K\n", + "remote: Total 4152 (delta 476), reused 765 (delta 401), pack-reused 3158\u001b[K\n", + "Receiving objects: 100% (4152/4152), 8.20 MiB | 21.00 MiB/s, done.\n", + "Resolving deltas: 100% (2524/2524), done.\n" + ] + } + ], + "source": [ + "# Clone mmcls repository\n", + "!git clone https://github.com/open-mmlab/mmclassification.git\n", + "%cd mmclassification/\n", + "\n", + "# Install MMClassification from source\n", + "!pip install -e . " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hFg_oSG4j3zB", + "outputId": "05a91f9b-d41c-4ae7-d4fe-c30a30d3f639" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.16.0\n" + ] + } + ], + "source": [ + "# Check MMClassification installation\n", + "import mmcls\n", + "print(mmcls.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Mi3g6yzj96L" + }, + "source": [ + "## Inference a model with Python API\n", + "\n", + "MMClassification provides many pre-trained models, and you can check them by the link of [model zoo](https://mmclassification.readthedocs.io/en/latest/model_zoo.html). Almost all models can reproduce the results in original papers or reach higher metrics. And we can use these models directly.\n", + "\n", + "To use the pre-trained model, we need to do the following steps:\n", + "\n", + "- Prepare the model\n", + " - Prepare the config file\n", + " - Prepare the checkpoint file\n", + "- Build the model\n", + "- Inference with the model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nDQchz8CkJaT", + "outputId": "9805bd7d-cc2a-4269-b43d-257412f1df93" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-10-21 03:52:36-- https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.3.18, 2620:100:601b:18::a27d:812\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.3.18|:443... connected.\n", + "HTTP request sent, awaiting response... 301 Moved Permanently\n", + "Location: /s/raw/k5fsqi6qha09l1v/banana.png [following]\n", + "--2021-10-21 03:52:36-- https://www.dropbox.com/s/raw/k5fsqi6qha09l1v/banana.png\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com/cd/0/inline/BYYklQk6LNPXNm7o5xE_fxE2GA9reePyNajQgoe9roPlSrtsJd4WN6RVww7zrtNZWFq8iZv349MNQJlm7vVaqRBxTcd0ufxkqbcJYJvOrORpxOPV7mHmhMjKYUncez8YNqELGwDd-aeZqLGKBC8spSnx/file# [following]\n", + "--2021-10-21 03:52:36-- https://uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com/cd/0/inline/BYYklQk6LNPXNm7o5xE_fxE2GA9reePyNajQgoe9roPlSrtsJd4WN6RVww7zrtNZWFq8iZv349MNQJlm7vVaqRBxTcd0ufxkqbcJYJvOrORpxOPV7mHmhMjKYUncez8YNqELGwDd-aeZqLGKBC8spSnx/file\n", + "Resolving uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com (uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com)... 162.125.3.15, 2620:100:601b:15::a27d:80f\n", + "Connecting to uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com (uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com)|162.125.3.15|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 297299 (290K) [image/png]\n", + "Saving to: ‘demo/banana.png’\n", + "\n", + "demo/banana.png 100%[===================>] 290.33K --.-KB/s in 0.08s \n", + "\n", + "2021-10-21 03:52:36 (3.47 MB/s) - ‘demo/banana.png’ saved [297299/297299]\n", + "\n" + ] + } + ], + "source": [ + "# Get the demo image\n", + "!wget https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0 -O demo/banana.png" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 420 + }, + "id": "o2eiitWnkQq_", + "outputId": "192b3ebb-202b-4d6e-e178-561223024318" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "Image.open('demo/banana.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sRfAui8EkTDX" + }, + "source": [ + "### Prepare the config file and checkpoint file\n", + "\n", + "We configure a model with a config file and save weights with a checkpoint file.\n", + "\n", + "On GitHub, you can find all these pre-trained models in the config folder of MMClassification. For example, you can find the config files and checkpoints of Mobilenet V2 in [this link](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2).\n", + "\n", + "We have integrated many config files for various models in the MMClassification repository. As for the checkpoint, we can download it in advance, or just pass an URL to API, and MMClassification will download it before load weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VvRoZpBGkgpC", + "outputId": "68282782-015e-4f5c-cef2-79be3bf6a9b7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n" + ] + } + ], + "source": [ + "# Confirm the config file exists\n", + "!ls configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n", + "\n", + "# Specify the path of the config file and checkpoint file.\n", + "config_file = 'configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py'\n", + "checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eiYdsHoIkpD1" + }, + "source": [ + "### Inference the model\n", + "\n", + "MMClassification provides high-level Python API to inference models.\n", + "\n", + "At first, we build the MobilenetV2 model and load the checkpoint." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323, + "referenced_widgets": [ + "badf240bbb7d442fbd214e837edbffe2", + "520112917e0f4844995d418c5041d23a", + "9f3f6b72b4d14e2a96b9185331c8081b", + "a275bef3584b49ab9b680b528420d461", + "c4b2c6914a05497b8d2b691bd6dda6da", + "863d2a8cc4074f2e890ba6aea7c54384", + "be55ab36267d4dcab1d83dfaa8540270", + "31475aa888da4c8d844ba99a0b3397f5", + "e310c50e610248dd897fbbf5dd09dd7a", + "8a8ab7c27e404459951cffe7a32b8faa", + "e1a3dce90c1a4804a9ef0c687a9c0703" + ] + }, + "id": "KwJWlR2QkpiV", + "outputId": "982b365e-d3be-4e3d-dee7-c507a8020292" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use load_from_http loader\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "badf240bbb7d442fbd214e837edbffe2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0.00/13.5M [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "# Visualize the inference result\n", + "show_result_pyplot(model, img, result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oDMr3Bx_lESy" + }, + "source": [ + "## Fine-tune a model with Python API\n", + "\n", + "Fine-tuning is to re-train a model which has been trained on another dataset (like ImageNet) to fit our target dataset. Compared with training from scratch, fine-tuning is much faster can avoid over-fitting problems during training on a small dataset.\n", + "\n", + "The basic steps of fine-tuning are as below:\n", + "\n", + "1. Prepare the target dataset and meet MMClassification's requirements.\n", + "2. Modify the training config.\n", + "3. Start training and validation.\n", + "\n", + "More details are in [the docs](https://mmclassification.readthedocs.io/en/latest/tutorials/finetune.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TJtKKwAvlHX_" + }, + "source": [ + "### Prepare the target dataset\n", + "\n", + "Here we download the cats & dogs dataset directly. You can find more introduction about the dataset in the [tools tutorial](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/tutorials/MMClassification_tools.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3vBfU8GGlFPS", + "outputId": "b12dadb4-ccbc-45b4-bb08-3d24977ed93c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-10-21 03:57:58-- https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.80.18, 2620:100:6018:18::a27d:312\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.80.18|:443... connected.\n", + "HTTP request sent, awaiting response... 301 Moved Permanently\n", + "Location: /s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip [following]\n", + "--2021-10-21 03:57:58-- https://www.dropbox.com/s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://ucfd8157272a6270e100392293da.dl.dropboxusercontent.com/cd/0/inline/BYbFG6Zo1S3l2kJtqLrJIne9lTLgQn-uoJxmUjhLSkp36V7AoiwlyR2gP0XVoUQt9WzF2ZsmeERagMy7rpsNoIYG4MjsYA90i_JsarFDs9PHhXHw9qwHpHqBvgd4YU_mwDQHuouJ_oCU1kft04QgCVRg/file# [following]\n", + "--2021-10-21 03:57:59-- https://ucfd8157272a6270e100392293da.dl.dropboxusercontent.com/cd/0/inline/BYbFG6Zo1S3l2kJtqLrJIne9lTLgQn-uoJxmUjhLSkp36V7AoiwlyR2gP0XVoUQt9WzF2ZsmeERagMy7rpsNoIYG4MjsYA90i_JsarFDs9PHhXHw9qwHpHqBvgd4YU_mwDQHuouJ_oCU1kft04QgCVRg/file\n", + "Resolving ucfd8157272a6270e100392293da.dl.dropboxusercontent.com (ucfd8157272a6270e100392293da.dl.dropboxusercontent.com)... 162.125.3.15, 2620:100:6018:15::a27d:30f\n", + "Connecting to ucfd8157272a6270e100392293da.dl.dropboxusercontent.com (ucfd8157272a6270e100392293da.dl.dropboxusercontent.com)|162.125.3.15|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: /cd/0/inline2/BYYSXb-0kWS7Lpk-cdrgBGzcOBfsvy7KjhqWEgjI5L9xfcaXohKlVeFMNFVyqvCwZLym2kWCD0nwURRpQ2mnHICrNsrvTvavbn24hk1Bd3_lXX08LBBe3C6YvD2U_iP8UMXROqm-B3JtnBjeMpk1R4YZ0O6aVLgKu0eET9RXsRaNCczD2lTK_i72zmbYhGmBvlRWmf_yQnnS5WKpGhSAobznIqKzw78yPzo5FsgGiEj5VXb91AElrKVAW8HFC9EhdUs7RrL3q9f0mQ9TbQpauoAp32TL3YQcuAp891Rv-EmDVxzfMwKVTGU8hxR2SiIWkse4u2QGhliqhdha7qBu7sIPcIoeI5-DdSoc6XG77vTYTRhrs_cf7rQuTPH2gTIUwTY/file [following]\n", + "--2021-10-21 03:57:59-- https://ucfd8157272a6270e100392293da.dl.dropboxusercontent.com/cd/0/inline2/BYYSXb-0kWS7Lpk-cdrgBGzcOBfsvy7KjhqWEgjI5L9xfcaXohKlVeFMNFVyqvCwZLym2kWCD0nwURRpQ2mnHICrNsrvTvavbn24hk1Bd3_lXX08LBBe3C6YvD2U_iP8UMXROqm-B3JtnBjeMpk1R4YZ0O6aVLgKu0eET9RXsRaNCczD2lTK_i72zmbYhGmBvlRWmf_yQnnS5WKpGhSAobznIqKzw78yPzo5FsgGiEj5VXb91AElrKVAW8HFC9EhdUs7RrL3q9f0mQ9TbQpauoAp32TL3YQcuAp891Rv-EmDVxzfMwKVTGU8hxR2SiIWkse4u2QGhliqhdha7qBu7sIPcIoeI5-DdSoc6XG77vTYTRhrs_cf7rQuTPH2gTIUwTY/file\n", + "Reusing existing connection to ucfd8157272a6270e100392293da.dl.dropboxusercontent.com:443.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 228802825 (218M) [application/zip]\n", + "Saving to: ‘cats_dogs_dataset.zip’\n", + "\n", + "cats_dogs_dataset.z 100%[===================>] 218.20M 86.3MB/s in 2.5s \n", + "\n", + "2021-10-21 03:58:02 (86.3 MB/s) - ‘cats_dogs_dataset.zip’ saved [228802825/228802825]\n", + "\n" + ] + } + ], + "source": [ + "# Download the cats & dogs dataset\n", + "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", + "!mkdir -p data\n", + "!unzip -qo cats_dogs_dataset.zip -d ./data/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15iKNG0SlV9y" + }, + "source": [ + "### Read the config file and modify the config\n", + "\n", + "In the [tools tutorial](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/tutorials/MMClassification_tools.ipynb), we have introduced all parts of the config file, and here we can modify the loaded config by Python code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WCfnDavFlWrK" + }, + "outputs": [], + "source": [ + "# Load the base config file\n", + "from mmcv import Config\n", + "from mmcls.utils import auto_select_device\n", + "\n", + "cfg = Config.fromfile('configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py')\n", + "cfg.device = auto_select_device()\n", + "\n", + "# Modify the number of classes in the head.\n", + "cfg.model.head.num_classes = 2\n", + "cfg.model.head.topk = (1, )\n", + "\n", + "# Load the pre-trained model's checkpoint.\n", + "cfg.model.backbone.init_cfg = dict(type='Pretrained', checkpoint=checkpoint_file, prefix='backbone')\n", + "\n", + "# Specify sample size and number of workers.\n", + "cfg.data.samples_per_gpu = 32\n", + "cfg.data.workers_per_gpu = 2\n", + "\n", + "# Specify the path and meta files of training dataset\n", + "cfg.data.train.data_prefix = 'data/cats_dogs_dataset/training_set/training_set'\n", + "cfg.data.train.classes = 'data/cats_dogs_dataset/classes.txt'\n", + "\n", + "# Specify the path and meta files of validation dataset\n", + "cfg.data.val.data_prefix = 'data/cats_dogs_dataset/val_set/val_set'\n", + "cfg.data.val.ann_file = 'data/cats_dogs_dataset/val.txt'\n", + "cfg.data.val.classes = 'data/cats_dogs_dataset/classes.txt'\n", + "\n", + "# Specify the path and meta files of test dataset\n", + "cfg.data.test.data_prefix = 'data/cats_dogs_dataset/test_set/test_set'\n", + "cfg.data.test.ann_file = 'data/cats_dogs_dataset/test.txt'\n", + "cfg.data.test.classes = 'data/cats_dogs_dataset/classes.txt'\n", + "\n", + "# Specify the normalization parameters in data pipeline\n", + "normalize_cfg = dict(type='Normalize', mean=[124.508, 116.050, 106.438], std=[58.577, 57.310, 57.437], to_rgb=True)\n", + "cfg.data.train.pipeline[3] = normalize_cfg\n", + "cfg.data.val.pipeline[3] = normalize_cfg\n", + "cfg.data.test.pipeline[3] = normalize_cfg\n", + "\n", + "# Modify the evaluation metric\n", + "cfg.evaluation['metric_options']={'topk': (1, )}\n", + "\n", + "# Specify the optimizer\n", + "cfg.optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n", + "cfg.optimizer_config = dict(grad_clip=None)\n", + "\n", + "# Specify the learning rate scheduler\n", + "cfg.lr_config = dict(policy='step', step=1, gamma=0.1)\n", + "cfg.runner = dict(type='EpochBasedRunner', max_epochs=2)\n", + "\n", + "# Specify the work directory\n", + "cfg.work_dir = './work_dirs/cats_dogs_dataset'\n", + "\n", + "# Output logs for every 10 iterations\n", + "cfg.log_config.interval = 10\n", + "\n", + "# Set the random seed and enable the deterministic option of cuDNN\n", + "# to keep the results' reproducible.\n", + "from mmcls.apis import set_random_seed\n", + "cfg.seed = 0\n", + "set_random_seed(0, deterministic=True)\n", + "\n", + "cfg.gpu_ids = range(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HDerVUPFmNR0" + }, + "source": [ + "### Fine-tune the model\n", + "\n", + "Use the API `train_model` to fine-tune our model on the cats & dogs dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P7unq5cNmN8G", + "outputId": "bf32711b-7bdf-45ee-8db5-e8699d3eff91" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + 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"output_type": "stream", + "text": [ + "Use load_from_http loader\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:04:12,965 - mmcv - INFO - \n", + "backbone.layer5.0.conv.2.conv.weight - torch.Size([96, 384, 1, 1]): \n", + "PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth \n", + " \n", + "2021-10-21 04:04:12,967 - mmcv - INFO - \n", + "backbone.layer5.0.conv.2.bn.weight - torch.Size([96]): \n", + "PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth \n", + " \n", + "2021-10-21 04:04:12,969 - mmcv - INFO - \n", + "backbone.layer5.0.conv.2.bn.bias - torch.Size([96]): \n", + "PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth \n", + " \n", + "2021-10-21 04:04:12,970 - mmcv - 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"backbone.conv2.bn.bias - torch.Size([1280]): \n", + "PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth \n", + " \n", + "2021-10-21 04:04:13,055 - mmcv - INFO - \n", + "head.fc.weight - torch.Size([2, 1280]): \n", + "NormalInit: mean=0, std=0.01, bias=0 \n", + " \n", + "2021-10-21 04:04:13,057 - mmcv - INFO - \n", + "head.fc.bias - torch.Size([2]): \n", + "NormalInit: mean=0, std=0.01, bias=0 \n", + " \n", + "2021-10-21 04:04:13,408 - mmcls - INFO - Start running, host: root@cc5b42005207, work_dir: /content/mmclassification/work_dirs/cats_dogs_dataset\n", + "2021-10-21 04:04:13,412 - mmcls - INFO - Hooks will be executed in the following order:\n", + "before_run:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_epoch:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_iter:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + " -------------------- \n", + "after_train_iter:\n", + "(ABOVE_NORMAL) OptimizerHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "after_train_epoch:\n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_epoch:\n", + "(LOW ) IterTimerHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_epoch:\n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "2021-10-21 04:04:13,417 - mmcls - INFO - workflow: [('train', 1)], max: 2 epochs\n", + "2021-10-21 04:04:18,924 - mmcls - INFO - Epoch [1][10/201]\tlr: 5.000e-03, eta: 0:03:29, time: 0.535, data_time: 0.259, memory: 1709, loss: 0.3917\n", + "2021-10-21 04:04:21,743 - mmcls - INFO - Epoch [1][20/201]\tlr: 5.000e-03, eta: 0:02:35, time: 0.281, data_time: 0.019, memory: 1709, loss: 0.3508\n", + "2021-10-21 04:04:24,552 - mmcls - INFO - Epoch [1][30/201]\tlr: 5.000e-03, eta: 0:02:15, time: 0.280, data_time: 0.020, memory: 1709, loss: 0.3955\n", + "2021-10-21 04:04:27,371 - mmcls - INFO - Epoch [1][40/201]\tlr: 5.000e-03, eta: 0:02:04, time: 0.282, data_time: 0.021, memory: 1709, loss: 0.2485\n", + "2021-10-21 04:04:30,202 - mmcls - INFO - Epoch [1][50/201]\tlr: 5.000e-03, eta: 0:01:56, time: 0.283, data_time: 0.021, memory: 1709, loss: 0.4196\n", + "2021-10-21 04:04:33,021 - mmcls - INFO - Epoch [1][60/201]\tlr: 5.000e-03, eta: 0:01:50, time: 0.282, data_time: 0.023, memory: 1709, loss: 0.4994\n", + "2021-10-21 04:04:35,800 - mmcls - INFO - Epoch [1][70/201]\tlr: 5.000e-03, eta: 0:01:45, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.4372\n", + "2021-10-21 04:04:38,595 - mmcls - INFO - Epoch [1][80/201]\tlr: 5.000e-03, eta: 0:01:40, time: 0.280, data_time: 0.019, memory: 1709, loss: 0.3179\n", + "2021-10-21 04:04:41,351 - mmcls - INFO - Epoch [1][90/201]\tlr: 5.000e-03, eta: 0:01:36, time: 0.276, data_time: 0.018, memory: 1709, loss: 0.3175\n", + "2021-10-21 04:04:44,157 - mmcls - INFO - Epoch [1][100/201]\tlr: 5.000e-03, eta: 0:01:32, time: 0.280, data_time: 0.021, memory: 1709, loss: 0.3412\n", + "2021-10-21 04:04:46,974 - mmcls - INFO - Epoch [1][110/201]\tlr: 5.000e-03, eta: 0:01:28, time: 0.282, data_time: 0.019, memory: 1709, loss: 0.2985\n", + "2021-10-21 04:04:49,767 - mmcls - INFO - Epoch [1][120/201]\tlr: 5.000e-03, eta: 0:01:25, time: 0.280, data_time: 0.021, memory: 1709, loss: 0.2778\n", + "2021-10-21 04:04:52,553 - mmcls - INFO - Epoch [1][130/201]\tlr: 5.000e-03, eta: 0:01:21, time: 0.278, data_time: 0.021, memory: 1709, loss: 0.2229\n", + "2021-10-21 04:04:55,356 - mmcls - INFO - Epoch [1][140/201]\tlr: 5.000e-03, eta: 0:01:18, time: 0.280, data_time: 0.021, memory: 1709, loss: 0.2318\n", + "2021-10-21 04:04:58,177 - mmcls - INFO - Epoch [1][150/201]\tlr: 5.000e-03, eta: 0:01:14, time: 0.282, data_time: 0.022, memory: 1709, loss: 0.2333\n", + "2021-10-21 04:05:01,025 - mmcls - INFO - Epoch [1][160/201]\tlr: 5.000e-03, eta: 0:01:11, time: 0.285, data_time: 0.020, memory: 1709, loss: 0.2783\n", + "2021-10-21 04:05:03,833 - mmcls - INFO - Epoch [1][170/201]\tlr: 5.000e-03, eta: 0:01:08, time: 0.281, data_time: 0.022, memory: 1709, loss: 0.2132\n", + "2021-10-21 04:05:06,648 - mmcls - INFO - Epoch [1][180/201]\tlr: 5.000e-03, eta: 0:01:05, time: 0.281, data_time: 0.019, memory: 1709, loss: 0.2096\n", + "2021-10-21 04:05:09,472 - mmcls - INFO - Epoch [1][190/201]\tlr: 5.000e-03, eta: 0:01:02, time: 0.282, data_time: 0.020, memory: 1709, loss: 0.1729\n", + "2021-10-21 04:05:12,229 - mmcls - INFO - Epoch [1][200/201]\tlr: 5.000e-03, eta: 0:00:59, time: 0.275, data_time: 0.018, memory: 1709, loss: 0.1969\n", + "2021-10-21 04:05:12,275 - mmcls - INFO - Saving checkpoint at 1 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 104.1 task/s, elapsed: 15s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:05:27,767 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", + "2021-10-21 04:05:32,987 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:57, time: 0.505, data_time: 0.238, memory: 1709, loss: 0.1764\n", + "2021-10-21 04:05:35,779 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:54, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1514\n", + "2021-10-21 04:05:38,537 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:51, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1395\n", + "2021-10-21 04:05:41,283 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:48, time: 0.275, data_time: 0.020, memory: 1709, loss: 0.1508\n", + "2021-10-21 04:05:44,017 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:44, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.1771\n", + "2021-10-21 04:05:46,800 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:41, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1438\n", + "2021-10-21 04:05:49,570 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:38, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1321\n", + "2021-10-21 04:05:52,314 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:35, time: 0.275, data_time: 0.021, memory: 1709, loss: 0.1629\n", + "2021-10-21 04:05:55,052 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:32, time: 0.273, data_time: 0.021, memory: 1709, loss: 0.1574\n", + "2021-10-21 04:05:57,791 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:29, time: 0.274, data_time: 0.019, memory: 1709, loss: 0.1220\n", + "2021-10-21 04:06:00,534 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:26, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.2550\n", + "2021-10-21 04:06:03,295 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:23, time: 0.276, data_time: 0.019, memory: 1709, loss: 0.1528\n", + "2021-10-21 04:06:06,048 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:20, time: 0.275, data_time: 0.022, memory: 1709, loss: 0.1223\n", + "2021-10-21 04:06:08,811 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:17, time: 0.276, data_time: 0.021, memory: 1709, loss: 0.1734\n", + "2021-10-21 04:06:11,576 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:14, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1527\n", + "2021-10-21 04:06:14,330 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:11, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1910\n", + "2021-10-21 04:06:17,106 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:09, time: 0.277, data_time: 0.019, memory: 1709, loss: 0.1922\n", + "2021-10-21 04:06:19,855 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:06, time: 0.274, data_time: 0.023, memory: 1709, loss: 0.1760\n", + "2021-10-21 04:06:22,638 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.278, data_time: 0.019, memory: 1709, loss: 0.1739\n", + "2021-10-21 04:06:25,367 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.272, data_time: 0.020, memory: 1709, loss: 0.1654\n", + "2021-10-21 04:06:25,410 - mmcls - INFO - Saving checkpoint at 2 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 105.5 task/s, elapsed: 15s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:06:40,694 - mmcls - INFO - Epoch(val) [2][51]\taccuracy_top-1: 97.5016\n" + ] + } + ], + "source": [ + "import time\n", + "import mmcv\n", + "import os.path as osp\n", + "\n", + "from mmcls.datasets import build_dataset\n", + "from mmcls.models import build_classifier\n", + "from mmcls.apis import train_model\n", + "\n", + "# Create the work directory\n", + "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", + "# Build the classifier\n", + "model = build_classifier(cfg.model)\n", + "model.init_weights()\n", + "# Build the dataset\n", + "datasets = [build_dataset(cfg.data.train)]\n", + "# Add `CLASSES` attributes to help visualization\n", + "model.CLASSES = datasets[0].CLASSES\n", + "# Start fine-tuning\n", + "train_model(\n", + " model,\n", + " datasets,\n", + " cfg,\n", + " distributed=False,\n", + " validate=True,\n", + " timestamp=time.strftime('%Y%m%d_%H%M%S', time.localtime()),\n", + " meta=dict())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "HsoGBZA3miui", + "outputId": "eb2e09f5-55ce-4165-b754-3b75dbc829ab" + }, + "outputs": [ + { + "data": { + "image/png": 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"min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/en/tutorials/MMClassification_tools.ipynb b/docs/en/tutorials/MMClassification_tools.ipynb new file mode 100755 index 0000000..ee87e71 --- /dev/null +++ b/docs/en/tutorials/MMClassification_tools.ipynb @@ -0,0 +1,1249 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "MMClassification_tools.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XjQxmm04iTx4", + "tags": [] + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4z0JDgisPRr-" + }, + "source": [ + "# MMClassification tools tutorial on Colab\n", + "\n", + "In this tutorial, we will introduce the following content:\n", + "\n", + "* How to install MMCls\n", + "* Prepare data\n", + "* Prepare the config file\n", + "* Train and test model with shell command" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "inm7Ciy5PXrU" + }, + "source": [ + "## Install MMClassification\n", + "\n", + "Before using MMClassification, we need to prepare the environment with the following steps:\n", + "\n", + "1. Install Python, CUDA, C/C++ compiler and git\n", + "2. Install PyTorch (CUDA version)\n", + "3. Install mmcv\n", + "4. Clone mmcls source code from GitHub and install it\n", + "\n", + "Because this tutorial is on Google Colab, and the basic environment has been completed, we can skip the first two steps." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TDOxbcDvPbNk" + }, + "source": [ + "### Check environment" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c6MbAw10iUJI", + "outputId": "8d3d6b53-c69b-4425-ce0c-bfb8d31ab971" + }, + "source": [ + "%cd /content" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4IyFL3MaiYRu", + "outputId": "c46dc718-27de-418b-da17-9d5a717e8424" + }, + "source": [ + "!pwd" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DMw7QwvpiiUO", + "outputId": "0d852285-07c4-48d3-e537-4a51dea04d10" + }, + "source": [ + "# Check nvcc version\n", + "!nvcc -V" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2020 NVIDIA Corporation\n", + "Built on Mon_Oct_12_20:09:46_PDT_2020\n", + "Cuda compilation tools, release 11.1, V11.1.105\n", + "Build cuda_11.1.TC455_06.29190527_0\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4VIBU7Fain4D", + "outputId": "fb34a7b6-8eda-4180-e706-1bf67d1a6fd4" + }, + "source": [ + "# Check GCC version\n", + "!gcc --version" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24lDLCqFisZ9", + "outputId": "304ad2f7-a9bb-4441-d25b-09b5516ccd74" + }, + "source": [ + "# Check PyTorch installation\n", + "import torch, torchvision\n", + "print(torch.__version__)\n", + "print(torch.cuda.is_available())" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1.9.0+cu111\n", + "True\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R2aZNLUwizBs" + }, + "source": [ + "### Install MMCV\n", + "\n", + "MMCV is the basic package of all OpenMMLab packages. We have pre-built wheels on Linux, so we can download and install them directly.\n", + "\n", + "Please pay attention to PyTorch and CUDA versions to match the wheel.\n", + "\n", + "In the above steps, we have checked the version of PyTorch and CUDA, and they are 1.9.0 and 11.1 respectively, so we need to choose the corresponding wheel.\n", + "\n", + "In addition, we can also install the full version of mmcv (mmcv-full). It includes full features and various CUDA ops out of the box, but needs a longer time to build." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nla40LrLi7oo", + "outputId": "a17d50d6-05b7-45d6-c3fb-6a2507415cf5" + }, + "source": [ + "# Install mmcv\n", + "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "Collecting mmcv\n", + " Downloading mmcv-1.3.15.tar.gz (352 kB)\n", + "\u001b[K |████████████████████████████████| 352 kB 12.8 MB/s \n", + "\u001b[?25hCollecting addict\n", + " Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv) (1.19.5)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv) (21.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv) (7.1.2)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv) (3.13)\n", + "Collecting yapf\n", + " Downloading yapf-0.31.0-py2.py3-none-any.whl (185 kB)\n", + "\u001b[K |████████████████████████████████| 185 kB 49.3 MB/s \n", + "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv) (2.4.7)\n", + "Building wheels for collected packages: mmcv\n", + " Building wheel for mmcv (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for mmcv: filename=mmcv-1.3.15-py2.py3-none-any.whl size=509835 sha256=13b8c5d70c29029916f661f2dc9b773b74a9ea4e0758491a7b5c15c798efaa61\n", + " Stored in directory: /root/.cache/pip/wheels/b2/f4/4e/8f6d2dd2bef6b7eb8c89aa0e5d61acd7bff60aaf3d4d4b29b0\n", + "Successfully built mmcv\n", + "Installing collected packages: yapf, addict, mmcv\n", + "Successfully installed addict-2.4.0 mmcv-1.3.15 yapf-0.31.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GDTUrYvXjlRb" + }, + "source": [ + "### Clone and install MMClassification\n", + "\n", + "Next, we clone the latest mmcls repository from GitHub and install it." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Bwme6tWHjl5s", + "outputId": "7e2d54c8-b134-405a-b014-194da1708776" + }, + "source": [ + "# Clone mmcls repository\n", + "!git clone https://github.com/open-mmlab/mmclassification.git\n", + "%cd mmclassification/\n", + "\n", + "# Install MMClassification from source\n", + "!pip install -e . " + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'mmclassification'...\n", + "remote: Enumerating objects: 4152, done.\u001b[K\n", + "remote: Counting objects: 100% (994/994), done.\u001b[K\n", + "remote: Compressing objects: 100% (579/579), done.\u001b[K\n", + "remote: Total 4152 (delta 476), reused 761 (delta 398), pack-reused 3158\u001b[K\n", + "Receiving objects: 100% (4152/4152), 8.21 MiB | 19.02 MiB/s, done.\n", + "Resolving deltas: 100% (2518/2518), done.\n", + "/content/mmclassification\n", + "Obtaining file:///content/mmclassification\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmcls==0.16.0) (3.2.2)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcls==0.16.0) (1.19.5)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcls==0.16.0) (21.0)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (0.10.0)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (1.3.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (2.4.7)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (2.8.2)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->mmcls==0.16.0) (1.15.0)\n", + "Installing collected packages: mmcls\n", + " Running setup.py develop for mmcls\n", + "Successfully installed mmcls-0.16.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hFg_oSG4j3zB", + "outputId": "1cc74bac-f918-4f0e-bf56-9f13447dfce1" + }, + "source": [ + "# Check MMClassification installation\n", + "import mmcls\n", + "print(mmcls.__version__)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.16.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HCOHRp3iV5Xk" + }, + "source": [ + "## Prepare data" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XHCHnKb_Qd3P", + "outputId": "35496010-ee57-4e72-af00-2af55dc80f47" + }, + "source": [ + "# Download the dataset (cats & dogs dataset)\n", + "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", + "!mkdir -p data\n", + "!unzip -q cats_dogs_dataset.zip -d ./data/" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2021-10-21 02:47:54-- https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.67.18, 2620:100:6020:18::a27d:4012\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.67.18|:443... connected.\n", + "HTTP request sent, awaiting response... 301 Moved Permanently\n", + "Location: /s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip [following]\n", + "--2021-10-21 02:47:54-- https://www.dropbox.com/s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc88da1070f63f9a78ee48b59098.dl.dropboxusercontent.com/cd/0/inline/BYb26ayxWasysNPC1wSer1N9YqdOShCMIBzSIQ5NKaIoKQQ47lxZ3y7DkjKNLrYiSHkA_KgTE47_9jUHaHW79JqDtcSNEAO3unPfo8bPwsxaQUHqo97L_RjsSBhWg4HZStWRbLIJUl5WUOtpETbSQtvD/file# [following]\n", + "--2021-10-21 02:47:54-- 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uc88da1070f63f9a78ee48b59098.dl.dropboxusercontent.com:443.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 228802825 (218M) [application/zip]\n", + "Saving to: ‘cats_dogs_dataset.zip’\n", + "\n", + "cats_dogs_dataset.z 100%[===================>] 218.20M 16.9MB/s in 13s \n", + "\n", + "2021-10-21 02:48:08 (16.9 MB/s) - ‘cats_dogs_dataset.zip’ saved [228802825/228802825]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e4t2P2aTQokX" + }, + "source": [ + "**After downloading and extraction,** we get \"Cats and Dogs Dataset\" and the file structure is as below:\n", + "```\n", + "data/cats_dogs_dataset\n", + "├── classes.txt\n", + "├── test.txt\n", + "├── val.txt\n", + "├── training_set\n", + "│ ├── training_set\n", + "│ │ ├── cats\n", + "│ │ │ ├── cat.1.jpg\n", + "│ │ │ ├── cat.2.jpg\n", + "│ │ │ ├── ...\n", + "│ │ ├── dogs\n", + "│ │ │ ├── dog.2.jpg\n", + "│ │ │ ├── dog.3.jpg\n", + "│ │ │ ├── ...\n", + "├── val_set\n", + "│ ├── val_set\n", + "│ │ ├── cats\n", + "│ │ │ ├── cat.3.jpg\n", + "│ │ │ ├── cat.5.jpg\n", + "│ │ │ ├── ...\n", + "│ │ ├── dogs\n", + "│ │ │ ├── dog.1.jpg\n", + "│ │ │ ├── dog.6.jpg\n", + "│ │ │ ├── ...\n", + "├── test_set\n", + "│ ├── test_set\n", + "│ │ ├── cats\n", + "│ │ │ ├── cat.4001.jpg\n", + "│ │ │ ├── cat.4002.jpg\n", + "│ │ │ ├── ...\n", + "│ │ ├── dogs\n", + "│ │ │ ├── dog.4001.jpg\n", + "│ │ │ ├── dog.4002.jpg\n", + "│ │ │ ├── ...\n", + "```\n", + "\n", + "You can use shell command `tree data/cats_dogs_dataset` to check the structure." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "46tyHTdtQy_Z", + "outputId": "6124a89e-03eb-4917-a0bf-df6a391eb280" + }, + "source": [ + "# Pick an image and visualize it\n", + "from PIL import Image\n", + "Image.open('data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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fKKwXY4g+1HVdliVymk6nHzx+GPo+BC+QMkvPn399c3PVdc3sYHzx5uXJ6WGemf/8N/+xblZHx7PQN5NR8dGTR7/77a+Cbwn59avnTx4/9H3TtQ0Bhr7zfZtnVlK6vb5ZLm4NQe6yIssIxfdtW29C7xHEWLSOjLVElERSSiHGEEJdr9fr9XK5nK/m8/n8dn59e3t7u1wMDpuxuIsPO0U1jNmhgH2j/vxqtQKALMum0+l0Os3zTLXDpl4horWkf82yDFGcM9bavm36tvFdrxGawW1smTnHzE2z8V2v77KOBNLbt2/X67VzTjezWjPd5JPJJMuyrus05NZ4SiN5NbaDX9Z1nbqsek7btgMU5JxzRW7IKXZojHMuR0ZDzhpjAFFkcNNokMB9+6PH4DFaa7uuUxX44MEDNd/OuTt37uhbFJO01up1VFX1ve99786dOxrOqgevTrmKlt6DSvtgAP/wUIR6ODRm0AMM6X/qNuDO+UyAaipZUASZgRlEsKiqdV0XVeVj/Og7n9g8Ozo9sXk2OZjdLhfj2TQri6vbm1W9+dN/9hO05uTk5NWrV9/5znfOz88vLi5++MMfnpyc6FV57w8PD4uiUP/wt7/97cuXL//xH//x9va2KIrZbKaPqmma29vb3/72t4vFYjabVVUFAOPxWCMTl5lqVLRd3fXNerO8ur5EEmMRgcsia5vN4cF0ubh1lgyBcJzP5xqBiPBmtT44nOaFOz06/MlP/jQvXAy9c/b09CT6vl4v1svbm5urosgeP35EBASSO/v0q99H3x/OpkXmzk+OFzfX7Wb9wYP7yffL25sQeiIQSEQwHo+so029WixuQ+jJQF44a01KofdtjB4GvBoSAACKSNI9swvIt3uJmVmiCLddHZMnAxrpEMHOSw9bwCb2KqXOuTzPVZdZa8qyqKoqz3PryFrbdY2i9+r3xRjbtl0ul7rXU0pkMLPbXAincHBwsFotrt9eEdF4UnH0vu0Kl63Xa0Q8OTm5d+/eeDweHDcAUIezaRqVKI3xUkoqhAPerkZF10FxpqZpFNExxhhnsywja3ST6+5VxzAz1lpryWzFTz9XDaC6ixqeWWvH43Ge53pxAKB46Xw+1/iwqqrXr1+/ePFCjaR+AgAcHx//5je/2Ww2RVE45/RD6rper9d93zdNo8pG10ghx6Io9BtVC2RZpiC1Yll6q3onapb1nxpDF0VRVVVZVU3TZFmWlZVzzthMAej5fEHGhpgOj45Diud375zdOe98/3/8X//ng0cPL6/edn0vAOWoCilOZtO79++9fP1qNBk/evzBZ599Nh6Pf/Ob30wmkxcvXnzzzTfq1Olz+sUvfqH48KtXr87Pz3//+99/9NFHZVl++umnZ2dnuhSImOf5V199dXx8/Mtf/hIATk5OVqvVJ598olFQ3/ez2Wy9XnddN5lM1DXS9wLAcrlURHS5XFprD2cH6+UKRRWT2azWfdt99fWX8+ubTz75KMssp74s86LIvv76933fssS2q1+9fhGTn87GL14+G0+q3rdX15fXN2/bzVpiiH3XNzUJHx/M2ratmzUixhgXiwURHR0dKDaY0hb922LxBgZrYMlYS5mxItJ3zaZeT6fTssrzLBuX1fHJ0Z07Z4eHh7l1VVXps3PO5blTxA6AQwjWUVFuN7e1xhgS4aoo+rZt64Zjij6AJGcssBAgsFRF3tZN9CF3WdvU41FlLLEkH3oOERGJIEXfNM3Fxaury7cicnJ6NCrK29vb+Xyu9uPo6EizoLe3t8+ePVPtUJbl8+fPnXP3799X4+acSykVRQbAKQVEEUld1zTNxvuu79s8dyH08/lNCH1KIUY/m02Yeb1eA7CGuCIphP7wcDYZV+PxuMwLYwwNDqe8n6AbPEZFhBQUUo9xOp2GEN68eXNxcaEJrsvLS5UuPV/zM+pfPX369ObmRmGMIRuxb131ngeveECi1B/eoRdOA9+iKPq+L4qirmt9Wl3X9n2fhPvgF8vbLMu64K21LitijHXdANJ0dti2PaLp+/CTn/zk5ORkvV5XVaUZBSIajUbX19eHh4eLxaJpGiIqiuJv//Zv792790d/9EfMfHNzs1gsUkp/+Zd/qeqQiMqyfP36dYzx+Ph4sVhsNpu+71erVQjh6dOnimDleX5wcFCWZdd1mqW4uLjQ/G9d18457/2ACetT2OpXQGCRxJIYWHTbWTIaKRFRkeW5y1KKIXiO6eunv0+hn89v8txNJ2VV5p9+7zsHh9OUYowhxpBSJAACkJRSCNH7FAJw4hii70PfpeCB08F07AhD16bgncFxWYyrYlTmKAklISfgCJwgReSEkjgGjoFDhMQAkFlbuKxwWb1ZJR+MMWWRFVnujEUBBN45WSmloNspy22e58agKmUVv8E7G2DzEPvEYReA6OfwLkoRpG1g0tVN6HqJiZlFEiE658oy36zWVVWdnh1PR+Msy0ajMsts1zUhhNlsNh6P5/P5L3/5y+l0ul6vDw4OFK3RdMAQWCkmR0RqG3Qn617VZ0dEapk0mdQ0jXPGWHSZzXNnLaWUhKOztm3btt40m3W9WtKQlhjQkSESVbOjO7Isy0G01Ka9ePHi9evXCl71fT9cxAByKtz6/PlzTdnroQs9JItUzDTqHdB/VbT6NFTX6mld16niVJtZluVoNGq6thyVWZbNZjMRKUYVACmQBYAhxjwv79+/H0JC6y6vb44ODo8PjzimB/furxbL3GUoIMxnp6dd007Hk+jDqxcv7925+4uf/fzDx0/UNKl38fjx46dPn+olaf7m+vo6hPD48WO1FXme39zcvHnzRm9EobajoyOVHMXfXr9+rVn7V69eGWM630YOAIpJJJFEBJk1qm45eP1PYkBOFoEAUMQ5d3BwMJvNcpdZa0ej6sWL519//dXHHz55ffHq9cWrH/7x5yAxc+QyQwYA2RAYi2SAJYa+TaFHSQRCIChMIJk1ZZ4hCiICSgh9Xa839SqEYC2Nx1VVVdYRAHvfNW29Xq+Xy7mkFL3vu8b3vXAkAINkkKIPwGxAJHHsve86jh5YmCNzHJQsIA/wIzOL8ADux+RZorUWkGPyQ8CieQqVQGYW4P19q/Kw3cJbdQm5y4iorHIDeH19/ebNa9/3usOIqGmau3fv/sM//IPqSn2UxpjDw8MQgtJllOalSXl1d9VfVauufqb6hppn0qxy3/dZZgUZgF1mjaXEIQTPHB0hGQRg4bgVwn2kZIgJY4xd130LUGmaRhHh29vbm5sbvRoF69WHUYd20O6avdAIFXYZc13NQc4HRYCIm81G8564lz9UKUVEBWY1Lq3rWgHihw8fZpl7/Pjx4fGxc246nW7qtu/7LM+Losrz8vT0vKjGs+nhcrH+zW9+o67vvXv3UkqvXr2azWZ1XSuGmef5d7/7Xc2YP3r06OnTp4vFQnXe27dvz87OTk9P27a11qqKQcTb29uDg4O7d++KSFVVRVG8fPny/Px8Pp+rlFZVNZ1O9QllWdb3/cXFRdd1mr1Q/bKfZbFksiwDAEgcYxRmYU4xgojR3AVHg5LlNi+cdZQ5MxqXhHJ9/db7DlJcr5fX129fv37ZdU1RZFlmM2s092AMIqcYve/bGHqWSMAEbFAyS6M829RrlpRl1lrbNM3bt29vb68VnFQIepcqiokjS0IBSZxCTMGLjxxTip6jz52xRBxiW9eL+c1yfts1LSRWy7/NMO0g0BB67733vUqmYjPqPaEkSIwsJGCRLAIkTqFXNS8pShxOYORkLWWZzTNLiIr6pBBFJMstsrRtu1ot6s1Gcwm6/k3TXF9f931/fn6uN6jpQWVfXlxcPHr0SDfAkydPUgptW282q75vEUUZkESwg+gTEThnjEHm2PctOWKOPnTMUdWLD13bbVyGuTWZM84Z2reBg/iptCgSpYGpopcas+nmUxF9/fr17e2tAkqDN6Uepoh47zUS1bSP7NKsKpYauw/uqMZ+aiSVQaIGTe07Ik6n077vJ5PJcrmsqmo+n6uLeHx8vKo3eVmWZbnZbE5OT0XE+1CWozzP27btfDw+Pn748IOTk7OLV68OZzMCCH1fuOzy9cX3v/tpmeVvXr3+3ne+29XNw3v3/+xHP754+eqjx0/+z7/839+8eXP37t2yLH/5y1/e3t5+97vfnc/neZ5r0uz09FRd8fPzc9XB0+l0uVxqQL9arbqu22w29+7dIyIlH45Go4uLCyLSuPpb6m/AyQqXGWMMbQEni2QAM2N1GUMIoetVB6nWK8vy5Pj4//u3//nxk0enx4f/7//X/7Oqiq5rUgocg+4PADYoZMAQeN8l33PwMfQx9MF3vm+7trZkJDEKVGV+eDCdjCoQburNzfXVYn7bbNYpemuwKsrpeDKbTDNrCmty6xwSsEBMEhPHxDFJYkicQuA+BO+TD7zTXMbgAMkwb32iAZBPvHWLBiWle8aYrW3w3ouoalJ2VFRASFFQ986HikqpA2SLFGME4MODg5OTk7IsU4p93y2Xy/F4/Pd///d/8id/ogp3vV4fHh6ORiNjjKbEx+OxhkWz2Uz5cX3fa/6jKArl0zRNo3jMZrNZr9dqS7quMxYBJKXA4q2DIndE0Pdt7zsfuph64R13VB//gMfssgUppcSSiIAIALd025TSeDy21k4mk/V6rRen1qksy4ODA2WNrlarGCNmOHgIuOPEqEwOmY99yVe4YrFYNM1GBVt3YUrddFKuVpuiKEKIjx49IrLe+zos1019fX395s2bpmnmy8VHWVZV1XK1NsYgmtvFyl1cHB+fnp+fA8DbN78rsnyzWndNm1kXen/3/M6ds/Pr6+u2ae6cn//i5z8/P7t7dnL6i5/93JL52c9+dufOnclkotv90aNHX//ut6EzKlH3799/8fTr29vbs5PjLMvatu2bPqX0+9///uOPP95sNnnuXr16dXh4qNB23/cnJ7OLi4vRaKJ2nnbMISAgwMFByLIMAaJzFqlwmbr6lkxZZoMHgbx1zrqmjpyOjo7unt9Rnsfx8eHRwfTNmzdNu2XbEpElEiKDRFlGAsYYQjTGAAsKhN4H8LPZtG3brm8Oq8N79+50XfPm8kKNkjHGbqM1gyTqP1siY50lI1HUxAEZFOPbLoJDkCLLghj2sWNIwYNRdU8iggCIAkAxBjIIrNe5TVYrQKraX3dgSluUVcVywCQHDHbA22XHQ1a7YkBZ/q1qAQBAhMzYLMvmu/S6eqF1XaszCQBv375FxPPz89VqxcwPHjy4vb2dzWa6jXU/a3ilnBtVqcqXUt2R5zlzsg5ALKRABGWZIzuQFPoOLSBHRKFB++qLAZgZMgHqDQ8KW/Njmp0fjUaKT04mk4GHpXmL6XSqkOY2Zef9AMmoXA3wxi7Pg+rgTafT8Xisfp0ybJQ+PgikgoeHh4f379+fTCaj0ej169dEdHFxoV7TYrHQ98YYwRAiXl/fImII4YPHTxDx+vq667r1eq3a9+uvvy7L8vz8/He/+50CZUpMf/v27dHR0fPnzxeLhTHm6OjIe399fX16eppS0rsry1KBzaurq4ODA72A8Xj8q1/96t69ez/84Q+rqmLmZ8+e6bPR3aO+9MHBQYxR3csQ+hi9ujQx+tC3gKzhk+71FH3wXYg9wZb2oKsxLqsss+o7LJfLzz///uXlxYsXz773ve9tF00iICMJS4zRh9CrXySQhKNIMghZZrPMWgLgqMrxnXkB9W4kz12e51nunDOIol7cer1u6ybGaACJiABRwAA65wgQZWsVt+moFHkXCirYrlDC4IKJvMtwqBnIc6ffrn/VN+6CoyGdtkUitgQpgF3A6bfMctmGPGqylsvl7e0NJFbc3lo7n88//vjjL7/8Urfuo0ePbm5u6rpWL0xdQt2lr1+/HtIn+gjirsJDYYI8z/W1ZjJGo1Fdr40xZZkLQggeCVxmicAYdNZkWVbm7r+ZIRygywEgUeHUr8mybLPZxBjX67X3frVaDUkVBQkHVTF4XCrVGsUOf8rzXNOGKjZE9PTp04uLi9VqS9VVudUiKb2qt2/f6jemlBaLxQBsKmxTluXNzY3q0YH+qr7N9c1tlmVv31w+/+aZJVNkue/7tmn+y9/+re/6zWqdQnz14uX/8G//+8l4/PLFiz/70z99+eLFwcGBItQi8uzZs7/5m79R1RO3Kem6LMumaV69ejUajbqu+/DDD8/Ozl6+fDmfzx8/frxcLu/cuaPMJg2PVe8qPNu2rZq+fXhsx2/eHvpdmsno+36z2SinVF0yDd5ijFVVdV33/Pnz8/Pzo6OjL7/8crPZqE4cXKZ3JlREKwbatt1ua9waYV1YY4z3/vLy8uXLl8vlUlXk4M4MWhsAmvXGt12MUdKOSAhgCcejkTNWVU+7qX3bcYiay+77XsOcATMfLNiA1WvegraS/c5BUIB0gFDxD+jEg2cxwA0q8FdXV87aw8PDqqo0A6EKHXcpQe/9YrH4i7/4C2vts2fPLi4u7t27Z629vb1VRsrt7a3m2BSe2HeMrbUDCUF/PyiL9WYJwM455qgR1iBuxqB15JwhMVkQamPsUoiYxAlbjuSpgOqgooK8BDboRRabdr5ummYlEvq+nc0mGpuqsW7bNkWxJntz8fbe3YfrVdPUPYKtu5SVU3JV6yWKZbStT00f123XxXS7Wv/5v/wX1XSSEPoUPafFKgiVqzoKlU0PJhv1EUfT4/HsKAh5Rsoyz/Ly8uLt7Q1YO6tm/+Zf/ZsMs9RHi1lh8na9aTZ1ledXby6OpiNH8cGd48cPzn/9i39Y3l7c//BhE/2ybT2Qq6bgRid3PnpzU883sfZ47/GHXz57Fk367/6Hf/XXf/9Xk9MiXne0kcrOTk4fZrOztbjrjt3ouKyOTmYn9fUybVqHPJ1k8/XbYmp/9et/StyenR39zd/8ddNsfvSjH725eHtyfJZn5aiajMfjEPqiyMbjIsR6dlCaLhYJ8wjY+LhusAuZYIamcnmZ5dPR2DnXtm1iNtb23i831ybj6awwNq3W14vltaR+XGXs20mZZYSxaTAEJ2KZnYgVxMjiI4SkJREi0ocwnk5NniWEgOKB6xRaSdFS4l4gWEshhOVyzQmLfMTJ5NnYmlyYmJXDxGQYKdE485br1NXcBytSuGig4fD87cVNvWlBAoInis5GlwUwI8s5+ALDNMdRBlYCpd5RwuQJIgGjJOaYUlBvxSdfjArKsIstYDKGUvIxdCCJJBpIBsRwgNClrvb1qmtDDGLAVcUkyyqJEtoUfTo5OkXG2MfC5IXJmk2zXqySj8eZbS4v4vzmBx9+8IMPP7h5+c2r3//mbDbC2D7/6jeWosFAGA4Pqq5dFjm2c5hmp7P8rJ1zO+fj0b2HZx9O8+OSJqnB1CB6wy1QsOjN+ro+KU6y4Py8cR6PyknOGNdNJuBAJETugwS2A2VMHVy1IUR0e3ur8WiMsSzLoihU38TYISLAFkce3Nc8zxG2/rEa4sGNfN/f2Lq1CqLc3Ny8evXq/v37P/jBD7744ouvv/7amhIARqNR33eIqKFmCOHg4ECkSUkyl2/LF3xKKV1dXX322We///3v9bSu6+7du/f02YuBF/bJJ588f/ZSWazffPPNarUqyxKA6roGgNlsVlWFiKzX6yyzbduen59+9PHj29vru3fvapS7XC6b6GcnR6enp/Pb67quHz9+3Ly90uWKMcQUWZBRdB1ub2/v3bv3u9/97ptvvlkul8aYuq5VTW6x0B1BbyC+D5CVrs8+WqZRB+8IGQMdAnaFY/qZqvXVYvCW7gsiYmkbTQwxhT4LDcsHaFofunNOJO7jc8O71BPZ2aitdTLGgGEAQAEEQjSIhGCI7EcffXSzap1P0+MiUN4LMJksL5//7p9gR8dXbE+vX9NO+5tEz9EsubFb+5ZSMgadtYPjoC7sYELRGBCWrX8RgBkAGMUYs80qEjKIOmtE1PZdSNFm7ujoqKjK1Wq1rmtF4Bnh5OSERbZMtOBDiqFfbWpnjMkLhwgCse+7ELo8dyGEmDoyGSKyBBZBsogGjLHGQbJExCAAQGQSgzAjRBHZAjODd7vLsbxb/WF/6J/UNVVGGO6x24hIQzje5ihdlmVN02h+dvAThvM15z6dTp8/f350dHR0dNT3fZ7nIEbLL66uLvXzFa/fppUAlElDZBEiIq1WN2/fvh3SNVojOx6Pl8vl4eHR119//fjx46Zpnj9/PhqN3r5969lnWUZkEXpni5RSXdd3794PIfR9cXt7O5tNHj38YLmc3717d7Vaee99DHXoJ0cHZVm+DWE+nz84Poq5HYprYgwCQM4YY8hkSjguiuKnP/3pv/t3/+7ly5dd16l2U19F10oXITdGwzCidxUtvKtiGVZVAa2UEjrUDbTrS+B1M/GuUlbPHNBsZ+ywWYf119M0IlA3VU/Q/Bj8QR2Zao0BAJe9hPCe2LBBQdziS5vN5vXry8v5WrLCk1s2XUI7Gk8fnZ0MW04dRfW9YVfXpqpBP1m5oAMwkbYLYlRiCWSLJsp+ols0fbiVSRHljTNICGn7+YJtUMl3LDiZHliXL5bruFiGEIzLsqK8Xa271h8cHgPAYrUUkbIcgbHzNzd1tymKLK8sInahafyq67p8lPt1E2IkAUMUJQBLgozQEJIxVtiBoRQ5cBIN01iAhVOyWhk5RCOqQTUStdaqa6vPVTl1k0kJu+rgfb3lvdfiWY0lFIQYlPc+8DNsCAV8r6+vLy8v27Z9+vTpyclJvfFaLFtV1XK5HI8rxRWbpjHGGrPVx0WRgVDf+9PT01//+tdKBEPEk5OTb7755t6DR+v1ej6fHx4efvHFF/P54t69e7//4vc//vGPw3rjW49opqMxkX3+/OXyZvnR4yeTquybenk7f2Xo8vVFmZUWLYqs1+vJbCrONE1jcqsG/M2bN2OirUEgIiEAJgJj3HrTKOnx7Ozspz/96TfffFNVlYhoQkLdBNxh0SIylBUMcRHs2hToybQrXdumUolUCDWEVuMwRE2D/Mgu9k4hiAiIbEHHnVfSd53G/EPFatu2XddV5RgRQQB3ZWXMggASE4hovg6UzSOJkRModiPILGCMSSAIQOf3HyRy2Xi16tO6iz1DBIMkX3311QBAyC4OFJGDg4Mh4Bx0BO5VvRljtP59wEJpKAZIMmgHRAEQFXNEERJNhrR9TwAMeZ7nQEaQ0JDN8rprZ7OZyTOtUytHo7wsg/Cde3cjp01Ti8h8tayqajYeReG8IsaeEfMiF+G2W3ddl1KoxidoBCSSY2MpQWJIUQxGQyTWIBExQxd870NIHJPWuKaU0rtSpm8dbdsOyk+XRsml+td9G4i4LTVC2C7K27dvNbi31qb4zhHaX2U1fUqUcc5tdvnTtm1VkouiePPmzWQy0hJYpdID0Gq5Xq/X1mYx8nq9/vjJ9xSeKctSAIeLF5G/+Iu/+Puf/gMzf/rpp0+fPj06Onr58uVkWoTQp+Q9hXv3Hty9e/err756+fIlACCJiFxdXf3DP/zD5z/4bL1et02vd0E2C6AsmfL27eXr1+nju3eVTumIDCBuZVJ+9KMf/fKXv1Sq0Gefffazn/3sRz/60d/93d9pC5NBcoYQQP5rbEG1ErqxhjTGcAoIg4BwEhAQJgRD6KzZuSrvaboYw/DI9qtljCHlsgFIWZZa5qdyKvLeT70MVcGD+hiEB40dPh9xdy8sX331e6E8pbRY3C7qPtkMbVbX4Xg82jv/3aG0Kg15UkoxBACQZIuigF1hgKokDRcRty1qmFlSGKIqJ1vpFQOAuNOTGAMzAHJCTgmBMsfMEaT1fRY8WDM7PDR51jQNgwjCJx8+eXnxet3UzJyYBaBp26vr60nlUkpJApADkQSeMZDDBN5kAMaQBTRinGGJfWwFMhRGJGROzD6ktvNtn+bLlSTPKcQY7a7MXoabBAC1PIOVG57/sGm0bcT+7jHGcAIi0mhQ/as8z5vYyc6hVX2mLzabzWazSSlpNkKTE4N3VBRFSkYLYfWXTdOMx1Nrs5RSSn0IwZpMKX8ff/zxixcvrLU+pM1m8yd/8ie//fKrzWajqY6UkoLOeVn87ne/m84ejcdT7/311c3HH1ff/e53U0qvXr84PDxMKRVF1nXd119//cHjRylJ13WT0WHTbKjIEkEm+Xg8Xt5c13Wd5zkipJSM0VgupQQMcOfs5Ory6Je//GVVVZNRObc0KvOzkyNmRpTQt2qXfBdUnwuqchIkQRAkAREkAWQWiUkTzRYQkIQAkohyjwaO0YAhx105GO6BmcLvMTEGVFOLPwa+oTo+RVFs6yH2NYIwAIS4rY9BJERAEBBmThYdAFi0iIigKDomgOhDORtVRG5hyfSuyMjl0IeUwhC1yh5Tcthp72ucXTG3JGOMEsS998H7PM+1TGlwH7YheuqNUdwR0zb1bZAImYksEIYUBSjLyz6GNsTj05OmaTZNff/hg2lmF6vlKM8Ojo+avgshtG1LxoxGozzPlbJiTMbMiUNM26xbnmvTsFYh28RBIBlrkE0IwWAQjiZYSwKEzBAj+5CQQTiyMCSm4c6H4qDBBRpeD5pvoHoO1n9YO+34gIiHh4dKUsM9LFt2xfuDy6QtrtRgdl23XC5ns1kIQX3R4+NjRPzoo49SSrPZTJdAO7jpJlND/eDBg7dv3ypTnJm7rqvr+uTk5Pj4+OTk5H/9X//XP/7jP14ul7/5zW/+/b//903TPH78uO9DURTnZ3estX3fzmaz4+NDIlByCRE5Z+q6/s2vf3t0dORcruvgnFssFpt69fHHH3/22Wd5kZ2eHk8mEzVTuyAnppT++q//ejabnZ+ff/LJJ1999ZX3/vnz5//8n//zDz74QNnnGrnxjoe97yAM/KH9/g6Kd6f0LoGm7cZSSpp93SviBtiVfeKOG2h2x951bg/nXFVV+uBUF9PgY79fUzYo3OHRDxec4raHDxFtjakIS5zOxkSEzFlmtVhWhK0j/aLhdoYb3F8Hs3foJoGdgz1ETMPGG96orrX3XYxRgJFgl0vcfhEaAIAueCGsJmNN4drMuTyr20aJX8WoOjg+MsY8e/ZMCZsiorlxETk9PdXLAyEtjSciLXHue2+MtdZ1ra83rTAacpwASZJsOdjO5WQzQStC5ArryiyvsmL0Tvb22Rv6gIfFGvaHBpDfEr/BEurJVVUpJU1XbT/a2cfWtBJPmT6qj6uqurq6stZqn6imac7OzpqmUS7bkJ3Teo7VaqUdNKy1T58+VR9PE18vXrw4OTnJ8/zTTz/96U9/enp6ent7+/Lly//wH/4DIq4Wq67pZtPpdDKZ394+++br9XJ1MJ2hAMcUfKfX+ptf//r0+HRcjYno9PT09PR0tVp0XffDH/7gz//8J7PZTDvKqBYkA7Rl8XNK6eLiou/7L7/88u7du2dnZ1dXV7/+9a8fPnw4Go1UDQ3aTUEX2KGgeuw/jkF5DSoMOeXWZIaQEwdvQHJrHKGBLXnSgOjrLZfSoP5HKIQCkkCScOza2hBMJ6OD2STPLKfg+zb4ThPuKECA+lp/WnpX2KEMG2ds7jKOkRXy3WX4tK9E37Rv31y8fv16tVr4rms2q3qzijH0bRN9r5WNVZGPyqIq8qrIo++j71PwWpYhKep/cddeSaEs9bC0h1W/O/b3qsYIzPE9cw5pvz8akozGpXaXefbsaZbZqirqep1SuHPnbDodf/PN13W9zjJbljkC+75dLee+bw8PptbkzhbW5JwwJRE2nMj3MQYgskS272PbemYxxgKgMQYFNOfpnHMuI7KAzlBGrnC2zPKRVX9mXzzMrtRotVqNx2Mt7jg+Pv7oo4+ePn2q4ZbITvkZY4zDHYxJRH3fi4AGeOPxWBibpjG7ekWtu48xLpdLEdFWUSrDNzc3GigulvPb21vNvE8mE2PMYrE4Pj72PhJR13XWZsfHx1mW/frXvy6cVVWq1ZnW2ouLC6CrO3fuNE2j4UTTNIvFIqT45MkTjun2ZlEUb/6n/+l/+l/+l//l6dOnxuJqvsrzvKzy1Wo1Go20edazb1589zvfe/P8ddNubt6+Oj09Lcry6dOnn3/63R//+E/++q//+l/86Iez2axvlr0PKXljzXK9zh12bWMIq7LYrFciQgiXby4OZtOubTJnASDFkBTJjASipQNGy8ri1usz3nsRVl2uh4hyuaXve+U5DICKcgn1KWgdwNZMpRT3ukjum46Ukmp6RT4066Nu4uD+hBCHOFOZDztojfWTVDWDVk4Y48gxgomCDLPJgQdadp4AkIQEwaAlODg7GxzjfUs7pLv0RoawBRGLokACZlbZ8z7kRdF1nbW77MsuctZABgBiZMRIWu+fOMY4nY1jYEA+mIyrqoLEHH2RWeB8vV4agylxjD50bbNeFc5uNhv2/aiqjHDXNrlz4/FYgp9fLZ1zWW4NmeQ1IpDM5FmVYzQppdKOwDGxjR1DpOX81jqDPhUWDdq27b//vc/HVebbNqaOY0B6v8fMoJX1cZ6enmrZnppjbb+3c+Jxf/kQseu6ELbdINOOLN/3PcC2y5PZFoamAePBHe2Ad2VQzrnRwUHT1tqv7dmzZ1o66JwLIXgfjo9PM5cb4xaLFaF1zhWF0dimKAqXFQDQtq2PbK3Nsvz29vbTTz/drJv/8l/+y2c/+DzGeH529vsvv7x7544z9uH9By9evDg8PDCIoe+vr68//PCj5XKJAqfHZ4v5/L/7V/+qX7c8F9c1Mcj19dt//Md/JE5N0zx58sFms7m8eTut8sm0jLFHIwcHBxzfsfz2j9vbW6VfDjHMgBVraPeH79rT5O8dg084OBf7zuq+J4mIKcRviZ++yF0GAByTkLHOWWsiYOAgIPp0RYR2SIs2z3r31He/BO39wxIALFKwmTGGgQBwuZyvN+uurb2PLARkARmAm2az20Lv/czzbTcjZhZR/U5E1Ie4vw78fo5kf/du/4RGTURktjsKCwFGH5gZEgSQThgAfNdx6KzRloPCMabQ+Q5TSpJ8ZtECE8eMgDJrrbHAEvrC5tZYh4aQxFhGZo4ShQyEtheR3NgYY7tqnHOjvBofFQAMHKvMlWU5LsfT0WwyLuf+GgAEDHN8J4S8l4RVRTiZTHTrpF1nYg0bREQJuMO7hueiSlQ16DaGTDDkKrTtpHL2dDvKLv8xCOHp6en1DTRNE6NXOyYimizRMOb4+DiENJ8vu64zxmo0qBWPdbMlxT969Oji4uLJkw8/++yzGKP6t1p+VdhiOjm4urr6T//pP//Zn/345ub6+vr67PxE/V7FWqzNmHkxX11f3T558mTT1G3bUm6stW8uX//TF6m+vvl//Ot/nXOKm+UWXiFBRAEevKZ9SQOAq6urIevDu654zAx/IIT7Dv8AaA1SJxw5SQIWEU5JRPSfnKIwIwhoty4RBCAUNIql6ecP2hY1uZxS8J4RxRgTow/BE2X7KmAQ6W9FicPWz2zOW27A1jkiskAm9pF2smQAkwinlCjwnr7YnU9q7lJKnJJoxk+2tObth7MwM/KWPmr3QlzFZId1I+N0WzFLSgyAJICEMUYEABYW6aMXkRgjpoQYSEBEiAP7GJERsSRASalv+uQJ0REZgdRFn5ITR0lrhQ0RJhGfJCbvKO99MERllvcM63ppUirK8vryjXOuKnN0Lvp0efF2M58jp5gCSmCOKUUre3Ed77IRiKgFjog4VDG+efNmUD/b9i3bnbEtjLDO7ZoFy1A9mOK2eGxnJLc0drNr9pjeb1cBAN772Wz2/Pk35+fnV1eX2iKp7/s8L+q6fvjgUdsu7t27d311G0I4mEzU7RQRbZwxnU5PTk7UT/vTn/zZf/7P/znG+Omnn15cvhGR5XJ9enq6Xq+/+OKL73zn45OTE+0sjoiPPnjw4vkrAJzNRqvlpiiKv/u7f/jxDz8PoTcGq9HI5FlTr+fz+fLq8vnz53/83Y+rR49Wt5dt2wqIIbPZbCq7SwCqAdmlBdbrJjOEYCQGFMmc1bWFbUF9Gnb5nrliFRnmbTKDmWVPYvchCt7rjcB7CcPBr5FdRmT4Pe11lYYdFlAUTvEXAEAUIv18VkxH3qv8Vli1ZGaJCREtGTKGyCSk6WzsAVph6byEGCNHjhykIDNc8L54D1TPwX9O24p1YWZAdbi22MyWijCkWHeOq4gAGr1XSQIgJAIGDRICATIgCAeJzBwNoiVsfEAkQ6SNok3iLMuwqqxq0r5HJJvnRi/ABxOFiAwmZ8GREySK3Idkk1datqVEhtkRJ9+vF2KjBVtYVxWlA1ovlldN3debyXRkichAkmhlh3QNClhFsaqq29tbhfiJqKqqi4uLg4OD21st5YJhB6gKxL3ksnqwupTaUk6ldyiY0EUcdsCAqqeULi8vQwhaJzHgE0P6/vr6On2cmqb5sz/7Z79Mv765uf3kk0++/vrr6+trtYez2SzG+PLly3v37n3xxc8Ojg5DCKenp/rkZrMZFDCfz++cnR3OJv/H//aX53fOPvv0e8+ePd3Um+ODw8PZbL3acIiT0cjZvN3UP/3pT0eT8cnJiVjazOsQwmw0Oj8///1XX96ZjWd5posQUyT3bhm/pdcQceDjD+yF4a+yo5IN0jJsTXmfljR4lcPD0m9Je/zB4YTtY43v3LnhJwAoOc1pjLBzN/KiUCuq56g7qo2ctsqXRa8SEYUFQGKMqG6gAANLEC8xCIyzDAA0sDOgrSgQgEXe9ZseLph2hD7YFbKKOroANq+stYACAGYXIOsWSgj7cK6+TmAFBCAKCidmYENEgAJMIAiJOUpMBtgQGTJgyRkgAuNIEjvk3EBmTWXHSpQ3iBmBIUAkJudRjAHnqCxNnjsA8Bl7TyGEvNhG6ZVz03K6Xq8Xi+uDR/dFUgo+9H1wOTprwYgtQ8eQYUbWorHqB6oIfUsVad8hremoqkpLrXY8xndjJBDf8dZ3rd23n5lSgoEastNVKg/69oFBMnhcl5eXxydHbds+fPjw7du3h4eHfd8rUmJ2pZMppcPDw4ODg6Zp5/M5ACgpHtAolKqklrIsf/WrXznn8ry8vr7+5JNPnj17Bv0W0z88vHNx8WqxuL1z5+QnP/nJX/3VX3Vd8+d//pMv/ukXFxeX9+8/vLmeHx4eC3ej0Wher7VqhAhTCr7rzo6PF4tFw+FoOqqqar6ouy5ZS5hwX2x2QY4ob+adbOyA/uFM2mOBKYI6iOX+MUSS+4L6LYu37wkP5++bQdkVpuj66++13qLetPsfPlwS7eo/9w+VJWSBrQuscs6SZLFYLNfr9bpuQgxAyIAIZCzBUE84FJGqMZOBCQKACkGJiPY6ARQV5gFD7vte5B11ZDiECZCRHKQEAMwCCdACsBAkbeQBJAaBCCxBUY220puMehkZgTNorTMpOmBEtCha5GWdHbltC2wlkJCBlHS8Sqc9rELoVVPM55ZS+/bqrYiMq2rscimZkdknDly3tbGoUNe2JTbtVfQOUO/Z2VlZltrNQWuC6rq228r6fRSHiCiEwCnsdNK29CmEEGHbutvspk0MmeU8z4cpNgNRq+887AiQfd9rq3OteAohFEWxWq2I6NWrVwo6f/nll7PZbDKZaAcATbIx0OvXr4+PTwRB/dKUUlmWIQRfx8PD2e3tbQj9/Qf3NpvVN9988/3PPj05PWqb/uDg4OGjB5eXV1ptmFK6f+8ug6zfrNvYlVVpDfV9v1ksvvvggXMOeu1Mp2wpZGbkd1zZfediu18RtQWQuvqKaorsLyZ+a2N9S9gMwZ7carsVARAEJNWGwsJRREAIwRhjB29lPyZEVLsESrhDBJGkcfhwPYOd+dY10C73KyKOTJJ3aXdjjDXOAc3XdfTBex9iCkBREMiISSgWAREJcPdFAsJsrdsqd5ZtgoS22c7hfvn9lRzUFsi7SqiIQEgGELYXGEVEkmTGkggIWyDrjDO0raatCjXjAA4AaIf0SkyFNTnliJhCDCEQ2sJmZNg5Kgp0jo0JiJg5wMxgNTZW21Jti2AlZvb+yepiISJVUU4mk+lkQgJtEsZY5hVLTCFEZDssK+3R+UQkz/OPP/5Yy7TVvTw4OLi4uChLx8wAabBvtBuKMFg55959GoBkWab9KbSoQs/vum40Gk0mE96lJTWNIZXVFoBv374tiuLm5kr7fFZVdX19qz2wqmr8u9/9jhNog0O1kMvl0mXF0dFRjPHi8mo8Hj979uzj73wiIutVfXFx8bOf/ezw8NCM3eXlZe9bY1AgqgH/j//xP5ZlaUz86quvjo5ODg4OLi4uHz54slwuq9JkRU5E4/EYne3axhlzenr6zTff3PvjH47H465rfEjOubwq1vVKb0Rxf7VmqlO08GxoM6OnDUOs9j3YP3RQh9/DLnje/82eS/Jt1rXswuxv+aIiot3r0l57Ic25OVsOH7j/Ff+tQ3YJdxQYTKs1bjSCJsa8D530feSUYmIBlC7G/c8c9JROaOFdUy/ctfTcpvKBY4wkoDxH2v11q+n2yhHFCIAB0HZViKyOGGSZwSQgkBvKHVmDBCiSIhILE24vHgWIyJHpu84ao01Bfds10lhriyzvwwIBQEgYtEPX1psz0LXBOVdVZUqp3iyD7yfj8tGjab3eSExd067sKicLzLnLXWV737ZtijFa4h5iFDHIbCRaSAwMSSZFUS9WVVFMy9F6XberJrYhQ+ebNoQQ+sgpGSJrjTOOiGxeKo5NgBjZCFQuL4zrYtJ844MHD6y1V1dXcdcFeLVaaS3z1dXV8fHx69evi6KgSblpm/rVsyKzjrBw1K1uoa8f3H/ULRZN2374/c+7yE+/eTk7OBTnkPnm5mYymdy9ezcGdsZm1pUum1YjOpb55VWe5xnRw/O70IV2vuq7W0tQzAprsGmS75OzJFDkxcGL118FeUtFNjsfz9u38+7F5Hy26dvYbLRCP8VYWMddl3p/UFTduq4mJSFaaxPKstmgpcwCIhNEFEZAAnZGDIL2ESOIvvMpxtwhEUTfZNbsQBwBiAhoDGbWisRdCzbRdg+KHLJx+4KmLxCBCDVGQARrDcC2GALJ6LnMPETvCNArpxS3BEvZ/gPEcoqRWQNCIrSMLkHsO6/uInOPws4Zm5El8cEzsikwyzJjsWmXb19dLxaLruusy9u2596v3t6mKE+efGTYvKHDIs9VuhAxt5aZ+75f1w0RIRnjUBR3QQIylFKGWFWTvu/X66UBmY7GRNuuakhiHSWyMfrESUASrhMPlcfIiAKYQFJiC2gFYwhtH4iZhBHFWnLOCVHfbNsjiUid0ng8rpt6E4JW7UwORjHGNjYA0nRtH/zQhFtRfe/9eDxmz7fzt8aYyWRSVrOmae5Wo6uu71LKDCDyslutNss+Bj0HTVqsVnZQZoNl0ye8XC6JrHY3bNtewRJEbLtaHU6zK7cfyBzGuK1Fhb1OaizGmPl8/oMf/EBd0GfPng2emJIwlTiiI7smh0dlnqXgtcslAgAnrTGv26btUxJWVpcirjqobOjemVJyzo3H4w8++GC1Wi2XKy0eTykxWwCwWY6IeVYaY0JiFyIYUldA/VVt02StjTH0TQtBABEzq/xnIlK0feuw7aRh61wJ7E1b2AHoIog6tBQHt21A2AdD9IcWZvjTkOfAve4m8H4stO/9Dif/V79C3s8lfuvYdZt+l4IzFomydlUfHEzzPNvUi7bbiKQYIcQ+chyPxyL8zdMvv3r6+67r7tw9v//o4ZuLy4PD8b37d8ajg5ub29cXl9533zx7WTz5M82Xpr3iD94l9IYrl11kKwmGdpiyQ01T2kISsHM6hze+t4AAoD63sO/7wmrOI6a+hRgdaeqLNfWl66yyrdMptNbxWx0hQPodB80Nj1Ur67Ub/T4B0Hs/Pbq/Wq0SyGiigyizyJULYbVa9FtbDnZ42O9DxsjM2h9aZFe1hViWZdstjTGGnOwBANbapmkQeZf94303Q9u/P3v27Ec/+tEXX3xxeHg4FEbUda39aYhIh54Cc5ZZJuzbOnhvAKwxArhcr4EskVzd3BblpCgrtK7btKcPzxTI1baLIfbW2rt3z3VMD/OWf2etJQLN5xhjjMvQGPQRjGWGvu+bts+ybLNZ31xd51VWlqXWEkIAmzmLxhhjEcHaRJR2vd9FRDNy260g/xXiJe4Ym/sCM/iHw7J/SzZ0a8oOsBlcL+97+IOIEfcqgPSj9uKL9wR7P7T7rx5DvICImnwi2gLUyuTUcorRaGQMth0eHJy+vHj98uXzlNInn3xwfHzo8izG+IP/8b8TxvW6RrJFaY+OZtZm9+6efHH17ZpV3UL76zMIp4gQGM1VKLPXbJt0JGMMIO/QnS3KJdtSJlDhZBESYAEBDSMtEYEgx5RCT9Y4Mpu2Tikpdq3fq/kw7YKxTx4EAGutvCPEvbuFIcdjjKmqSvt3aCBwc33ddZ11LoVoEI0ydV0PMDUWLZkin1nciwNhANNYk6eiXM0sM8ysA4ZCnCBiirIbcGGKwunAakTULwZB3BFqxVgFBl+8ePGDH/xAu6NqCTwRDYMju67TvvHL5XwyGjuDHFPf92WWj0ajqhwtFqvxZOayeHl1nRfd9ODYutzaTmlK67VhjsrzFkiz2eznP/+5cy4lSSkaQ3meAWDTNJ4zS2AASIQFEzOHVDddiFdFmanqcdksd5khAkmKyLldffpWGFAAeAdOsQgCAgnIH+TlcC9sG3wN2iHGgxb/Q6nYV/D6pX9owYb34q4rwrcO3BUZ/qEc/rcOZ60h7YUHacsyT8aYMs/apu66xloajSfWbGdUfPX110gwnRQHB9Pj48NyPAKAkOLt9YVzOQtOqrwsRmFSJIbrGxos3rDpdavoNhgiveFqdzIQAECjrxD6lHCr9N5fPcR31VQqhCAALAicOWcI1e8mIiBi5hCCoq8DRE97fN1BHIY5MESEe43S95WpiGjhkfakh53aTYElMViRmDiGzOaZM0i2Kg+bpo7JF1n+bvDi8LmIyABEpKCl5up41yTb7Goq425ksYqic455V/8uqIOmiqJ4/fbq4cOHX3755QcffPD3f//3n3/++c9//vM8z3Wmmq6+RgUHBwchhL5ryiLLbEEGRCSBlNXo9OxO04YuRLCYfIwsPgbK8icff7RcvlXKpQhro38AbtqNy4xzhqLE3TRibQhPMZGxrGNDBQQpcep8X9f1eXaa57lOJmFRgJsdkUFi5tB1AIIAkN7LcaEAIpIAoiC82z3f2tw0tDbcQxe/dfL/jYF693XvS/Xwe2XqDP/8loTvn/x/L4ckoN3yQUkCwigKmsfk+8zSZDIS4VcvXlxcvGrbdnSYHx8fn54eF1UeQttcL40xNssuLy8ns1melVVhgSwSoDCIR3S8x80aFoTfJ2zpb5iZU0xp4M/B0PcJcJtyHIbS7BZHbzltH44IQCIRp+MEQzQpkEFkE30ffffg0aOhKe4gVzFGnTlJu+JPHMpcCAlQPdhB8Wl6sO/7tm6AJcsyZyxTSill1gJnhMQpcYwAzlpCsoDYdnW9XhVlZoe7hf2c0p47FEJANPpivV4nbq21hrbtfZWkol4l7Lx54XdNfjWc03Y1Oo+BiNq21czeeDwe2hlqIgESh67tYevUNk2z2jTTg1hNptcvXorgdHZcVKNN3Qv2f/qnH//8P33JzCjJEqTQaVP3xe3VpCpTSol70NGFQRCxzE2P6JAYQQQYAY0zwAAQOaWU8rzQftAsgUC8D5kTZubAffICkBEyR0hsUVEodUC1heYWFv/WXtefQ58Y3qOzDHI17KFvOZmyxw3U9xr4tvRuf2rlu+wq3wUgsbDA+wTL4ev+W3Ko+SlELQ7czuchQiLI85wlrtfrm5vrq+tLY+jBgwc//LPvzec38/m8blaj0ags8xhjs1l9+NFjBLNYLK+uLkVwMpkdHZ7cu3Mmr1aaYgCwzMi7kkjtlyiS5H2Xvu+3TEnmLb0OAJCE+Z2wicjW+3r/nkh4C0Vtqw3FMFprM2PR2YjoA2SWrEFEUJDVEokAGpyOK93AzGy3mUNbVZWzOEwC1bAQ9iIy9e/yPB+Px+qU+s6AdUja8D4hgCUSIESxRpBYYrT7fs47Db2rbBhyMgqE9H0PGEW2LfJUVaj16/vemG0VXIoMAErS12TD8fHxer2+f/++pvWU4anW7/Ly0lqroAgAOEtt0yQdI+5svWqur6+tzWyWA4B1+RaPstTVm2a9Ikwh9taAiHRda60tclOvV7vJctGQqrdEaLIia2JKwpET8DYHjYCAnFnXdZ11VebcZtOSkVEx4hA5Re3EM3h9aAwbRERLGomhJsWRUQnO+9H18EZr3/XUgvexlj8UiWEXDqF1Gub27DCh4S24o33ty/AQYpBz/39ZQoO0deO2zxdEmFOKAgjSbNZvry7btj2cHdy7d+fw8PD1ywvmCIy99zqnZHowOTo6atYNWRcjg5Ag9l24vr559uyZMSeD4dqJFoqIpk/3HezhancrEPSFy4wj1/e9SNrl6/c8CFQlhMiyKzVAQokxGkg6OqogQINioIzZarUaRrvAziZr5/jh6WhKSTEYzswwpFnhHL1arfIbCNLj8Vgly5GxGQCRIwRgiSFG7kOXldloNLLWoIT3aBm4A9mIcGjppc9eCym895NpzrueQrJjXROR9z7PjZJRIiWN+rz3J3fuzufz+Xx+fHw8n8/VuI1GI21We+fOnYEMoFWFRZGv15sQwmg0Go+mXes7H1abGqEdjSZZll3fzIloOpldX19//btfZyhd6Lf+QL1y42lGxWqzzMwUUp9brMoxETVNE2OPmGLElGwKKIIpBZRtTwxjsOu6IrdIkFLKXVYUed+3yYslAmssMBFkWSbMYq0ytpGEGBGBEAnf8dr3PUN9kKrRBhH91rIPz/IPj8F4bsk3u5Kl/ZgKERWyG97yTgj/W9L23zi0DUeMmiAB2XUequt1URRNW7dtOx5XT548Kcvy9nb+9MWLoijGk6qqptZa50zmcmGLIJzI9zoP1BgqQCgGGjwCtWm81zzlW8W+sq0eBO2Zv9VWJDvGTLv1muUdTxARaadqgDR3AwhCIpvVqspcljtAjswkbIlcTovVUvGOYQUUmJnNZvtCMQSrOo8gCUBioB3VHmDdtHmeI0vyIZKhEVhjc+tYCGg7sx0FQgh99E1Xu2SsNUWRhSjbTl4AYK1NiZumQcSyGGVZttk0tCtLSynpqmm3D2bRFzokNaWkkyQ0Ka+sF4Uly7LUpL/S35RWNjCttaC+bdt3A1CNVFUZQqzrOs/K4+Pjum4vLy+Pjo5EBFiOZtPMke/WBOm3v/5Fe/nryWSiTdYcudRvbppl2/bEARGzrODQJBGIyYgYwYPJRFhS6JgBGAxuya5d22ora4NSZnkIvu87YxwYk1ICBIvAAL5vIXiDpIN+OCbmmBkQ3A7rMiZLu8YT+7EN7MovB6EaktG0O4YQYDhZa754x4D/Nq6o+1VEH98OVNum4AmRjAnbru/vObrD3sJd8b7sKITMuFrXZGA2mzHzfH7jnLtz53z+5c1vf/7bxEFHL15dX79584aZZ8dHxhgRC5xbk1nnCK0w5WUlDOORrZtOUuZ7WS4XvhdEyTILAEQ6FVN3V+z7ranfqRJgTlqbO1ybD526XTrTN6XIvJUQ3LWqMwZj3HZ8tGQAEZhjilmW5YUziH3fBo5Vnim9qCoLAOjaRvNnVVU5awihqTdbxhyANQTOikiKQex2Wq7Z9YLhHbklyzIVCgVKtr9hA8Axhab1NrfFuEBLXeo361ooaXNxq7tfRdEYN51OU0q+D1VVFcUWrQ4hKdUQERWG4fROcxuznRmg4R+9T2LW7nQqk4PhNsZoenD3CWbgyoHwsEcnk8l4PO37fj5fAkvTNBG76XjifbdY3EYfRlX1Z//inw1CKIJauLTZNNbaruu6tu/qOoRAZEej0XRatn1iYWBwZMlaYQzBhzYaY5BFUmLablDVsgQsKQoy4xadEyLZoqMMYLboKChSAPtCsi8z74MH2xMGs7lvOQcFvJ+i2LdUQ6C472fyXqpDD33XPmq6b6WH7T6YHdzt/dPTUx20WFXF/fv33rx587/95f+n7/t79+7duXNH1ejR0RERXV9fI2TWZC4rsjzPstxZZzJjjPE+JuGUDEKBJgdyLF1i2/luX8vAu+qNAne1vO/fguCOcWB4O6s0pW0/KwAQSCwMu4o5JJKUgBnRAACBUpsNCKMAkhChAUJJzNtWUYNKgj0XRvYycPuqc3go+6uq8cLQZ0Sb02iCMfap8z5KLPMqQZwvbiNIgkTWCCq1Ba2GmLDthyk6I7betJvNJoSkGTzEd22aWLRv+buGJeoVqBCqM73Ph3z69KmurCI3tIOGBwap3r+6u2ppNUvp++1IDSKq15uubogTSAgNO0PHk3w2Pjk6OPzux8daPyU67rzzmeGM5IMPPggh9H1Yr9c3Nzc3Nzf16qbdzO3hnZQSImUG87wAwYa4TRFAOMUArNMqCS0gJwm4I50DonVkjAXC+C7bzpoVlsQi2u3vnRgMUoe7STiwxzuT9ysJ9yVQdvn9ISAf7NguE/seYAN7Xu6+SA/vkvePQS98S1mIyGx2dDu/9t5XVdHF8Ox3v7m8vOhif3R6dHLntBiXN7e3bduPRiMim1Uj68q8KMvRqCxzZa2RRWNMv+kDp95DAiIwiclH6QN/ywnXK/wWODl4BIgIyID7Dvz2T9YqLLHN16ekk8kEaRh0lVAMEBMSQRoSSoaIIAGApJQ4kXtXSER7lMBBx+n6w676PMWIAIa2FF7Q2hDm4L3oOBaiPsa+68qicNYai6kNgkyORGRTNz1Hl1ubOQYEBkTZNurCXTSo3Ss0hRDj1iJpabxKl3K+hXEQJ5EteWfQavsR9sXFxXg81lntuBuolnblhd573PEAt7cagoYHbeeX65W1mfbCsCCzUSEhhHYzPTr8/qefnB4eeO8dhNj6br0drxVjCiEQx8XVhXOuKkeT08ODUX40KZumAaDny42RXdUZApDNiEzp2s4z67RZq85/gsSccmcjs7AXABFLIAwASaEZ1PZnIEPi/tv5Bt7V76Y9QGU/bBu07H4QOMjGALeYXU9eiTu2pwiKwBYOBQRE0Yp4UIDIkjFkmvRecnn4in1XindV14jYB399c5NSQnPw+vWLX/zyZwcH07/4iz/XgbUvXj1nBmbo5rd5Vo7HY6HcFkVRlnlRkAElsANgAI5JkjALJAHfh7rp6qYz06kumAZ7qGE1Q+JgjAEEEY4p6MUMgpG2JRFxZ7KEaNtqUrYlB2lno5JaCEQUSMg0GDqRBEy715xS5JicMyxsQAiBhCUGANAOPZAipN0kKkOQIHCKvI0jhhhhsIQxRrNrESQiurfROJvbJJxSMmhdnvmem7ZHvSMAALDaOcIYUxSF99si9Lt37x4eHs7nyy1DD2XwgHc7YEi+JZGtKTPmXcf14cyjoyO1hIr06HUrFroDALYToLYZSCKXZS4rEm/6PszncxTg6F2eEbKzcng4efLg7P7pzIH0i3lEUTdYRFDIgpCRjGy7mW9SmgMNnsPBuCzL8rrZIBgRDLFrVm0SYygzrsAtSxM5JjZGSPW0yYwRFuMxgkjihNpoNhKhQVXDiRlFy7Pl27jLvmYdfrm/PrjjbeFewn3fZH1LRFN8Vzo4/HL4/H2TqDtYOxjum53h7UPlmiIiehmXt2tErJv66Yuvmqa+++DukycfFONysVxe3rxNkc/Pz7OsWC7WImjzLDAKgRgBC2CRwGhzKOscYEwExCQgXeia0HapD3uTpb8VVg3XP+xvVSUi4n033Jpzdmc/cQfgx2FBCDQjvzMJIAKIBIYIOSUI1hgSBE779h92ePXgsOCeo067ahLZVZlpMK9SN6yqloYT0dArNYTgysrlLgXfdI04Gh1MI+HN25X0yCDCKAq+D1w4haacc9qX/vnzl5eXlwrIqkpWl09ECN+NTCMy6tDuXfe7/TFYAJ27pC6rao7Bs1KLui2rZ0+0lcmUYtd1lrDIHIqs57cns9H3Pvn4g3tn3Dfr1cLElPrAkR1RURZqq30fUkqz0VTHNbZ+O+RIRKy1ZydnxjhhU7d+uWrqto2QmJnIERBvnU/GbaWkRUSLFIh01ntCIP52Da6IZvATsRnEabBp+1L3LaM0/NR12A//BudC9oKT/bfsX8OwzvJ+Q+FBsId1HmRbt4iujO4nVYvz2qeUFsvbvq8fffDws8+/m2X2xcuXV9eXzuZFUWyamjd1ilKWo8RCFsGkJFEw2iwnIgCOwlnlTDSUOEXxPvbcBe5xx5L8Q4gY9jzAQSSYmSzIXpf7YfhSiD2JttV8j7gn70Zq75hrBlGIDEpSi7ol+oqIMpf2JW3AqAaZVCLx8ASHWHowIYqVKOqrYqmx1bZxhMGyLAW57RvIbCkTMBRYfO8ZMKUUGWzadddSdajPrGmaoigGyZa9Y/tE4V3DBUV41CXY3oy8W5R10wxokgbfQ1cL1Rm4NzdbRFigaZo8z53Ls4x81yeBPHPsO2fkYDI+Ppw65Pnqln1/dDhbxcgpEYo14iwAAyEDSdusfd8agumkJBq1bbtZN33fQ+iJKMttnlWFy5Z1X7fBR06SCBHQ6HQRECFrDRnmLX0J49ZWGMQhSwuwNYHvjBez2auy2d9YfyiHg1wNdmz/YQ+PU4VE/Zw/tKWw52TCnuUcXgyfMHQqYWZtf64/dT/pc1lHXC4XZZV/+tn3P3j8IEl6c3XZ9U1WFEVRANBivQo+jUaTSZ7ZPCsnFREah2TBWkKLKYGEBJTQgjEklELX+9BG9mjA7LVRU6ALd3nOwTXY4bS8j2ztqycRUVRZZ/furSeEMDQuEI3XEC0RWKLILJIYAczA9SPZC4kHoBjfr3G39h27E1i0GSTHFH0wSGjBkhFjRUQSi0YGzNonUiRFiUREzvoUV5t1BKiqql+tE0NkiilZZtZEn6a2EVGHSVxcXNR1S7s04LB22/IF2AJrMUbmbVofBwWDZlBmx8fHOuTg9vZWa9jUqOr4F8VUNDjU3TAuy9b3jJDnuYikECVFAhGC89Oz87OTvqlf31zGZjmucodyOJ1sNpsQAqQoSCgps0SU5c4RcN9tk6qOCFksweWbl1U5ns6Oq/FsMq6AsiRN2HTAghYNQhLYGnMjiBhDxAGIE0ER5ZHQtiRINdI2Rh+YxIN07RulQU5x79jfQ/siKrt2r2mv5/Jwzh/+1EzXvrnbZjXUgoegY2q6rtNRFnE3qTPuzfELIdw04ejo8PMffP+zz74rEJ8//6bruvF4rOSHGPn4+DhzRZYVo9FkPJrkoyKlgIjkCJ0gQgyhD10IXsiAEHMKyXe+jckj8f4iDC9g17eW9+jjaa9nx55DuAUdYorGGOZ35cUADLCNvfUz6V2967YrsbCS3QBA64Zp/xpgzxoPOnQf4BDZUaMAZFf+PiiU4QENiJpzLlgQETJYVVWfuK5rdG40Hq/qRhBYkAQsGmRgESFril242dY1CkBMAmAI8zyjCBDT1JXXkBAzQIwA6CjLjDD3HIpi20MlxpRlWOYFMzdN0/k1GnDW9mHjY304neWZvb6+nlQlokhsY2gBwIoYEkHYdGvnLJi07JeJGY1khiRxFvCDo/sHWLroesB1CMYdzu2Me4/ZyBSEBAlALKNI4pgwJPKRWEBSClGSLbPZqDg/P3v58uXtm2/idDaZHpyOZgelu856L3C7Xq67hK40tgiMEcRY22IGkgCIrKEo0ncpJeAYebxsVliVxjruehAuyKFAKqMzCJB8vy1pQWs0lRclAYDNHOzCG2utj7hcb2KMVVWhlb7vACDPs6ZpEnCm7eUdEEkS38VQGmTmwDFFVtsYY2z6zqTtwOembQGoKArLKbUNJdpsNiFJjHFdbzwnBlm3Xd21XfA+BCYDhIHTum/btv3OlH78Rw8+/6MPEndfP3t+fXNlrAsR13UDQNFzntOonFow/YYNBxqfkhMEhsjcobMmiz71YtHVdW+yPImpN2HVpkBFdE5iop27DtpoUGTrAao9T0kArHNIlEIQ7t/BM3HLq0YAwyA+KkxigABsTJB8mhRjBawTSBTmxH1sNpKmVYEZYuA21Chm7BywD21D+di5HLaYolhyAACMEsWAscZmYCmCCJuEObqeODA4Z/JqlAATi48hz3NIrOADQSLaNUKLMWbImKx1gMYaIUDGKLGd5M772MeYhO0gygCIhCKoVfMuy5iBmWNKFAICEZFB3OIPahZY0q5HXdt3uSusddaStRYIY0wxRi9bzaR6XafGxhhDrw2jWEmAg/YKIQiCMUS7Rg64y0keHByMrK3Xi8jh4OBgMpm0bTseF9sP3zY810dgRCwBZ1kWow/9dviWMcbm2Wg6cb7YMveB8moym01uFptRVYLl1kvnvRBLhLbeGAskQCgICCDaBQCjz3KbZZnNnEmJOXFSshc3bZsza4SQQAREQtAaSGZOOl1ExKctIrVarNfrZUpp1PdK3UKS3Od932VZlnNuzHvueqJ3LBZjjMnylFLf+5Ral2UiqMWfQzHO7XJRb1pmjpzW9SYyC0LdtDbPYtsg4nhU1k2nI8EfP3z0b378nclkcnl5efn2mpHu3r1XN82zZ89Oz+7ArkWA9z4rq9FofHJysgF9vqAt5y1u5+uGtM2zxyB9CNtG/vzOO9j3CGhHWhgcbLMb2MZ7qbx952I/lh72j5qBbXkTbkNiBCQgUTKmvDtwz6Xfd0n0i1RLGsC0ayKRUpLELnfMyfvtQGwkAOUMAgCAQRTZYxQKpJTAkCFnMpdYQuKQxIe0c0AkJbHDRRAZ7e6onm1pDFIS5iTsUyQgi1aHrWl3Ld6dLIwikFgSJSKKBBCYk7qHVnZjD9XR1yY54/F4GRbqFEncBl1DJCA78ioRgTAkBgH1iFCEiDKblWWW5a5PnhkQAVGSiAFBJCVmx5gyV2RZlpKO9UD12ep1jUhlWbLgarXa1O0osHVFVRQ5ZbaLPqyT74Qy6wCMNeTIgEFjkAVsTFGAWSSl5LeCseOhKqpnMAoIS2TxIWnHjz74siy74AeI2Mft2Oe2hrquvfebpiUi7zvF38oyz7IsLzLFuzWoY47ljuafUjLbelNUfkbb+T74EIIhR8Y1bQ9t33a+7Tt1SjvvGUQIIwe/CZAYCNtN3XbtqMjvPbj/0UcfffDBvYuLi5vbhTGGBd6+fQtoHjz8wLncGNP3Xu+1Cz4LQfcmOGMMWUJryRpKbKy1ne91k4SQutb7PjLS4G7vQ01q6OKO2bPvYyMip3ftZAZHXXasN9nLtQ54iWYoYPcuQjIoKXoSpm0XDEhE2p5+P0AgQG3tiEMCc1eerhK19Wt2v7FkdH5OCtG590rJEBEFgFADycxmZVm1IUJMSXwInYYA6u5aEUFBAiJAwXcKxseYQIRQCBmBgQEZQXLrtpC6XqsQE4sIIXICzzrgKVhrHdk8L8TIUGisSUIiUshHVzC9j0flmSXlRyRmBNqS5UUbrgXm6WSSFa5uVyGEsix3HTs1ZjDacAnRel9bS7k1iJhlKcaYQkwp9T7GJLm1jsjarO26erUMsrr/8EkfORiZFC543/QNEY4q5zlSQiJRIM2KCBm2KYTgU98G4xAERNEoFiRbMFDwqW37rut8itq7JSua7dQEYQ3ANGbg6JS1p3bAh06Xazodq85S9G8ITgraRh0pimJduoPvPXywWS5Xq5W19vBw5IpC5+TkWdmnXYNJTiLCvB22Xo1GrQ8319d5UXz3O9/58MMPR6PR119/s1qvjTHVdFY3jV+vJ9ODR48evXp1kecFkVHPpm1b9uycy04mFo2z5Cw5g0SgfVmEEYA4Qdv7vg8xCjqjlSZDlMW7JOqQ4pIdf2OwbHGv6H4/iv6vGsb9SHKQQ8JtRgK3USEnkESEtGXADJI/fObWt8KdVLOICOmov/e+MXFiEbFbT1rj0l2zEKOqwSCDc3lRVH2oEdnaDLhBNERICGCSJdgHvklAe9NhHzwzEBE4BDTMnBCIhBT90ZQMKKEBRYi23GXU1STasr+MmAE9DyFo4aP2UPiWR6EvxmUVhQNLSL0IEqBhVA5NSokkIlZ57pIUZG2C5FyOiFuaC2k9H8vO/ds0Xehb5TqJCAc+ODndLFcA4JybzqgoyxBSaFqOXd94g+b8aFoV+eX13KfOcZ6SERHRShdOBIzOkMnAAAAETglYUiQAnV4SIjNz23XDxMU+buswO9+HEBi2WXjded0GYowC7yZAAIAALFeN7LqYkXnXiWujpaIpKdWpTEnZQk3b+RDJWJflAti0bV3XddMEgbpvVYabrmVmIGzbNssyTglSPJiM7969+8mTx7PppGmay1dvjk6OyZrb29uirD77/g+S8IsXL9br2vtARAQmcwUAiNliUWTYOnSWLKlHshUDYfACfR98zwzWYf4H5Lx3x+AH4Q7O1Rf7juK+qHzrGM4hIiCEXWEFM4NyDBHhHXosKalfL8wRUQxZRGSdTrd9Aru6YU4sUZSSuOvUaMkgiffsvbdkjMtEBCSJCAoIyja+Q7QmY+YsKzJXxLhOgbMsI7TWEkAERqZt3lMLzQd9YBAlCoM2ijQGtn3VGQg5RLXX246uAsIiSRgAALPMTSaTIq+0ikIzs1vvGVFEdDvGGM22AGg7OHowjBllMXGKPih7BW0SEpa+rauqsJzqek1WxtMx5fbN1ZtqZIkIGBFZBLeeoSQBYo5d55umNSg6BdEY44ppSNzWTRQgorIsi5wRcT2f+5iq0exgXExHFUm8vpmHzU1enQKqk8CIWrwkBq33fiPcdW2MPoaemTnEGL1PkFLquq5uGpW0JBxCsNb2MWyZH7vUjmPqg+ogirBt4AkAkIIxJsawTVvZd6rNsFL/gBlBKCeHrjBEN4ultbaaTK21rQ+NfrvLWt+3fjviS31gFNQs1nK5nExG3//edx8/+iDLMt82BuTw+CjLchbJs8K5vPN914e27cvxyJBNIQBSlmXO2jzPq2kFJMZQbo2zhMASZeetUeTUe982wUdBsEg60utdr/EhGQh7Gc6BeqL4pNnmG95J4JDVwPdR6MGxlD2JBQAWBmCrBBsmQB3XyYzbTomwI83hNtcPAMAh6mVt/T5tJ8OCZQksQqwpAI4pGQbIUJJoIIcCu17JBtCgBeIir7Ks8H3wMbisRERLBgjBqkiTfd8iEaKgNRnaJIwEIsKg8TEBCSniZwjRCFIU7ll0k6bEkNiaLM9zRNP3ISUv8G7teI8Cq5lGay3gO8J3Sin0fYghxsAsYChBSIw+bqdYgt+RPCRlxtrMKUmOSJiREEWSSEKRtu2cswp1MEdD23GLddf1IbEgpBT6AMDOWmtwuV7neY7Jt6t5WY3Pj6YQ+1evLrL8YFC3hKzWKUq6rZfGGBHu+ib2fpeKi8wEu1Q47wrefQxlWcbIUcQYIyBBGAGEBY01O8Jk3JXMMcc8p5BSVOeKDTEAQmJOQAYBkISQyZB1Li+std53USAmMdvCbQMoaLDrW5+2Dp4YskhElKXgvS/z7O75nQ/uPRiX1Xxxq1hOVY3Wm41z+fHx6aZrv/rqqQCc3rlbFAUn6TpfuOzwaOZsDlsoQawR68gQYsLA2+SHiMTAbeubrg9BwGQINkWvvsqQcoC9rADuEV+HWJE4DUIoeyTPtGtkTjteGw8jHxEGUQQt6dUWISBIxLtIklHofWhHY0IRIYG4az+s7zWAIJA4lZkJTc+BVZA0USkpCYEkHUIvoA0KjEo5EphRURV5mVLyPpbMzO/lriyhRe08AGrhtkuQZVnkxMwC2whSa2UMiEGwRMbYKGwEQSRItNbVofNd37dt7pzmP40xWnugYPqg81QIt0uD24SMon++awNHFRNCHUnLEMPR0VH0PrTNqMizzK7X64p4Oh13rRJNlPEgKCiCIknb5ufjsXNuuVwu1qubRUop2XLW9T5zziK2bRv7jp2TxIagLLKU/M3lcnZweHb3XpyMLyH6zRJ2BCsRSaxMg+BjIMKUUtc1uudYYkgJowEABUKMMcAcUup7b4siioA+ZtAAUoC5770GfiICZDSGIXFdCImFAXXbJUSFVS2RJpcAUBCZCKwlayfl4XK5XK5XU5xOp9OsyNfLVdM0jQ99H8hs8cZk0JAg4ng8fnD/3uOHj6wzby/fzOc3ItLXG4MgCIK8WteBU1mMQorL5VILtZnBTfLJdGrJaS+jHMkSGQTa9uDdTYNNECP7PvZdjImNNQyUmIDSt3xIeR9cUZFTJiMisu/3fdFBPtOuB8zwOe9SqSqE9K26LVQhlL2ErQgIJBFEMQSoHWkIdKC9oqJskZzVMd0ppe3An+iDM9uaJo4xRp9Zt20nxYKonTc027ntiZhlGQlxTKLde0CYBVlEwPKWhC1qKBInffdms6mqKsvyzvcxBCIS5q5tpzGVWZ65LMbku9b76EPsvC+KKiND1qbgu6ZFQwbQIkUkLTrue51jTDtmzDbrqvxj2SF+ZW4cGBbxiX1sDdoiyybF1FkKoS+yPEbfNDw6mCCi9z4mgpSIyBp0zjhnsyyzBvM8P5xNYozz+fzt22uRdHp6enh4+PPfPs1yW5aj8biajMfJ98JRUry9ZkvALM5SDL6v10cH43/24z/+T3/3m6qq8sxpqts5h4Rd15I1b99etb4vy5xBdDjUaDSCd8OPrSBFQAY0LgM048mImVf1RuuyAaHpehKnII1WxO58LY4xWpMxKTEqL8ttr84UkYwBgKLMTo6OFcxwed40tTEGskKT8syszXK0brMsy2dPvzLGTEbjFy9eHB0clmXxL//lv3Rk/tNf/V/GmO9+8slicfuLX/zi7OyOdh5JSZq28zFMDw/OTs+/+PnPsix7/OjJZ599dnxwuFwudd9nFlMK3ps8c87aFCMz+z72PgHA9e18uawnk6MmyGq1mc6O27CQPZIK7UYwDCJEuyrHbVlcfEdAlx2cM+xYld6dD6LMUkfWZFlm8wwQvfdNvW7aBnNnEZx6IhFjjAbRWScMW4OlwpkSKCMlRBFBFsZtAE+AlozvWo5hQMskBREmsmQQmEhAKGk0qMQag7Sp6+Vi8ejJY20lUde1mkoAYSIRtrtoEAC0/eY2SwNaIBeTA0BjAMABOGNOp+PpdJqVRdf5xZI22GSElXNgbIicUpIQJQuIGUhKHBjfURBU9WxVEG51vArhAN4k30cWhm1HdMaoEK4iaXnhCKzNyTkDBMCQZYX+NTFDRCJOiRHN3bv3r64uL169atv6yYcfTqfTzXr9+vUbk7nlYlUVpUi5WCxKZ8ej8vrt5ePHjxeLRXc97/tWhyWdnZnDw6NPP/7wzZs3r14+Ozk5+eTjJ6/fXNzc3Nx/9PDizRu0iEGSsHG2Go9CSmLICDGzziVDQ1u/SMtHgldhE9z2ZcuKHMM2JNaqU9qWhPY2c1mWqTSKSOTkDNnM+eBB8OTkJPownk6Wy+XBdKLEmsy5osySD13XpBQz58aj0fV6vVgstMckIjZNM5vN7tw5/7f/9t+G0H/51ddNuymK4sWLZ1VV/It/8c/rTRhNJ3meb7ouAnDXtk33+s3FRx995H2s2+bnP//5wcHB0exgPBk55wLXzlhCJEFh5iTIW1b6el2v12sfxIKklHxIre/fG3G4J10Dc1r2yJy0BcphL1x6j2/9LfO4r82l70TNY0rGmBACEFoSQ6RET2vRWpti0GopAgbgpKMBmELotaaGiBBQXRTQ4lGl+CBqfIuIhMDMkFhprjo8BhABIYRQ5oX3vsxyBFbGUt+1LssQwZDANnggFBbeVnMYInDWFVnuuy7FkDtXWBdjNGRGeXZcFAcH07IsV5s69R1EF41jIgbs+tCGKJyi95ZFOGFkcPsJUmEZOjRu6020cxHvampC9FFYkNTLkJiYEjnWTGNRFMagdWgzl4iBPVGmFBZmFmHEhBhE+KuvnlZVcXx6upzb5XLVtt1oVD14+LDY9KOPPmrWm03dnpycBN8tl8vDw+NN07RNj9YcHBwpivv27VViOTs76bpG5yePqqIsMuZYVdXp6anJ3PXtTd02ITIYQoS+D9mu1dU2ttnB630MUTgrcpdnGEk7RGZloelWJDF2W9KlQC4ROWeIHDN3fZOYRMQ5V4zGOpo3xqjjeqy1y/V6Mpls1svlqrEIVVWNR2XbtvP5zXaydNMeHBxs1kvvuz/6/PNPPvpouVy+vbxoNquPv/PJ8eHB1dVVu6lD9F8/e+6cK8pyNJ2cn58fnRwbl4cU58tllmWZDskqysJl683m9ubmk+88BmBOECFK4r5pvdeaDFiu1pvNRkwFAFE4CseUjPmvVE4O0jXAE0Og6Oy7+knYA2D2iemDWCqWo50afYqsWWVnMudSHwGQiAwYEmtSwl37Q9y6hTt5VlKbgDquQ05/QDRo+1h3k60JcVtilkREYRuRXXFCiOPDg/V6rbig9x3H0PftQKHVwVAW2SFHEBIRBDRgCOBoOruNIXCqbGYM9iKGqDTOSjTRYyDLMUeIBiMBI4J1zpALzidOwftdh4XB+x9+7uszRER493u9T2RBw4Ysx5g47vSNNnsnY4CMIYcMhBG3DwuRyOqixSQisayqEMJ6vVpvNpnNRqORMabr+izLvvrq6fHBzFr7xRc/m00n9++cP3vxLPTeWjsaTfKi6PvQ+dVytal7f36Gp2dHH3/y5Pmrl7/5za+q8ejo+ODq6vLo5BitScKd77vWW8qMMUmYZNvHdptU2AlhTFvKi7ZP7/s+gZTWtrFGRBRKIfodO8Qa1M4sAEwoBncfxDw7nHVdN5/PtRxsPB5772+vr/K7513XNetVkbuqzEGga5vlYk7FdFxW9WYTQ+i6bjaZ/OAHPzg7OfmnL34aQg9Em83KOSpHJRlcN/WP/uRP1+v1arNumu5Xv/rNum0EIc/z73z66e3tbd/3uXNnJ6d37tw5PT09Pz+X5GOMiZOghBCaTdM0nfeRWbTlkSscIDKzMegyk+I7IRxEbvBC6Q9my+n+GcTyWwL8h0IYY1TMTwOo4UOMMdYaa8GqwVO6LIu1dnDWAEAjQ2TZVpnv1/gmrdwHRNEWHCJiCEXIAMYdOgrIoIkGFkRABGvNcj4HlKoqueEMnettSkpAN4RgrXUppaD9a4UEOKVEKFVR9nnRM5TOWjIUGQUyQG5rT8i+jyFYiaUlz+JZMmOKLK8A2t43bd+FiIjWZmk3RWgLKMN7oqgrtL+y2VBkbLSrR9RhD9p+N6WECMYZIiJIiBhjQkTnMiI0qN4fg8jp6fF8frtaLazNxtPxaDxeL5dv3rx5dnnz8OHDzWbTtfWd+/fY+5cvX84Ojha38/F4bG1WN33XdSbLKaMY4/X88v69h5999r0udL/+9W8fPnp0en7+1dOnaKgcjU5OTgRhvaoZQS8mttsGWZr4gl2ePQrrMx7kE0ALoOKOrxxSAmN1RNmYSIOfRISjUaXNZrQWwlrquu7u3fPr6+vj48N1V1tLF69fl5k7OT4kQt93m3UnMR1OJ1ervigK7z2HOB2NP//8s/Pz85fPn4vIZDKJyS+Xi7przk6OjDXz5XJx82VIsSgKk2e9X61Wm/O7Z9/77LOrqysdv3P3/Pzk7DTPc2PMaDRq1qsEzJxEMPnUdb7v+5TE+xhCIGvzPI8cIkfjLFmM4V1ufZClQVT2LR7sWOzv9PUfvEv+4AghoCFjjDNkt9Are+8tvPuW3Wdtn8XWZDHT3hB49T4HydxOQBZJuzJrEQEZulduc/RqUrd1ppiYJcsLSaHrm75tjg6mQOJTJILOt7oNLJEV3BYeASECYhIB4SgQU06WXGYEjUBBhgBsEkoeghEQEsgBjLOGxbAAgXMWXaaFEa7tAydEQmTaNTnfFzbeVZQleOeKEJElDJQUUVZo2FpblaVzjhBTCsZYYzHLMuCACJo32nUsRxRhjszx+vp6PB4/+fDjxfxmPp+/fv3aew9kHz165L3/+MMPJ+Pq+bOno+Pj6WTy9PdfHhwdTyYTYfC8Ikbn8igc6hYxzRdXjz/8+NNPv/P6zcWmXk26KQDU9Xo0mZweH00mk+ub+XK53LRtSh609MkaIiAC1p6vMRoEMATIMXoR2TU4ClohSqTwOGZZNhqVZVl67/u+875PKWW5LcpMRBKH1WpljHEuOzk5+fJ3v5mORwbw/8fafwVJlqXpgdj/n3OudC1D60gtK0tXV1frnumeQcNmsRhgbWcNtnhZmHEfCBiMLyQf8cAXgHzh8oGEGWwAcgcYLGZmZ3pqplpWl+ysqqzUkZkRGTrCPVyrK4/gw3H39IysbgIkr4WFRXi4X79+4/znV9//fdlsttNuUoaUEkbRQMOgikexEIIRIuKYKHBc5/Kli9evXgs8b2Njw7SYTzGXy8zOz/mDfhBFhGp2Quz3+z1vUCyVSqWSm0path1F/JVXXjk4ONBCHf1+H6TKZDLT09P7QSxjKQiCEqCIrosiEk10QBnCkF5IMNPUn/2URelHdG9wPGI6dk1jB3jKPeoawfhP43QGxvPTbCipHUdxFIcWRQpKIJEg6XClMYNQQlAIDSuVGjZGlMZmiDF2kqKuFaGUOIhjwzC0eKoc4UuHmwVwlDqJAqWEjkhNk4Wh7zhWGPn5fDaIA8KJ7bD+gOJIt4TFeqoa1Gg2EkEpVGowGICUBhDgAlAZenhSKAPBZtS0TA0NUUJalDJGQiERpEHRchKu63pB2B/4YRgOSVieDSKOlCWfL9GMjRBAaLAoGKBvgWmarmtreigdVziO4ziOisaOdDj8MYw+pFBKuk4iDEPPG/iep4UQe72O53nJjDs9PR0EQafdTCQSDEmtVqOm5Vq2BARC3EQKjTgK4yiKJKBpms1OO9k4KU+VX3nlxubWdrvdpmyYfhBA23IdJ+j1+iCezTExypg5FAzR4OVxkyOOIsqYZVkaDG0aVEqpJDcMw3Vd17W1k4mjEJRSkmuGIgIolQApGDN5GNkZBxEtyxKSI6pBvzc7Xfb6/TDwJBLbMU2k/qDf7XZNlh0MBulkanZ2dmVlBRGfPNkElIVC7uSk0u1iOpPqewMp+dTU1NT09KDBAGm91Ww0Wplc2jRNz/Pu3btXq9UMgxYKhdLUVC6TYYQGQbC7t82kCUBAciSoFAiuhBBAqQClhhAKLoSyLIuYpucHDJ+TKz0VWI7tc2xg/Hlpt0lXeeoMurbnOE7E9Qy6bqRRSankE20MkCglGXfnCSGj9s/IrSnQ/Ktk+MNkVzOOY0YoMJCjUWOdBTJCNY3NMCIdKdVoUuDpuVkh4nQmeXyilIiTmbTWltRnYJrbYIxYRaQAAhH8/sBilKJSsRAomWEwJFJKXf13HcsLIi+UQnBC0KBmzCPFBUhlMiNp26Yd69hAied2tbF2I6gRqQE8SxoJIYJHQojx1BchRGO+dTNNt1y0pmwkY333uRBSoi70AyiiZUlTjPNI4zZt29Tk0I7jSGY2Go3I98LAAyXmpmcymUwQBG4q2W51+p6PQCMhB56vgCSSSaH6cRydnJwUisULFy54fvjw4UMBKITo9XoAhJkGj2JCiG3bjuOEg6Gco2EYSMmIaIREUURHlESUUk3vGYYhIZpBTHNGWslkEhG5iA3DkJJzbgkhGCOUIqWGlDKVyB3t7wnhDvrdbDZLKY2i8ODgYKpUSCQSBiOB12/WG74/EFGsQColAs8vFmZLxSIhZHt7+8mTJ+tnVjX4wTCZrtZyKSIeD3zPdacvXbrU972Do8OB7znJRKFYFKCYaSSTrms7mog9k0pblqH3PsGVlEAVSCkF53Eck2cUjMM6p+VYwMxOt0ufzUM/d5ARgh8ngLIAwONo8mmT/nDCbGDcZ04kEuB7Y4C7/l+A5AaB4TYohBACheCgkCiDWc9dhgLdzbdMc7g+1dDxDucNxHPjhUSBntMnhIAiUmoU57MrJCA5jwqFnN40OedRFDI2HNceRkNggGEYCFKKKOQxRbANajBK0ANEStBMAiqJyhM8lko6juPYaFlGECsujTBmiC5jqUHkZdL5dD4nQAipyqVSKpkJw0fFLGk0Wopzkxp6rsIwzCgIkSAIqUBSRgjKOI6E4AAykozZyVhBJJAL5ftxwo8JtSSSMBIGEgIUJPJQEMR0OjNoj/lFqByOnQAimlai2eq46aykuL3ztBjmC6X8Xu0AY5pIJLKZDGazeuqq1R0MwvCzn/40m0rNzc0pEMsL0wcHB77v9Zq1lDmdYSXRj092TtbWV65fOCfDwaNHj1yWQNELej6CEQlJubARFChuYq1WLRaLCpVBTcMy6s2Gm0qGPK4326Wp8szslO+HQRQRYgAxJaURjyWQGNVhrXZ1ZqbTagSeBwAEoVTInZycpJNJilAsFsMw7HfYfHEBQA5aPQDZjTxC5OrakuWaALLrd4hrd1oNK5Hsx23GLOTe8tqcZVnpbOLoYD+KIsdyMMbNp09npqeZYvWDtm2n1ubOhmG493DPnBLJKKmUAgsybrZcLudyOYL4k/d+lnCcbDY7Pz/v5u36cdM0zbm5OcP0wshLJ5M7OzuO5QouAahrpsNwAMLkgW8Y1KZG6EUCedKyMAp0vYoorXqiuJJKSlBKU8XHnKNWzlMyiiKLTjAbAZCJFjyA5GJoopQhZQwAvD4KaTJCJUoAJRQoUMSgIoqEjONIMURLMgMojSRR0BFd0zQN05RSxoorVJGIwjBMpVL6TRGRoF50SlJUxI4NhpRQpJKgjoYIxZ7gqAAIEUopgaAkBSSIQRiFlAnLvPd0q+N1uypyStmuCjiNJEoBsULBlAAkChUSZIxRg5GEZTkGIVIYCqgWu5QghUCJKDGKRSwk4TLkIop4LMEmjBpGrpB3k0nbtoGCk3AWFuYME4ul/Kdbt30/9DxPSR0wEFTPxqhhJF2hYRYAkiqqw1ZtS2QELxzVdSiORxPRBUrEcFRcMcYIVTxWSilK6WAwqFardsKemi5Kudjvd9utlmO76VwmCILBYKCU0nt5s944qVWuX7+eTiRs2z46Omq1Wp7nMcay2WzUE4VCYTDo1ev1bC49uzA9MzOzvb2tlDIYSyRTBnMGQTiAgFIjkUxS0xiqiIVcckG0MoHjMsa8QaCENE0zCKJBr0cpNSjV8xPT09PddkepeH9nlzIUMUeEhGsrpISQbrebSaU1JYxUaNu2aTJAPhj0giCwLEop7XQ6ALLf71NK41gQEmt/4vf9qamp9fX1brdrMqNWq6XTaSnlwsKCVo9Lp9PNZvOjjz4ql8uXL1++tbMlpdQBfxRFR0dHnXY7mUx+7e03DWLoRlEcx5zHpmkAqFQq5TiONwhs2263O4yxQbOF1NI54RALBUIIEKiEksZwxEfXu4c4ax0L6aoaGfYIhkUsJb8iIXyusPf8EfNQSikUBwqIlBCChIGCKI4kgAQUSsZKEY20BkUNkxomZYyMY11CFRI55NNHDbIBAAlKapQSoNDsUWQ444IgRcyJFjgBlFr1VaFSGIahYVuI9Lhy1Op2qEGYZXQa3Vw+g6h0h44RhShAZx2IaAAzgBpg2BZBwYFTGYeCc8WFkkoJqVwWxahQBCEPhQA0kVFmmsx2mGmYtl2aKi6vLK2tLXERJRLORn2n3/dEFEeR5rEbMl8ITRen2zI4zq2FpsyTSkqgSIY8RRoIrltq2okDAGPMMRyZJDGP4jhGNBCJUpFSSCnd3NxcWFhYXl7sDrrHx8dRxA3DSDjJhw8fzszMzExPR1G0u7t9cnKScNy1tbV6vR75PiGk0WiUivl0Og0AWmAUUWmS/Hq9nswk0+nMxYsXP//yFqEskfJcJ+lHca/bt22XGYZFiRJy4PmaYdEwTQLACHVMK+m4ANButnq9HiqwDDPiscGsOI6z2Wy33bFtt9frJZPJOIp0wc515fTUbKfTieP45KSulEo4icFg0G4HhomWZSSTySjyarVaOp3EkTSdaWr4LjJmFtKF8+fPr62tPXnyRA9/6c5hrVZLJBJWKkUpLZfLmUym22p/+OGH2ZXFRCJhGUYYhqZpJnNpg7Eoio6Pj9OJpGVZmVS2XC5rNcwgCAKLaeYLABgMBvl8gdbbBGkQRVIpRKqGfmz4X4bni5y6SgkAZGJGaSxnT5GMZzImLfC3GKFSQik54vBHpYYrmzJGQKEY9hC4BKQKFYQ8VERx9SwnFIoIoCIetQFHLBhcgBAqVoRzGcnYZGpYVAPJJUpigC7voAQKAFSTOiqFUgIgNQyLIOMxD4Mojnm301cwZGpkSdNWSikhCSiGYFFqokEVsZmlFAqpdzBUiqBCQhiyRCRp4Md+LKnhWGbCTqUN2wJGy9PTFy5dnJ2dNh2mEAaeF/GYkSFoeHjrhwPBkiGJR4mxbuzAqMxFCEopJShKh0odQ5E2BZLKOI6jMGZRxKiBjCYd2+uRiIBuJCJIQMIYOXdmPZvPBUHQajTS6XS5XGy320+fbv7gBz84OTlpt1pKqWKx6DhO4Pn9fp8QMjc3p2El3W5bCKEzTw/CZrOplHBd1/f9jY2NQin/+uuvH59Um612v98HRYhhas1TAGBIMslUwIyE7QAQXS2zTavT6+ZzGaWwWasLIc6cOZNMph8/fpzO57qdTuhHOo20UmkAKTifmZlq1Oq+H87PzLdanV7fC8MwnUoN5MDz+mHom5wZRtJ2TCFCz4+DIEA9jKbQdV3TNB0nQSlN2YlCoVCv103TrFaruVyu2WzqLcx13W6v19rdXVlZef3116MounXrllMobGxsDAaD5eXlYrHY67ZN05yamrp47vxgMDg+Pq7VqtlsemqqpP8v3W5Nc4L1BgNEFAqR0SAKIy4kEsKQUoODIkgQqe6gDf/XI0scVwSelekmquV6avZUGea3HIxIQRQqlKik5FIolIhKSlBEylAoTRLMpGQKKWLPC41YjqeohsWz+Jm6DpWoWfc5V5xLrlApCSAYU6aJBtVeIR7SAYNSQkmJAEAZUARBoNfqxDGfm11wEqmtvad+GCWT6YHf1+UkUIo51FBKEQYGoZbJEqbpGMzSwH6FwgKBRHGToDIoEkBFjTCGMOaxok4ykcwUrERaUQMYXVhaPHv+HGO48ejB4eFuFAdRFHmep5mFUI8x6lm4KNbJLoJUBJXmvudCFxKUrtngEFWolAqCQHAFQ2VEzfYtFCMI0mJoWsyKKUUQSr8LJYTMzs42Ws1er0eQ2YbV7w84l/Pzi/V6vdvt6mE/3x9wzpNuolgsZjIZxzQPDw+bzSajWCqV9DRWKpXs9/tRFCTTSQDodDq2a9m2c+XK1Y1Hj/YPj+I4zqTSqWRaSpCKM8B8OhMlEoSQKIq6nb4fBq1+3TQtygwASDquk3BXV1Yopft7O8lEQgoRhmEqlRl0e+l8odfrJRKJ2dl5iqzbbXf7g06nWyoVCwVLSlk9qudyuUIhF8WB5/XDyLdtpgNmXR7QwNQo4oaBSqluv1c5qQaeH0VRo9HQaFLXdeeXFuMgjKIomUrpcDSfz8/OztYD7/z58/1+v9vt9ru95eVlx3H6ne7BwV46nZ6bnXYcZ25+xrKN6nHl5ORkupQMw9CyjV5vwEyzXq+3O71u3yeGLaRCyhRBJbQPHJU0dLCmRwdx7P1QKQVCAR3NhIIidEhCObZA9Tyn44uHVLEeNwcEBVKzAgLBOJKcc+CCcE6FYqCYBnYKZSiFOCY71/YGenwPh6OquqKmhADK2BBfDcCJpIJIqYcohhAcJaRUHACYZJSiohDGXCpkpmOYvpIoOCogrpMcMosrxUTgEUIMZjgGS9pmyrFtw2REqShCYiqCymAUkFE0mUEpPWkNhJIcKDJqOik7mWG2LQGtRPK4Wvnww1/Fkh8e7XZbTSHjMAy6YXc42zacJgHFh3PlUghERQjgsN4rpRIMNWuY0HUmHbjqMyhiUkoZM7UlA0cEajCwLENKHkcKhKQUGaOUkP39/WQyOT8/32zWd3arOu9ijP3VX/w555xRmkqlUqkUIoqY1+t1IUQcBJq0f252emZmplqt9vv9lGsDyFYrHAwG6Uwym80KoR4/3rQtN5XKWFYjjngQBGiTOI6DKFRh7Lou59yyXRAyDn0Z8X63Vy47rU7HsqxyMZ/JFWzTEkKkkynGWNJxlVKmabYbTSEUj+JEIvHk8VYi6czMzPm+n87mL1+9ls/nNx486LXDZNK1bIP3AkTUPX3PCzXjE2PM933DsMIwAIVCiFKhYBhGebl89+7dVCrVaDSKxaJtWu12GwByudzy8nKn09nf26tWq8VicYDq6tWr8/Pzjzce6QAkDv1K9SiXy6VTCUTa63X2duJkMplMJq9cuVSr7GoYXRCFScPs9gfdwaDVG1gOBBEnBuFCxlxIQNDyZlrFTvP8g8JRf5yMBiMoosRhFYA9rzM1jpV+i1eMooAQApSgREUVIZQxhoxKKSSlCrkEImE4fScJAQJSgyU0WQ4xAEAB0+y4AECkolJbPpWAgpgaECIQASjXieuQj0+3yJRUDBG1oAKAzKRzyWQ6CKKe5xumQw0SRLqDioiKIjCXAaKymHIYWKhQxEJEQgkDEQQHJQxUjFDLoJbBGGPESlBEpjBWwIH6YUgUcFCDOKw+qnqhZ1qUMWIZKATvdDoh5XEYCc6RGdrudRnGGOm/0RGOnlKKSmMH9C0XiAgoBedaxckGUK6rgy4hpIwFKkkFt0ytpTEQIjaZyUyGqNKZrFJKUzxlMpmlpaUg8H/yk590Op2LFy+ur615nre19aTZbKYSyWIp7/v+/MxMLpcDgNpJ5eDgoN/v53K5fqNvmMx2LM/3hRDUMAI/Ojw8nJqZKZenTctpNpteGAEq0zIIowyoyYw4jFAKg7JUIpkoJ3K5XBiGWgZIciF5xJCk08nFuflBLGr9gWb+z+VyjFDTNMMwbLfbQmQXF5Yty6OUJhKpmZm5Wq0x6Aau63IRSSkt23AcKwz9TqeTz2flkFQfDcMIg4gxphSm0+np6elkMqkZbNPptGVZvX5f92l1hJJIJM6dP4+IjLHPH9z/8V/+ValUunjxvBDi1uc3oyhaXFy0TSubzZRKJUqI7td3um09AMl5NPA9wzD8KFaUeWFEmOnHsQREMuQyH/sxAc/q9/C8Hqic1KUR+tUAEx35ye9kQpJl8lAyVshGAS4qpSQCQ2SWJWPOla6pSKEAKdUCMYpSqRTX0qiEDQ3SpHLEXCiR0hE5Oh+GyQYiSpAEkDKDUiJijogU1RDshgoJRUoJCKRATcsGyxr0FRKuAAAHXoCIlAAhwKaKGaUERa3MLhQP/SiWUegYDJSkSkokwAgFk2CMwAwnb5qmAtIPwiCKgl4fiKcQeoO+H/mOYxNC+v1uX3LbthzH6QUdOTpASAUElAKldEeIETQMhqhAClRKCFRiSESiS9aIKJWK43gwGCSobqkpIYTkClCCJIxxy7IAjL6SGkVhECoActn0/uGBZeeXFhcPDvaOj4/q9fr+/v7Zs2fjOL5582a326UU8/l8KpHknF+5cqXbaj19+tR1XdsydD+g2+026r1sNmuaJhIihGg2mwohnU43m+2pmelsLmea5u7BYRRFlmU5ju0q0zGtdrs9XC5SJpNJwzCazXa5WBRCdFttKeW1K1eXlpYYxf1K7WBvl4AKgmBxcREAENXR0dHly5drtZrOmqNYPHq8mcsXi6Wpva3ddCZpmowQ1em2dIMxl8sNBv04jhkz4zjW+RZjplJxrdFYjqKjzc3BYNBoNC5evFirnuzv7y8uLvba3TAMbdsOw7BWq8VxnE6nOedLSwvFYpFSOj87e+XSpXa7tb+7l0i4fjA4Pgp1n1aJGKRAZboJJxa8XTmxE26t3kJCewPfspNSoSTUZAYhDKlEBIKUiOcsatKhqdEBz0oDZPI5OGJAkxOq0i8ejDHCKBAiQQkllFCKDxuJMZcR19xiQBANpQhRAFQoopSKJQKAQRARhURm2Aq51AyLGotECAB4XmAySoEioFJAkCAalNHQj4aDxEM/D5QqCsoistvs9gf+3OJCpMTG5uMwjrP5DGMGDikMgQne15+RAJGSICgETgj3A89mVF+mUkwqFfpBL4p61E5nbSDoBYEXBMyxlCADvz8IfEQQMmIeEAIGIyMAK2o1L4bEdMwo4iKWOpWnSAijACCiOAg0wZH+B3DGGIDyfZ8ApQpCHu7v71vz88V0OooiwtA2HUQS8ZiYpNvtSylt2zUNNxZSCMGY2W6352fnfH9wfHxICPZ7nWajdunieTeRiOM44bq+77uuvba21m62tp4+yWaztmHMzMxowLuu3ff7/cXFeanH5qWKAiElSFDtdncQ+IZpnz1/LpFI9bzBo0ePUqnU+sKZ6tbhYNBjmgQIFIA6PNiTCqempnZ399fPnjk+Pl5bXX/48OHCwoKUUshYKh5GPmPG/v5+JpPRsOzj42POuU7VBoOB53m9Xu+f//N//sufvLdkLezubvf7Pce1bJvZjqVAmibzPE9Hhr1ejxDGOU+lMlJGWkjQtO2FhYUoiphpaEbmmbnZdDJ1dHTU6/XKhWKhULBte+n8mVKpdHBwEHjezs5Ov9+bmZpOpRPJlJvPZpRStm2LOAy5KBbzhJBBv6fp83jX84OIS4yFpABomI5BFRkKJAohIhEJKU3TmbQ3HOG2fc8zDMO0rCHIUbudOB4r4E6+amyKMFHXGXoqGUMUSyBAEDSXklScc6mQS8GlUkjBAIJMAAgA27SH56cAABwIJZQyM+ZcEcaFxqYRykwuZRiGhDFgTGgpQUREGkkVDnykLIpjgso0TQTgYaBAUmYEQYiUmo49NT0tQCTTWRfEIBjosTVKiUGRKRlLKWPJoxBMxgxKTEKpRTlSQslI4UVKyaWUkYgCwc0oBIJ+FPhhQCSXSvW9PuccGBhEa8eAjFUUAgXkJJKcy2FSCGPAge7SCCEoDrkJdBkbUQ/djxJxEEoRKdWYN0mNaD8oEEpZMPCEEEoCUN2QUswwGSPBoI+oDMNIJtyB35eSMwaIYBjG5ubm3OzsjRs3Wq1Gr9dDxGKx2Gw2s6lUGIbFYpFRTCQSnU4HEU2Dca61uJg0QICSgvNY5LL5/f39ge+trKwsLi56ntdsNg8P9w0cj9gIBQpRK5+SSqVy5sza1tOnP/rRjx4/fpxMJra2tmq1mmUaBiWuYzuO0+v1gtAjhOQL2UqlQik1LdbpdXd3d3//93//5s2bf/Pe37766suEEMsyFxbPISohYs8f6MjTshylAh6LKI4BYkoNznkUBc1m0/f9ZrNpmmbSdZVSGuVTKBSWl5fn5ub0Teh2u41WKyYKpMym05zzRMKdLpdPahXP86LAV2I+m0unk66UVq1Wq9dr3W53ZnoaEahhIKGU0kgoRRk1TDQsociQmkGjkyklE9DQsQPUKZ+eKZ9M/+AU0P/5Y9IIJz0qIQxGAEl4xmzKQElESoimUBzOQehdQPvlIfGPlFwIHZnrkwz/NPLdzBjOfEqpWw+EAlFIheJKKc15QBCRGQpAIjFMI/a5pro0HduwzFjGFtoAGu0qpFRsjKlVUnHOlQCgjFGUSlGtdqPZhygBBQoJECVBaAAgoNDEmjEPCSEU0WQGMwhFApKLmMeCBxgM+7aoGIHhdMioUUFAciAaDQRSaSIPADKsjqHSA1YAONItkXHEKWNxLMCgiiACoYQCAaUw4jHnQg+CZjKZMAqoYyRsJ4r7yZSTzizZtj0YiK2trSgMv/71rxsG9X3f6w8454Zh9Pt9RIzjOOGm6vV6u90Ow9DKOUJwIUbcwcqIueCc7+zszC3Mu26iVqt5oee6NkC27w1EGFFKkTIFoKRSiiM1CWIQBPVmo1jMNxoNpPTe7dvdbjeZTF44t/Z0+8lg0A9Cv9frcc5jLmzbbrfbqVSKC5VMJl3XtizDde1bd27/j//4v//www8qlcrA6/X7XUIgl88iomHYmsswDKIgCKQEBIMSw06wMI78cCjNGUQRATBNs9VqPXr0qFarpVIpRiilNJ1OT01NPdne0swgX355Swl5/aWr8/PzP/zd3zmpVWzbDj2v3WkIIXgcJpOuaWQ8r0+ZCbpkLZQXRlKBQMIIlRI5KC64lFKNJC75hLo1Uc/MTJNZDJe6HOq9kd9skzqbnTS/oVFRJqXU8w0AgIoQoIQQPxa6+KdGMaN+iRiRf8oJqpvJY7zp40g3e/wqgKHyCKAUXOgWHIwmpzTdkUTlR0G31wvjiDEGFPTcj+bOAK6k5CyMhGEYJjN0uVLDMogCRjAmhChAkCbVVV/KlZIghIwVokIJFAgqQE5QUAKMMtOgtmEySkUUBrGQQgY8EEIqISUCl8NcdNiNFVLvHkqOlcqVkEIqLpDoyS39yQmgazsEWRRFvu8jReYYRCghOAMNmDaVUiYFzbloW9bZc2d2d7f10HQYeqDidCrtum42m3rnnXdMw6hUKo8fbyilVpdXXnvttc3NzXajkUgkKpVKMuHs7e3phTXwepxzHksglKAWBkEhVD5fVAqPj4/jOEplU/l8ljJstOoYEwMMgyAo1Kp0RApCjYWl+cPD4z/8b/7h559/ns8VT05OYsG//vWvnz23fuvLz5v1EyFEIpUqlRYHg0Gz1SEEgygYDLz1M6ulmdLP3//l9PR0q9X4kz/5nyuVyrnzZ1KphO8Pjo6OXNd98uTJ6uoqY4wSRqnmQFSIGEUxs4aex0m4tmkFQeAFgSPEK6+8cnh4WKvVTNNUplk5qZaLpXQ6ffnyZc/zlJCvvfJqIpEAlLdvffHZzY+vXbtWzOUMk+bzWQLo+d1Wu1av1wv5KcO0FbJuv+eHQa8fhLEwhRJccAlCgQaO6dE+JEoOSXcBETWSWFsL0xetFIjhikdEVCAmFDjGdqi+ijNbP6LtV3N3a4dJlAKBMuaSc8G5EAIpIYoQZADDnVI+ryc3aYSjuGZonFJwRERQhCAAUgJIlJKAcsjPj6BluVApikoSShRAEPmRiAyLUUo5j2CksSRkLLlgUUyRGIyaoBSPleCUAiOEcsGlQJAKJMRUCV2lFdgXXc4jRVQQhZxzhcilUErEMQcpBEFJUCmQXIlQhH4kkOs6gZQSUBqUoa53CSm0rLEiI1C3ktobAwhUcjh7hwSBAEkmk4QQ3/cpAjNZcliwIoNWn1JqWJJSQylkyBg1GWMbDx7uH+5ZNi2WcgpixkABD8Lep59+kU6nGaXb29vJZDKbzSql2u22lJIx1u/3bdv2PE8Py+ZyubAfyBEp+LCfKaWUkEtnKyfHCuXMzLRS6uHDh4ByemaqXekhIh/q/gmlRzYJC+KIWWx/fz+ZSu0d7Nqu9fTp05WVlf297W63FQQDzsXUVOnCxTOBH20+3TJMWq2dKCUT6SSltNtruwkbKXEcZ3p6+vr16z/96XutVqPX73734nd93x8MBjpYIIS4rkuJQSkDgEajoSsKtm2PQcOxEIyxqampZDK5srKiyVpiwR9vPtHBOedxPp8HlNXjyrlz54qlfDAYKBCDgWdQYlrMMIxk0nUc6/CgZpi25ST7/X7EhR8GElAoFccCCAUAwhgq1E1UNam1OKxfIpkcMR1GeaAFTwGfCzsnQ9Nx2eaUwWgNU83YggIkAckVEI4KUA19rJIKQCmqzQYB9GzueIBDW5kkCJSgQD1NKEEBKBkGoZZIYhQJISbVjG0xGYrpAUihkV5aaMO0DTdhIyLnUcJJGhaTIAgQAVKBkCiBKMYFgwjjiHPORRQzStLJpJNIRH6AACKKYxEQgVIRooBzCNGTKlaIseAAEhklqAyGQkgCSnIuCRcERcxFHIuQc8Y1q6KGnSmiEIGo4UATQQV0dCul0qTjCoiiw5heKaEm/kmc8yjiccx1OZ4xSixHSskjFcogiiKpeBzHUvHDw4MgHhQKWSlTrmvH3Ds5OfKDQavVtm17EEWI+PLLLxcKhY8//OiTTz5ZXV0tFAp7e3tra2sn1eO5uTltpchBCCm4EgoEf5bPHBwc2K6VL+YYY4eVw4OD/WTKzeQy6XQ6DMPBYBBEoZSSMUNTJtdqNcMy//LH/+v5cxcrtZNOv7d6ZrXZae7t7SDIYiHXbLfa7ValchzHvF4/iaK41+tkMrlOpwVAvv+7379///65c+dq+5V0JqnBPQDged7+/v6DB/ey2TwhRArNo8mIOUyNQh4TLReNmEgk3GTCcRzXdZ/u7MzNzCilarWabdvpdLpUKmlITRiGT548VkrNzk2bJut0W1tPHuXzuUIx51imSgshVLfbFnHUaDQALSGBWk4QRZRSjZhRoAMZQgihBJFQEEQqopTy4yHdPZKRsqAa5nhCKzMoRYeGqTEbz6FGxynlqdrp2FtybYFAQCuHSVRcKSJMw0A9ykS4ZgDSuFXDGColD7dZeKYGqcc8KdUAHjFCTMYGQYKMIBAikRCQCpUyhkBzIblUCITBqOihCEMv9Kq1k6yMhgxGFA2DAiqiCEFgng+MAVEqigSPhcXANNF2DMMyQUglIn2XlGICQCEjZCAlF6CU5Eh1JkgoNVCaSkoKKIUQAiXnPBIy5gKHeFGpZwjp0OUN4+whTzdSQKGUlEPeSDWBYhNKcAljympEDMPQ9307mbBMo5CfGgwG/X4/9KMwDgDAMDjnfGFhznaY6TDLogp4t6d6/U4YBr/zO7/TaDSajcZgMNjY2BBCDHr9tbW1MAz1jFmz2RRC+L6vUeaMMUqVoDLmIAVXEglhpgnEYKZltZqd/qBjOua582fa7davf/3rly69HMVcIWgaC4mgZckyubxpmgpxEPjpdJoQ8s43v9FsNs+eXUdU/X5/Z2+3Vmvs7++GYdxsNcIgSqUSs7PTlUrFNM2FpXnDMFKpVI3v1uvBxsYD0zSz2Wwi6Uop8/m8lGBZFgLxvCAIAh5L23YYG85Yj1vPhmEAVQCQTqeTyWSn0xn4XqfTUUotLi5qnHc2mzVNo9vtHh8eSSmYwRYXF5aWFwiBZqNWq9U0L2CpVMrn81tPD3SoJoSghgkAgJQrSRGlUhIUKgSlKCGUGADgcT6emyNIYFSCGyL4R434ob1JOZJrPd2dHz+iRlNy+lepNO0ZQUQKqAVbpATLoEAkJwACpVIwhqQqAUozbymAZwYvhSDaw1DNNKN5rrlBkVAALWUmhh6ZgKSMooKYx0IKJpGAoUCikn4YCSE63dbu7vYgKHe73TAMqElNO4F6mgEo8wahbaNhGACUi1gKafQjAoNcJgtaf4LaRAEzTAIoiFC8K+UQ8IogKEWlh8gZihilkLGIpRRxGMVBGIahZHJsTqg0EkKpZ6ovEwEAgFKKMqoQpB7i0gLVCnSlWIs6EUDO+WAwcFJJalFmuQAwisSY41j5fDaTz1SrR6m0I6UIoyibTS2vLM7OTaXTyY8+3P6Lv/iLOIoWFxcRlZ4toJQ2Gg2/308mk59//vn1a1f29/dnZ2dBC+5M7AgAQ8lyg2DMuVLKsiypZLvd9n1fQ1IIIaZpJxIJYrAgCJq9VqvTzuTypm1/7fr1drt7/+GDVCbdbrcJIQuLc8eVQ8/v61Q+8LyIc8YY2LC2fiaTL3zyyScJN/Xnf/7njuM8fMi+8/bbX3755e3bt/VgRzqTOjo6Yoxls/lUKgUKa7VGFDU5F9rkZCTHA+ae52ksnoi5ZVm9TieKoitXrvgDb2Njo16vr6+vb25uJhKJdDpVq9WymdTa2srR8QEPw9u3b+cyaSQqk0kxZsRh0Ov1NDxFjlwZ0qGn4rGEEdGRQKREKYrmaDoWxuNIBHXYqUbsvSAlRZ16IygllRqDuV+0Q3iBZkYppWmvh05M5zmgtf+U0hSGUkoQIIfg1UhGL4a1iKgZ93BEezN+L4sZlFClJBccERkhehyeaVuNQQkuKdHESFobiRCIoqjVahmOEfFYaRJ8AogUNT1Tww0MEluGySwaM8GDMFJCgFIxWkgJVWBIVKAgirmI49hESwhBpFQEqaJEUJSoADSZhSIgpezLIJRhSENuSzUs2VAlUAghFScAkkhiAiqCiDFILlAxAwAVZ0P2MaUMBQwJGfI3QsxDI2lxk2ZS6ZNKZffexqtOaqY4MwgqMfcUDEwmHCeRdC2I/Ppep1mrYugDU0HkH1LpR8FJvdoddNdnX3rj6kthGFqWEYahi4QQIv1gZXbG9/1z587UasuWbTgW7XRajDEnXdIZVxAEIY8VoOmYhmlGUdw8qYdBnMlkTMceDAbKdNNJM4o7UspYhhgrEUnfD6mJ03NTnX57Zn768y9vrq6snT27qlR878Htc+fO3bn/pDi1sLVzlEjl+GGVGvbmxj3LMouFstf1ZCSDjmcqtjq7kMlktra29maevvXWa/v7+7nc2ffff991Enu7+7lcDoEc7B9ms1nP6wPw2blpKaVtk8qBX3ByKJWbzkZR1Ot2FhYWhBB60LnRaDze2Dg4OFhcXDxzZv3BgwdvvHI5DEPGWDHr7O3tdVv1TqMxMzNTyOdN09zf3+9141TKHnjQ6/cBIGW7hmmFvkg66d1KM5LScGxghkA1gnAKANDVQAWYNp1hECT0fAGICYV6ZFQhxiBRKUBAg47y8FEtlKBCIqUMeDzkaEXUJU/tXB0eAAAqBDFkk0dESUFJ1N4PJRJFgRJGGaWUD6ujihCkhOKQz1tSZgqphgzoQJQCRGKYTMSEK0REYAoQQ83ARYArSUChRSg1CUWpqzLIlOkKEfcDLE5lGLUdaszM5I6PD00CBBVIJSVnRI3GiHAYZGvahZCGQBlVILlgCoExRACCfFgAVqjhsXq70o4ClJIqHivLgtIDY6fKWZPpNbwAkNdFAiklyOHQPQISRNu2O50ORTI/O2cyFgXhYDDodDqFrG0yI47j6lG11Wim01mTMs/zas2aAhGLuD1ouUnHck0pIQ7iVqsVRZFpMsZYtVq1LGN9fX3g9S3L0FQOepoJAHTXvuedSClBEcMwtHQHYyYzjMHAY4xFJBoMBoPA17z3ngdCeolEwrZtpVQYxFLKRCKRzuYfP358clJ7urU9VZ4ulUr5fPGoUm23u4jqz//8z4Mg+M53vhOG4f7+fiaTTiaTURjt7Ow4jgMAmUxGTw8JIZ4+fbqwsJDP5/WU4BdffJHL5TRoZqzFa5qmZkPsdDr5fF6HCdq0MpmM5tGYn5/d2trKZNJhGL7zzju6ZmOYtNvt6t5MPp+/ePGi4ziJRKLVaiWTyX6/L4QwDOPp06d62LLf79vUVEBj+az3oBSAUoKPWmugBAqFIBEIKGoZ45EaHA3R6wx/vDYmazDP6WHqFHJysP2Zgq86ta5e9Jn6h1Nn+MpXTSafX+V8n6vHjuJ8hkoqMYyNpRAoRRzHxDEJMaTig8EgnXE5571BP45DiRaj1LQppa52/MMLopQCpVIpL/ANQgVlRAEKSQAtUAyJRD3aqIbOXLfxcWiBQiohRDysC3KlFBAE8WwsGl+oL0/eDngB/SClxFH2rpeRrq0VCgXJhWEYjUbDpMyyLIoQR4E3CEzGhGEMvL5jWVEURTwSsTw+rFRODhvtFjNZzQ2+9rU3TdPM5/Nf+9rXKpWjo6Ojp0+fLi7OE0Lq9bplWb3uIJNN6aKo53mGYTCGoBSPoijiQnSFkgSZ49iI2Ov1okDLmqsgiAgF3w/jWDDGTNtKJtJKqVarxZhx69YtZljZbP7mzc/jOHZdd2tru1Y5OHfu3Jkz5/b29m7duqWLlvV6PZvNtnZblNJCIaflPnU7u91ub25uuq57/vz5hYWF//gf/3J1dS6RSEgpXdcFgH6/r0ag0CiKHMNtt9uGYQCA41oas55IOHEcnz9/fmFhXncLhRD9QbdYLEZhjEAs087nCvNzC1EUxRH3Bv7hwREhJJvJFYvFzSdbiUQik85aps3DiAv0hYpCTVmNKJXUdJ2jf7dUoMFDkgCdaK/jaEJicq2f+lnh6U18WMWbYIWZNIZnrx1jw0eHHKNtEEZzR89Fs6fW3lcaIQ6fpvSQudJRMwiltMqT7n4rRYYam0HgOa4lJQz8vmnOJNJJLoJkJikUF0hjUEJJxiiFMRUfpcQweBQHUWQZkdQkcFygAg7KoFQpJRVyQD1nBEgkICjgursghRAi5oJzTZyjG7Ry8iZqKxrDYU8ZIQDoWFxKqYQkCpDptUd7vd5UseS6brfbtQxjdXVVKVWr1aKgL3kgeZxKJW3DNCiTXKCCfC4fC14qlWbmZir16vsf/NKwnRs3blT2W81m03XdXq/3H//jf7As67vf+07MI9/3DYNKCXEcm6aJQDOZDCKGnFuWOaRwRilEHARBGHPHcdxEghDieQMQyjCZkhCGoSYy1/zblBiIpN/vNdqtXLZAiTE1M21Zzu3bd13XLZSKqVTqpWsXFhcX6/X6zZufuq5brZ5wzpeXV3K5XBjE2Ww2nU53u/3BYJDNZovFot9ra97RTqdzfHy8uDilDe/o6Gh6erpQKBiGsbOzU6lUtHShpnzWk5PJlJtMJrWHZ4xNTZU/+uijdrvd6bZ++MMf+r7vuu7du3dc152bmwOA3d1d27ZbrdbMzIxhGLlcjjFWKpX29vbCMKzX6/V6fX5mMYy4F8ZBEPKR96OAxDSlnqlVAqQkIBEVUaMOxATv49gNvmiHv8UjfeWD4/KMmrA/OS60vhCCfeXJxyY9Xpww6T9RV1Oe1YqUUgpUHId6uAIkB0IZAjJGAPt8YLKEYVIE7rhmKu10Or5tm1JFiMAl5xFnupA8pMgkqBhDISTnoeBIiSb5UkLGsaKCAIBBUKtAoxadGkEKtPgv5zyWQg9c6Q80NjwYhR9f6QnH38VI5FEpBYCEENM0HdOKwlD/yfO8MJl0XVdzqDE47rTaMQ/TSVdYdrfTC/2IEmLbtvC9KOS9bn/Q8/xB0O13T45OEM1MJnNycuK69je+8Y16vb63t6er9gBSCNFs1h3H8X2/kC8FQcBVqOnPhDDthEtKRAgRhHG1WiUUDKCWZQgQqDV1UCmJmXQ2mUz6vl9vNDwvYIxZphOG8fe//7u9QX/76W4+V+ScV45P/u7f/bvZrCWEcF338uXLmUzmZz/7hZRydnbWNM1sLm0apmmaUvJEwikWi4h46+Yn2Ww2juNWq/XZZ5+9/vrrt2/fbjabcRzrMFVKWa1WdcGTc86U4bpuvpBljOmAQjN8P3782DDY/fv3F5fmV1dXLctqt9sbGxuri3NBECAlQRTWarWFhYWt7adra2u26+QK+W63G/H44uVL9Xr90aNHzXZrtrzo+0FvEAQR17V9RKTUoIRqIxwiREGhkkCGGrfjiFRXvMlIqfeUHSqlJt3Zi1v2i5HUUAVswgKHdDQTbnDMNqaeT4VOLcWvPLQPRNSjvajGqqAIBNRIcEwAGNqxJSyWSdjZXJrzMGkbQcIZdJQEnk6nhIijKJKSM30j2LCUq6cYKackFpwqxgiVBLiUsYypIkopm1Ht2obTgRp5M2QclAJ0p0oB6h3omd7VJFsMvBCaT+5Do/uCDIfcaq7tpFMpPWmacFxKqe/7QohyudxvVYLA8we9hJ1GRCUFYySdzj68/8B2HS1wmS/m3nrrbSEEEMwVih9//LFhGMlMcmtnmxk0mUzuHx0uLi4iEiF4z/MTSPq+V56ZBkqIZemxa87RMaxEIgEAnh9IyYWScSwy2RTzWRBEQsSJhGMwWwjVbnd93+92+57nWaaTzBiUklqt4QfB9vaOUkAIZcwIw+j48GRpaWV5cbHX6dTr9QvnzqwsrymlqtUTxUWjXY3DkBGyvLhYKBQoqjiO9/f3NfeEBqweHx+7rouIvu+3Wi3DMIrFolJKY9+ogY6bUEpFUSSECENfCNFoNFZWlqenp7/7vW/rucqbN2/Ozs5OT5cvXry4sbFxcnKiuQX0Z9/e3kbEVCplGEaz2eScN5tNx3GWlpY6/X670xv4MScUkVFKpUICugg3ZtYTQxEuAAXPEDB6sZGRAsxXrn5EeM7njEK+cX6oU0o1noGaOIEcmxZqYMyzQaoxKHRc/Jw0wvEqVS+EwRpPrWsiSikgCkEiISajqKQAKcWwrA9EKiESFiYoLk2VOQ/KuUw2YaUdFovY8/pcxpHBAkqY4oJQpusxSkiiaW0IiQVnUhBGBUUpUSklQCpQko/kYxUhisCowzMckFdSIShyOsqf9OnjD3bK4+vjGRGGJj0ekeFls9lapRoEgSYRlFI2m01E7DUroe8pJYWMURLDMKjFXNd97bU3ylNTfW9wcHyQyqSn56b9MKhWq9Vq9cyZMysrK5XqcbfX0fNNb7/9dqVSKRbzQohMJhOGISEsCCLfD/PFnBBCy8ozgDhmnMtur1ssFaonNc4jx7GQ0jBsGAZNpVJhAJqs2rbdZCJNiQapqHK5/OEHH6WzmXq93un0CCGFYvHosPIP/uvvPXr06Cc/+dtWq5NIJBhjCkSpNLW9vZ1IJNrtdrvdTCRSmkV7MBg4jqMT1zAM19bWdnd3c7mclFKPIwVBMDc3l8/nNRY0lUo1TxrJZDIIAsdxEglHr7mTk+r58+e+uPXZ7u6ulPLatWtB4BkGTSRy29u79Xqz1WpxLl966aW9vYPV1fWDg4N8Pm8Y1uzs7ObmZqVysrHxeHp6ut/3+u2o2x/ECu1ExrAMU0rOhZRcCiLgWb1EgKSAEsAwhkp4MAr8yGiQYtIRjfdoTa82GR/pHV9nuXKC83d4ktEz5cgf6tB0bIE6QRq/1+T7jteknNDoPuUeNP+FVEJKhagIaOJ3hQSI0MMHEpFQVBQpEGVEPvp+OZlQyio6iUQpv1DORlGwtb1JDMYYAaRMfzxUoIQcf1SdCxlSMgCFQyQaaDj5KO7XDNNqJIY+Cut1QqrUBK/2ZAj6lY7+xahgeH/lSOxOSkTUG3O/39cI42az+fjxY4c1TWradooAiQOp6Xx83//yyztLy8tA8LByHIvIuu/EIvJ9f2554f6Dewpkr9cbDAaFQmFra+uzzz6bm5srFEqUGrOz80+fPiWENJvNTqdTni5JKTnnYegLpQzDCIKg0+kkEgnPG/R6/UwmAyg1gXQqlapWDoVQmUwul8tFcSyEQsR8qVgslI6PKp1O5/iktjg3ryPGfD7/8ccfr62tcc41ZuXLL+/85V/+5Y0bNxBxaqocBEG73fa8frfbjmNRrVYdx5mfn7dtW88EahP6kz/5E7036fTPNM10Or2wsFAul282f51MJoPAy+ezU1NThJAwDI6PjzYePWi1Wrlcbm5u5uzZ9ZdffunRo0c7OzuXL166cu1as9m0LKs0NfWXP/7xH/zBH+wfHi4sLdWbTTeZ9MPQcpx8sTgzN/dkayvoq74XUMOw3LTFjEjEhEshuVIUFMhngSFq1D+ZoJ2ftMYXw8JxxDR2euMXjgOr8XnGT9NObXyucXlGDhEycGr9jU8++f0rF+TwDSgOC01KKAVIFAClSGTMFQwLGZSCQRmjlFFqBT0a+ElABYQFfirpJm3XR1goFFOpVL5YzBXyzGQGSBXL+NmUECihJGGUSxGLoS6v1MpVBMUQ1QcKlVBC89cZ1NAyQ2PwK4x2qTAMNMxlTETNRjP1pzY/bWymYegXMsYUF7pfIiw7CALGWBiGhVxeKVWpVHTfGVmQKKQSdiL0QwnCMAzBYTAYJJNJy7LmFubPXbzQ6bUr9Vq313YTiUIpf9G40Gw3opCvrq0RQoIwNE0zm8sxw8jmcpzzMIrCiB8dHSmltra2lpaWNDtg7fg4lUppcSjd5u50uoyx7qAvuGKMHR0d5XI5x3Fu3bqlgammab/88st7hwfz8/Obm5ux4EnH3Ts8KGRzuXy+2+1+42tvSCkf3n+Qy+Webm51262pUrl6XHnzza/dvHmz1+3EYTQ3N5fP5hBR8rjf7dRqtWQyWSwWC4WCdo+zs7OtVuvkpAYAtVrt8PDw8uXL+/v7zWZzZWUpnU5vb29r4M7GxkPHcV5//fVur23bdrvdHAwG77333vT0tG2b8/Ozu7u7hUJBA1Dv3Lmzurp6eHjIGPvlL39548aNzc1N7Yf1uKNpmiHjEhQjJJlMGo7VHQyiwEfDAqJ1FIfsLABACCWEcB6ON2K9u+kKzTieHKc2oxXyjKl6aAUjLnN9Er2c1KigYE5KHbKhsLbQmB4lY8GFbvNRggQVQBzHmtVBozJ0GVCfajJMG+8IABq7+Ez0WykhJDimRZXk2olqaCqAAignk8Vc0avWssX0oFrvVY8VUYRCp9v27Vb3pH7i6hbFRGMAEWEiVdP3YrwJAUAshh+eTmwPoAC0HAs+M0KtPCh4NDknppTiL8g+njrGD46dp5Sy3++blElmBEHQ7/cNOmwnmrYDQPwo7HZ6/b5HwKCESQmpdFohHFaOJYhUNrO4NH9cYQ8ebex+tGua5szMjOM4A68XRVEunzl/7uJ7772nlAqCwLKsfD4fxzwMo9nZ2e2dxzs7O77vSwnnzp3jnLtustVpM8a63V61WjUMQ8/Laf/T6wa+P7h27YplOSe1WiqVOjo62NvbY4x1Op3eoI+ocunM9PR0Kp3O5XJPnz7NZDKOa62sLh0eHk5NTW1vb6fT2R//+C8RaTabLZUs3ZozTTOO47Nnz56cnOhKxvz8PCHkV7/6lW3bP/zhD//Df/gPnufdvn377bffbjabFy5caDQat27d+r3f+735+fmDg/16vaYj3jDyCSFRFLRaLULI9HT50qULT548effdd7/zre/HsVCK/+3f/uT69euLi8uffvop5zyKol/96sNr1661Wh0p5RtvvPXBBx8YhpHJJk3LMUwrlU4wK8EVUMsTinb6fVBICRUAOKSSHaoyTq6u8RobpyHjHG/8tMnlNz5OvWq86Ws6DAnPXJkcucSxb4SJ7sUYJ6BPKCfULOD5MHXiykeQN5QwbHaAklyqcayIksRc24vf7ymoxGGjwoSIQh4qEEjBdR2khBiMPVMjmVBoGQ4R6TMKPeSnUD1ru+unCS06P7oR4/muYYgPwyvWbzH+AOMRaT00Of54k592+OCERI4uvJqJJCIGQeB5XiaV1ucMY8GCUErZHXj9vmdS03UThmml02lmmhGPu91+q9+2XZsrmc1np2YLx8fHSolEIqs33Vqt9knrk2vXrsVxPD09PT09s7+/L6XMZrNCiNdff9M0Te0NkBqtVmt3d7fZas/NzcWCx7FApBqaYBjW9HTq+OhOqTRFKTVN1ut3hIwvX768f3TIOWcGDcMgCMJUyoiioNuTVtOYLk7/u3/37374wx8+fvy4VC406q1arUYIyWazr7zyCiHs6dOnOslMJBKBH1YqlV6vt7q6ure3FwTBW2+9VSqVPM978uTJzMzM1tbT1dUVfbWPHj06Pj5++eWX4zjiPC6Xy1EUOY5Tq1cNY35j40GhULh06ZLjWJ1OZ3t7u9lsGobhBf7Tp0/7/f73vve9hYWFf/kv/6WUcmVlJZ/Pc865FLbrlMvljz/+uFgu7e7uEoiEkhxUq9M0LD+MZBgM+kGExCTAFQIhRElUChVIPVMytsBTRjWZhk16ocnvL+7dk1atzW/MKayUUs8HlS+2LtTIbZ4ywnHYDM8fQxFCAKDaLCUgEqWpuBVolI1SHBGFBJC2aVAlY9+LQ+A8kpKjoRhjzW5PgoZGaxaA8dfEgYg6FEClKCLTGk6EEEZ1PKDrJVqM3jAMRqkeb3wmWCO0fvdzNRj8DVpzkzd3sg8LEzID2tQ9zxuSrykVx3HMVawAqWVajuW4yJhQGAtRrdX2Dvbbva6TsIUS2wc7zXatPFu0E66dcPOlYnmmXJouLa+uLiwtWa5zcHwElGzv7X1x+0tmmZWT+mdffBnG4t7dB81Ge3dnP5vJVyrVYrGUyWTffPNrrVYnDONMOpdJ5xgzlVJSQjKZvnjxolLi4sXztVp1MOjlcrm/fe9dpcSjRw/b7SYXUSLhZjJp0zQIwSgKH9y9C0K8/sqrv/e7P3jr9Tc2H2/MzcwsLSysLC3kM1mv1/X7A5Rq0O15vT4BVS6XlVJTU1O5XO6TTz7Z3d1dXFyklH7xxRfnzp2bmZm+fPlyu93OZDL9fr/f76+sLH/55Ze7u7vpdDoIgkazVq1Wq9XjqampXq/XaNQ453qoP4yCZCqRTqevXbu2sLCwvb398ccf37hx45/9s3/26NGjR48eaSkFwzB+9rOfTU1N6XBUobRt23EtxqhhUDdhJl3boIDAAQVBhagQFRAgFBV5NkQ76eLGv45Tu8mN+5TVvWiHX/mgUkqOSjIS1Fda4IsvP+WBX3xQ52uKDLWHQNdTRpaJRKECxQXnXPBIcO6Hnhf4/cGg3+/7vh9FkYwlCBnHMY9iHnEZxWycmD5ngUoZlFIkQ93tkUKbAhUCII4/7bArTYDoiUSlNwPts4etwmcfY3IG7Ld/VP1C/TydOTi2rZSKoogD6gY0pTSKIsclgNSyXWY4zPSCQRBFkR8G7V631+vZrrW4tlyem0qXM7GIFMqjo4NSqXT9+vU4jh8/fsyoeebMmWKxvLe3t7OzUy6X0+l05fik1+tNTU0dHx8nEknGjCAIT05q29vbYRC3Wi2pqFIYBjGjNueSEGbbbuCHnXb3pFZdXll+uPGAMnL9+rVKpcp5FIQegMzlMrlCNplMAwDn3LCMKA5+57vfHAwG/9P/9H/9gz/4g2r1pFwuf+tb39ra2vY878GDBxpAp3Owk5OT2dnZl199rdVqdbtdDVe4devW6upqKpX6+te/ns1mL168WK1WX3311W63++qrr968efPhw4eVSuXq1auIeHi0Pzc3p2n8X3755Vqt2u22wzBMphJKKc3e/2jjSbVa/Z3f+R0p5c2bN4+Pjzvt3o2XXgGA2knDYNY3v/nN7333d/7Fv/gXf/RHf8Q5/9M/+wvbti3LMkzDtE0kVEgZxGbPjxA4IAUAJIgKFCGEYORF8FyKNTx0G3OyoADDwd/nGlovms2p3sZw7WmdUKXUhGOctMCxzx1XMSayUJh0g6feV4JC1GzVz10YSoGEEiSCgE7DJFBUwKlCjexUSohYKo6xIv5wbSMiAmUSQZ2yfl0XIVQTglIClKBWMJVK4uiTK6VQKkEBFQBRRM+FaaiMeBbWa7nsyQ/82+/pZAygE1SlFOdcgzxQAUUCAHEcE0Icx2l26iFXsUTXdCzblQq5kkqK5ZWVmIe9Qa/dbZkJY2Fplhqs3W4tLy8jYqVy1Gi0jo6OTNMcDAZhGO7u7jqOW61WNx4+LpfLu7u7tVpteXn1zNpKv+/Ztru3d1AqToVhnM8XgyBwnWSz3YrjjpTSdt1UMl2v1yuVE6XE0dGB4zjb20ez8zMvzVz/27/92zfeeD2K4lqt1un2pZQHBweEkHOL55XCL7/8Us/pPnjwoFarr66uPn78+MmTrenp6Ww2u5Zd29jY8H3/zJk1zwsMw/jkk0/CMPz88887nc6rr766s7ODqIdg4M6dO7qo67ru/fv3HcfR25brusmUi0TNzc2VSiVK8enTTc0ymkwmDw4O9H04f/58IpH44P2by8vLvu9nMplvf/vbBwcHn3/+udYDTSaTV69e/fzzz3u93j/5J//kww8/DIJAKREEXhj6hBmG71FKvTAOQ58RVIRJkJqJHgklFJEimTC2SV832bQY/wkm5hjG1nLK3U3+ipqzdIRYBt0h1M8ZmZ18Hs42aXjjtx6Xdk6tRqWUBKEpEIdXq7mShk+SOhHTrwSpCKAwKTEoMSmlhEkqJVeSA0AUhwQoAhClmPbaI2zZkDVRCYlIpZIUUQkJSCghUilQQCYUP8c2o/eaYf9FKqUlowARUVI6uaPgC63CF7+f/tgjVSpBqMkMRlkcx/1+nzGWSqWqTT8II2/gu27SNkzJVcwlF4oxli/mcrJwUj/2Q88LvFyykCvm0m7m4cOHW1tbSql0OhuGYeX4SbvdPjw8brc7uVxOcHVwcCClXF8/q6PuZDK5ML9Ur9dNx261WoSwQd+jlBFCbdvu9zwpwbZdQlgcB5ls4sKFCw8ePEink2EY1mqbC4tzrVZramqqVqsxRhgzCYFCoXDjxg3f9xMk+uyzzwghn3/++fz8/DvvvPOf/tN/ImQIZC+Xy/fv3xdCvPTSS/1+PwzjT359M5PJcM47nc7s7KxSam5u7vHjx7rDWSgU3n777SdPnly6dMl13StXrlQPDxNJ5+DgwDCM5eXlWq3mOFY2m7VtEwBs287mMtlchnNuWZbneUEULi4v7ezs6Lgxl8tlclnGmCYpD6Lw4OhwfX395uefuclE5aRaLBUGfW/gexAJLiUhRAJathHFQqDej4lmTyAEKGPMNMcWOF5y6oXyzOQaGNvk5AuHFjWRWOrXak4mNS40nPZjz2/6XxWUjs37RSMHAAXjEsazqgwAcM4ZstGAMpdINOlTs983DGpajDFKUAFIPbyOgERJUASVGDYJxDODenbgyKehVCA1Rb3USSCllGl6rSEGcOS+pQINHRzJzY1F5ya3txfd4Fc6xsm/djqdfr8fhmEURb1er9FoaAEJN5VExN7Aq9fr1WrtpF47qdfqzcYHH334+Ze3Or32zPzcysqSbdtRFCAqKWUqlZqdnV1aWtLdtoODg2QyKaXM5/MahmLbNgDxfZ8xdv/ewzCIEbFUKnU6HUSqaQVbrZZpmrMz8/l8XodSjJnJZLpSqVQqFe1ker2Onk6o108AwLLM+YW5a9eura6uFot5w6CVylGn0/n617++sbHxwx/+EBGbzWYymVxdXb148eLu7m6tXi0Wi6lUamdn51e/+tXu7vb58+ebzebU1NTa2prulywuLq6srGxsbBwcHPi+3+v1Dg8PLcuKoqjZbO7t7ywuLtq2/fTpUyllr9dJp9NvvfWWlFKL/i4uLmoqxGazGUXRtWvXHj16hIjb29ubm5s//OEPdXr59OnTSqXy/vvva2EcXRZ+4403MpkMM5kQPAxDPxj4vs85Z0z/xxWgUjDKl7T+5BjeGMdRFIVhGARBEATy+UM935Z48RhXccZeC34zGelXL6pRxngq/nrxh8lfXzRLbdrDduXzua5SAkymGOGAoRBBzP0w6Id+3/O4FLEUkeCxkMymBudccK6egV+RmEaslGb4QBnHSGzLkBLCMFaxP747hFJCEVDEikuUwEBRNXkHAcAQz+BpMOEPx7cbJrAIiCgkR4YoFZexjuwpoFRRIulQmwZy4McqwVxUYMcsibbXEUEgTNOMlWh7bde1E4lEPOhGKjYs48mTJ5mT1PnzZw3DCCM/DMOZ2ZVmo7u+vl6pVMIgRqBnzpy5devW1NSURqjk8knAeGY2XywWc7lc9fiwPJ358MMP9/cOX3rlZUpZo94ybbNcLinEhw8fUmok0qlOe+A6yZOTWtBH1yycnFQ6DS9fyLz26svbO1t37ny5Z9FsNjtTSGdT5pnF6ffee69VOajVaiDS5XIxm17kkflf/70/+vjjD4rFYr1e29i4T4h68OAeALEt9969hzPTc/VaY25u4dqVyx988MH8N75RP6lm06mdp1tn19cSjl0oFOon1YO93Rs3brRarU6r2Ww233n769lsNplMzs/OJBNuu9n68IP333777f29vVKpaDCadBN/9Vf/ayaTefTwQaFQuHDx2o2Xrjx48ODv/uiHtVrt//F//79ZltXvtdbX1w8Odvr9fiaT+fLW/ptvvnn50rlf/OIX8aA/l0/P5jI3f30LgU3PLpbKM082d6amptt9r+cHiGgaVHAZiyAOQwPpcysflCKodIRFkIMiqCdkiZJSSGmBBCWEiJWUusmhzdh1Xc0nQ4Y+UIESinNAXajQ4ocjrkAc6q2hUkNaaQCt/kkQlZRIiA7pYcipARp6CQDyuYKlIhyJ5iZVFCUSJTVxBDVYpFSoFJiOZFY0MoG5uK2UORQ+UlRKoutFQUyHjlQR9uIeA8/3EqSUOgETQoRhyMgQdjR2gJNbxW9ycS9+nzTC8QamtPa30uwWMMpch7NWpmnapouIFInv+/v7/Xa7baOpQZK+P8jlcoi00WgwgxSLRcZYOp1OJNxeb2CarN/v375z6wc/nL5z545pmk+fPg2C4M0339RYMMMwFhcX7969q0d46/U6Iu7s7IT+IAiiQqEwP7dYrdfq9cbc7EKxXP71p5+5yWQcx5zLM7PnKDEGg8HOzm4ul6vVahpLoMmplpaWSqVCPp+7ffv2wcHBzZs3y+WpK1eubW5uIuL6mTOVylEun3306FGxlB8MButnlj/44INer5tMJnO5/Orq+p3b9wBgd2+bUfPu3buGYVy9enV7e/vs2bMbGxv9fj+dTj969CibzepPsby8XKlUzp8/3+l0CoXCxsaGUuqb3/zmn/3Z/1IulxljzWbz+vXr1Wrl7t27lNJMJnflyqVCoRDH8dbWlg527t27Z1nWt771rVqt1u/3Hcf53d/93VarpVPoSqXy13/915zz6WLh448/Nk17dXXVYPaDjU0u4Oq1yzv7x6DLLZQKRKEkgCTEGOsNvuh/vvIYgiKV0msDR7yDp+Bm4zPIySI8PkOujQc1EBEmIjIppSbBUBNwGTlRySHquVKqhptP4nX0NZjGs/M/l7gqPUfCQLNFSQVKKoWUapsngMAmDeZUsD52WZMoWzGUCnzWToHny1zwvENXLzyifzjVkx1f9yj6/4pggDGmiw2SiyAIBr1Ou90uZEoLCwsS214Y5Sjxo7DTH8zPz9Zq1bnFhWvXrlqW8Ytf/mxjY2N+fr5QmvrTP/3TTqejad5v377d6/WOj48Hg0G3202lUmEYFgoFvQqnpqZardbc3EKlUuGcX7l8bWFhAQDjOK5Wq91ud2pmxrbcdrsbBAGPvTiOGaNra2vVanVubu74+HB1dfXOnTvFUl6IeGdnWzcAq9WTpaVl23ajKBr0/bt3bwdBUCjmX331VR1nci6FEOfPn9/f32+1WicnJ2EYplLJdruDiIuLi0dHR1NTU7OzszpPS6fTH3zwgeu6UkqNQygUCr1eL5vNRlF0++6dUqn0+PHjge8hZe1uR0r55ptvbm9vOY6TzmYQ0XVdpKxyUj04OLhw/qpuAuVyOb271et1TUizv79///5913VfffVVPZUPABcuXEilUo1G68njbduKzp5bz+aKBwcHQgBoqRWKCMi5Ekqh4AoovGA8z+zheYtSE90LeH4A6tSSm1hvEysKQBdG1W/OgHR/W02IW0yWiPRBX3ihmoiTx4985fUMWd50tUgB6tEjbZCjy2OT55WjyS4cVWxhDB4f9QYHcXzKnE6Z/viejj3eixc3ebyQLo7pZ8a3FWDEpazTibECrmEYvX4/jKJkKhMLPvCDMPQJpaZt+WF0+869k1rt+kvXCsVp09rlQp05e6ZR76VSmZ/85GdKqXw+L4S6ceOVTqfz/vvvV6s13w8tK6CU9vve1tb2yUn96uXLiUTi1he3Dw8P3/7GNwihW5vbJqHlcrlQKBweHBsGPTk5AQDfC7UQfCqVIgQWFxd3d3c3Nzc3t2SxmM/n89///vefPNlUCsIw3tzcLORLCTcVeCSby8zOTi8uLr7/q59HUSBk/Morr6yvr//4xz8OguDBgwe5bEEIUSwWNAHy7OzsnTt3vvvd7yYSienpaULIX/3VX/X7ng7q6vX65uamnsbqdrsEmWMnms3mzZs3v/3tb29sbHQ6rSiKpqZmFhfnnz59Wq/Xa7WaZVmpVObq1WK3MwCAZrN58eJFpZTOD7WAx+HhYT6fTyaTzWaz0WjMzMwUi8VardZotKSAbDarUbupNC8W8/VmN5ZCciEAFKEAmmRTmuw5rOKpZTPOdMZP0FTOkwtJP2EsAwzPV93hq4zhK+1k0gGMf3j2oEabvGDpGu817pCPsQdj2t9RZ214fjb6IoAKlVA4HPDi2o6Ifs5wExpbnd4ShBBDwPgEu5GcoCHAiSLyqf3gOU/4G27KZA49aYSaoQilAgCCY8eI+gPjCNNgmqZB0bKs0JPNZtt1XdOwm80moZBKJaqV2ptvfC2bS1cqR416k3NeqzWq1dr8/GIqlXrppZeEEAcHB+vr6x9//LHmz/3BD35Qq9VgNFWsFSBefvnlzz777OqV6+vr6/1+/6OPPmo22pTSkpsoFoue5+3sPj2zfk4Auq7baGwLrvr9/vz8/MbGg5dffun/9T//29dee6Xba7/zzts///nPDcOcm5uLY+G6rmMnXDcZBEFD9DmPZmZm7t2/c/Xq1Tt3vkylUoVC7uDgYGpqilJ6eHgMANVKLZ8vFosFXSy5d+/erVu3zp07Rynd29u7cuWKHmLK5/PdbvfBgwfLy8vVarVSqWTTmVar9d3vfL9YLO7vHbbb7W9969ubm48//fRTjYY/f/68RsN1u93Dw0PTcPSM0t7enpaIOTk5oZRWq9U4jsvlsmVZ6XRaA3empqbu37svpVxdWc/ni/Vao/pkM5VKlaZmB37ox5HkoQCiKANAJTiXihFj0ga+cp1M/jze/cdQfr0etBGOPRKMYyiYXJajX/Grl+I4xD3lCSbOdvqQExeMiFrQdtJXP+9RlEkoI9QgI/MhIKUCpCPnotf5RAg6WVNxHEerz47xrPrQd2TS6sbH5B2c9IST8IgXb+5kADzeDsY3YgzNIXq613G0gK5pmvofk85m+t6g1qh3ev0gCgk1FJBqrSaUsp3E9s7ehx/9mhrmN7/13StXX1KAX//612u12q1bt/7sz/7M9/1Lly4RQjKZzPHx8e3bt3d3d3V0qgd55+bmFheWEXF9ff3ixUue55mmqQX9giDo9XqpVGpubs4w6GAwUEoBylar1e22S6XSyclJKpWyLOvq1av5fLHf77/77rudTi8MQ9dJvv32O9lsFgA5jwBgd3f7s88+syzrtddec103CAK9RyDi/Py8aZqFYo4QyOUyL7/8cqfTeeWVV05OTnZ3dzOZjBaEymaza2trZ86c0ZIy2k6azeba2roQsl5vcM63t7fzuWKz2fzlL3+1tnbGMIxGo7W3d3B0dNTtdmemZ+dm5xcWFhBxdnZ2b2/v+Pi4VCodHw+zOwB49OiRhgecO3euVqvt7u7Ozs6+8fpbKysrJycnnj+4fv3q6ury1uYTQiUjoNkBFQiQHAAYQfVVx3jRv7hOxj5AE3xMgkX/i44X7fxFy3nRGvGFr7FmBk7AueI4PmXh44/Dhl/IFBpAGICBaCA4Bht/Md2PmrQrbRLjeeexkxy+8cTN0vHhqTsIL2xyk1c2dqGT/nPcGH1WLAVERErJsBdCIIoiOaLGQAKEEFRCKRVxbrtur9fzPM+yDWoayOjUzNztu3cLpVJ5ZvbguPL+Bx8xxpLJJDWsmXLfdd1yuZzNZm/evGnbtu/7nudls1kNwjRNM5PJCCFM02y326Zp3b17T0q5sLBAkAkVN5vt3d3dleU1LsXq6ioXUa/Xq9Vqi4uLcRzXq82Dg4OZmanNrce6BWKY9MmTJ9/73u98+OGH9Xq9WqlFobx69drq6hqP1Te/8e2NjQ0/GDx+/PiXv/zlxYvnw8jP5Qqu687Pz3/00UeImMsVNIupkPHZMxfff/993Vd4+PDh3/k7f8dxnI8++ujoqFoqlXRLXZdegiBwXffWl3fOnF1rNBrbu7vNdjvi/O79e7NzCzdeflkIsbTsD7xeIplutNqLyytCwe72juM4rVarUCgAQLPZTCQS+Xw+iiLDMObm5nZ2drRKlGEYlmWls/njkyoI6fuDXK6QTCVs28zlU34QIBGubVEpg1hyyRllhmEE8VdLfL74oH5EN4S0M8DRJBSMREVfLOw9t/YAlJI4JM6dOO0Ly1WdjkVPH2R8VvLc/PEp//niIyPrHY24j/i/2bAYiwAw7OZNOmV9oiiKoijSRF3jCtWkacnRyMmk7zp1nDLLU99PeT/9LuPLOAVP1VA1fUlKKS25nkwm2+22TlSklJpVxTTt69evFwqlarVaO6kzaliWk83mE4lUpXLyk5/8NIpiRPKDH/xwdnYum81dvnzl+LiSSCTn5ubjmHue3+32Go0mpaxeb3S7XcZYv+8BkLNnzyYSCcMwfvSjH+Xzec/zhBCPHj3y/QGAvHz5crlcnpoqLS0t1ev1lZWVZrN59uzZTrs3GAz29/c9z9vbPbAsp9vtPXm8NT+3KCUsLs2Hkf/aa6/atlUqlW7fvp3LFtbX1zmXlA7p1bLZdLNZZ4w0m/VHjx5p73ThwgXHccrlsl6pc3PTFy5cuHLlyhtvvPH666+fnJzoIZ1Go1GvNU+q9adbO/Nzi8VikVJ27tz5RqO1tbVtGIZtudev3Ui4qXQqe3h4eHJy8s4775imefbsWT1xn0ql9Kz95uZmuVxeXl7Wqvdnz54lhOzs7Ekp55cW19fX0+lkHIQxj2zb9LwuKuXYLGFbjCBIwRBcyz71r//KZTO5oPUY93j/1UBlc3SM/dL4tF/Zb/zKWEwfQ8D3ZIo4+pmoZ184+hpbhLbb8fqc9OfPfQSJ4y+QGsk++SVRSWaapu/7ajTnPx6cxVH2NeYg1NOAMLIQGHk2bTnPgDzPQ++EeDa4NLYuHE1djKec4NmMswAYQsBjKaSUQBkwMjMz47puIuHGcez1B0EQMAKU0kwms7+/77jWxUvn9e44GPT+5m/+5uWXX3769KlSSvfNs7lMEASlUulw58nW1lapVELEw8ND/T8GgA8++KBYLJbL5b29PQ0Z09JL8zPz3W5fi6483txcXz9bKpW2trZSyUy5XE6nU7du3fpv/tv/9vbt241GLY5DnSvq0cdz587NzMycO3fu019/XDk+mZmesyynWj15tLFhGu7Pf/7LVCrzs5/99Fvf+ub9+/f10Mo//sf/eGNj4xc/f//MmTONRmN9fX15eTmKwlQqyRgtFPL9nqeFEwuFwvT09L/+1/+6VCqFYfjtb3/76tWrpmnu7OxwzqenpweDAec8m82bpu374blz5xBVoVBAoIVCARFN06pWK2trZ/7Nv/njH/3o93d3d/O54ltvvKlXtsb3HR0d2bady+WazWY+n1dK5XK5Xq8npczn87pO+9lnn+3s7OXz2Watvrq6+uTJo0KhBEpkc8lYkUazA0oggSgMCepxgGfH2CrIV024A4DeWOM4HnfFJt3AZGFmHEaN0xwBI5AnwTgWw/MrpZ5TCFZKKQGSjOqd+i10FqfhKEopzrmIYyEEY0yHh8P0bCRPgqPGnhztA8MPJUH7OTkxi6yxCmN7GcJfTkENcKI6qibKxFJK9lUjHpPbzH/mPqeeP8aP654kQ/0uEoa1Ncjn845lE/Ksk6GUjON44IUntcra2oplGZVKZWpqampqtdVqbWw80DrP8/PzlUrl448+sSzr1VdfzWbylNJGvTWw/bnZBV3Hf3B/QyNUHMdZXzu7uLh47949wzDeevPtJ48egSJrq2ds237waKNcLqdSqV/+6v1ioXzn3t2rV6/atrWzu/3Sjevdbvfk5CRUMaX4ta99Lebh3/zNu3fv3p2ZmWk22oyxIIja7a5SYNvu/Pxit9u1TDtbSObzWc/r/+Ef/uHNmze1AMbly5c554lE4o033vj0009937vx8vXt7e3dve1SYVGb0MnJybVr13K53K1bt2CUY+uuhud5W1tbOsw+c/bq5tOdfLEc8nh9fZ0QEu7u2G7y3XffNQx648YNapjf+u53ugNPSvnw8ZP1tZUHDx7k8/lsNqttT0fpxWKx3W7rjSmVSsVxPDMzg4g7Wwf5fP7b3/52pXKUz6YZY3MzU/VmM5dPu7Y5CCPDoGnimpbwgphSRuG5mGu8GH7LOhkvPzI6xvHaGPY5PmccBuPtHhFBzwSLUywyvy2rVEppV0MAKRK94GE4y6DEb5hAOGU+zz4doUgZUkaI0iT/qKQatk+GA5Zs8l5IKfWIkM70JvFockSTTC3rK2/ZOFA+ZY2TofbIeJ79dbKyNLxmQhhjhpYnlYIx5tqOY5saKsBRaX4RwzB4FARBADJKJSx/0NvfHXDOk+7S/Ow0gZf29/efPn363t/8TalUKpVKxXy+VCqKOOr3+6urq+12W5dVNA5zfn7+5s2b58+fz+Vyh4eHug0gpXz48GEwCHQvpFQqzfa6iUSi3++7rru4uLiytlqr1XK53Pz8fL1en5qaqp58rGKyuDjf7/ePjg/OnDkDAA8fPkwmk41Go9/3qtWTTDoHALOzs4LLc+cu9P2D48rht7/zzQ8++GBra2t6ejqfz6+urk5PT3t+/8MPfzU9PRVG/k9/+tMw9PU0vRBicXFRx8nz8/OffvppoVDIZDKWZWkumTiOe70eYyyKoq2tp3qJbj/dXVs9I6Sam1vY3Hw6MzMTxzEi1fLAukV54fzFo6Oj/f39tbW1hw8fdjodwzAuXbokhDg6OiqXy8lkcmlpSatWcc6DIFg/e6byQfWLL744ONjLpBILC3OWZXARmaYlZRz6nuAhY44BjEDEowCo+1sM4DdZxbgwcSp70vHXZPc8iEJ8VnQApUBKKZQEeAYsUc/X9odvgUBG2kPjtUq1nUulkAgcmc5EW04+G8Ianhafb9dJQKlQAVGgEToEQJCh5x9eBhundkOfO3K4zyEMJmb8XizIjt8PXnCD6oUcVz0fb0we41CWDJG4ilKqyaRTSdfzPIpEh6AmM6SUmn/FMoyz6+uVSqXTap07d67X6fz8pz/N5/MEYGVpKZVImKYphOh3u65t8yiuVCpLS0tBEBweHvZ6vXa7nUwmE4mEZsUFgLm5Oa2sVK/Xu91u0knrUqdp2jMzc73eYGtr69rVl1rtxrVz1wmB3d3dpaWFv/7rv06lEufOnSHSiONYVw7X19dM06zX60EQJBIpzwsdOxEEkWFYJ9Xa3t7BwsJSp3/cbLa++c1vXrx4MZPOPX78+Oiocu/eg1/84hf1ev2b33pHiOjjTz58++23Egnn888/55x/8cUXCwsLpVLp4cOHUkqtRm7bdrfbdV1XZ0qFQqHZbBJC9vb2Ll26tLu7pwmCK5XKpUuXfn3zk9XVVcdxoigol6ebzSYAefDgASHk0427c3NzQRAopXZ3dy9fvryxsaGVNmq12oMHD2ZnZx8+fPj9738/juPl5WXHzhYKDzudzo0bN0BGs7Mz9+/f9/v9QtkRUiqQBCSAVFzwKJZSCniW9r8Yjr544ETlHF844Pn4EwA0nGO8BE/5i7EbHL+vtr1T61MpUEJqL6qE1COykxY4sTuM3+u0Yesj4AqFJuAf5pmAyBUwJMPWIoFnQbZ+5TDQff5Ek8Y2tsYXTevU41/5tFPH+MHxD5pURtdgdBZqmqZt247jaCIQz/P6/b4u0Pm+X57KWzaN4oAyTKZcypAZZHZuOoz8bq+dL2QvXb6wtr6iGV++9rWv5fNFIVSj0QrD2LKcIIhqtUa/77322hvpdDaORbFYTqeznMswjKenZ5PJZCqV0ngUSmkYhkqpZDJpGMbNmzeXl5ejKKpWq/Pz8w8ePBhDxtyEvbKy8vjx4yiKLl26JIRqNBq+75fLZdM0C/ni4eFxFPFWq+N53ve//733338/l8sVi8WpqSlE/Pf//t8rhf/oH/2jdrudy+VWV1d/8YufdXvtVrvh+z4hRLfjpZSa996yLMbYzs4OY0ynT4VCodVqcc6npmc9P2y0mmfOnbUcJ4iiIAovXb5anppZWV3N5Yul8jRSEvH40ZPNh48eLyws5HK5vb29jY0NAIii6NGjR9vb267rnjt37urVq6+++uq5c+cIIb/+9a9/8pOfvPvuu4OBXyqV5ufn0+l0r9fjIpqaLuTz2WIxXy7k8/lswnUoAYpK9zn+i45TIejE6n+OPWycECYSCcuyJlf1ZEB3alkO/Qo++1VNNOqEECLmIuZxHCsu9JA6PB+OTlZ9XjQWpVSsVCRlJGWsMAaIAWIFHNCPIz/mARd+HA8TQv1dC2jhaD5t8spwVFA59fnHzzm1sb14/KY/jT/P6ANoVCtoPiXHcXR5Wn/aMAzDMGSEOo6TSqUcxzEYebr1hKBaXJiJAt80zXNn1l3b+vt/77+6efPm4eHxg3v3TdPMpJMn1eOPPvwV57zf7wNAuVyenp7W1q5r+rlc7vj4+NNPPzVNc2VlJZVKdbtd27Bbrdbq6qqUst3rJpPJGzduVE9OVldX792/PxgMrl+//vDhw0KhQCn95JNPrl58SYMwdaw7NTUlpcxkMnt7e3HMwzDsdvsGs5XCs2fPz88tommvri1fODz/2We/tm334sWLnEs9S6Vjvw8/fH/gddbW1iqVytRUOQzDa9eu3b17lzF25syZ27dvdzqdpaWlsevzfV9XTXSTybCsZrM5VZ7xvXBzc3NhYaFeb6ytrepNwfcHu7u7nudVqkda62J/f7/b7RaLRdd1L1y4sLOzQyl1XVeTcGvU6NzcnBBienradV1GUlPTpUTCeXD/Do+8J5sbQsSU0pn5OUBDIUFq9vyo2/MAQE/eTPqx3742xstj8vt4Bb7YV6OUGsYzODQhiECklEoNSRO1J1QTWdJz16AAAKieldeoaRkLQDJUiFXk+e6alBJHKDmNByCEjONSbTgcUCAROOTdp1rpGRA1EgglIn22M403DP0e+qInyQj0E+REr+KU/ZyyzNGHe26E99RrJ1+uf9VhMENiWVYi4TqOg4hRFPX7fQIoRzVVy7JSCYcxdrC9oThfWFiYmZkdDAaDQa/fbfd6vaODg3q9XigU2s16IpO6du3Kndv32o1md9DX6RMhpN/v60omIaTZbPZ6vWazmUqlCCG9Xk/nfsCRxyKdylSqx0opz/NKpdJxpXJ8fJxKpZ48eTI1NdVut4WIL148/2d/9mfn1i5+7Wtfu/Xl5wCwtLSUyWQ++OCDMAxnZ+fiOD6pNqWUBwcH+Xz5ymWn3+9fubH8F3/xFz/43d978mQznc5KAfv7+8vLy4lE4l/9q3917vyZP/zDP3zvJ3/9y1/+fHllYXd3N2HzYrE4Oztbq9XOnDkTBEEikXBdV0fUUkrHcXQSqzPVdqejlEqn03t7e6Zprq+vSykbjeadO3cMw8hkMvfu3UPEoWnxMJVK5XI5SulgMLh//363233jjTeklBqFI6Ws1+uZTGZ7e1srGTbr/rXrVwyDVo4Pzp9dTafTntdNJpODbk8h9cNYSDAINShhBAlD+I3m9tWHnJizgVEFcrxyyAjUNV70WqJchy3EYKCQc87HpdEJOxxb7+kFqxQqoBS1aJLGUOKzOtBzNR6p5IuLf/KcQgGXigxh61Iq1KB0ZtDhmD6oZznh+NBp4fgqJ9uAOCJO/crj1O7yWxzjZCBx6i4MadIp0/5ZKRUEgS+57/uu7WiSC4pkvE3oFdnpdJ4+3dIlmHQ6c/bs2XfffXdz87FlXZmZmbly5cqFCxe2t7cNk85a8zMzM61WS8d1k9ZYKBSy2ezc3JxSynXdw8NDSqnNHC2yeXh4OLsw/+DBg8FgkEqn/WCQTCYXFhY++eSTXLGQyaRN0zxz5sze3p6ueUxPTx8c7CPi9PR0s9nc3d1LJtKtVqtQKJ5UG8lk0jTNRqMRx5lcLieEWF1dff/9Dy5euCyl3Nvb63a78/OzhJA//dM/PXtu9dvf+ea/+3f//vz55UEX9/b2Xn/99YODg263a5rmG2+8kU6nB4MBpbTX62mfHARBPp8/PDx0nIwQsW4wZLPZyvHJzOyUUuratWtCCN/3AeD4+FgjgZLJZKO6c/HixV6vl0gkbt26NT8/H8fxu+++WygUVlZWjo+Pdb6wurraarVeffXVW59vrK+vP3nyaHV11XWdTCbTatVeffXV+/fvS2BRLNFwKXUsy7JtGykTg/+ynPDUHg0TIFIcUdpOeDmIo1Abp27fgcLxEMZvWrdf8Y4KCCFKyJEVAn3GUPwcEdszp/obCq4xl5RLJFLz0BAplFJCxGGsADRtKZA46pqGUDII/R4jYJsmSpQxyBhRsnymOF2aTdgJRggBCDxvMpaQE5AimLDYF2ue8LzrG0cU8ELB1zRchqYShAi0wHTRMAQQXywWpmgYBe1O2rbnp8vFbFozRKLJ0GSl2ekLVy+jyRLZtBcHH938JAbxO7//g53Dnb3jvYPqwa8++dXm7maz1/zy9t16o7W5tX3/wUat3tx49EQBqVRr2Vzhzt37SNj9BxupdDbmkjKzUCwvr68EPPzi7peLqytuIlkslW/duUOZ1en4Cu39w3o2PwNg+z5dXr7suuVCPt/tdF575VUd7208eSxAOalkfmYqUEKY9KTbVo4ZMezwAJP28WHv0cO9Rxs7J9Xm4uKi7bBLl9cTSWY7mM44c3MzjFkEEj977/Nz6y/Vq6o4taSII9HeP6pVa+3Np7tcqtnZWd8fGCZaJpyc7B0ebnW7FX9QTyVBmBgz9Hg0CHyCiinJ+x4LedzpfvnRhwlGTg73Svn07Gzx7LmVRufkf/jf/I9WIjkIg67fNR1ycPyUWfKP/ru//0f/3d/vdRsH+7ulQjlhpB7d3dq8f9A4GlRre//n/8v/6enWxtWrl3/x/ocDjwtpv/ezjxsd7seGmSpZifxAQDsITwa9p0dHJkhDCUMJJjmTnCrBQDKQqLj+UsClivV3qeIYZAwyUkJ/xSAFAUEADMpR6QcFAUmRowol1/QWAlQkeBiGcRwjom1aJjMMpFQhSoWxIFwSLqlQTFGmdMZKlEKtdxQjDoTwQAWMhAYNLDYwSI9ClyiqJJGCKmkgmAQZpQSRIEqpxxuBS+QShSISqEI2sJNNNE4E1pXRRKuh7CY4HZquS7cu3RpP1HiCTSa1zw9bgW3b2WzWsiylRH8AnHPdwDiV48oJYYBTlga/oSOjXmhdwAuec2zP4z9ZlpVOF+YXF6QU+/v7YRzl89nLly/rJDYMw62tLc0qrcsht27d+qf/9J/W6/Uf//jH09PTL7/8MiKeWSNHR0e1Wm1qaspxnGKxWK1Ws9msUurVV1/d29t75ZVXfvKTn5w7d+7hw4cvv/zyL37+829+85uXstl33303lUpduHDx0qVLd+/dnp6a7XRae3t7qVSmenKSyWS+/PKLbDbtMJrL5bRchC5dLi8vd3s9ANREt1IBoEJE0zSTyaROIOM43tzcPHtuHQDOnDmzu7t7//79a9eucc7PnDnz8MFGoVCoVCrZbE5H5lLK69ev1+v1RqPR6bRmpsprayumxUAKyzYBIIqCQa8vZNw6juM4FlLpd9cgeCFEFITvvPNOPp///ne/92/+5H/5P/7v//nV61cR8Ve/+nBvb+fSpQuZbGp5YZEy3N3dvn/33vT0bCGff+utt9KpfLPWXFlZ2ds7uHXr1oXLF37v937v6GD/6OjozTffBICbN28uLC0KIYIgEApiSToDPwzDdDKVzxU73fA/0yP99mPSHY0fmdzNdXN/zP8yLmoMR1Wfr+hMvvwrUqqJdXsqdpsM6L7yhZHgiEjU6drSJLkhG+PRxkaoa6kanqNpiXU55CsvaNzTP5Vwj211nAScMjn5VUi3UzasxvP7hnF0dDQzXTZNc29vjzE6MzNju04chz9576fr6+ual/7C+YtBENy9cy+fz6dTmVKx/HRre3t7+7vf+d7Ozs4Hv/pwfX393t3Hq6ur3/jGN3Qb8OLFi6lUan19/csvv+z1eouLi9Vq9R/8g3/w2WefXb9+/fDwMJtNP368kc5mbMfMZrP37t31wyCdzuwf7Nqum8lkGGPZXNr3gi+//NK27aRl7u3tZbPpo6OjarWyuLK8u7u7tLzc6/UopQQUNUwA1E1ZAOh2u5ZlWZbl+/7+/r6UPAwDRNTRMiJms9l2uz09Pb2/v6+USiYTe3u7u7u7b775+v1790ql0t7eTqPRuHjxvB9wPwhMyygWi4wRg7I4jveaNSEECmnbpk4XpZRRGN145eU//uN/owd//w//u//tF5991m639/Z3pVALi/MLC0uua5+oI0Q4f/5iPltAxHarG4YRo3atVgsDXiqVlpeXj+p7m5ubUkrGOvqC8/l8s9k+f+ESECoBu4Mwijrtdjvi0rJdJM5vWuL/XxwvLv1xjDa0llEbf4yChOez0skh4MkFPF6HL3qXU+/1m4xWn4oaxvgCYMLTTK7/8TTU0AjHl6jrbHpSrtNpScUdx5m05smkEV6gMxz/iZLTO9aLHwZeMD8yum5KqUEpYUYy6Rby2TgOB4NBIuG6rouUVKvNV199tVwub25uptPplZUV7asXFhZ+/vOff+Mb31BKxXF8+/btTCbzox/96P3333/ttde63e6jR4+63W4ul7ty5Uqj0fjTP/3TpaWldrstpZyfn6eU6uwuDMN8Put5XrNZDwKv0235weCkVqvX6zMzM2EYLiwsvPfee4VSsVqtaVrOs8ur77zzTqfX/s53vnPnzu04js+cWTNMO4oiEcUAYJkMkIFS/sDrW53ATS0sLCQSiStXrrTajWw2fXh4UC6XhRC7u7v5fKFSqayvr6dSGQDodLqDwQARG41at9u1LCOfz29vbwFAs9m0HRMAeCw6nQ4iUkqTyWQqFTAkgkd67oRLLuI49AfvvvvuK6+8tre384tfvD87O/vWG28KJQkAEDY3N/fe3/5seWXJYjTm/jvvvNPr9A3DMJldOT558uTJ/s7++XMXUqnM/v5+rX0Sx4elciGRSMwOG6omYca9e/csx83k8nYiU8jmJKAfxoyZvQGH/38cL+aK8HytYZgrqmfSDCNjeL4iODH6BJMR3MjAXrTDF4+vtMahLSADRBjqdg85tAFAPA9QIafeXh+6DaU7clJKnVjrf+SYvuk3vXbypkzGrpM/fGXqOP60Y1zsuEmYz+c1IGt9fV1XBfb29gAgjuNPPvlEKZVOp//6r//6888/L5VK/X7/H/7Df/jrX//6z//8z8+ePcsY63a7U1NT3/rWtxYXF3UhsVwuSyn/+I//+MmTJ7lcDhFzuZzWCfvFL37R7/cfPXo0Pz9PKJSnismkm8vlwtBfXV2eX5jN5TKFQi6Xz5anStMzU9lsNpNJeV6/1+vU6/VPPvkklczoYk8unfnoo48OD/YAgDFmGQYhBJRUggsehZFfrVaPjo7u3r27v79v23a5XC4Wi4ZhrK+v625kv99PJBKaTK3dbm9tbeXz2Ww2u729rZTq97u1Wq3X61UqFcMwEomUlHJ7e/vevXudTs80zWI+m0g6OGIuD8MQCBq2pQemHj58+Ps/+OHS0ko2nalVT1658XIymb5w4dI3v/ntt978Wqk01Wr2OJcXL1xZWV7d3zu8f/++ZVnLy8uGYSgl5udnL125XK2dzM0urK6uPnz4qN3uXrx4eW9vz7UdKaU3CHQ7d9DrN2v14+PD32pZ/wXHi0vu1AJTEx2/UwUIMnFMwjbVuD34nEjmc0CU33Qxp2LAsc1zgTGH8VcUK/1dSDL+YmM2NCE0Y4VSapgWju3Btm3TYoSQMAzHVaxJKC28MA/2lRsVTDhAOTG5rx8Z/zBErhnGeOaQAnY6nSiKbNuM45jz2PM8REylUp1OB0aVaz1wqAljPvrooxs3bnQ6nTt37gwGg7W1tV//+teXL1++/fhhtVrVyz2VSt29e1drSDiO0+/3X3nllY2Njampqd3dXT1SqJTsdNrMNC5cOLe/v28YtFgsahZ6J5FCVH/v7/3Bk82n6+trX375peu6x3tVQumnn3565eqlmZmZIAiuXLnS6/XSqYSfz/V6vTCMfS8kjmOajBFaLucZo8VisVarLizO1Wq1TCaTSqXS6XQmk6lWq4Swg/0jRKpRAaZp53I5ANjb33ntlVc1E/b9+/eXlxejKGo3WwDSdRKO42i5mGLRDf2g2+4IyQnBZDKZTqYQZNtoV4+OX331dWaxZDL55MkT23b/7b/9f77y+tc+/eTm1HQpCKLFxSXbtr+8dUdJaZpseXnZ98NarZZyU6++9koYhre/vNMJBm+99bUgin76k59PT0/nsumnT5+eO3uh3W4PPK/T9RJBSA3bNM1SqeQ4Tq0d/Bcb3Fcd4/UzubTURINuHJeOVuZYHez5EVZAnFBHnDzGS3rSE04iV+EFz3nqQESlkWfaGU5c53PhqG7pwnAbEFKCUkgIjoY1dDNAjmnnTdMcv8Gkyx6DEiaNcLjrP98knPw+PtX4B0IoHYnj6LvDOVcKUqlUsZATIu71eoRgKpVCSnzfO7N+NpfLPX78eHt71zRtzuX+/uGNGzfm5xd3d/eiKJqfn5+bm0PEzc3NL7+883RrL5FIZLPZIAgIIcvLyycnJ4i4tLRECHn48KHWkCoUCoPBQJMapdPpYrnUbreZQROJhOXYvV4nm806iUSn0zIM4/GTjQsXLpw9t57P57kvd3d3FxYWHj16lMtl5+dnj6r+0tJSrVabKpfDIGg2WjyWtmEalEgedTodPZWjuyD1+onj2I8fP37y5Ekcx61We3FxOQxix0msrKycnNSIaUVx0GjWhBC2bV6/fv3zLz472j+IosgbBK1WK5VKZTJpPWVfrZ4QACUFKOladrFYzBeLFLHXaZuWNbMwbxhGMpn84tZtyvDi6ophGEopy7JmpucMg0khHMc1TWduZqZer3f8ZiKRWFlZQakePXr45MmWEKI4vxALhSCpYbTb7cXFxYXFZakwlUp3et2IAxqmH4Rerx/EkWe7QBP/v9je+JAThNnjR04trbEdSikBhgRlOKEdNo7G4HlfMml+MGGHAEDoc4IZpyz2xXdHYpw6J2gsJ04Y4Rj8qi9VSkCgAKB9kZ7eD4KACeI4zrh/OPl+k1c/6fr1g5pd68Xj1LWOf9Dx8ThCkFJy3eNErFarhkFX1lbn5+eUUt1+L47DDz/8UHMNEUJu3boVx7Ee+avVaqZp6r7Z+fPnf/rTn66srHiex2PQ0XW32w2C4NKlS/v7+ysrK7pDvbOzk8/n9cWsrq7atr22dqNSqUzPzrRanTNnztTrdT8IPM8HgJmZmUQioUkxwjDMZrP7+/vlcjmTyZgmk4rHcby3t5fNZm2ToVSJhFMoFASXYRhbJlOSD3qhbchisaBRL+lMslDIPX261e12k8mk53m1Wo0xc3FheTDwi8WilFJy3uv1PK+fy+UajcaZM2fW19cNQvf39+fm5rSsfL3e9DxP19l6rXav3eFhZNtmNp9xE3a9Xt8+2Hv77bf7ne7jx49TmfRrb7yue8Wm5czOL5oWe7q9mclkirlsFPJCodBstsIwHAz84+Nqt9vVknUA8pVXbqCbabdavV7HsRNB6B0dHW9tbbZarU6nC0gz+UIxn7YdHoQx+tS2nX70n2Vj/x+P8dp7MQQ75ZrGbmfkCUcrcyieeRp8MunT1Aup4FfWRH5TmIqIoVQwhJU+F9NOIvgYIcTzvCiKCKGccymBUSaEFiIdyneYpknokHVGf8fnDzVBkXjKSjXGZfyS8XNM09R4cUTUBq+U0nNWzLKEEP1+nwI6lsGF8Lo9QqBYLAoRHxwchGGwsLCg0cmpVEoLOGslCaXU7OysnrRYX19/8ODBgwcPdK1F45tff/31vb29vb29dDqt1Y7+36z9V7AkWZoeiB3l2sM9tLj65k2dpUVXy6kW0z0zmCUGoC3Apa0ZZ7kGGtb4vEvjE1/JB7yAfADNCBJY0NpADBc7GEwPMHpaVFd1VXdVV6WoyryZV8vQruURfDj3Rkbem1XTDdCtLCtuhIeHe8T5/Vff/32O42iatre3J0fRMcYSdVmr1Vqt1kcffYQxxgpRFK1SqaRp2mg2CVEODg4kVf5kMpHU19J1F0WhqiohqNlqr6wse97k6OioP0Ddbrcsy+WFXhrFEwQELaej4fXrN7M8khUUXVcRQpqmrq6uTqfTer2+v79fq9W++93vnp4Mut2FSqVy69ath08e27bZaNTW19ffeeed23dura+vO5b92WcPRqPRjevX4zgsy3IymdCSFzl99PBTXdebjdrXvvpVx3WTNIUIbVy9WnB2PB5OkygTLMjT3d39ZrNZ73bzIh6OglqtBqFgjHmet7K6dOfW7X6/f+ocS9gahFAOGWq68slnjzzP46wMw7BiGghAhLDrVhcWFoMwPu4PipKVjE+nnqabeZ4CaM0WKIQQn9fz+PlgHhVndT6ZKF0uy8ttFr7Nm9AssATnGYosziOE5BQFnxMXku/FWLlgvfK9EigyM7AZfrUontHhnGE5ZR8BPOtUZse5bNJ8bhLyGUCtmMOYz+jEhQSji6d3lHnv/J+/yY+Y2XBZUgwhApBAwDknhBiaZqqa49i+76+vr46nE8kjdnRy3Gq1To5Lw7AqFbfXW9Q0LY7jPC9939/c3BwOx3JOvNtd+NM//XPP8958800ggjzPpceo1WrtdlvKm5ZlGQSBEAIhJNlydV1njF25chUhVHEdKbV9fHSyvLqyuLjY7S6wkxNKabPZrrhOq9XinOd5fnrobW0/BoDrhvoXf/EXGxvr7U5THlzBxDRN17HdSoUxTss8Cn2AhK7rnHPf9xvN2sHBwZMnjyuVShiGb7zxxo9+9OOiKK5evQoA+qu/+qsoivM8v3nzhm3brVbrXVb+8R//seu6tmFKzhvp/23buX7dWV+/4jhOf+AnSRInicwSBYITb5pl2dif3nn11Ve+9CUhxOPHj70kspibsnKwt7m1tZWmebPZvLK6Vq3WPvroow/ZLzY2NnwvCMMwCAICgbwvk1M0nnpRFGEIsrRAAnh+mMShEGI4HBJFkwBDzIWu6wjjLMugYc8WK4RQXB4VeFYS74uRNJe3iyUGMYvRnoad8w/mSxtiDh8Gzxtvs1M9C80uAGXmPuuC2zz7FAjALBA9w9ZAeRx564EAPDNPeOEi5/2seFbI+8L1XDih+S/oaSg898YL39QMdsQ5N00TQ0iLsijLJEniOMYQYgCyLJNTFI7jMEYnk0kURYyVt27d2t/ff/DggZxk73Q6vu8Ph8NGo7G0tCQJiP7pP/2ntm2//fbb9+/fZ1TIafetra2dnZ2Dg4Pl5eWrV69+8MEHvV5PUjPFcSxTNdu2t7YeyHYIoxxBWRDGEOLBYBBFiarqiqIM+qO93QOpEb+xseG4dpZlN25cG42GnPPj4+MiyxcXexhDXdcd26q6lSwrhBBlnt24c8uyrFqtJqnya7WaBFL3+/1vfvObf//v//3RaOJUqh999DGEcGNj4+6n9z3POzg4SNN0ZWVFOoqTk5OlpaWFhYWjo6O1tbW7d+/euHHjvffeE0IstDocAsrZldUV1TIKynXbNCp2EEeKqh8eHnqBr1n6a1/5MoRwa2vrcPshgEA3UJxM//wvPr2yfvXo6KTTahdFkaeZnGnSFVKW5WQ62t/fh5Ue5wBiWDLqhVlZlkkcYozr9bpl2zhKiiyPkiRLUt1ArKTEfGqB8OmA3lOMKEZ4Zg8zM7i8PbfPfHm7sLw552cZ2fMqmTOvw8+nZ+ed3gXruPzGC0sdPC+rvOAJ5UZm38j80Wfl3HPEC4Twojk99/MuHOfyns+1z/naFEIInPvGLMviOFYw1jCxLGNpceXJk83l1RVVNQ4ODhYWFh4+/HRxYcEwjFqtFgSB5GKwbfvmzZuDwWAwGKytrb311lue50nZsKIoIMBJkty/f1+mi2tra1mW3b179+rVq+PxWCIwW62WEKIoiv39/V5vgXOua4bS0LtdRVV1RPBoOKnYrlWpSfmKvf2Dk5MTQghCOAiChd7SyemRhI8rilKU2UsvvUAIYZRqKtE0rV51OAOU8qpTURRla2vrtddeK8vytH/81ltvZln6l3/5l9evX8cY37hxI0k+efz4MSHktddeOzo6RhhoulJvLIeRT2lxdHSAEMAAGoZTFFTXzNFwoqlGmuSKommaBqnQNK0UXFGUJEnCNIGahi3DS6KNhYXbneYnd+/9+Mc/Pjw8tCzLsqyuCXu9HsbKZBx88N57mqbs7x/bpjWdTvI0H4/H4/HY1HRCUBQHeZ6TRCnL0jL1sqQInBGOSAxDrVZDiIwmXp7njDEExXysePbg0vqR5Amz4sLnGeEXOA/wjCeYfcLMxp6prEomh1kVcMYqKF3ObGh4Zp/oeRYl0zTwrNWdfeoZH6l8aW79CwbOWaDIvOVcvs4zjbdzpOnsOufd8bzhzYfFZ7udv2vey4NnXeK8Hfq+TxBCACoYywl6TdNs3VhbW1MVLKPWPM89z2t3O91u9+S4jxCCALtOLYqiMIgDP1JVtdFo6Vr85PF2p937ype/9uDBg3/5L/6Vruv379//2te+Jif3fv7zn3uel+e5pJp2XVf26Hzfz/O82WwuLS0F/jjwgzQryrJ0nGoQRIqqcg5s2y5ZGQZRpVLpdDq1WmM0Gu3v7xcpsyxreXkZQlGvVuI4rNdcSUiRxBFC2K1YhqZpmlakpWmaaZpKNc80TSV+TRaiNU2TCsHdbvff/k9/+Lu/+7+Qx3/ppZem06kUeFnsLchwenByypjY2dl57ZVX33333TfffPPg4OC1V1/VNO360npW5MeDfqtZLwColPkoDIfeJCny/8f/+C8++uTj0XgCCe52u9evbrz55pu3LF9VdM8LojBVCVpeXt16stuotW3LGY9GxyeIUsoIU1XiVKqoCrjRyLJEVRSaF3mWJElGCHIBCMOwYruSpFRAmGclVpU0zeeNRC4jcY4gma/+P9dj/OpGOGchswDtqRz3fC4373bAnIu+YBczI7wccF5Y/5f8zVNqtmdf4s8Y4YVrkKf0bMQIwbNgnwuObv7GcNlhXth/fof5T5eXTQWFEKqKqitEahLKud6yLHe2nywtLem6PpmMAQD3799/443XmvXWZDIZjUb1er3dbkMIT09PkyT58MMPV1dXGWPvv//+4eGhZIw3DEOGrJ7nLS4uvvjii91u9+TkZDqdGoaRZdnp6Wmj0eh0OlJGoixLjBXHqVbrNc65ZVZURbcqtqKoSZLkZcmY8LxA1bVuZ2FpcWVxYfn48IQQ8vrrr1cq1mcP7g4GQNeUzz777M7tWwghS9fVBlGJYpp2HMcK0WLGGo267FIsLS1tb29//PEvFxYWqtWqYRij0bgsmZQBjeN4dXU1pzljbHFx8eTkBCFkWVaj0ag57vb2ruu6YRjWarXT01PTNPf3969cuTIdDpGq5ll2cnTsZXGOoJ/GkyT59NHmz+9+nJel3aojhLws/WznSXtlcblTtFqt3kJzPPJqdfvRo89sq1qvVzudnm1ZEMKVlRVT04sik1FDobdCP+CcJklURCUNSwyFrmrNVl2CrvI0lWJehmkWRRFfWNyXVshs4aA57unL2+flihcynVlO+FxnMx9tzm8SYQuetUl5evjpeV5c4fNWMNtHiAu293TlP/24p+f3bKJ7vuvFZy4ccXau8833+X3QXKg9u/LLXnT2vGmaCsYqUVSMIIRSQKtUy93d3Sj0Fxa60+k0DMNWqzX1PYTQhx9+KE2UMSYVMw3D2NjYkL3+9fV1Xdffe++90WhUrVYxxrVabTAYfPbZZ/1+X9M0ORk8nU7zPJdxlIRxyuJNo9GIIwIAqNcaEpJ/dHQcp0me565Tk0xbnhdEcdzpdBYXFxFCuq7HcfzZZ581m/XDw8NGo1arOoqCMcaGqlmWxSjFiJimiQE0DIMnmaap/X4/z3NVI8PhsCiKer3+xhtvSNF5RdH+0T/6R3lenpycMMaBwj1vsr+/a1lWp92R+hz3Dg51XW80GlLBdzKZvPHGm48fbXba3fh02F7oOZYtiWdU16k4DtNUP4kAQablCATTggIAwjQZTidDHDSa7sLiGkKo223/hz/5s29/+3v1etVxHMAhhFBRFBUTqbmNMd6bZEmWyuEyTdMwhAqGtm23W92iKDzPi6KEcoEVYnJTURQsnh9eAjZTin+6QAEA5bnswoXtgg3Mthl88uxfMbNJNHfUi8eZDSGAZ7PQy0t3/tYw840zw7vsJwV/DvgGnLFuyx4+eD7dwOyDz1NBIQSfN5sLbvC5j794E8/mkPIxQmiuKPuU2hQAkGWZ1Ljc3t1RFPLSSy+9+vprURRI/XrGmOM4Evgix8CHw6HjOMfHx5PJRAKgZWlnf3/fsqw4jnd2diQc/MqVK/J2GEVRURSS4VvTtDAMi6JYX1sLgkDq/pUlHY1G/eFgNJxomlYwLnF8aZYdHBy9++7PptPp22+/vbK0vLW1hTHsdrsbG+vHRwerq6tJHDFS6rqexHFRFApK5aoFSTadTqXWYlEUq6urGxtXDg4O3n333c8++8wwzN/6rd+ZTqf7+4ebm5tLS8ubOw8BAJ1OZ2lpZTqeHB4e/vZv/3an2XrnnXfDMOy02kdHR6+99ppUtknTtNPpWIZ5kB2VBZR0VVGZH03Gx6enOS0rFTtJUwGh67pMiKP+qbFcOz4+KgvGGLx567pbrVSr1dPT09XVjdCPJENkgXAURUKISqXS00zdUzllRZ4mYZSkcZnnUIBarSbp3hBCpq5xIMqyTNMUW/aFdTC/HjjnApx5p3lzeu76ee7z8/BmaYTnK/ai/cHzouDMhcBnyzBCUhg+K5OIVMznZoh/lXU+/+CpEc5hrQlEDEIBoayccggBwgAhPLsDibNRYikezqFA880TCKDgZ3VUxsCMqRsAAIXAUPINX2AiPbs3yCuE51g+eRBTVRSsAEaDuMAYI9wouRLlotlZHftR4MWdzqpC8KA/zZJiMhm9euuaFEK6d+/e2mL7ypWrg9HwF+//1LAsADkmsN1t6abmxf76+kYURZXmchRFBTTDqV/SwND0NNuqWHYYeLwsrqytMz0DhglNff94U1XV7bt7jHPN1qGOBpOjadCvtbQXX9ugNOp224phT2N25BUP90eHB+PQ0D/2Dh6l48Od/esnfVDQ5fanL968rhtOo95u1Jw/+P/+fzauXL/9wmu+nx8eDUZhXnG1ovAcxwVAJGnAGKO0LGm++fihaentdvODD96L42R1ddXzR0QBb73y5Xffffev/+PffO1rX1tcXGSOePDxZ4ZhfPMbv/mzn/0sjuhCb/3e3c0XXnjh5q1X9vb2kKs9PHo8CYZra2uL9SZSTNNs3v20/8f//hc/+vmH/6f/y/9ZrSuNpv3w4ce9Tv10MhmewKsbNxE3LMPByNhYf2l/d7S8tE6palh1t8a4KBSFVEGe8mgcjcbTMwknQytME6iqNp0mUEWD6TCOU4EgxkgIXhZFliQYoSbKfd/XDUvR1IkXKIqqmZafxDrGDMJSACY4g4goaiF4URRVeO6XhFx/QIqoMM6FlLEFApwPCkEIgaCcPxNznZcYz9pgMoxlrGQMYIwxIjOPBM+A1tJYJLSFIygABAid2WdSzPUtz5NBcN7qkO+VL8mTgORchercp8r/YYzA2ZS+OCP2nXOvQPqgz7PsGSgWzqXU/NKE7vnRBL6kIzf/6lzofPYgTdOyYAAAwABCiFIqmSa2trbWVpYBAK1Wa2lxYWvrcVEUb7311ns/+qs0zSGEDMDxeKqbJ7Va7c23vvKnf/qndhAWZalpmh/FeZ5LtqWFpRsSylwUBYCiLMvxeDwZjS1Tp3k2GAwEK23TarVaAgJN07Ks+OUnHxFdfeVLLwdB8JWvfOXll68ppGA8UzUSpQWaJiEItGFsOhXFUaN4gLFi2laz3bJV3VKU3YP9z+5/dPvGxtpq7+2338ZIPT09dd1O1aktLa+leZ9SurK6dHx8VJwmhmHs7e1CCI+Pj2/fvv3yyy+//7OfD4fD733ve3fuvPjRRx8dHBx885vfLIoCIbS3t5ckme/7Jycn/+v/6r+u1+vD4QgAsLKyAiE8ONh3HCcryvF0ktMSYmBVTKxaJ9OIw/Lf/MH3d0cTiBjCOEziJEujKFpe6RGixnEchsnGFUfGF+NRIPmRdV1XFGU4GuV5lqSBEMAwDDWmqqpKnvwojGXPWla2JWk6hFBisBRFkQUw3/ctRm3oQiQEYAIwVVXiPOcICybkxCoXgnNelJTjc7qjp0b4nO1CKDh7EpznRzPm7NlME4QQYyxX+uX1OctF5x3j7NVfK/q7fHA4ByoAM8pDxhiEZ10Uzrn4vGu9ZDxgzoTmP/LCPs89lQvPz79F5h4SQi0Jj998801WFluPH2EMFxd6N27cODza/+u//uuX7tzgnGdpQZk4PjjojyfXrl3r9LpxmmQlZYwRTT0rPFI6HA7D+GxUEgqmEEVVVU0hBGHL1IGh27ZtaMrCwsLaympRFJSV/iS1bWvojUxTv3Pnzne+851Wx84yryhDiFG4fzTyp35SKJYFtcwbBy7mKcu5ggtIia7Vm4121TYJfPcnf/Vk67MvfelLluUcH41cN4vC0nXq2/ubvV5vd3fXMHRGxUcffdTptA8ODr797W/X63WZsrbbbUqpZVWuXbv20c/vjkajOI6vXLmysrJyejqQrI2MMamvWK83X331VUldRQj+bPdxWZRX1tfcei0p07JIh9NJtWH+wR/9a8WpqSZOioQIUK1WqWCMsSTOYiPpdpYQQoEf7e7ucobRKqKU2rad5+5gcOr7fpZHElAYx7FlWfM2INHF4/GYMQEhlBQ+CCFZfDKwIgTXDMO2TaSQOC8YKwnGqkIEQogAwAXCClYVDoRSMp7O4dwE5BBIsXg4K6UCB215mAABAABJREFUAKAA4pmexOXU6UJ2d2EFznsO+YDP6688b60+jfieV4mcP7pM5y5YFD+PB4GcJzxvTYLZSV6GqM82eGm2av5cL1z2F5zchXfNDkgwQRBDCME5dhSpqorU4+PjimV+9RtfR4L/0R/9Eef0lVdfwgqp1lqnp6cZY4ZdqVRrRFHCKBl/9khR9ThLi7xEWV7QkhDCudA0fTQamaZpmjqGkHNOCK5WqzXXMQ1DUKYouOq4Gxsb6+vraZwMh0PE0At3bvz0/en+/t5v/Rffwxg/ePiZYytE4UEa+2mqmFYyip/s7wYFvnL7BoiOB6dDVtICCrddc6uuW3O7Nfs7xnf/3f/0B59++qnr1peXNpr19uPNXyz21izLWlxcFIBVqy5jpedPFheXiqKwbEPX9U8+vgchfOWVV/K8/OCDD770pS9Vq26WpRjjoijSNL179+Nms/29731P1cje7gnnvNVqyN5XtVrd29s7PBkuLnRXr15hZer7U4FRkI4Vw4iSISKMKHo0ndqO1e22R8PTLMsYU5Mka7U6gkMIcOBHjUZbUkhJqFq9Xs+LhE6KsT9N09SwaoqipGkqqfoAAIqiyJ1loxIhlGdlHMfT6VQIsdBqckGLIs+LTGoWUM6IbmiWLRAWlJWM0TJlvGRCUEqJmCtbiFn1UK5pLteOEAKcq2DPFub8DR2eswbPIr5ZWw8h8txVOmuZyG1mwxdM47I5/K3bZaN9qk94Phr//MrvbJvhqmdZ7HlR6xnDm8Wo8FKzdXbGzzVCyhhCEAFYlqxIs0KnhJCKa7uu++DBvTgJf/Nb3/zG279xcnRYr9c1TTkdjTJK7YrjVmumZZeMjkajew8e2Lbt+wHEZ9T/mmkAWmJVWVnpyrYH4JwxppwT5CyvrGCEaJ5pisoEjOPYtu0OwYjziW9UHPP45NAwDD8KNdXQbaOk6XB6RAFeXr/yeBQ/3N5url3/vf/qH6w2lH/5//of7//iEy8Lsa1RAsbBBIN84+qV/8P/8X/4b/43v//b3/svNjY23n3no7/7u7/34MHDRs+5d+/etesbn376YDIZdbvdd955p9Np05K3l7t5VvZ6C4ZhaJqRJMnhwTE6J6e6d++ubVcIIdevX5XC4IZh9Pv9yWSiaZptO47jCCGKkmu6yTiY+FMBcrda1QIeJpOr1xe2T0ZJmgKeIa7GkZ+GQXNjw7Z1VdF9P6jX2lE0vX37NqWiUqnoui5XW6VSSbPqdDr2/XA6HX/rO6/leT6dTqXu1Tl5NC+KgpCz6VNFAXIinFJ6OjihJSeEACSIqiMEEACaplQdmyOcFwVI0rQoGC/OCFuwMrd0ZjO4s6xPyBUr7ZALgcHTkgnnF0mZnpsWzfYRz7KufF78eWG3L7bDC5Hg7LPmWyxPBd/OjQpjjOd95XO3C37sQjg6yzOFOE+EL4XOs1sLvFRoEkJwCDgXZVkKAUu7hBD6YXjz5s0sjbd2d9rNhmlbnud1Oq3JNDQtlxDk+/5w6mVZNplMsqzgII7TrNlsAoSyLEOQFDmVWk5lWQrGTNOoVqu2aRGEGCt93+92Oo7jVCs2QiiM02q92ak1/MGJPx22m/UCMkXFiqKsrVwL4lEeZrphjcfTx4NHx4NBc6HbaLf60/GdazeBSqChelE4CTyaJW3bchoLJaBxFPzDf/gP/+gPfyCY+spLX7l7916vu2w5jBCCIDEMs9PpKCrudruu69Tr9SiK2u12peJ8+OEvIYSNeivPc4nelLTi9XqtVqvdunXr4ODA87yl5YUgCCCEq6urEq1aluVgMLl9g5RlycuCgyyLPdOATU2/cXXxdDIIJoGtq4iV/aO+AtHtjQ2FTxYXV8qCIUR++tP3bKs6HEx0XbcsK8sy3/cluD8M4zTJTdOWA9O+75um2W4bsr6f56V0MpLQrSzOiCoRQpZONE0TAGiaYVZsh9WKsjRte+wHACPGBOQFFoxghIhGFY6edihmqxYCAATkQMilyyF8imgBc9DTeTuRswGSThqdb/J+8dxgbWYtF1zi5f7kvHU9d7t8/AtRLpmdEDzTSJKn+LlN0vnYev6eMSNcE+c6TfCckQ48z2vPF4TmD6UrhHMAITxjcRUwL4soicu8uPrWm6enx5988kmjVpUaRv3hKReqbdtpmg5GwyxLAAAY47X1jdFopJug1ekIITzPAwCkaWoYxmQ6NjW9Wq02m816vW4ZJoRQsHJw2lcUxTSMarWqa3qWJQUtSVlAxE1Ly/txiVAcx05ePT0ZJHlAQa4ZlbwcPHryeOSlrYU2V+CPfvLDn/3ojz755ce9WttwLAp4lIQaoF44BUxrVCpf/upbP/jBn967d29p4ZpttTgH0+l0fX398eNHlUpFVev7B9s3btwYjUae5z169Gh9/YplBZTSx5tbb7zxRqVSQRioGmk1W2tra48ePYqi6ODggHO+tbXV6/VUjTSbTV3XKaVpGt+9+7E/DlnBYi80VK0s0lH/ULXNTr0WZu4LGyuc7k29WEfqSq3RqNc7lqukiW1VVVXf2d7/7NNH6+sbhmHpuo4QCENfckxlWTYejyGEVzeu9fv98Xg8nU7b7bZTceWvXJas2WwmSTYcDqMoCsNY/sqUUsizaq2R53lWFpVKBUCc5BnR9HqrhTCCUBAEORYQAogFAZCx+UX/1A6Z4BCdiS6dCQc9uzIvL9HnesLnGszMU8086sxG5LjPcw3h+Rt/jtohAM8QoJFnEANn7Zm/3QjnW/PS0mQ6PjPCp/oWZQmeV7yakZ3K52fXTAVnXBCIsKIQQCjlSZJAATRFPTw5btVrnXZzOp1wWtTr1TDy949GWVH4vh9FIcY4iKMsy6pVx3YdkqnVajXPc7l0ZLw0HfmSQtNxbFqWcRLKNvrrb74RBeHp6WmSpq7rIoXoli0gXF1brLj2J589OJ1OOAOKouzvH968c3U0HaRlVKs2bt++UzzZe+/+I6aGt15uZ97IMAzHcQqac0HtimmamhCMENRo1LzheG1tzR9njx9v/db37uzvHXVWdM/zTNOMk5BzpqnGaf9YCKaq6pUrG6urq7u7+1/60pcG/VGa5kFw1G63R6PR1BvfNK5Pp9NutxsE/t7e/srKymAwcF13cak3Gg9URZcyiXWzWoTZwdbexnqz5bpZPIK0YHFY15U379yOvPgXB6eVhvPCyy+0ay0eRLV2y/fiTqfy859/WK3WVVW7c+eOpmkyBWWMqarCqIiimBBlZWVtc+tJURQQwmq1WqvWx+OxEAJCvLu7WxQ0DMM8z+UUtWmaQoiNK4vdbjdNcj8K3VpV07Q4zYmqYKJyCJIkHU8mXhjkeU6ZYIydnkbPLkB0vn7kWubnz3AAuLgk/ALOfRQhTznNZhV+earzOz81krkYdb6nhxCet4JfxbAvH/bCxxGpHyKtglEOISYEUPqUgu38bOTbhGTBgOcwvNkdQhI8AgAopUVRSA5fCKFyLm8ozjm85RtnsofgvMEqznuPEjQkmBAQqKqqqxpRFUzI6elpMJ1YtlGxLKKpJycnU2+cUZzmOUJIM8yyLDudjqZpZZlLrlvD0FhZtho1AECe50mSdFvdXq8n2RxHw2EQBAsLC2mafvjhh9VqtdlulWV5dHxsmrZuBJK2rNqovvTSS8d//cPxeHxTu7OyfOXRp4+RwoECNVVturWVRdYP0n6YRv6kaTu9ZjsK/bXOEuAMAaASJABzHPvDj37RqjZef/317/+//y1CzuMn206lUa06hmHJryUvEhky5Xkqv42yLI+Pj994/Uvr6+sLC0vvvvvu4kJ3aWmJcz4YDG7dvoEgQQg1m804ToSIkyTyfR8hJKsgo/HArOij40HrxmLqxXmYmETRFB2rRrVi+Rn71htfXm2shF7UMCvXF5fWlpZZcHLr1p1/+S/+1Q/++D/8t//tP3r/Z7/43/2jf7yzs3d6etputz3P++lPf7q5+bBarVYqlYcPHym6urKy8vjx448++qhRb8p46ujopFKpEAJee+01QggtOedcesvj45293YOJ743H45JRy7J008YYE1VhHCRJsriyXBSFomqHuztxHGtW+3zCG3HOqaAAACAQwgicEWmflWfmSy+zuHFmJDOhMXGu6TBbhxdMbj6aA+dB3Aw0oigYPGve8BzZMxtBlGtYsiRr+tM5+Hm3KQ3kGSNECHF+ZmZfbNyXM0B4PtQ478HRpSneCzWl2V1E7iZzCTFHbM6ogEIICFIAGGN6tVaWZY6hUuBRniVxSGmhKJKNDY/Hw6IoGo0GpXQymRiqstDpagq2TR1Cg1KKIJBAtsFwQhTkuLamaRDCPM8fP37s+76u64ZViePU8wLXdevNFlbIcDx68+byLz+5ZzmV2y/c8f3w3t0HVzZW6tVWyZM4DSM/CLMUFpkOBcjjcHiKPMLzrGraTcdxdN1QiKVqFdNgtKzXq4KJ034/itMkzSeer6qO5OGXGUueF3lecgYgxJqmHR4eUsoVRRmPx5ubT8qSdbu9Ws2RpKkff3x3fX1d1+FPf/reCy+8ILWZLMtyHFv+lJJh6fT4sa3h9W5NdetIEF6UhGITmWFcsIx13PrG166CEsReoCJsUNpcvf6z937x/e//6//+v/8ffvjDH373u9/9/vf/9T/4B/9gc3NTCLG1tbW9va2qapqk3jS4fv26bmt7e3vT6fTVV191ner7778fx3G1Wh8MBmEY+75fliWCRELVfd+nLJtf4rTkvDxjTqlWqxXLrujmydRPo1jDuNbtTtMZ0b2AEMwGnQAAQvDzsI4DIOaC1V97uxypfcGTF8xBPpCsKNLZzkA2GGNB2cU3CgEAwHMBKZGKKzPACgDPcGNf+NTZ85ftUHZp5+808t+ZdsWFs39aPj3Pd88OiyCngjN2JuWNiDymqmu0yKUvjaJ4MplgDFuthmCo3z9ZWFhYXV3lgrl2pVZzw8ifjieclnmWWpalKDhJkmA6Kcuy2V2SFfw8SygtVFWdTqcyoLIsy3acwWBw0u8TVau41bUrG15wZNiV6zduPtk7+uu/+tHXvsFevHMn8Ee9Vmc0hb7vGxC27cpyozGd+mNvnEDV0PSeW207bkVRFSBUCDSMCBA119nbPtjc3AyT2I/inYMDy2kR7GiapusqQkDPVdma4YKORqPV1XVK6d7uwd27dw3D0DTd9/04jnVdf+edd7rd7ssvv/yjH/3oysbawsLC9vZ2GIaGYQAAKC3KsvQ8bzQaxaF6uHe003KrNm7UdShIFpUCpu2FFRgkcUphznRFdet1BSFdUf7kB3/67/7dv/vOt793/96nN67farU6v/M7v/uTn/zEMAxJJhDHsaZpACBVVTnnH330Ua/Xe+WVV3q9XppkUq1xZWXt/v37um7K2xxEghAiKSFP+4Ner3fz5m27UmGMSf2PMAw9LxhmQw4BLUuMkKkZkEvWWUEpo+WZaDTA4NwBivm1PcsMf93tuUv68/aEc3rA84VJMIcJmwHCznBg50rVF6xgHqFKJAwCISS58sEZYOZp1+EMjH5++3nu6V5w+uBZ/w4+56Zy+bsQQsivWABAMFZUVVd0NIfxgxBSzhVFsSsVBGUNIL9x40ZZlnEYrK6upmk8GQ9ffvnlvb2dyWiYRqFOcKXqqArGiKcpiMPg+Pg49L0sK4Ig4Jz3+31CyPr6+mA0PDw+ct3q1atX6/W6LAiJMGh1e3a9bdm/2Nt7b2nlIPRix3QQBRZW67pBMGwYJgAgDkOFlYaw3EqlZ1VdRTchMiDUEVIAUAnOkuSzzz579OSxgCBMk+3d/cWVa+MxBgAoKqEyaOM8y7IsTwzDkFXHjY0NTTPSNOec37hxI038/f39lZWVOI6fPNms1+v379+vVCqyPWDblkQ4AAA8D1uWwbh2Mjz5+BNqqOLO7Y2KpVHBC5+O/J1mZ7lRreVpAblAEB7v7zx++Nnf/OyT27df8Lzg5ZdfXltbq1Qqm5ubpmlOJpNf/vKXMmQYDoftdrvTWTo8PIqiSMo2QQgXFhZ0Xf/ggw/+/M//8stf/nK1WpU/epFTibiIoqjV7uYFvXvvQRAERVHIQbBud2Fxcbnb7kgl8Pv37+9v7cjBTlmeKGkua6EIQelpEIZn6R886xqeL7BfI0Obt4rPC/Fmz8AzSbIzK0BzIsGzHWQ8jM5lVL44opw9JjIWvbDHPKj84pmBZ56fWSa6JKN99ufchP6FS5pPUp/WgdiZ8CKfUXcDAITwPM/UNQEYpUXFMhcXF4syCwLvxrWbjx8/rlRsRXHu3f3YsoyNjY2tx49u3bp5Yqj9/gktU29aAiA0jJ1Go0RGGIamaUrpIkppGCUQwp+9//6NG7ekWj3n/OjoyDRs16l13KoAEGLy1a//xnAUPnm08x9/8B//m//6f5V4I0MnDcvBCaAEL7huvNB1NVVjtq7ojm03NLNqW25FrzuGo6tlmjzZfPThhx8OBr5VqZWUnQ4HI8+3rEXLsjRdBUBGXDAIzgg48jxP4qxer+d5SYhyfHwchmEcjQEArVYny7LNzc1vfOMbUjxYVRUt04QQnjfVNB0AIHniJnHqTb008xUVlKzodFpmxdYs5+B0kFCj1uBJFKeRlwaT3ScPHz246zauJ3H+0ouvttvtdrs7Ho9dt7K9vb23tyeJXgEAiqK4bg1jZTr1v/vd7wIApFc8PDzc29vjnP/O7/zOkydP6vXmeccil3NPZVmGcVypVBqNZqfTjeM4CIKTo5OToxMAwEeMVSyz2Wwu9HrLi4uMsTzPx4OpDNdnEdYswQGAnYWgUAAA4Fk4+p9ohM99ad5U5J8zZwDOvZ9czFJXFyEkXRpCSApFKucTueCc2+IsX5sPR2fWMosM590X+JU9IUKIzammgfOAEz1vygM82/SfN3g+tydjLAMFgQgCgMQ5SoJzRdeazSZlBQA8TZONjStREKoK/s53vsWKcjA8NW17f28HQ9BuNfIk8X2/yFNs27pmUwZs25aaoWVZTjw/L4swiJeWVlzXHQyGp4PhK6+8IrOav/rh33z7a7c13QJQuXLl6mtvvLGzs/s3f/k3q63WG6/dabWbnZp7PD6ZponQxJVuu1N1Se5CCFWEK5bdrruNaoXgktHk0aOHH3zw/vbWYyGAomgCgKyko/H45KTPGLNsEyEgBBuNRoPBwPMmkqzRsqzJxEuSxDAM3/cffrb5W7/1DSmTeuPGje3t7R/+8IdXr17t9/sLCwtydv7w8LDb7cnKDcY44rlQcQrY48ODsTd13Uqr0220F3THff/De0kWZ2kYTAaJPzQUXqtZnIPl5dVvfes7JydHw8HolVdf/tGPfvRnf/Zntm0vLi5KxNyLL77suu7x8fHS0tLW1tbp6anrujdv3lxcWMqybGdnZzLxbNuWNE2yNysl3IqimEyDOErLgkmbLPICQqyqhJe0Ua0tLi4iCIus8DyvUqmYpiFbAnL1I4TgWaGSF6WUsj2rjkIIf13zmy3d2eN5nzaPjp49ObMUmb7NFvYsHZtvQs6CVThnAjNDmD8ymbUKwFMn+0WcGQB+ruMW562LeRc3M/3nxqWz/qTE6wghOORIIQRhCDGGCCGiIEww1oiiaYpCEIZAfgWWZa2srAhKgiDoLXQajUYSRrqhtpvN05Oj8Xi40G03mw2nYhm6qhHsuq5t23/yo/d93x8MBlLp8nQwTNN0MvEsq1Kr1Uy74nneZOKNx9M0TRcWFj57+OjK1Wu64RIOGvXWV9/66kc/e+8P/+0f0sRTv/b62nrP1UzGSoghxrioMK1oAQAwQpaqVyuWjsBoND493nvn3Z/cu38/yzLbUZjgEEOiqSPfT9NUCKGqKsaQsVLX9Xq9btvm3t6e7/tJkty4ccuyrOPjk1q1ceP6rTxPfvCDH3z729++f/+u69YopQ8fPnzrra+UZVmrKUmcCSFc18nzIsuySqWiWInlWCrkoMjHUeIn+TjKjX6o206YZhwwVQGs9AXNalWnt7xYc178O3/n7wjBoii5c+fWD3/4w3/2z/7Z1atX5OylJOGv1+tCCCDg4sLS3vHj27dvb2xsJEmyvb3d7/cVRel0ekIIxrgkFqlWqzJq9X1fMy1KKaUcAESIqiqKZCSheTGdTvf39sqybDdbk8mkf3I6nU4bt+9I2S94Rj9RzhdgZgtyzop+bU/4zHL9nJee+cRz5yHmhErBeUAnzguTMiiVStlfvJFZTw88Hbf9Ih89s+bLkeeFWYp5v/rc65n5dEl6L30jA0xRNE1RESIIQIwVXVE1VRVCYCAIhgTBsiynga/pzXa7vfV417T0KIqGpyeaprVbjcXFhS9/+cu7208whnmWjkIfA1Gr1bI8yYv0zp07w+Hw8PAQYyxDi4WFBdtyHj/e2tnfM3Sz2+1eu3bj8PBQMilyDpI4U1THsuyFhQXXcrIg+MW7P/wX//z/mYWjb//m13Rbg1AYWBEIlYIbik0QxggpCCoQRb6392T7088+uffxJ/vHUaWmVlTbC0pVgbqqx3Hc7/cdxzZNU1ERpQXn3DRN1634vn/jxo0kSYIg8jwPY2yZZhzHUTR4+eWXdV3f2tr61re+s7Oz8+Uvf3mWmAGQSVJjSqksOFlV21AVHSHMmSqQgnHJYJKxNEwUXSNEFaKoONWVhdWbG8uLvWav+Zbk1b969er3v//9e/fv/t7v/V6axr7vHx4elmVpmvbR0ZFcf1mWSR1lSRgnhEjTNE1TySk8nfplWZqm2Wp2FEUJw9D3fctpIYQMTbcsC0KYxkkSZ3mSFkVR5oXquqxM5SCibdudTiejdLbSGGOUczkQe7kW+sVFh8/b5nPCv3XPy4t/vtEtzkkDAQDSJRJCePH8wsz8RggHsGSgoJADLgADgmBVanHOducASKlSABCY4z6chZSzNuCs5jOrfFqKJq0RzY1Lzk56hpvheSmTwjTPVLWgigIA4JRBCDVNM1QtjmNVVTVVPZObREoYUQATQwNxNEYItdp1AMBgNDwd9B9tOqZpW1ZF0+pYMz0vmCZFl9V7vd6Cgxa7PVPTf/GLDxlj167e0HW9iPP15ZWDJ9sKUYswnp6OKpVKw3Z2Hm7S5UVdy7JkdHxwWq/X7Yp27YXrQe7/8pe//ItPHh/k6ObNm6urq7WaW9E0AIBmar7vjz0vjuPJZLK7u/vo0aOjo5PxCHBhT8YEIUyAAuNMFaGmCH9QUzYUR7dVlTBI8yROBbd0c2mxE0UTzrlhwDyH/cGx1MfOkrRZ16sV9bd+81tlQZvV2ulBX1W05eVlb+ClWdLptGge58nUsbBgQaPQVFY6FRsiHAc+wqBXdXAFj8d9XOLXXnuz013gCDY6XdO2sEKqLTVJaCHYJDhZudLLWRIm/vb27rWrN/KS5SVvd3sYK5ubm5RSRVFuvHRlaWnp8fZ2HEUY44k3zcvC96cPHtzTVdU2rSSYHoY+hFAn5LUXbwqkaJo2Go08b6CqKqMlRqDb7Z6ennIhNBMvrV4Nw1DL1FIUmqbdqLpFUYymXpDEUIA8yxhE1UYjyQoAEBAIMAYFRAArBGOMS5Ryzjk762ALhBjjjDHBhQIBRpBxSUgtIIQIQoFKAIC4hGyBT4WMoPzvrOCPz94Lz0QEzwyFi7OJLbm7AFQAIQAFc33FWU4HzqkTz4xQ1icUReEcQC4YE5TSgrIZ3f3l+8FzrfnCHWVmb7KJP/tzZoQz1MIF2IGiqUKIPM+FEIALIURRFAmIpKbXzP7l3de2bauuHh0dlSVzXdcwTMdxbctZXl5+8OAzSdq/uLh49epV3w/39vY+/fTTrJj87u/+7te+9jVFUX7845882vzs6sb1Gzdu+L4fhuHh4SFCqFKpWJaVpiml9PT0FGN89erV5ZWlVqtFCGKsFIKtrq7+7P13/+f/+Q+rVffmzZvr66u9Xq/ZbCZ5Eoah53lh5Pu+PxqNhoNxGMZu1SFYEwIWOeMMqaqpEFKW+f7+/sbGhlutQCgAEWmaJkkWx+HW9uNrN64RQo6OjtI0dR3H87yf/OSd3/nOV4+Ojr3p9PHmzvLyqoLJytIyxsSbThVF0VUtDqNHnz0cj4fedOrYlb3jsYqJ6zjLS4sV87ptGY5tQChM0+QQLK+v6aYhMLFchwlAVIXmdGdn5+4n9waDwXg81jSj1WoRQp48eSKJsGzbFgJ2Oh0IoRwKk97A87woisIwpJQeHx93u908TeXcY9VxJddzkiRPdva73a6maZJtZDweSzwqhHA6ncqCsLxHt1qtRqNx75f3HccxbKvWakKVHA+GByen/f5ptd6QSwYJDASEAHBOOecQw/l7/SwQO/sTPNW75vCMYuK56/zX3Walo/mkUVZrwFwl5XKKByTvKDwXIcMAUsplQfnzjHB2bZcrNBf+lEfmJbvwpHww60xe+JpkxfJsCBqcpbyUcZmgz2fGckg0DoMoSgEAQoAsy05PBhAO87y8c+fOw4cPP/30U1VV19fXF3qLL730wq1bN4Lo9Ec/+pu/+Zu/efXVV7/73d/85S8/3j/YlfGbaeqqSqI4HI2HktuGMZZlYnt7GwAgAJ9Op7Wa67ru7du3d3d3S/q667rT6eT09PTo6EAS3XJRyAhfCIEQxBhbhmabZlkwTTM4gyFLqWAYMlZmWVb0anVCiKropcgF55pmaJrmeWGr1UIASZ5Vx3GSOM2zouaKvb296dRbW7tSr1fX1tbiKEuSaDSaXLt2TbLIcA50Q1VUwjmXqteQi2q1euvmnZWlrmloGiGMl7ZrCwg0w0jyQiCYJNnx6YlC1CeffRaFsVTCkqoHp6enjIk4StfW1mq1epIku7u7nhc0Gg0AAPKZN51maer7/ng8lgO+0/Ek9H1d1y3HgRBKy5TJv2maiqJIpnMhhGEYEMJKpbK2tra/vz+dTiULhvzdB4OB41Qghr7vT0Kf6EZeUsexDctOs6LkDDAmECFIgQIwzigtVXyGgBEQ8fPqBhMAzFkghwiALzK+52ZPX7zN1vB8asY5n+8HXqjQzJ4neZ7Lm5mqqgQTQgAEZcn+9r6nmKvciGdHM+bDX3xpygk8heGhWUNydk5Zll3IIQkhCJ89D88rsVEU+b6PMRZ5Zhp2pVJp1FsQQsExIaTT6QwGA0VRNjbWTdM2DMMPvKIoOOclDVZXV03TVBQ8mXgQAl3XsjzBGHc6raLIfD/M83Q8KQBAjUaDYMUPvP7gNC+ysixXV5dv374NIej1ei+9/ILv+++99+4nn3wSx3FRFOPx2DAkaz5RVdW2TckmDACaTjwhIC2BpqpZWpQlZ1SYBg6C4OTkpNVqKLpCWU5ZVhSFELDV7ERxIDFoqqrmeW6a5uLiYh4PbfuMpuX09DSKAjkemaZxWZZ5nqoagVDIFfzw4fbqxutFkUnlqVqtoavKGeaEM83QAUB5XgKMfD843D/UDYuWLMuyRqNh23a93oQQJknW6XRUVV1dWY+iSPorhEClUoEQcp4NBgPf9yEXGEDXdbvtDispFKLVarUaTc6553lREMrf2jSFJICSN744jgEARVHI+4WmaVmWycQ4SZK9vT1qFKZlIYwBgkIwVuR5nlJKEVFYyShlGBKocYIUDIWAnAMoIALwbF1SfiaGIgCSNRsOEbhU87ywdP8Ttpl6CpgLAy87KvC85JPMqjKzPWSZZC7oFGdXcXaSYmYk86Yyw6xdCC+fjnvN1XNlJHPZDQIA8iyVyoRCCEoZAEBVVYUoUpWNYIwQkhM9UIAwDF1DM00TAHJ8PJCGKimG6vW6hFD2+ydhGBJCVldXr11bj5Ix57wsS8Hh8vJip9N6/Hjr008/vXbtmqoRw9TyPFdVQikNQy/PY8epCyEURSnLMgi8MKwGQUBpIesKlUrl6tVrUm17OBw+efKk0bAoLeSQq6oSRcGc0zzPq7VKFCWMlYoK8pzFiQ8EsqzKxPM2n2xbTmVpqct4UZSZXN9pmkoM7XQ6VVVV0zTLsDEkSwsLYRD5fqgrqj8Z16uugqBVqRzu766trfU6a0WRTSaTg9290JuaGgrDsMyLwI88z/erDrPNim1qmtofDvSSCoymU09AGCUpLQGxlIWFhdFodHx8jBDpdrsY435/6LruwsJCFCabm5ue50uV76WlxbIsp0F/MhxJo1JVlVOW53mZ50tLS5qmyV/Ndd2KZQdB4Hme7H9KPlLGmCTRYoxtb283Gg3LsjjnhmGsra3FcRyGoe9NDNNUFcwgABghDIBgrMyFEKygJS05JBgCRQMAAYwhnaO3ZbMSI8RACA4RhwDIkQuAZnWOeV/yn2OH89tz1/bnbWS2CSHKomBMcPYrnYeYi4Bn+8/Z3nmvjz4jFAPnkLVg7spnm6xHS3i3YE8LvrZtE0JkYUbi7CSFScVYtJ2a67ppGpdMlnjK0cSLszQIgiiKyjKngqqKatiaU3OSbCphPRCJJI2iMFFVcv361a2t7V6vt7a2CtdhmmYnJyejcToaD6bT0LIMVSUY4zRNZemSECK5qJut+sbGxu3bt2XxsNFoKLiQM41B6CkKllzURVEYhhUGcRBE06kX+gFnBcGKpgJccZIkGY/H1aoNIGe80HUCOdQUJUkSAlGv3YEQjsfTlKWGphUFK4oiDP12uyvN+Ojo5OjogDGqKNgwtLLMkzTOsowQ4rpuzgVCiFMWRZE3DRACmqYACDVNS/OCcjYeTcMkLilPo5QQdZpGCBEIseu6tVotCCKJAjk5OeFnrIQwTZN+n8pSwng4Ojk5iaKo1WppiprneRyGMpLnlOU8l+m9/NVqtZofpaPRSN7UpCcsyzLLsuXlZdu2IYRSyVSqJiOEut0uRCgMgzDNDNsyTNtxKpppHh6dAAIQwBACDDnnJQIYAsEYRAgJiAQAZxSBECP4FMsFziwQzC/U/3zD488O5cFz2Pe8LXxeiEtkNogQopRleUEpx+isJ/MFnnD2eU+NbQ4jOl8LlTetmfOcvQTmzHj+G4ECzSZH5IyixLmbpgnO+xkSdM5KWhTFg/ufTcbe8vKyW62Ypk1pIb8RBAkhxDR1IbQkiZIk2tvbo5TG0WRpaUkIsbm5CSG8du1avVHd3toVggnBGo2abdu+76uqYlnm6enpdBJRSkejEUKoKIokibIsWVxcrFaraZoGfgTPZ8yWlpZu3LiRBKcAgDRNoyhQFNxoNCzb4JxHYQIhPDnpf/LxgzLLLUPTNZMQcnRYTCaTvb09zgvLNqq1CoF85E+XV3pxHIdhUHOrCCF/6uV57tiVTqPmjb0sSQenfdu2gRAYAoxEwWj/9GTQP5WORcGIFuWwP20tLAjCMMZZlkVZZGZqGJKcTmzbnvoeAEi2SYFAURzHccJoKvWqgiAaDEZlWdZqNcdxNjc3u52FdrutaVq/3/d9jxDMOR+PjvI8B5wrGOuqmkKIEDIMA4ozYighRJ7niqJUq1VN06I4l2mIrusyQpHez7IsmRkJIbIsk0KurutOvSGCpGDUtoyFxZ5pO1ESx0lmaSrXVcHPOPMpZSUrGGMM2QAADKGY08FG5zT1f+v2n+wM4RyaR65zcd5RBM/OWzwnHOVzrBtlWTImsKrIm/cXn+uFY81GM+D5SKK0bZGXYq6JL/vy0o9d8KLwfBJKsi0ihBQ8i0vPtllOyDmnRZnned1t5Hk5Gk2EEISQvEgxhpZl7e3tWbbR63Wq1epoNNjc3Nzd3T05Pb62sXp4eGjb9q1btzjncojh+o2rQeidnJwM3ju1LadSqfR6PcexAeCGbkMIJf44SZLBYLC9va0oiiT2RAjFcSwBkLpmpEm+0FsyDEMAFoY+Y8yyDEXFlFLLspxKlRBy9+7dLI8VoikqTJI4SQrJ+KbrRFE77caqZRlxEhBCVpYWj4/h0dGRRpRuqwkhFpTlacYZbdTrURRhBMbDwVlHWJRFnsqalq7rFdvWNU1wkMYhQijN4igKk6SSOTaL/CAIHj3ePD09tStuWbKJFxBCwjDyfR9AtrKysrd3kGVZq9WSSdrDhw/7/X5RFEmSMMZ0Q7NtuyiKzc1N28ASHGMZJiEEAYgQ0hSVc+77viw3OI5TrVYRQp7nmZar6zpCSGZ9cviGMWbb9mg0AgDIzv5kMpmpNRe0zPNcFnVqbqUsy7AMTF1HCGFMKGdZliUsKYq8LEuhm9IYEARM+gN4tlbROYkgBHzmDP/W+uKvuM0a3TPsqFztn2f88+ZzNkSDMcZYUEoJUXVdT7IcnutjQCQAQjPwmjSh+dbCzCCl7YE5zdT5aFs8C02QG3+Wq4afz3fJ90IAZ/cV2eqY/TAzp1pSzgT3Ap9y5rqVSqVimjpR0GA08E8CAKFbq66uX6k16v1+fzgcAoH6p8OD4qjT6aysrPR6PTkAKTFfjuPohnpyekQUJAXrb926dXp6Oh6P5WC+FPTNskwKJzmOs7S0BACQyvL1ej3L1DwvEEIQKrquQYhoyRln7Xb78eaW3G0ymTiVaq/X8zyPAwggz7IkTsIktU5PTxEW/cEJEGWWZVHgs7IQBGOECMFFUQxP+0Wec86btVql4jSqtTAMoWCAKxgyhFHBKCtyQ9V67U4axaMwKcsSIVGxdFVDELKClsfHx2EcRVFk6BNV1bOsiMIYEgwAeu3VV588eUKwevXq0vLyMmPs6OhoNBrJezQhxPO88XgsDa/eqAKaDwaD80DdPT09VVV1eXlZiuEcHBzI8uDR0ZGu68fHx9VaCwAgoaTSknVdl89IdeQ8zw3DSJLE9/12u61oGhNn925W0iSKaV5oRDk8PLQct1qtGqrKypJTBgHXVFIAoSkEIZSXFHCGMRac5UUBIeQCQM4xAOIcniV7ZE/X2yUfNV9unN/zwlvk+pwfWZQB+YVwdOYbpR3N6iNEQpllrUIatLxgeo5UEOdsbdJsZjRVs6PLE50fZbpQpLlwYV+crcI5mJuYXfmMGh0AuRRmB8mLlDGmaZpt25VKxXVdxso4jr/yla8GgV+WpTcNBIeuWysKenR0kiRZp9Nrt9vVqsMYm078LMsAAN/59ncRQoeHh7JMV6vVIISUUlUjr7z6kjRgwzAqtithmZKLOoqi0WgimyULCwtXroAiDg3DsCzDtHTZDZb3rqPDkzRNCVGbzebKykoUJsfHxwcH+7X6jeEwjCKa53Ych4PBKVFgniZFmdMipbSAgmMIIGCcgjLPu0sNqR4DABCCY0xM04AQZFm/LHPOOYCIEFUWjdrtNkU0z1PD1BACRZF5gZem6XA8BABM/Cn0/ZXFFSGYW3WajdbS0sruwQ7G+MqVK+12+/j4+Be/+AXn/MbNa81mUyqEjsfjsiw5p6ZpIgSiKP7aV74qyTgIIcvLy7quf3r/wer6Wr1ed113dXXVsqzj4+ODg4ODgwO7UpNz+icnJ7I4J8OKsizjOK7ValEUXblyRQixsLCgadrj3SdYIVLIFQCQZQUvSgLglfV13/dZVpiuDoSYjEd5UXQ6HVYWZQExxrKJDwUEQkDBMcICinOoNwMACyC44AI9Y3i/lhucT/8u1ClnBsbn5vUuLP7Z24mkfpBtN9M0MVYkH4S8Pz3d+6lhPGNCsw+bJYEzP3m5DSg+X9/juZfHnyaN8Mz45+ZEZBdRwqZMU7csAwAQx6Gsqu3u7jqO0+12KpVKFCWKolRs98r6VdfWJ5PJ4eHRZDJ1nAqldDyejsfjPM+bjXbVre/t7zx48GB7e3ttba1ardq2Va/XHz58uL29LauCnXZPktg2Go0kyYIg6HQ6d+7cUVV1PJ5mumaaZV7ygnFdV1WVqBpWFIVyBLEWRX6SFbppJ1lRUFZtNARnWR5xoRAFaZoCIAMQGKZepElRZIJTjABGQAgGOAW8APKeKITglNGCYwEBUwhQCCqKglIOASq44BwALtyKAxXshyFRkBAsTiKAOWUMEkgp7Xa7/dPB/tHhZDi5c+fF3Z2dD95/f3XjCmNMlqaKopCS4PKLGgwGx8fHQrBer9NsNsuynE6nNcc9OTyK43h1aVnqcOi6/vrrr0+nU3mfkjFnlmXVeu1r3/g6EMRxHIyxZIWr1WqLi4tS9dU0TZkHyX7PZDLJ8xxrqqYaQhEEY1YwKigUwNQNxgSkIC9TjDHNCwIRNgzLNGMvp0VGZxUHrnDOaVkqpk0A54ALiBCCAEh2Xc7RU3qLy0WKX3GtzrvEC9Dt+ZdmJjMfGAohiJR31nWdsZQQASHiAsjZ0LP88pLdg7kbxnxGd+E2cNawvuT3fkVTFEJIiByEEIjzsREhZPSIEIIII4QYYJZlVNwKA+zo9EiWJSCEvV7PsoxOb6Hdbo/H4939Iwnnj8KYlpyWfDL2wiC2bKPb7V25cuXk5KRWqyMEEEK6Zk6mI0n+vbu7K4TgnN26dbNeb0wmkzDygyBkjCFEhBB5nodhvLOzJyv7v/3dv8NYnhfM80OiIFXFbrXiODbnAkASp1leMLda4wJRSru9xU/v72GMDEPTdaIbimnpCPI8jynLIKCqAgEACFBOM4IU09AiPyjLEnJGsIIAF6zklNIit009x7DIKaUijZM0zTkHhqZpFQdjUDDKRZkkAVGh6Vo9sxVGSa+3WKlUsjSP/KBimVEQJlF0fHzc6XQIIYPBYDqdWpbJGEvSSJZSrl+/Kr+Wk5MjCVrquXVJexNFERDCNE0kwMnJiWwRQQ3KPoQfBmEc2bbdbHRn8Zj8V5bfdnd3FUU5OTnp9Xr7+/u9Xs/3fUVRGMJZSWleIAAVpKhEwZAQpAz7J1gqiARByVi73UYIcSGqtikjZ0oZFwiCEglBECCQUQjgGff8LLbjCCnz6/PXssML3ujC47NQc07gafaWCyZARqNRURSSOjIrSowVTTV005IdHiEEhGimpSHE0wlifmlK8IKrlbGrnJuaDzJnl/p5FzbvOWeecHZkfk4ZTpAs8EQIcUoLIUSWZYwJ2VV7+PBhURRbWzvLy6uGYViW1W63a7UaKJKtra3BYIQQWllZqdfqURTt7u5yziVRN6XUdV3G2OPHj8fjcRz/Ynl5Ncuyer0ehmEQhEVReNPgjTfeaDQapmlqmkEpvX//Psb4zu0XppNYCAGRIAQAyAmBbtWq1ap5kVqWoSrGwsKS6zYODw+Pjo4o5apK6g3HMLSSFmHoGxpSCEjj0LZNTJCqEMA456wsctUilmUiATDGlCKE5YCKAEAgBBUFYayrKk+TUpZPVFV3HCcTimmpOlQBEmmRh7FPDKKbxpUrawih7kJnZWmVIJQXKQT8zTffnESxqurdbvuVV16Jomh7e2s6ncqcByGEMSSEuG6lUrFkP7lbb/e6vTSKj46OFEVpNBoAwslw5LpuEAREVer1+urqasnowcHBaDSyraoQIkkSecx+vy/rApJPMY5j2bhWVbXRaGCM9076nPMiy0zdUBXN0o08SYs01xWNEMKhUJjCgeAA5GVRpGmz3cqyTPZpKaUcCACBimQGxgUQACEAuBCCAQYAK4UKnvUlX2yH8wne/KpGl4Zyn/uuC4ZwZoQnJyeuU6vX65pmlIynaZ5n5Rn49Tz+PJ/jfWY4anYq8/Yji11wrn8oKdzmne8XnCu4dHeBF9zx+T4y3IUQVhw7z/PJNEYQ67puWTrGihDCNM1Go2UYxunJoCiKdrudZ+WTx9sapKZprq5cGY2HDx9uQiiazWa9Xi3LstGsqaoqi2xSoVT2x2TN5ujo+PT09Nat2zdu3ECQfPrpp7VagzE2HA51Xa9V6zIBGI0DxpiiQsPQiQIAoEwAxkGSRu12yzAMwyQAKpbtANifTEe6qakaUlWFiSLNYsZtSzMMQ0OQIwAR5ggBWjAhABJcxcjUrTzP00yUZV4wijBECBAMozAlhECBBKdQAE0lmqphiCjNLFO3KpbA6Lh/HEQ+A8yq2GbFzrJMlAAAvrKynETZaDDcfPjpKEpd15XoIk3TbLsShqHv+RAJVSWKolQqlm3bMlYqaWEycu/evUePHtXr9VqtNhqPDcO4fft2VhZFUWRFHgRBkqXLy8srKysrKyu7O4ey6CD7THKSCwBw9epVKRoph/cfPnyo63qSJABhDLGuIdetNmpNTVF9BrIkduyK53kCgmqjjgieTKcZE5VKBbAciVJBnGNIS0aLAiKCVZUAxhEUAkAoOEDiDLT9nKDsV1yolxf/hUU+81XzQSJ41lfJjZRlaVmWadqcA6Jqvh/2T4ccQErpzAgFhACetTtmLY15OwFzFIaSX/XCqxdO7le8tlmSiWYSxxCCc2dIIQUAEILKEkAoMEGarpqGiRDiHAwGIwk8yLIsCKIwjHd29j3Pe/XONTmyXZa5aZqtVrNarRKC87w8Oe4jDGq1Wq/XlSp/JycnlUolDMMrV67s7R1sbW1JQnjD1OQmFcIajYahmwcHB48ePer17uR5hnLAAdKBgjHiAHJANMNiAiVZLpHiaV6ougkQ0Q1EC2DZhuOYCsGMlZRi3dBYmVHGAAeIEEXFgAHKiiSNFKzO94U5ZxBiCKEMpEtKpcvSdYUzMZlMiG2bltnttgVBfuIFSSAgxyoejQYQYg2rg8Gp67orSysKJh9/+NEgiLvdbrvdPjnpHx8fCiHq9drKyoofTG/dutHr9Uajwe7uLmOs3Wk1Go3+kwNNUd2KI9M/WdLb3d2VIHin6h4dHR0cHGRZVpalpmlhGBuGIWv3sj1o27asjXme12q1ZK7heV6v11MUhXKiqSoSQFMNxkTJcyiAqZlJkhQFpbRQDV0zdFnUIESZjscQQkSIShDDsOBUAAABQVAAOREBBEKAc8EBR4DPE7LMp3C/7jYzLVlhmX/mQh532YGRIAgGgwGlp1lW2I5LKQ+CiKja3Amdy0ZJI2T8gpHMMsCzlsa55czO7/JVfYEdPnPw809AEM76kLNAlyPMGJOeStM0QpBMBizLMgyr11t89PDxcDjsdHoLC0uci+l0Gsex53mSji5N8yiKxuOxALwoCggFALzdbiuKwhhXVVUyt3e73SAIrl+/3ustxnHsedOjo6PVlfWyLH/+859HUdLr9WzbfnD/0/F4/PWvfz2KSFGwsswYYzbVDVMVAmKMEcZCiKJgYRhPJp5Ee2OMNUVJBbdts9PplEUaBZMk9lUCHdsqy5wJQDBWFEUAVpa5YBwDVQ6qqSoBkMnRASGYFIgPgwjjXNMQxkrgJ5PJZLFWM03dcRyOhUSZVutup9erVCpBEGlYBwIMh/2DvYN6tfG9733vL995//DwcGdnx3EcJKV287zb7d64eS2Oww8++ODo6MA0zSsb66Zpjsfjg4ODRqOh6zpWiGmaIEuzLJNCWpZT0QxdCCGJWCWjsVOpdzqdNE3H47FsD0odGyllMa80LBuMqZdhjHlJoyg6KakKMcHYta08zy3diFIxPO0TQ7MrFYTQZDIp81zWOFRVxec+w9BVJgCCgp8NIYkZK9Q8XcWva36Xl/G8M5TbbIrighHO+OYAACQryP7RSMafYZKfHbpIIYScMdnePFPb5RxwUSIhzoVv0HnnAAHIOccIyT2xAIBxAiHEhM1dsAACwLN/ARACyBInOPv3bJ74/HoA4EAgIRjjJQAEYwEABQJDKKeAOQScM6RZjPE057SkJQRCgyY2dJ0cb23DPNVAKQofUogx0vWs0cCawSEsq5ZzeppNJn5ZijwrHceROXCtSqbj5KPp/XrdabbqhmF+9NEn+/v7EKg3btxY6C1vbm6GQbq/f3h0dLS6uq4oyqNHj5I43djYaDabH330EcAoCILJxPvN3/zNW7evTqdTRsVw4C0sLIRBGIaeputRFNQbNUUFtboNssn6UldV9DQNypIxirOyFLpWlgaCmooxAggzhIGQrIiNXgVCGMVBFkQYobIsiixXVZUDoaqaQqGOlZSVWZZM/dHEG5cH6k3rJkIEQtZt1C0VY4zUIjt9coKIIjTr8OBUN8wvf/nrllnxPB+ohVklUZSlxdQ0zXbPtW2L8vjkdNfSjV6n7joqhsjSlTwOIt/Xm2aGi0Kl0+mQRKTd7tZrDbNmjYaT1fXV3d1dwzCvX79+fHz86suvbG5unvaHURRwzuXwsRCsLHMAQJqWrlspy7LfPxGC27ZZFFkQeJwpAAFdVXNGwziQBpbHNKQZAKBEgmFM87KkPuecl8yoVDRNo4xVHQcQUm02JQheEkBSSuU8qkKwqkAhMBUlZ4xzjgEmSMEQcQ44EwAgAZAAiAMoAOLnYDcCmezeYYxl0V5WX2bhiXybOP9PkTpLsx7jrGCDZ15KnL2ZzzFqzxyRDC/BeTZ59id6jtzx3+rZLgSis6T2Cyo0zz3UhZ3F+bXOTl46SUrpN77xjTAM/WAaJVFZ5hCAbrfrOM64fzKdenmeV6uOU3GLgnpeYJkVRVEgEq1WgxAyngym0+HJ6VFR5L1er9vtLvSWfN/f399P01Q25b/yla/Eceo4jqqqhwdHw+FQuk2A0dtvv40QopTLSkO73WaMRXFACHr55Ze3d54AAN5///3FxR7G2K3Xq9UqZ2DiTYfDcRJnlmU5lQrGGApMsBRUIRgJBRNVJWmeSU8ufzchBCKYqEpRFCWlaVGmaZrmlHIBISSaeu/evZWVFcuykjyhJVcUDRFYlvTVV1+beEEYxpVKxa5Ui6Ioco8Q8vbbbwdBcHh4ODztCyEsy9I1jVLKWNms1SGEN25eOzo41DTt/v37k8lkRjwhh5KCIJDa4zdv3D4+PvY87/S0X6lUVFV95513GGMvvPhylmUzWiQJqUEItdttGbJyzqMoGo1GUuVXjozI0Qpd103TlPjBGaO2XBWyCIkxJqpaFIXMaTnnUiBRKp9HUZQkiThn6JSVRV1RAFE455wKTnkpGIFEwbhkshUEEAAcQQgBBE9LE//JznO2PVMdnRnDfAZ5OX6d3+Bljhlw5uTPXuUXnfJTm3k2RAbPGvCvEqZ+3jMQQuk1iyJLkmhzc5OygjGWl3lRZAJBDhjGuFqtWpbVbLbKQhwfnw76Q01TllcWkyRRVdJqNSqOXW+4aRZmWVoUuRDiyZMnO9t7vV7v6tWrnHNNM770pS89fPjw5z//EADQ6/WOj06Kouh2uwght171vKmqapTSbnfh4HBimJqu641G5969Tz797F4Y+s1m/aWXXlhYWCiKLJ2eSGLcOI4Hg0ESZ61WS7Q6RVFAATiGgmPEmYIRhgBCHKWJoWolpSVniPGyLBmniGAuAGM0y/O0KPOy5AICTFRNs22Hcx6nGRdCIuMNS1cUxfdC3w/TJLcrzvLyqqroYRjV681rL93Z3d0Npt5xeQghlDYWhuGrr748GY6EED//4Geu647H44WFhW63K3XRIISWZTHGJhMvjmNJS2WaZqfTURTVcRzJf/HSSy9t7+xNJhNJ9atpmqxIG4ZRFIX0jRIkHEURhNC2bYxMCU7AGDuOY9v2DOE0W7cSRyXNksKnY98AAIlcUVVVgpyeqjOcz+Bahi2n7WhOsyxjJccIE6JwVvKzXAxAwJFAHHIAgRDsfCxYLmBZImHgIreNOGfmR/PLdWYX83A2Mn8xYC7NE3NjR/PmLj6fnnH2AZdfuuwJL9ve31qwAXNWLea8Hzh31BDAsiyldkUY+QAAXdecqttuN5FCIAaEEFamw+FwMpnqmtlstq5cueJ7AcZKvV6ltBCAp2nMWFmW5WQyGY0Gn3762dWrVyHAf/VXf8UYW1lZsazK4eHhm2+++eqrr8ZxfHR0tLu722w2r127trGxsXu4U6vVOOfv/vRnnU7n1q0buq7//Oc/z/MUAAEhvHPnVhgFlmUkSeS6bhkixliSJEmSSD9QFEWcJgghBARnQJSAl1hBUEEYIoAARwhRAbgQTIC0yPM8LyhTFEUAxIAs0UBKOeOcQ3Ttxq2spP3+cGlpYWFhZepPIBS27TzZ3tV1vVarIKw4lWoUxScnfUXRFteXLd2QtyrP8+Ioarfba2trWZwkSeK6rqSikgNWQghN06T8E8ZYdtiLopBI0S996UsPHjzQdePo6OjJkycSKGOYdlmWcnJC/qaappmm+eDBA8aYoiiWZQEApKsUQlQqFUqphIAbhiG1biTDvPRm0nmcAZWFKMtMGjw6n1YFAMi5sBkj68wIMcYZ55ADIQCCGCkaA5xzwCkjQDAgmEyJABdQcC4EBBg+053/VdbtZT90wX+Seej3BSOZf/N8mDp/eHDJE86OAP+2pudlT/sF0enls589I2bdTwGYYJAzCHi16srbSlnmeY5VBA3NNEwtDfNbt264bm17e2d3d9uyKoSojuPIoDGOQ9+fFmVWFLmmKaurq9VqYzQa5VnZ7XZl7V7y7Z+cnAwGg3v37g0Gg+WllRdffFFRlOFwqCjwwYNP6vXm8spiveH+8qNPqtXqq6++cnBwYFdM01Idx4mTIMsSiESzVQfZZBa4YowNgwAAgyBwKw4+v60xJiAAEHIkEKWlwlQIASQKBBwRzHNRMlqUDGAkIMKKAijPaBqlWRTHULHzzGCMaZqhaCTL8yiKJl7EmHDdumlVgiDK8wJCbFdct1r/+OOP5QxUq9UaD0fHx8eWZa0uLW9uPmw0GnEUvf322w8fPnzxxRefbD6WnL9SGVsIked5EAQAAEppv9/f2dk5OjqqVBxCyO3bt4UQuq5L6if52+V5Lu0kiqJGoyGBaZRSQogMK4qiGI+mYRjKvh8AQNK9yUbibEHOLLAoipIXjluhlMZJlKRxq9XSdJUL1h+MZrEepVSCOhBCll2RfGiEEJOoHIE4TYu8UFQNQ1QKToFAApSccSAEFwAjCAAEQMKpZ4Y4W5fw/En54LnmJy7oE15AeV+Am80A1uB8oWPl+TnhBTf4jJE8j0T1/3+eEHMhtcgBhJKKWQjBh8O+aZq2bcuVQQV3q9VGo+HaqsRblWWxura8ceWazEBOTw/b7XazWXerlhCcUqppSqfTOTg4Ho/He7sHe3t77Xb7pZde2tnZ+4u/+AtFUer1Zq1WW15eXl+7IiVsr1y58md//cfNVmOht7Czs5Pn6Qsv3nZd1zRNLuhw2E+S6LR/dPv2TcbKZqvuOI7a6Y69qYxPCCEQYCGEnKkjEBEsR+MEAAAhAAUUEDIBABcAQVXRhRCUA4xxGIYIKgACJkTBWZKlQRxFScpAsLy8WrHd8cQTgOm6XnHraZq2Wz3TcgWH00kQhnmr1VlYWFhcXD69eyIR6rqqLSwsUFYSiA4PDxcWFhCEw8Hg008/Pdjbl7P2lmVJi9V1Xf4cknXbMIzr127KqvLu7h6E8MaNG+dM3mMZOkp7m9UdsixzHAcAIDlmarWaoihxHEOgKgq2bVMIQWkRRaWmaYZhlGXOWAkAVxQs5VSKosjzFCBBIKKMp1nMitI2zLN2SEnP2xhkZoSEkCiImBAAAA0T2zAhhARhBWYCIy5kKCoAEwwIweWk7Rdh0L5g0V4wivn9ybyZgfNolT/LOjH/GZdjTgBm+qnPGQyZBbfzz1z2tLOjffHFXC7MyB+Rcy4gQhgRhDSVaKqiaqhSser1mqKqaZqHSSx/aUMV7XZ7cXFRJjbDUT/LMqKgvOAlzcsoB0A4joMQSNN0OBzKW9Irr7zy6quv9vt9KQb45S9/eTwej8dTCGG1Wm2325L4aHt7+/r1K9vbu8cnB0TBYehPp36WZbdu3RoO+/V6fWNjPQj9drt5cnLkeV6e523TcCmtVqv1ai1K0iKnaVHAgo6nnkoUQ1V0RREEQagQAQHEmqFDDLM854JhjCEmRNUQQrplcw5KxgtO85JmeVkwziEwbUvVtYKyaOIRQjTDMnSdUnDt+vXJZOp5Xkl5lie6HmZZsbOz0213hBBlXjDG2u12r9PO83x/f79im3GWLSws7Gw9uXLlShAE3W7X930OZrQDQZqmEuTAGNvf39/f33/llVfiOJbd1CAIMMZS/LQsSzltOJtR6vf7EuokRx/lsqxWq5p6xuUhg/aiKOS4xs7OjhyskZhnVVUl2kkQKKsyssMk1VSlk5SDCzLgl64bAKAizASDACsAISAIViqGrhGlYLQUgHAGGYWQCnYOGQP8jH7wrEzDn00Rn67NL84J53c9Y8WZDRPNnB6ao/ieRdK/lnmAOQv8Yk/4q2yX95/dIM4BdBwhrKhY0xRdVw1DK8tyPB5iogIA8pKGUTSZeFndOKOrgVASfuV5xjlrNOqe521tbUEIX3zxTr1el1xPk8nENM1r165JXJVlWZrGZWNa6goeHh6envRfe+2169evR1FkuGxpaQFjfHh47PnTWq2W56ZhGEKIWq2WZUm1Wk2SZHFxceqN6/V65g0IIfV6vdUKseePpz7PspyXcRxzTScQKYQIiBFRsKIqqqoZhqTDK/NMwhgoY0gIx6mmRc7STGSQUUEFwIiomm6apu/7qqq2Wq1qo06wOp0E4+lkbf3qyUk/zTJdN1VVrbi1oiiCKMon4cLCAiHk5OQkjWOVYNd1DU1/+PBhrVZjJW02m4qiLPZ6QRDEYVhrNQkhsgGbZVm1Wpe54mAwiOP45OSk1WrVarXpdHr9+nUJe5B4QFnykaNMskVUqVQqlUq1Wh2Px1EUAQAcxxn0p3J9SsxqkiSmacqcULYHZJf4XE9FMEbzPCWESBQUQiAIQkppterIZVMURZYlYRhwzhVF0YEMa1nBhWBcVUuMFVXBEEIEOGACACIEZ5IsigPIxVnvQgAAzqA3kAt0Po1xoUXxq6xnIhG00mXLe4n02hjj2YgUAEByHIhnqzpP3SMAiqKIWUsDPHVx7JIW79m5ztHgg2cd9DMe8rwVOfvcWbQsj1CWTCEEQoyBwBhrmoIJzPNkMhkyxoqiZIJrquHWG/VGy7btyB94XvDZZ48cxzFNHZxjfZKEm6b+0ksv5HkeBFEUJVJlZWNjAwBwenqaZZlhGJzzIIiiKGo2mxDilZWVv/t3/65pWE+ePEmSRFEUVcXj8bjIabVahRBnaa7ruu/7zWYziiKEgKZpcRyapl6vNSGEEAFaMLkQJ55/Nl3OmRdEpX42jcY5l+SZRUGJpaVpDhAyLJszltMCAUSIGmdpxXbygiFSBlEc+GGz3ZlMJr7vX716tdPp5CXNs2IcTQ6OjoMg6LQXS8qTJPM8j6i6W2s8ePAgz/MrG0tCCMuyNtbXhRCtVrPMC9kq4JTFcaxpim2alUql2WxeuXIFEJymabPZrFQqnueVJZOFTV3Xm82mLJnqun7r1q0wDBuNxvb2dp7ncq5XrhbP86bT6euvv46QbLFO4viMnkPTtIpj27ataVocx5ggRSVCiDiJIAK6qkkPgQkyTJ1xO8vTeqseBIFt20WeK4qSpWkUhoqihEHQ7/dd122326WmlbpOKQ2CIGNnxNMqJipxTV3nAGRZtrS8Mva94Xjke76fRJQxxdQty6IUnS+8clbdkTWk5xobf1boYba8nwlHJRLlPK7js5fn6Srms8R5LPVTuxKAUipnmTDGEIpZLRigzw0vn7td8Kiz5ubnhakYY4wwIUBXSM216zXXMlQI+GQy2d/fp7RstttOpZqVNAgChBAAMI6TarW2sbFRluWDB/fCMJQYjnq93mw2G41WWZZlSQkhUuekLMuyYAiher1er9fjOI2i6MGDB7/xG9+8fv16nud7u/uU0kajwRir180oilRFr9ebSZKlSSa/BzmPl6ZpWVCpXZckWZ6nNUVACHWFGIYhWQYZFXme2bYDCaZMZAXFGKsqAAgjTLJM1gWZQAxwUZZUSqJEScIZTOKMM6CqqqIoRVEUOW133c5Cp6Ss3WkdHhxhRX28uZVlmR/86euvv9nrLa5dubqzuzWeDFfXlgEAjq0Dzv3ptN/vU0rTOC7L0vf90bBvmqbjOIamFEUxHA7jOE6SRDUN2fdLkoRzLlHjhBCMlNFoVJalpukYYzlfJpm5JaeRZMeSTg9jfHJyIsejJLuMbdtCiN3d3W53SXLwJEkiR5zkjx4EgbRSybiFEHIcxzCMaTANgkDGHZL2u9/vM8YWFhYcx+l0OpJpSlZoIYQcCICgPI7jOoamZVlRluXHH3+k6ppZsddWl5M8mwZ+nCZ5mii6fZZOAQYghxBAJCASYmY7MzgK5AByhM6CzflA9EIk+NQIZ8YG5/oTGM9rMn5RAMkYA+eUFrKJfFY+fl5z/1cxxfkYevZgdjGzHQhSAOCUUoakNgBASHpm7LoV0zQNQy+KIi9Kt2q1Wm1As6tXr4Zh+Gd/9udFkb/88stS1aTdbhuGITlXwjBK4gxCqKpqliemaRKsRlF0xu0HsWEYvV5PFiHkMMFgMAAANBqNw8Ptfr9fqzYopScnJ6cn/Xa722i0IMQIEc5BmuZ5VmCkViqaadowPhGcFUVBWSG7ZIyCgjImOMsKmtMyLwC3CcIK4QhxGiaUFZALBgUQjJcUQM45BwykaZ7npYDY0E1VTfKkoJRubKxXKtZwOAYAuLWqlNzwAv/FF19eXFwsKHVcuyzL49OjSqUyGAy+9Nqrsj5p23YUhHEcc841VV1fXwcAEInkFQIKYBmmoel2/YyNQs7ixHEsw6V6rWlZFsZYtl6kqUyn0zjJKKW6rsvSyGQyybJMdiYIIbJvYVmW67ryOJVKRaoOW5ZlWZbs5TDG1tfXj46OKKW1Wg1jvLW1BQBYWlpy3cpkMpL65K5b0XU1DH1K6cnJkeM4kv9O1nIUBVuW4XsJJgRgBBBkgheMFqxgvGy26hBCQDBnOS8zAoRjGFhVgpQBAYAAGCKIAAAAI4whOpMREwIAIdm75QixuDTld9mUyOUpeHFOWjo/DzE7BJ8rtCB0bq7iqVcUQsBz+5GjGL/WdsHm5zEK4HMeCy4Y4wUoSppTVjIOAWcQilrNDYJoOp1ipLZ7C72FJdu2G+7iX/7lD9M0/upXv+xWK3fvfhJFwbVr16RYqjykqujIViGUNKy8Wq1WbFdO2QghdN1sNBqyLHF6enrz5s2VlZUoiiR1hWEYMnxijAEB6/Vmp9MzDXs4GFmWZRq2aellyQzD0jSFc753+CnnXK7Ler0uIFaVMMnyMIgppQXnQghVpQZlCmUAUEOFBBKAORRAcAEQQhBjiLGuFAUtioJyIBgQVKRprqtGo1kjCuoudMqyWFlZznP69d/4BmPs8PD43/7hHx4dHd154dZoMtQ0rSiyLEvefffdpaUlQ9OhAIwxzwuAEJVKpdHopFGcJElepARhwzBkuJ7xM55oyeobxyk8n0va2NhQVXVrazvLsoWFhUqlsru7W6vVdF2XTUXOeb1en/3usnHvuq6iKFEUKYrS7XYHg1NZ15G8dVmWpGmcJImEZCiKEgSexMRK0CTgAnIRB+FxXgjKXNd1LJtS2m40K5WKlLJREEYCpGnGOYeKQoGgZZ56uR8EukwviYxRIVEViDEhmLIyCMMyKqlSkUY1I3MBc3wuF5bxM4nVpZeeGqH8BueD1HlzuuA9GWNYUWc7Sy1VIQSUOd7Ml3JpogghVPJfieXqbzVCedu5HJcKIRCEiqLoOpaCZzXXIhh4njfoD6MoAkD0FjqLyyuUga2trYdJkcSpAODJk+1er7O0tMw5E4JtbW0ZhuW61apbd90aISpnglKu6VC2NDDGrVbLNM0sKySxRaPRStP08PBQIWq32x2NRtvb23deWRccFgUNgkhV1VqtBQTa2to2TTPLckoppazIqRAwirLpdBqGvqJomm7olm3ZVUS0JC38MDJNsywpKynEmANRMiHJIEzVkLBDwUoKEEGKvFfmeR6Eke8FaZZTAUI/KEq2sr6uaIpTdTRVj6KEUhrGgaZp3W6XqPq7P/tZXqTD8eD4+FDTtKOj/eWVxSJlg9O+PCBjjJ6XCSbDkSRAURUi6wVRFDHGpnHIGJtOpxLpImfEy7K0TF227E3TlNQn0mijKKpUKhhjSdsjYeKe51mWpaqqLOokSdLv9/GZTBCZFWaks5UeQg4fS1mLarW6uroqhDg+PlbVMwJBic6XY2hhGEoZ49kksTiXs/YlZoCLssh5STVFbdRqNbcqBM9zWhQFURUAoUawoRAgWJxn6FzCCM7J2c/Swrl6jADgc0eZnskJ5ZvlPX5WzJTOcNarkJmrRCqgeUDMJccq33hWOPrCMccv2J4pqIqngSi4lDECeV8giBBMiCzhMggFIQohCGHYbNZr9bbj1HzfP+2PwjDcfby3sNC9fv3q6tqKpilJGoRhUJal41QBAEmS5BnD2IMAcQ44B71Fh3OO4NmXwDlXFE3yjrZanWq1Gsfx5uam/BqDIPjpT3+6uLiMMR70x1lW6HoCBE6SrN3unp6e3rt3rygKyzIMwzRNI/CjhYWFsmQlZcX5byn7YEAgCFEuBKMsS3MEIGNMV1QdCkVRIOdZljFaYHjG8+X7fhgnWZJFScIAKQqqavqt6zdWVxerVWdra6fd6o1GE4zVD97/QDXMRr1FWbG4tPTyyy+/+urLtVrVC70XXry9fX8rSZKjo6MkSeSZZFnGSypXv0oUOQifpmmZ5WWWc3JWPJf6BVmWSROVReCiKGSrVmoYKooCQCYh1JIqSgJiOOej0Wh5eVnTNM/z0jSV2fhwOHzllddkLTqO4zRNEULNZpMQ0mw2kyQBAMg8YjgcysZGmsaUUtM0Za1LDoVmWSbFTPv9fr/fl7UuCfSJiwxCiACUfCGc8yTLTLOoVh2JZQWcQQRVRanYtsXNeJpKA5F3Fmk7su9/wRDOndjzPeH8M0TMlWTAnKuZTXzNyqfwfF5pdixwHoXC87nfs8IM/1Wb75e3uTuKAADg88H8eea1eVOklBEEAECU0iSJPA9jyHSNFEVRr9fVjoawNpn4h0f9OMlVRX/hzktc0DCMt7d2KMuLImu26isry61WqyxpFCbTqR/4cVkyjBWFaHfv3lUUxXVqruvKz63VGp1OZ0ZfqyjKZDyNosh1XQDAwcGR69YgxHEcm6ajKjqEqNdbpCWPwuTw4HgwGFi2cePGjbW1NVXVqlUSRUkZRvI+KLteecYmnlfQUgqG5SgXQqiqCg2c5zlgnHMeR1GZZ4qi6KqEPUFVVXXDVvWUCUCI6rjVt978Ur1VyfN8Oh3bVgVhXJbl7u7uxA++9a1v3bp1i6j4+vWrjlNRDXU0HigKls06aRhFludFnuc5BvBsqQHIGGGMZXEi+bW4RiRRmBywlHpmcvWnaTqZTHTdgBDK6ovUDJVJnWziSbI/WUlijIVhmGVnViqEIITIuFQattxTjixgjKXSweLiommae3t7CKFr165t72xOpqO8SG3b5oIGoRfFgaLi3b1t27Y9fxKEnm3buq7XiKsoSjQc5Xle5gUQnArOGIuSWMGQFrnjOK1Wq1qtljQfDIeDwSBJknZrMc/zGdYcAIDPWWXOPIecKDhHzMxXMcDndMLJbA7wQiFEfimze7Ps6lBK6fxQ75wRCiHweWEGwqeaZ7+u6M2FgUg0m+rnz+/jl2WpEYLQDFwgpCWbpmkYRpoUB4dHnheqqq7pVp6VrGTj8QQT2Ou1iIKTJJlMgOM4YRiZpmnoVrVaV4heFJxgVdf1Vlf3fZ9RzjmvVCqyspdl2cbGRhynsgAohGi1WpZlDYfDtbU1SunW1mPOxFe/eqNWbUwmvmnYh4eHmqatra2ZppnlCaVnbBqD07GASFVVy3EFIHg0HU+8GdpYToRICjzTtKrVKkpiRcGspBhjhhCBiGAVIWTbGsaKXXGCJC0oM7ygVm/cunXr0cldINDS0lJRFKpqPHnyUNf1OiaO47z8yitTb9zr9aIoPD4+pqzw/enJycm1a9dkc+9gb78/OCWESNyJpqi1Wq1WdymlIzIIwxAAIPGcMjaTLmuG6pReKE1TKTAqwaira1fkejMMQ1q7LALZtn18fDwYDOQkZ5ZlEMJarTYcDmUWAACQuG3Jzy37DTIclUVX+a7XXnsty7LpdCozbUkz1Ww2Hzx4IBmlZPUVnAt7tbud8Xg8ybIiz3heAi6Q4BBwWV5OkoQLmud5EsemadZqNVZpSx2UOI5loC5tUMbql23sgtuYZXbP5IRsjixx/mU54Cw51yThPITQMAyp4HF23BlOTkKQhBDn8SoXXEAg0Fw/Y+5fcA68lrgDcS7XxoXAkuJQmrYQQsyIwM+usAAAMCTQ2fSKiQudkIquWaapaZpODAJNgswoDOOpBwFvOU5d1+I4LmmCLDiMioojABBFEeW5KPI8oGKH7ne7XV2pVBpVmbyNx2PP84bDUW9hQSE08MeKUnS7blEUp8d9XVeXl5fjOBa0jPxJFEW0MEJ/VObR9kH21ltvLTL48/c/+L/93/+vv/3d7xmafnBw0Gv3KpZz5/oV19QsrdJzOya36q36ONVomWuEaIoCeF5zyMaKZQD19HgQ+lFRZEbdbrh1t1LXNBWXhauHSADFQpqjYahhca5koGhBkkW5h0Ta9/2GbrzywhorxrfX1v743/8AEWV1/VrBkEqUarW6vLauEG2hu9Rudp9sPkrjyLR0moCdzZ1J4D3eemKa5iuvvPx3/5e/ByH8xS8++NnPfjYZeLW6u967igkpswRVtCSaXL16VQ7j6qamGep4OomS0LQNoigIIcrLrMgVRUEMUc5sWLEqdp6k0+l0Y2Pj4OBgOBzKEd6z9l2WVyvOYDBQMeGct1qtMssnw4GgZRCFL770ku3aDzcf1Wo1igUjQABm6kTRFUGZppJOs2FZ1mDY1zQVABGGAWMMEVJxXYiVVm+BAVJrdjvLa2EY9/v9PM/1Sfj6izcCQg4oHzGQYS1J85xjU3O8HAhViCQ3SmaqpF6t8SJP0ySYnnjTKeCiZmiUIsYYLbMoiiqmJUHnBtEBYPI2SghJsSwmnxc+uQAAIclgJgAACApAZuv7spOZ7xzKZFRuT73cHNoGzJnKcxzcnO198Q7PVGWevWfIz5JLbnbXqJhmlmVRFBm62mq1HMtUdU3T9DiK0jQtyszQdALP4XgQIoRqtRo6E2aDtm0jhDgD+/v7w+FwOp0uLi7KwEnX9UajAdCZ4M50Or179y5jTFe1drsp7zuS29P3p1Iiqtls5go+Ojj8xS8+ePDg0xfv3HRdlzN6enq69fhxu9l5/bW3bt++XeRUJjBurSoEVBStyPMsDU0dq6parzUNRQWcaopXmnnFchrVlmU6mmKqqmqUSCVIU1RDIRhBwCkUAEDMIYxLmgRRnCTNduv2K69du3lHcPjhhx/WarV2Z/Hje/eJZn/1q191q48mfrC4uPjjH//44ODgK299aTrOTk6P4jC6fv36p5sPp9PpdDp95513/vAP/1AI8fWvf/Uf/+N/LIT47OGDP/mTPymKotVqfPOb38yybG9v7/bt23Ieem9vjxC10+kkSbK4sCznlSSoRQjBGJdTFEEZLC4uyo4oIUTX9cXFxf39fUmuJRnWkiSp1Woy+NzY2BiNRmmaPnz40HIqGGPP87I8F0IUZRlFUZkXrCiLPGclJYQAIqrVqmEYYRiGYZSXpRACQry+vr6zvXd4eGi7jhCQc+44zsrKypMnTzAmEMJqtUo59KM4z0skQJZlUHBGC2YaqmPrmgLkkuMcnNcdFUXRNQ1CqGmagrAkH5NjSQAALpl4FSixbkJgzrnEugkhIH8qz0aeeqpLmM+ZHQIA8Dlh8PlE6Zl9yqR8hn64bHKzg8O51t8X2OH8cS7vNisNn2OUmEwzbNt2HLvZaFQqFUltYBhGMJ1QSpGBFE1BWVYWVABOiFGtugghz/OLolAULJnmZBcrTVPGSk0z5FCcZZmDwZBS6jgO53w6nSqY9Hq9paWFo6MjuYZ83//kk3s7O1umaTabzQyw69ev/29///c3Nx9alkXLoigKKDgh5PDoqN3ai+N489FW6Ed37ry4tnGFx9ypOoFfhmGsKRYxdKTrTsVs1Nzd7Z2j/aMio9NgGoWxZVWqTq1qaQrGEIg0L2iRQyB0RcUqTNK8P54UDCyurK5fv/Xi629g3fj00aaqaMNk2hKiWmuYtiuEiKJoeXn53/ybf7O2tn779u00Tbd3dwAXZZH9+Mfv/L3/8u/pur6zs5OmqcSRHR4e/5N/8k9ardZv/873/vk//+cHBwf9fv8//IcfSJu5f//+0tJSr9djTMgxwkePHslIPssKmdepqiab7Lqu66pRa9SDIIjTJC8LytnC0mKcJlmRU86iJK7VakVRmLZVFIWAQFpys9ksKRVCuK47GAyEEIZhFBBqmlZ1XNeutFutquNSSg9PD1RVlTqtGGPbcWRG+uDBA4VonU5H0bXxeCqLOnEcB0FgmhbGCiYqhyjNizhOoyio1WpAgDzPC4I4N4UQlLGyLKGqAsGEELTMAQCQEBUTFSOCkUIQxlhXVCEEQWfCEBixubDuHFjDZ7UZhMScEV7e0Dml/rxNypwNnStjz3vCC43IeWObFTwvZJ6Xt8vlVvlAFmzm01d5nNF00m21a7WaYRh5ng/GIyEEbiBd13XLVBSl0ahhDNMolikWMJjEMcjsVQhOaVEUmeM4EMKizAbDUwiwnLjhnFoVxzCMVqslGC+yPE1Tz5skSSTLgEdHRycnJ0mSvP76m6+99lq9Xj8cHDPG+ifH4+Go025dv/5K6E0nw9G9e/dMS3/hpTtra1fCKN7aeXf67k8m3viNt16p1+uEKEAgoupYIYATw9DddgcAoCBlOp6Ox9PTUR8NBrbt0JpVr1erFQdAQBnkjArAVMwnQeiFaXtp8dU33uwsrxLdiLKcCq5pxkJvCSmqbdscgP39/TAM8Wj01a9+9f333ycEJ1G0sbEBBXj5lReTKH7v5+85jvPWW289fvz4Zz97bzgcrqysmKbZbrf/+N//yR/8wR90Op3f//3f/+3f/ju9XmdnZ+cv//IvDw8Pj49P4zi+cePG+vr6yvJavV6XJOWyhCidw9nMrm5J3icZC2RZJgnwp9OpTPlUVZUSogCAPM/zNJPLwDCMWq1mOxWE0OlggDEusiwMQ8E4BtDQ9TiMRqNRvV2ToO1+vx+GUavTOVOeUZWlxRVd15/sbO/vH0rBxiAIFhcXsaIWBWUcQKIAiCBCcmSFiVIwAIAhVzvSNGZbumlyWpZ5DuS8Py04wmVZClVVEEYIQsAxRkhTGEFCCJ2Vz67nC9VRDsDzjFBayEzmeobenrUxwLMDUfKlC23GCx2FmQX+iuHo5SNIGi95d5lx9SOEVlfW5FSb7/tFntq2XXNcVVWDwFdVXbfsar3GSko0VQbSCEPOKda0Wt0FACCEyoJVq24QBJZlCSEk/EpRlLxI+4MTJ00BAGWeMcZGo0FZllmWJEly584dz/M8z6OUdjqdWr2+f3Dw45/8pCizmzdvLvZ6t2/eSNMUcsY5f/PN169fv+p5gVt3j06P9g73GWCdTqPeaUiaas4BxAQhAgQCiGi6GUVRrdZo1ls0L0+OTjc3nxwfnnjR9P3Hn16/fv3G9WuWbnBAMlrkrIA59+K81Vu8fvvFdm+pACCeekhRm91eMBkWZfT48Van2xtPw/d+9kGl1rDK8v6Du/3h4Lvf/S7GOEuid378k62tLcPUsiLd2dmRTK0vvPDiZDKWTmwwGFy9enVzc3N1dfVf/at/ZVmWaZpXr1797/67/32e5z/5yU92dna2t7e3nuy8/fbbQohKxZ1OfcuydN2wLEvXdYnJPj09DYJgZWVF/qaGYezu7uJzjSBJZiGpKOTYhFtxCCGPHm+61apZsVGCu92uFwSu62IIoygydUPCvmlRyuUqZTB2dnYOD08o5wsLC9Vq1Q8DCfWsVquKogEARqPR/fuffvs33srLJIjikgnLqgAAMAJlWY68CeDM0DRD0yhnEELF0FVVVWsuhjBL0rIsEYCUUgwgBxAJgCBEEMqYU8rbc84NmEMIIXhqLHwWlZ5v5MKin23SCM9haGCWB+I5OkMxt80m9Oet7kII+kwD8HO2+VcvPBYznhsA8LmS6cLCQlEUEgOlaoYso3mBHwUhwQgphDN5LZhoKoRQswyJZZPHJIRwDsxcl7FlURSTyTTLMoQAY2UUBSfHfXCOX4cQNptNia15/PhxURTNZvPWrTuO41BKR6NRGMYbV1am48ng9OT2C7fzPC/yNM2Tbq+LMdZMw7SMJEuXV5c6vW6z2VpZWbE1Q1E0WuaaahRFKRjHiBcFFUJUHcexbU65pmkAQTnZEE5DAYgfxJNpIOVvVV3DhBwORm+/+MrGzduckDjNKUKM0ZKyshSj0WTryXa7szD1vcdbW29/a63Zai0t+cvLy0mWxmGUxuF/+Q//wc7W9tbWVr1ZV4h2fHy8s7MjhLBtu1KxJaZMwmgP9o9UVV1aWjo6Ovrxj3/8/e9//+tf//rGxsbf+3t/b3tr9969e6urq7u7+5LZkVIWBAFjTNfMIj/D1mCMbdv+/5H232GWp1d5KPp9v5x3jpVT5+7pMDM9WWKUJSSEQBIS0TZwbLiC4+NrAT72se8FTLA54CNfc4UIAgQCC0kocqWRJkdNT3dP566uXDvn/cvx+84fq2qrZkbisZ+7n3762V1dtfeuvX/rW2u977ve1Wq1QM7W7XZhbWgYhrquwyHreR4hxHEcFjMLCwvValXVNNd1G63mkSNH4P23xmNCSKRqmqxQSlVVzefzURR2u93xeEwISaV0SVHgyD569Kjr+LVarTvoW5aDEFJV9fjxo8PxCCFk2264P9s4Ho9c12MxE8UhIonnO67rcizmGIwQyhsqi5EgcDyLSSyGIWYQZhnEcRzPcgghEkeI7plrkDgWaDwBSClGFFECxSiDEUIUH8iE3y886Gu3mgFeOuHxD7KLB3/kjSGN3lCR/uO3g2nwdaEIumrYE0oI2dzegm6YFziO48IoanV7/X4/n8sgjKMoGQwGYeQTQgRBCigmhIgiz7IYhkfT6SxC1HXtbC4NL09VFYbZs8oTBIHFnCzLsCZaFGXAxyH+fT9sNFqm7UA3mMvlTmRPDloNSmmSRK9evmzbNiewpVKB5/mhOWQYzg8ChmEWlhd4XhRFuVyp8DETxyHLCalUmpIgSgKWZcOYpI20JCsxoZ7rIo4tV6teEFieXTx8VNf1kOLRyHQcj+M4HsUEx0auVKjOYlF2w1BSNTPwu91uQtGw1jAyueMnTzle0Gp2Tpw4cfbs2d167Z577tnbKkPi1Tu3EEJrq3emp6dfeumlpaUlSmkYxNMz1Wq1OhwOgZELw/D06dNXrlxJp9MvvfQS7HuhlK6vr1+8ePHa1RvHjh1bWVnJZHLpdPapp55SFJUQ0ul0RkNT0zTQx2iaFifJcDSCQ9S0LFGSEMYJIRzPR3FsGIbreQhjPwjwvkva1NSUrCj90dC0rVqt5vo+kBwMwyBCe72ewPNpI+V5XkTDzc3NWq1m27YoSkBL2rZru46q6LquswJPSKvb7QKuWyqkZFnlRRkWuULDFRlR6Pm2bSOaoIQkSQTEYBT4jMBalgUAIYuxyPMMw7AMTpKEIoIJjcMQISRQnmEYShIGXHs5WA2ECKYIU0IJphjhPej/u+jo68IGhBEHWzLo/SZOO/A70AME+vesbP8Ho+514Tf5WXSgHUX7e3yB24WXNx5ZmUxG0zSe5+MkimMiCoyqKqIscQwbR6HjWEkYMeweA9kf9NKZFKV0OBzCYATLCkDyAMcF4Q14j6Iord1WNpUGWpnneUri4dCFwXDTtG3XkWX59F1nzpw+q+janTt36ruNM2fuqkxVv/3tx+rthqYpR48eVlX1W48/rihacThADMdxPPVc1226vjOdnXIcS1HlbCYTx0GSuIrESxIXRJFCEcYcwwsZ3WARtl1vc3tnZ72WcnxBEMIwSjAbJtjpjnqj4S987Jdy5anOyI5IUqhmEs+3/aBQKIhVrtVqFYvV6blZ03K26/UwDgqFQq1ZazRaqiTXajWO43q9Qa3RtF3v7rvvNgwDnM6iMGk0GqIoPvTQw/V6rd1u7+zUoLYEtbc2ZbAMXylPzc9xYRhub2+3Wp1ut3vmzJl3v/s9sNHl0sVXd3d3YaQ9k8mA+ygMWCVJUq/XGYaBDhzML1RVdRxHFEXLsiRJymeyjUZDlCXdMDL5nGboN27caLRaGGNFkniedz1nPBiSJImLEcZ4dnEGvBQ4jqMUQe2WyaRN2xJFET5ZQRCq1arjOK1Wi+NQmk6EL5EoivlMmuf5Wq3mOjROYoypKIqiJBFCfIpgdlkURZ7d64k4lmUwHo1GwKwEQcBiBnY0YIwJZTDCDGL298bHkKMoQyfD8Difz74xANCB+hMhdFDJDa6sE+nMJEOC8TPGeDKXOAmkSSwdLF+Z/eVN+IAWllLKfZ9VMRPtjiRJYKYAT5TRjX3EKWEYJpMy5udnZ6anFVHoddvD4YASgkkSBEFCIo7jRE20LCuOklQqlc1mOY7z/TAIgjCMdV23bbvT7hmGMTc3Z9v2zZs3jx86miRJs9m0LIeXRNM02+0uRYhSmspkDcPI5wqHjh4xTdNxnB/+4R9ev3r9nnvO9YeDxx77xiuXX/nRD/5IdbqyvrFx/dbNr33tsamZ4g++7/2pVPrxJ566du3G/ffff8/xu3u9XqtdP3LkULmUW1qed50xpjHL4nwuo8lSGAUkThBC1tjsdrvPf/PF9fX1VCqVxKTdbntesLSy/JM//VOCosqqmmAaIyqocpwktucSRKOBOxgOr1+/furM6SCJrly7tluvlavT6+vr1WpVkpRRfzDoDSmlHMM4jjMY9AzD+OAHP0hp8sUvfjGdTi8sLFy7fuXRRx8lJAGraMsaZ7NZXdcHg8H8/DwhZDwe9/v9u+++98knn+R5PpvNtVotRdYOHTr0Az/wAxsbG48//uTFixez2ayuq7dv356dnZ2ZmUmn0zs7O7DLiVIKFSlUKLA623EcXVFTqZSRTm1tbyOWOX7yhOd5t1ZXB4MBgxDLstPVqXwm26jXPcddXl4WFB7ETN1ud3t7p9luI4Q0zdBTBkZsEARBHIVhDAvestksw9FMJjM/OwdHT+B5Yeibo3Gv1+v3+2k9nc/np6enYbUGy7KuZ8VxXCgUDE2HopfnOMuyhsMhhxlw9Tc0HY5pSZIkp78XXAyFC53lGMrgMNzzuaKYcP9IRjoYP5P4BLXOwViaZFEIS8Bv9g6JA8sGJg/L7E/xf/90+D1u3P4NcDa8zxkORyOO42RJ4nmexShM4vHYEoVOEgZh5OuqVq2UGYbpNBuDwQAhVMgXU0YaftxxnPHY4nk+m83OzhYdxynkS4dWjty6detrX/uaqmrnzp3b2dwqFIpT1erO7m6z2TQy2XPnznh+yDDcxtbm9tYOywvVapVh+eMnTjVbneWVQ41mZ319bafeePDBhxFmeE48cuTI5s728vJsJpsnURIGcSaVPnr4yOnTp48cOtZttUWRV1UdM1wYJJbp+b67tDBHqWC7set6DEWKomhGjhPlueXBwHK63f5wPNK11NnThxdXltVsMaEoxnyUhAmDSRDbrjsyRxhjqzEybWtoWnfWN+5/4IEwjvvDwclTxwuFQrPZZFnsRyHLc/lsrtVqkYS++c1v3tzc/M3f/M33vve9/+pf/etvfeubly9fXlxcvHXr1mAweNObHoEfzOVyzzzzVDqdXl9f932/WCzzvJjJZHzfVxT19u3bSUxLRyuNRuMP//CTnufdf//9jz76KMuyq+urnCg4jtPqdgbjEcaYEwWBJI7jjPs9hFA2nwOSsNls9kfDSrHUarWiJM7lcpbrXLlyxbZtUZbDMGQxTpI9WlwURd/1hsNhVa9A5QyAKmRUx3HS2YwkKoIgcGHAsiHYTCZJwgi84zjtbsfzvCD0aJxgRBGmi/NzAsfGcTIaD8BTw/E9RdFWlmbBQzEmCULI87xREPieV6lUwjAkPeJ5XkwSnGBKaRRFHCvuBRFDMcYE05ggRAjFAqyWQIhyb2znXheErwubiR09eq0GZ3IHQnTCqgNgc7B7nOTVg4/zPe8fvIGAkGGYiV4W/EgiipOEhlFECCEkdl03CSPbNleWl0G4ZFoWRiiIo1QqlU6n+2ZvMBghhMrl8tTUTLmcuK6fJMnq7bV8Pt/v1TY3twRBePvb3wGLZpeWlhiGeeWVVxzHfcc73lEolF58+TvNZrPRaOkp474HH3j00Uc/81efnZqampqa2draGUn9ZrNer9dnZuf/l3/+C1EcXLn2aqvRSsLk1KnTlXK1XK0oiha4AYe3IjeM41jRldnZ2Uw2bRhapVLSNM1xbMzKFPMMpixHoygw7SAIAtd1lUxuanEl4kUfc6WpmSOnzxqpzHaji1iGYZiYJJhFLIsH45HjWKIszU/NkVrtIz/2kwmNB6P+cGyWqhWGYbL5zK3Vm2kjA0pIjuNYljMy6WeffT6Tybz3ve995ZVXNjc3//k//+fz8/N/+7d/G8XBT/7kT966dYtSks1mNzY2yuXq6dOnwY+wVqstLCy88MILhIBDD0MJzufzrus7jjcYDC5dukQpjuO4VC08/PDDCwsLURQ9/vjjGxsb/X4fzlaO41RV9X3/xRdfNE3z5MmTH/3oR9u7ddM0eZ43DMP23GazOR6Py9WqZVlzMzMgolAleWZmppDLg85J07TRaDTp3IIgpBSbpomNPRMtmINxXc/3/enUdBAElmXxHEPjhCQxhxkW0ZnpKk2I7TqmaUVx4Eccz4vpdDpKCKVoZFocw8iynJVlkRfS6XQYhrZpea7vhxFGDKwjJRQFCDP4u3JOEHRSiliWxRO7mkIh9/2CcNLyHQRIDzaBB4OQvGb6/rtl6mQs+OD3oAPl7sFylBDC0+8dhIIg4AMS2ImsnEXsHneCCEJI5Ll0JpVLZ3LZtCzwsiIyCPue67ouQkQURT/0WJZVFA0caX3fb7e6wIal0xmWZU3TDoIAWpQkSdauvVoqlR568JFStXL58qtXr11r9/rNVufo0eNvffvbLl5+9fNf+PtHH330Iz/xkz//8z9/9913tzZrS0tLXuBOTVVKpYKR0m/dusGy+Oy504IgDMeW67qFQonBnGma6XTa9wMw+ZR4IU4ihmEcy3Rdt1It8SyHMd2bIbBM0zRt2w4pTpLEspxut2/oqZWVw4IgWZZVKBRYFlyCUETiwbAXx2E+n0/x6eu3rp88eZITuM6w2+o0wyS88MorlUplc3OzUCh5jt9utA0jpSlqLlcY9NpXrlxBCK2sLD3//POZTObjH//4cNj/0pe+1O403/ve97Isu729BZEGkxZBELTb7YcffvjatevAvK3d2WBZFobKIXN6XhBFUaVSabRrcKTed9991Wp1YWFhAmZeunRpY2MDFlcEQQAr71fmFizL0lOG5/uO70mKHARBlCS2bWNKBUFIGylD1TBCEAwDs5/JZMbj8Wg04ji+Pxx2Oh1BkBRNzeeKSZL0hoMgiOBEI4TouZTvuqmUXikWOIzjKGARJkmk63qvN6CU2o4XRTEnSAzLl8tlP3Cz2SzPciC1H4/HvuPm83nDMGzL2t3dHY/HkiACmCwIAo08juNEnuc4joVxe0IJgakDghFiKMGgwJrE0uRv5oDR0yRCDk48TWpU+gZmDyIN+kZQeE5IxYOw6vfsCb9fELL7G7xhGwGIg+M4FgSJxgnGWBRFTZUNQyvmC/lsptNu5vNZiRf6vY5pjnluT+yfyhgzMzOIMlevXl1bW1NVdWlpec84jKBUKqVpWrfT39rakiRpeXn5Zz7yI6ZpfvWrX33p5VckSbJshxXEBx96ZGFh6a8/+zeXr1750Q9+eGlp5T/8+v/7oQcf2djYmCnPTE1NCSIvivxubTOXyziW6ThWsVhkWdxqdRBChw8fLRQK1tjqdDpjx15eXj58+LAoikkYweyPLMssw4T7t8DzgPhGCK23ahhjx3bDMFZVvVSsFAolhmEYiiRZQCSJ4iD03NF4yLK4XC6uX98pT1V7g24Q+YVK/tbqzSAOnnjyyepURZFVjhPSRmZ3tz7o9o8dPaHIsjnqx3G8vb09Go3e/e531mq1xx9/4uMf/1fvfOc7//N//s+ixN+4cQOExPPzcxzHnT9//tq1a9lsThTF6enpp5566sTxU1/72tcEQTCMNKBZzWZbFEVAXyzfBF1EoVBotVqg7b7rrrviOIYJCQg/y7JAHu30R1EURUl8Z20tpuTw0SOe521sbaVSqcMrK5lMptfpupZdLBQMTbcsa2D2U6kU2HAlCbEcxzRNhuHS2Uw2k4+iqDccxDEY9oQMwwQ0iaIgl0mViwWRZVkGqaKAMbUty7E9RdPDKHH9IIhiy3T0dIbjkK7rNEng4zBNk2e5paUlQgiNk36/7zrOZEZXkiQaeSzLChzPC6zAciyDGIQYRBBNGEowQpgS7nXMwcHy8nWQ6SSbHWwUJ/ch2PCexwwGTgnodfzaMWS8v1L7dT3nPw6iwkPRfS93juNA0JQkFGEk8byqqpqmyLKKMQ7DMCGEEBSGoe06rutpmgqLtbudPiUYYxxF8czM7NTUVC5XQAjNzs4nMe12u83GerFY/tCHfqxYLAZB8Bef/rOdndpgNGQo1jTtzT/w6NTsXK8//OQnP4kZ9rd+63deuXjxDz7xf8Vx3Gg1wzjq9kZ31jeWlxcLhfyx43dREpJ8/tqVV9/5znffvn1ze7Pmum672a4UyudO321ZVmfcLRQKMzMztm333EG91kqlUplMRpG1IIgpQQwjMCyNk9APCMMwA8cOw9Aa25lMjhGVkesxlslQxrWddErnMI5CF9OEJoQiHLiBG4Qsy9u2m9Dw5e+8srmzdvTEUVmRXN/LF4qdVhtm5Fv1Zr/fr3vee971drTvQv/MM8+Nx8OHHnrwxo1bN27ceOihhy5fvux53iOPPLK+vm4YqUKhoOvG7Owcx3G1Wq3T6dR2G7qWWlhYePXVq4QgnucnRQd0VvlKDo7gUqk0MzNTr9dv3ry5vr4OLwMhZFkWxrhcLlcqFUmSzt91tt1ujy2T43k38MEvqxIEMDxRKBRInNA4SafTuqqBjRCMSgFcFEQRIWQ0GsQkiSNCKXU9N0koaCR4nmcUUZIksPAKkyRjKLlcRuD5HssKgpBKZwllXT8YmZbrh1EUxQQlIxMgD0opJ0j5bK5YqqyvrcmyrKczPC8CRkopYTmBZWOEEMUkSUiMEow4jsEsgzCBaKQYUVwqFQ5G3cF6clKLQvzsDfUemLfAB5ScE7AUggS+AplzMpUzybEY42ivi/sfRUcnywPANR0mreI4FgSFEMJxjK6okiTwHCdwLC9wgeuUisVqqcgLrGPZo9Ew8H2EEC+IkiSl0+lsNmsYac/zNjY2Nta3dF2fn58/c+bcysoKpXRjY3N1dbXb7W7fujg9PZ1QZBjGmXP3MCx35fqNXn947733yYp6c/XOyxcuXLt56/Cho2sb65Ik+S6ulIsrK0t+4BZzOcwkR48cKhbzKV3b2drudrvFfBFjrEpqtVrd3anbxGEYZmpqGiEGIbS9tZtL5zKZnGmavh9A6+v7Xrvdtk0LY9xMhpRSy3IqlSlV0k3TlnjJtR2JF3LZNEYk9l1VFjmW+q4bJ2Fte9juNBut+vKhpd3GjpKS773vnmvXr25sbS4sLLQbbVXVDS1T294NgzhJaOTZv/prv6Kq6qc+9SlYQL+zs3Xs2DHfdwuFwtve9rannn7ixRdfBGMewzByudzCwoKqqoZhbGxsiILcbrfPnj178eJl0zRd1y+XyyBhwxgvLS5nSilo2NLp9JkzZ4bD4auvvrq9vY0Qgmxp2zbM7ILXUzVbaDQaDMdatm17riCJcRwzHMey7M7W1uzs7Pl77rVG4zurq6qsLCws+LFn2za45Xe7PYbjCCFraxssz6WMjCRJmGMJQb1eD1pNIrDZbLpaLCCS+I6ZSxmlQp5jGd/3h8ORKMgRoUFELMfdrTcxZgmlgiDMzc1NT0+7rttutWAVR6VYyuVyHMOapjkej4fDIaY0nU6nNRrHcRyFJIoRSTgWiywjcIzIcSwhDCLMZIrijbfX5aVJfEIPPWkO9z0IGMuyJs0eQghKC6DaoKuBxVTotZK3/6kbxCrGGAJyIm0lhIRh7DMsQoTwPCIcwnR2dp4kked5DCtHUWSaFsvgQqHw0MOPAA6+sbH1jW88Ztv2XafO/NiP/djRo0dN0+52u88//3yv13ddVxCEYrHIx8uyLBtGmuHYa9eumZZdLlff8fa3y5r+xFNP37x5O53OLi4u1hp1gHBu3dg+cfIuyx7Xa80rV16tlAq3b946ddeJuempfq9naPr09HSr3qrVaojgeq2GVMY0zSAIZVlNGZnhcGzbfqfTL5VKju0BXxeGoWWPMca6phUVQZKU8cgyjDSJCWYZWVN5XrTNcRjGgW97luXLPIdJo1HvtBqmSfOFLHRusBdla2vrAx/4wH/87d8CYbrnBaIoFsvl+k69UilvrN783H//ux//iY9+/OMf/6u/+qs7d+6cO3eu2+1WKiXLsj772c8+/MiDnU6n3+8rinLs2DFIgIANnj59GiMWdPCNRgNcemF5PSHEc30E/suynCTJxsYGxng4HIJJ1OnTp03T7Ha7IL6HCUOMMcyyKJo6HI382q5t22EYSooCMxC6rt++fduznWw2K4vSzZs3C5V8p9MBLTHGGHRwlFJIITzPaykjSSg0nBhjPw4KhZwsy4Hn+r4/ogSRJI7CE0ePeZ4fBpHj+X5EXM/3fZ9hOF4S683GxAAuiiJd0yY6FhhlhqOEUsqL4nQ5FwaBS+MgTJIkRAnlODZKGIVjWUwZShlM8VS1iA5oryc3yHjkwBz9BFYBDgT6PfhVwzCE2daDuRQeVlRkTdMy6TQQjKZp9rs9y7I0TSNRvGfhelCVxpLXPQg8jizLE+4x2V/ymk6no8Egk8lJomJZznhk86yQzRYNPcUz/NLSEk3I2tqapitvectbThw7FsfxhevPbG1tWZazsnz47NlzqVSmXmtubW01Gg3XdcfmMEmiTCady2cwpq7rpnghn88/+9xzU1NTKysrzWbzgYcf0nX9rz771xzH3bhxI18sgLVeOp2+cOHCqZN3J0mCWfbSpcuEEITZ8lR1MBhRhHQ9dequM0mSlKemPM9Tdc0wjMR0KaWQ3uM4BoGloijD4RCUg6qqAvEFZYiSkZl9O3cGYT9wbdNyXddQNVEQwtDvd7qj0SiOIvCZdoMRz/NJTAVJEwRxMBjpWur8+fONVv3bjz/25je/6Zlnnzhz5tSPfvhH//d/+2uiyM/MnPvGN/7hRz7w/n/2z/7J9evXVu/c8l1na3tjenq6vrOrKEoYxrdv3vrAB350d3eXJEhNG/V6PZvNJkmyvn7n2OEjd911125t++knn1peXmRZtr67myRxNpvFGNu2HYTm7OzseDxeWFgwTWtra2dpaanV7Ph+yHG8KIqIMq7rg7V+GIYwrrm0sowY3B8ObdfZ3NwMk7hQKIgcLwjC4ZVDjmVhihzLzmazgsiura1FSTw1NSUK8ng8Hlum63qdTiedzWqaIcsyw7Htdrtea4ZhyIrorhMnBUHI53KB622sreeyWU3TdnZ2dF3P5nKaoXth0Ov3x+NxEEeuJ/i+H0UBy7I8z/IcIwi8KPGYklK5wPOcY4193w9DH2wdccQzDJPEcRT4CFNFFCSRZxFFScKzmOcYhhI8N1s9mPoOXvoHg2FyH0hziASWZcGZJwgCdKBpPBg8XhhgjAWeh6I/lUqldEOW5Xa77dnOeDy2bTuOIvCQ5Hne8pyDMTy5ua7reSEhSFUF2He3xxuGgeM4SZgoim7oaVGUWSxgxBTz+dHQXFhYeNMjDwmCcPXqFdd15xdmlTSTz+cVRWvUW5cuXa7Xm8VCeXl5+ebNm3EcJyQSRT6Xy+qG6vvucDi0O12GZU+ePMlx3MbGxgc/+MFOv/eZz3wmV8j3+33DMMaWOTc3NzMz8/nPf/7UqVOt5uDOnTvpbLbX6/d6vff84Ptqtdqdjc3ZubkgiDDL2Jb7zve8e2dnN5fL5fN5bzCGIw88pKHelmW53+8DvGYYBijUoe4gfAI1vCAIIi/EcWyZI9eyHcfJZlKiKA57/e2drWF/AP0zYiOGYQI/4nlREtX+aEwSNDc39453vfOLX/z8tetXfuFj/+Lf/O8fX1lZ/q3f+s1f+uX/x9raiNLkB978iKrK/9v/9i8vXX4lpWuf+uNPBkFweHnliSeeMIx0p9V+85sf/fCHP/yLv/Cxs+fvmZ6e3tzcvHHjhijyLMK6rp89d3ppYfHatSvdblfkeVmWqtUqyESNlFir1TBmjh8/Xi6X//zP/xIj1nVdjFnAioeDcRzHKysri4vLo9Go06zVarVMLivKEsNxDMdub2+bjp1KpXiGVRRlujrFYkziJPQDWZZTaW17e9vxXFmW44iMx+OROR6NxtVq1XKcJKG5XE5PGcPhsNPuEUIYgR5aWu71er1u9/iRoxzD9nu9Q4cOgRQEYSwpMuZYx3XH47Hje0Eoj0aD0WgUhiFCRBJ5VVVUTY4CP5fPFLLZdMbAGLdajeFwKAmioeQwxkkc+r6fxBEH2y3iSJElgWdlgedYFq8sz9MDwmi037Ml+47Xb+wVXyd2AaBpIuCmrwVLWYEnhJB9WJXneYHjWZadmZlJ60YmkxEEwbasWq22tbXV7XYVQ6P7yOpBSgO2hUD1CxQtRH7ZSFFKdT2la4Zleb12T5G16emZo4ePPProo/ls9ubNGyzLnj17OgiCF55/tjOqdTqdXm+AKKPrhqrqAi/tvTBB4HjYNUwSEoG13nypeuTIkcuXL2ua9sEPfvC/f/7vkiRRFGVrZ9vzPEmSKELHjx8H+2qe5zOZQhAE37lwgeO4fn9w733nL1+6stuo//N/8YtbWzuvXr3iOv5HP/rRJ55+KpPJaJo+V6yAwhjKCvDGpZQ2m00ISAjCSe/tJh6005LAi6JIkmQ47FujcX/QTaI4jqMkilzXdix7b7sYChHCvh8ymFM03XPDwcjEGH/kxz9ar+/+5V//5Y995MO3Vm+urt744Q+8//Tp07/x658gJE7icDweHj9+7Md/4iO+62Rz6du3bz/z5FMcx43HViGXt203CIL/49/9h1euXH722Wd7vV4+n7es8c7mFsMwhWKOY9iHH34QY7x+5w7DYLDcTqfTa+s3MpmMbbuDweDcuXOapm2sb7344ou6ntI0bTQaR1HEcVw2mz118vRgMOh32tvb27woyKrC8jzDscPh0AsDjuMkXiiXy2kjlTYMx7JJnMRx7AeOJEkE0eFwyGBuZmYmJsmtW7cxxs12O45JtVpVdW04HI5HFsMwksaDG2KjXl+YnSsXSzdv3ICLLY5jihDDsWESm5ZlmqYfhVEsQ8GJMeVYFiHCcazAsyyLWQ7rilKpljRNa7eb4+FIVVVDybEMQ2kShiElCYcZeHuTMBAFXhYlQeQ4dt/fHgrCyYc9SUSvowehoJ94gKP9EWNIiZO0OUE+Hd8DyTU8EXiPQ2G5E+/R7rqmlcvlRx99NJfL9UZD2DFo2zZoHQCdh/KM7lshpdMZWAPiDUZLS0sMwzYarerU1I99+KO5XKG+Uzt58mS73b5y9dV+v9vptP775/9G4JhMJlOayk5PT8/NLVim0+l0TdPMpPlCoaDruqIoHM+Y5qjTaZvWCESDLMtevnz53LlzU1NTf/hHn5ydnd3d3d2t11KplCzLlm2fPXt2c3Nzc3Pz0be99dlnn52amitVKtu7u/1+/95774UFQB/84AdHw3632+62W0srhwbDnm2OZEH0PI9NKMzOKhzLiYIgS0mSmKZVKJfgjYXXAJ9CQhKRl1jMERIzDBOFoeM45nA0Gg8cx+l1OmNzKLCcJAksx6Y0VVGU2u4Og1lEKMEEEaqqchjG/eHwS1/60i/+0seu3rj56T//zO/8zm/98Z/+8X/87U98+s/+8H3ve9/f/M1fZzMpyxr/8A//8Gc+85mP/eIvPPX0E+9617u21jcuXrzIMJxlWaIoq6p64cKFwyeOWZb12GOP9fv9xcX5yA/6/f7Kykqv0/3Sl7508uTJ+++/v9Gob2xsIIR6vd799z9s2/bFixcr5SnbcquV6a2trdnZ2X5/CFbZi4uLmUym2WzazjgI3SRJDMOQFJnluf5waDk2QoiXRMdxBJ0zDAMYY1BswlxLr9dDDC6VSiRB4/E4Jkkmk9nc3CSEpFIpsCGGN9bzPNsOrNH4wQcfPHbs2NrtVdu0oMuANVv5QoEXhf5oOByNoGuwbD9JEoZBgiByLCYkASIglysKHMMwOEkSRGhKN0ReUFU18SnGOEkwy7IMx4LmmUShGccRxUwcJzBF8T3LzklKxAcocoZhNE2DccyDIphJBL6ukkQITTgT6CElSeKMFMuyw+EQnNTiOO52u61W69KlSwghLZ3h92vXbDY/N7cAjpRgMs+yLABxDMPADgMJ4+FgPDU1dfbs3ebI+upXv37lyuckQfy7L34+m82mdBUhwrK4UM6qsswwDMxlY8zKkloqlXQ9xTI8dJiUUkCPYOkXQoRl2Tt37tx1110zMzN/98UvaJq2vr4+Go38MDAMY2yas7OzHMddfPXyAw8+sL29vby8HCXJhQsXwPR+bm725u3bhWLu/vvv//KXv9zrthmGOXHs6NVXL/f7fXCh7jXa6XQ6l8tls1myvwzLdd1cLgcZEqqSZH8tpqzKDMIMw4ehb9njYa/f63UsexyHIWZoSlMJjYPQYxiczRgzM9PDbpdlWYyYiFCBFzTDUFWdE4R6s/Unf/xnP/+//IuLr17+4z/58/f/yAcb7fZv/c7/+Tu/+XsXLnxnY/0Ox3G/+Zu/eeLksU996lMPPHjfr//6r//e7/6nz372s2trG71Od3NzO5VKlUvVzdqOJEmnTp2CYQswOwQTpPPnz9u2/corr9x//33Hjx+/ceNGv9+/fPmKpmlxTLrdvucFhw4d4XlREESMaRB4mqbJslguFwmJDUMjJLZHlq7rqq7JqhIlieXYgiBwPA+BxPM8IhTW0RiaLstyFEXVahUxuN/v+16YzWa9wN/a2gZuE8oogecqlYqmOqurq7OzU77jWpZVLBSCIGg5rTOnTzMM02q1wCRGUZQwiW1wFkc0l1Mty3Idx3UshBDGSBQ4XuQty5qdrhqGznOMLMuSJJimGQSByMrgBAn4sCiKHMdSQlhRoZQ6cURCn0MJwZTCchmoPjGilCSIUlCYUYoIoZQQhmUhbCa1KNqHOrl9A/M3tnOpVArKUagwwzCMKAK8IQ5C2H+CDgwfdpoduA8JGQgZjPHEHzaKInhDYZbih3/ofcVSeWw7f/CJ/7p6YxUmXADv8UNPQ5Kuq7zAcBjFNMAEgwubIEgYsRzH8zyvqYYgCDCjBJvlJUnkBdayxpZlPfjgg6VS6dN/+ReLi4tbW1uj0ahYLsG4TTabnZ2dffHl75w6dQowzIWFheHAAjXGu9/9bsdx2u3WRz/60Vs3rokir6rqzHR1qlr+2te/QimNwyAOA0Q4sC1jGEbXdRj2KRaLQA9AkQ9vHQyvJEmCMaKU+q49HPZ7vd5o3As8T9VkkeUZkSUk4XmGEOKFdqO9yzEsSQjDMighTmRxgsDxIsdxo9Fo/Ykn3v+BH6lWZm/dWTt85MT584+sr6//+Z//+fvf//7P/OWfy7L4wgvP12q1JApFif/xH//xX/7lX3744Yc//vGP/+kf/8mb3vQDt27devzxx7VMyjTNH/iBH3j44YdfeOG5fqfLsuzNmzfPnTl78eLFqampM3fdBVble8RgrXX16lWW5cIwlCT5c5/7u52d7cXFRWDwS+VCp9tSVEmUeMwkQegghMIwjEYjWVUMwzBti2EYN/BFUeQ4Lo5jgeNN03RdVxalQqHQatdFUWR5DiZjVFVleY7juFQqNRiNYLgxXyyUy+WUEYDAJZtK37hxo5FK8Rw3tuxerwe7ljHGvV7PcuwgjhzH6ff7Y9tSlJzveXEcYowYhmHw/ph7EnEcx7FsEPiQe6CeBN9Kx3MBnZY5RZAkjDEOAoAzwzDmDraC9ADHAKfypBwl+0uJgWyYzEzAxYFfy/XjAxT/eDxmGJj73ytuKdkDWqMgCMMQYywKAsMwYEEpicqkR40TEoV7rSmDuSSmXhwghHLZwtGjR0+dOjUzM/P0M0/evL26s10bDgY8J0J9EsfxwtK8IokUJZZjKkQoFvO8wDqO47t+kiQsG3CsYBipyVQUzDFNynIARXie5yX5xZcvzM7O3759R5blhcVlhmF2d2pLK8szMzNbuzuEoFOnTn/pS186d/fdnW5/Z3ubYdF4bB89evSv//qvFxYWTp488fTTTzWbzdFgqChKo1FLorBSqRi6bOjyaOjHcWRZJvgXh2GQzWY1TbMsC+ptoEMppbCqTeR4iEbPd3zXcT07Dvck9WDihRDhBRYh1vOcwaCnY81yHZbl4oTanh8nVJIVyx4fO3bsiaee+sQnPvELv/iL/6/f+I1/+S//n5/+9Kd/6X/9X8fj8Z07d970pjdtbKw9+uijmWzqwndeunPnDs/z73nPe55++ul2u/vjH/mo43jPP//83NycF4elUunFF19cW1t785sfETn+lVdeOXTo0Pb2drlctm37pZdeOnPmdCaTqdfrg8FAVdLT07OZTGptba1cqubz+Xvvvafb7bquTSldWlq6dOkV2zZFUWy3Q9d1EEJRFLmWl8qk4QqBqQCWZcFhqFQoxmEI7AvDMCsrK88//3xCyalTp6IweemllxzPLZcrcRybtg3uEp7n+b4vSXI+nw+Jl81mDcNAlCqiVC6WOJaVJKlcLnueF4RhEAR+FELAxHE8Gg2hIpMkWRRg2QvlGGZ2dlbXVLpPInCYSetpscDXdtuQpQD01lMZ8BYbDocEB2yCMWG4g2GDD0wbQRhMiIrJfcdxgiCA+vhgxzhh21+HahqGEUVRGASwe4DjOIHjwV8ZnojZ13MDnagoGXTAffQgVBtFka7rhw8fPnnypCRJly5d+qM/+iNVk0mCWJbN5nIs5jDGrMCn02nbtnu9Ds+hcrlUqhRVRaKUcJxhJZYoihwnUIKBEgj8vU3xoigaKQ0h4jg2pbRSqVSr1a3VjeXl5W984xv5QkE3DNd1y+UyxcgwDNXQv/Od7/zoj/7o7dVVTdehQvY8z3Xdu++5u97Y/dKXv/iff+/3/uqv/mowGABhpWmaNR5VSsViPgf9sKYpjuNQmoShPxz2HceCYSKAf6DbAUd6MBCYKlU9z3EcJwx9zFCMKcaIZ/Go3ytXirquWPZ4OBwmSSQIvKoqfMQTQhCKEcIIkSSJCE0IIZub6/fdd98rly4/88yzH/rQh37n9/7zE48/9Za3vO1T/5//srOz9bP/7J8cO3as3W59/R++urK0uLm1vr6+HvnBD/3QD62urv3sz/7shz70Y7/xG7/xXz/x39a2N2dmZrrd7u3bt5eXFx944AFVVb/z8ovHjhxtt5uwIA38s0HPOT21cPPmzSiK3vGOdwWB98yzT9XrdVVVVFX2fKdQKMzOziqKPB6Px+OxYRg0jGRZhpEFEKOBsMZxHEWUYKoo9H3Hss3R2PM8z7fL5XJMkl6vhxF76NChIAp7vT402IqiybJsu06j0SiXqtlsdmB2R6PRvffe2+t2V2/eKuYLlFJw6WYYhuU4gihEoCRJoiIjLNi27bte4LtJzAiCoMqyLIs0IaCAFTkeCxQYfJY10ul0EASYZcMwFESZ53nMMoRgQZRjxMYUEYbFhxfnXsdAkH0btdcBLVAfJvh7L7uYNIEHQxohxIlCFEVRGAKmAkU8wBUswlBtIkrhCvN9X9dyk4edlKMMw9x3330LCwuSJL366qvPPffcYDSYrk7Pzc21Os04jpMY9tLsQfyqLDIMo6qiIouKImeyeiZtUEps21QFfXNzM0noVHVG1w2OExRZE0WRUrq1tSWIXCaTkmVpfmHW85xvf/vbR5YOE0J836/Vau94xzu2drYtyzIM4z3ve++HPvShf/fv/p3tOE8//fReafrii77vX79+/bOf/ey//tf/+p577rnr1Knf+I3fOHLkSBAE+XyeZdlms3nPPfcMBoPNzc1isbiz04KtfWfOnGk0GmD4lyQJEGVQ5U5EDnEcK4IMjoCua0uSUCoXLWssSUKv38nnc5lMqtNt+b7L87znuYZhqIwex7Fp2cPh0PEDzHAMxyaUMS2nPxqLkuK4/n/9wz/8iz//zNrG+r/7d//+r/7iU//wD187d/b0f/gP/8edO6t/8ZefHg36iipls1l7bJ4/f/6uu858+7Fvra1t3H333W9+06N9c/SFL3wBYmM47BeyuRMnTmi6cvXVK0kSJUnSajQOHz6UzWbjOL506dIjD79lfX0dVr4QEucL2TAMJUk0zZHnO9PT06Y5EkVhNBqB5aFvEVEURVmyHLvb7/OioKqqH4Xdbndhdg4htLK0bGjarRs3VVmZmZkZjfvNZtMPA0EQwiAOwxAxOI6TMAyDKCIEsSyLYJERYhFCfuyU8oVcLuc6Tq/dETheVRTf94vF4mAwsB1H0dRCuSSI4mAwaHU7ju0HQQC+OwzDsHg/h1GSSqWqpXK1Ws1m0xzHhUHg+/7q+pbrumEY6ykDlLS5bKHRbi0uLouSZNv2cDjiDiac18UVs6/bpvu21hzHIfpd7fVBIHTys/i1KlAoEuBigt4GUwTVbBLFe054oqgoSjqd5jhuNHRBtcyyrGEYU1NT09PT2Wx2e3v761//+tbWFsMws7Ozx48f7/f7r776arlawJgyLGIxphRBWmBZNpXSSRJBDaApKqUUEjjP89PT0wzD8ZyI9u1MwB5b13XXsxFCMzMztVrt1q0b2WwWIWZ7e3txcfHUqdOb2zuO47qu/673vPf//L0/+PCHPjI9M/dHf/RH7Xb3nvvu/8Y3vjEcDlVJ/Lf/9t9+/etfb7fbR48e/YM/+IO5uZlMJjUajdbWVt/97ncHrsMxxDYHDz94vt1uF87e5fv+eNS3rVEYuDynciySJSkKvSj0JsUCu7/FlbAMolES+yQJkxj7rhP4LqGh6zpxnKKUAl6VK+bCKOptb88XFsIwtFzHDfwoiRhKMGUShGdmq6VqSZL1wWD0hc997r57726321/4u8+99a1vXV29tbm5+cQTT9x//32/+qu/+pm/+PNsLp3JZJ558qkXX3yx3x/ec889mmZsbW397ku/e/8jD/3SL/3Sl7/85a9+9atvetPDKU1fXV29+56zHMeZ5iiXy/3UT/0Uz3M3btyo1+vz8/Pf/OY3K5XKysoSITEsh0mS2DTNH/mRH7lz587YHG5tbYA6n+NYjDHIvnVdF2XJC4Juv9dqtSzXgTbStm3XdXVVBdEIIaTdbluWJSmypmk+G7Isq2iqqmobGxuNVqtSmUqn01s726IoplOpzc3NVE6DSXGWYeIgJPGe0NI0zZWVlWwuNxyPGu2WZdtw5Vy5chVhJCsiz7D7sAhhMM5kMjzDRlHUarWGgx7w2AzD8KLExYnrB1EUJRS7lsNwAsMwtuNQaHejkJsEzyTYDgYhfq21hCiKNI4m3/m6cnFy52BPSBFVFIVlGMuyAFBhGQZwJ1YQ9zjJOAavAT+IU3qqWCzOz8+Xy2WO4waDwZ07t/v9fqfTkWU5m00jhNrtZqNRS6VShw4tW/aQwZRhEceyHOYYhpFlRZWlbDozGPYCz4/CMI4JTRISJwIrXL58eXZ2dm5uAVHGNC3f913Hbzab4J1z/r57VlaWbt++tb2zqapqHMe3bt0C/cri8hL2mFdfffVXfu3X/uzP/mw0Gv3qv/m1X/7lX75x48aP/9RPfvWrX93Y2Hj3u999+8b1+fn5T3/60+fOndvZ3ZIkYXd3d3FxUeTZfDbNs3hufrpRq0mCkESRyPMch9zYn5kqMQyjyhVFUQaDAZA6cOKCjGEShBIvRZEsy6IkMizLihJLkcwwmGPY0WggSQKl1LRtThCSJBkMRmzSgMehlCIGY2BsKTUtvpAvmbZZyKdfufDi/ffd+5Y3P/zE409yj9z3Uz/1U7/9W7+5trb2pjc98uWvfP1nfuZn/uIvP33z5s2zZ8/WajWE0Obm5unTp6Mo4ljhzp07Tz/99L//9//+7rvvfvLJx+M4Ho/Hnud9+MMffv75Z2/dutXr9aIoBG4T7GHD0B+NBwiTOAnr9TrPc5Ikffvb36aUzsxOHTlyTFVV0zTr9d1Go5lPVaCLI4iCOkMQhIwoxHEMHxmlFLr3+m5tNBpVqkUoBXVdVxVUr9fX1tZGozF4Z8D5DlZdkx8EmYQgCKlUisQJRgg++vF43B8MLMfmREHTtG63u7q+pigKz+xJNaHZA5eLlKbDXVjcAA8oimLfDlU9ZTleGBOGYcBDQJZlczzEDI2TmKIEH56boa+9TUpKGNgDGADwTEVRzMADqAD2DaADdOIba1GEEMGoUCgIPD8YDGzbFgSBY1hIdJEfwK4fSRThwUVRXFlY8TwP+gfTNGFumlJaKBQ6nY7neZlMJp/PY4xt2/Z9X1Y5SimDOUEQeVbgOE6RNVVRcrlcp9MyhyNB5PL5TDpjyKLEsnhnp4YQ0jRDFGSEsCTtLQB1HOfYsWOYoY5jqarS63eGw/709HQcEFhCMB6PL1++/KEf+7Enn3zy6tWrf/B//ZdPfvKTq6urWsqYnZ29ceNGqVTK5XKYJOfOnfu1X/u1T/zX//L5z39+2B8oiuS7bi6XKxYLKd1wXMs0TUWSTdPM5XKDwci27XK5PFmE0u12wQ0FdJLAwQLAjBDKptLwGQVBQDCCxSkI47E53K3VpqamOI7b3NmGHp4Q4nuJ67pxHEqSJMkilPc8z6czmWPHjj/99LOHDx1vNluIMj/4g+/7yle+5pP4Pe951xOPfyudNiglb3nrD+xub1Wqpa9//esz1SmGYZ544qlTJ05SihVFabe6V2/dqFQq7Xb7rW996wc+8P5mrX7hwoWLly6wmHnzmx9RFGVzfb3RqB87diyO452dnVwu12g0ANuAYSW4oKvVsu/7kiTdvHlT07R8Pp/P5yVJGvdNx3HGlhmTxA/DVqc9GAwIRhzH3X3mbBRFiND52dlOq7166zbP8w8+dN+VK1fGllkoFDhWGA6Hpm2FYTQej6dmZhzHi+N4enYG5khTqRQrIvAQSuKYxglNCM9xMHefTqdZjrNdx49CzDBhGI4sM/RdIAYButMUVZZlDnOu6xqGAQaNsJI5jiJKacjJuVyu2aq7rsuy7Hg8xhgLHK8oUqFQ4DguDvcz4UFtCn7DlODBgvOgkAVuB7vBN96AUaCwdDqKKKUJwwLMBa4NsiynDMMwDGAgXr1yCUAIKF/321Hc7bUZFucLWU3TgtCDYfB8IRv4Y0IIxglGCaUxiWkcBXHAJWGkyVroeqY5YjHWVYMV2cAN5ubmbty4YZr2kcPHisVSFCW25WKMz58/Twi5/OrFdru5tLQoiBxCaDwee1YoSQ5J6PXr16dn52RZsSz7t3/3d1944cVr166fvOsuSZJqtdrszJykyN/69uO/9R9//RN/8F9+/Cc+sr6+HgSBLIuzs7O9TjuTSUdBmJrRa7vbKytL/X6/UimFvq8pAiICixNz1APPvyiKUrpMKeUYQlkqcIgmge95hBBJkhx7xLKsF/jwZjoOJ4pyQgnDMJSQOCYMg3hehN4hjmOCY8vzvShGfMQTXpSFbDadz+fN0bhYyBq6oqn8+XNn/u7vvtDc3Xrg3jN/+5Wvdzqd97///V/60hcJSY4fP/7tx7551+mTsizX6/XhcHj+/PmLF14RBCmfz7/7XT+YMKjdbouiOB6P/+Iv/kKTlVOnTrEcfuqJJ69cuXLq1Kn5+fn5+bnt7W3LsjzP293dVlUVLgxV1avVahzHV69ezWQyQRAyDJckNIqSfn/Y6w08zyvnS77v266jGfp0scjynGmaY9vK5/OgyR72BzzLMggDvf70008zDLO8sry4uDjoj2D/WRzHMzMzpUql2+0Ph0O4/PaG41i0Z5kZxzzDBp5PkgQW0QyHQ0mWwzjqDQeO6yqKksnnNE2D6zNJElmUYNdiEkb5fNbQdU01CCFhEDi2DU8tFqtVWdL0lB9ECGPYHmvbJqVJJqULrMRg8l2y/nWaT7o/OjgpLyd88Rtjj752MOJgMmRZ1vO8MAgADCSEJBRhjBVFUUQJBlgxQqZpbm5uWpbFYTwB5aEABrrMtu0oihzHRoiCcWgcx5ZlKhJKEKaUkDhKaIwSFAVB6PmqrCiKkktnaJywiMEEJQmNgpjhQ0mSVFUHSTGA3aVSybKsmzdvcjyzuLjYarUYFuVyGdM056eXUqnU+vr6/Pz8oaNHfv3Xf/13f/d3X3jhhb/5m785f//9u7u7Kysr6XS61WmPt8bvete7ut3uxVcv/9q/+ZVPfvKTqqpm06lOp3PmrrvM0bjZqnuOXSoVWq1WLpPieX61vptPp9VcBiGU1pROxyahzzMMSxNJkmKf5TFVVTlJEhL6BGNV5IfmUFG0MHAxxoTQJIh4no/CMIyTdDotCEK73bEtZ2ZmhlLcbrdZSSEMSiiJkyQiSUxjWRYLhdytm9dr9a181qBJyLH0xPHDG2u33/Oe95w9e3Y4HPI8XyqVHMf+7d/+7Y98+EN/+7d/+7GPfexLX/hivV5vt9vnz5+/fftOFEWf+cxn3vne93z729+em5u7fPny0tJCtVT+1Kc+9c53vf1jH/vY5csXm80mxzBzc7PwmQZBEIQWwxKeF1VNYRjGsseypKZSKc/zer2eKEqu66VSaZblzLHd6w1iPxIEwQt8lueKgpDNZovFopYyQNcO00PQ20OFefLkyeFw6DjOtWvXet2B4zjpdFrXDUVReoOBruuyLDfbLU3TFhYWarUamyBNVnieN3Q9Y6RGg6E5HiuKcvTo0WazORqNFE2dnp4OwtDzvDAMMylVFEUWM1DNCoIUer4VRpBCEN4rj7PZbDqdZll2c2DanhfEcYJoEsBeACZJkvGwn9JkFIdRHOwBM5PbRLAG7enBoILAIOg1JmsHRW0Hv3lyR1GUIAjCPWpFiuOYQZjjOMMwQs9vtVq+79P9spbjOA5j0LjhvRmlEE4UVVWhSCOEuK4L5F6SJAgjhChDMaIxJSiOEE0og9g4DKkoCoJkaDqoBVjMptMZwsZzc3NJQre2tizLzueLiwvLhmE89thji4uLCYksa5zL5Xr9DgRYv9+/fft2qVS66+yZb3/72x/96Ee7/d7LL7/8jne84+Lly3DSf/mrX0mn06IofuADH/hPv/tbP/MzP/XNb36zUCiUy8VbN26uHFq2bdu0RqlUajQaGYaxvblezGe3t7eDwDdHw5mZGdd1F+ZmMSUwOhCHQYioY5lxHIs8Bx5WSRKTOCJRKHKYSJwsq5RSzw8NTRlbNIwTVdHjOB4MBlFCoyhxHM+2XZqQmCBVN3K5lCiwCYm8MPB8t1wuDHs9TZF9z9naXOM53Gk0n3/m6XJl/oknvl0s5E6ePPn5z/9dLp8hhExPT//xH//x3WfOMgxz69bqxYsXK5WpbrdLCP3GN77x8MMP7+zswOAS7Db71re+tbm+cf/95zHGTz7+eKvVvOeeeziOe+6550rlNFjf67oahkGn08lm8gzDuK5LKc1ms5pqVCqVKIpUxTKMNCZROp3u9nsjc7y5uel4rmmaoiLD0NDU1BTPctZ4TBMCXYxmyePxmGKQauF0Os0JPDisjsdjUZShtmdZFuQ1Ed3zf0ilUuVSGVMUBgFsHzMMI5VOIwZ7YUAI4Xkec2wQBJqiQtOYJEkc78GN9VoNNr8KnAj5Ay5jQYyarc54PGQw9mw7Cv1sOqVIgu/FPIMFnkV0n6Cf1KKvQ0oPBhiEBN1XtLH7twl+M8mNB3lCGIEHQ2Vuf5sSwzAHdaF0X3bD8zzHM5ihCYnCyI/iAGEiiJymK1EcEBpHceAHbkIiXmB5gaUoQUmMSExRAgoGntszmCKEeJ4XBSHsWw6DWOSkarkCm150XRdFcWpqCsiDL3/5y/fccw+suXRd99q1a7qunzp1Coqora2te+65Z21tDWP89re//fd///fT6TQh5O6775Ykqd/vq6pqWdbZs2e/8pWv6Lr+4IMPHvTtS+vGcDis1+uyLKqqev3G1ZWVlcFgMBz006kURYkkC45rCSKHGcpymOOZKA54gUWYUJQIIqcbaiqta7oiK2IQehQlPMPK8t5wM5BJoDhxXY9SynHccDhutVoJIbbjMgxTKJUWV5bnFuZTqVScRKY5qlQq/UF3NB56ru3aZqfZmJufvfLqpWw2Wy6Xoyja3t7+4Ac/OD09ffXq1be//e2wY1gURYi34XDY7/fhE/z617/O8/xb3/rWTqfz0ksvaZp28uRJSulzzz337LPPHj9+fHl52TTNjY2NQ4cOKYoyMzOTy2WjKCSE5HI5SRY2NjbS6XShUCwUCjzPdzq9jY2t8Xis63q73Y7jGEDsbrfb7XZd1wX1WafTGY1GruuOx3uyb5CbEUJ0Xed5HuRslmXB+B8YpQP9SAjZ3t6GGQBCCIicbNseDAatVqvdbo9GI8hpEAggXRqPx6PBEC7d0WjUarXazeZw1Pd9H65hSZIgW4Dn4tbWlqjI3W630+mEYeg4DhTDiqLADE2xmK+Uy0zCMQnHUIGjAkd4lvAsfIXwbMziENMAkQCRENOIQTGLGUJxQmgUkzCiUYwTwiEsMCxLEUMo/MEJwQlBcYLixB6bPMPqqsZiJoliliCGosD1Yj+ghHAsK7IchxkWYZ5hBZYLYHKO7qnm9oKcIpEXcEIZgmRelDBH/Yj6kYhYL0AxYQnlowSFcUxQQnESEz/BfqGcNXLa2O6b9lhSRDd0X7rw8kyphKMoCdyZal4R0TNP/sPVq8/ff/+JwWCbF8PhqLGxeXN+aaoyVVzbuJPNZ9Y2t+4+f9/G9k6/P/yJn/ipr33tH04eP9VqtDfWNhmCZ6szu5s7b37gkdPHTp07ecaQtIfuObl69RXP7OUMqdfcVUTG9yxKwkIh1+12O71uOpPT0pkgIVomRzmhPzY3dnaxKL568+bAcvumY0XJyI8GXuhSxkP8eqt3u9bijIJamL6921EzRSdC6fKUG9EAseu7tZgTepbjJKQ1Ho5832dYKZ3q2BZWFYckVR1hpy0EY2yNarduFVU9r2TmKwuqoB9aOqFrWdsJsoUKLyt3NjcKU2UcD+49fajfqrmjcVrO3Lh4Z+Nm+7GvvPiLP/sr//UPPk0i7ud/9ucwE4+tBmYtxfADx4x9p9usqSL3yAP3v+mRh7Y213e3d2q1miBIa2sbrU6PE2TL8adnF+46c7cgqbyoveVt7z589PSNm5uN5jCdmVpaOdHpjR0varQ6mMNOYPqxlWCfEaITp08FSXRnfW08HuuqqooSTggTJdOFkshyta1tczQaDAYjc5wvFwuVUqZcwZLcGQwJxUZalxVeVQSS+IHviDyTzuilSllPZQaW2xmNsaKM2v3GzrY9GsaB7zgWy1FJ4cfWgONJEFq+MwzsAfKtoi4tlLIlRcgZeUNOCVg05NRUcTqlZWnMYMpUK9OnTp5OGZl6o2HZtqbrumGomhYMzJlC+dDsAvUjTGgxk2MxRgidO3cuncs6Qdgd9PfGQ6GPh7MEvdbX8P//2wTOId/Lbe1gXp2Yi05y7Pd8wNclXrTPi8CAhWEY6XQ6n8/D9+RyOdgRa5qmruvXrl2jlML/ApZt2/aNGzeuXLny0ksv1Wq1hx9+OJPJpFKpwWCwvr5+7ty5U6dOXbp06ed+7ueeeeaZIAhyudzZs2cXFhaAec/n81/4whcIIU888cSFCxeKxWKtVoPqALbt7cFompZOp+M4XlhYgMkA3/dhMHI4HhFCLNPRdR3QM4ZhoijSNAPG0scjazAYgG9CGMWCKHGcEMaJaZrd/rDV6kRR1O9044j0+31D07vdriRw1mjMIux5Hix5NwwDHt913evXrzcaje3tbVD8QqewuLi4uLi4trZWKpVYFkuS9MrFlz/+8Y97vguOLOfOnXv++ecLhdLU1MzKygrYb0IVmiTJ9evX19fXVVU9e/ZsEATHjh3L5/MnT54UBOH27dsbGxvPPPPMP/zDP5w/f75QKKytrYVhCPuAV1dXt7a2AKLjOA4akP7+LYqilZWVu+++O5PJOI4Dq4fG43GtVnMcR1XVXC535MgRSZJGo1Gz2YS+APKSpmkgSQNDCowxTPGDHykoLnO5TLFY5Hke3H4lSSkUCrBPW1VV6CEppa7rWrYNfVCSJJ7nWZZlWVYYhuCW0u/3x+PxYDCo1Wr1eh0KYIB/YEINdokXi8VMJoMx3tzcHA6HoihWq9MMv7+PHto8dn8D2T8SUf9TtzfGIcTG6/45ibrJoTCZ4Tj4UG8U5UyK4ck/IaRBcgEVJgjiZFleWlp6+OE3ZTK59fXNK1eu+X64tLSysnzY0NPn771f11NzcwvZbP7y5SutVuftb3/nO9/5blmWO53Oxz/+8T/90z89duwYOCAxDAMi1VarxTBMKpV605veND8//xM/8RP9fp9SOjc35zhOs9mE7fY3b96cyBXgjuM4AG/KqhLH8aA/goY5TGLLsjmO8/0QJnolUQnDEFjpKIoYVnTccHNrZ2e75gUJQszOzs729k69Xu/3+57jJlFI4oQmRBS4lKEpijI9PQ2zLDzP+74vCAKcDuC8RAgZDoe9Xi9Jkm63m8vlLly4cPTo0a2treeff351dTWdTj/+xLefe+65H/zBdxNCPvaxj/3Tf/pP6/V6uVxlGEaW5enp6Vwu1+/3t7e3G43GysrK3NzctWvXnn32Wdd1fd/vdDqqqsL+90984hO7u7vZbPb8+fOnT5/GGK+vryuKAhcxqIXgUAB2lFIqSRLoaSeXShiG/X4fLnRoWTHGsO8efKJgLBOMsTHG+Xx+amoKIQTDuJqmQWTCgyPEQGxjsAhLEp7n44gQQjDHyrICw+i5XK5YKkGkOY4DGQV0eaVSiVJ68+bNzc1NyAEgYJyenl5aWur3u2traxQRTdNM0xwOh4qiQPMFnRE3wWDI/u5B9AbpzGuD8PsG5/f8OsMyEzT1IH4z6Tkxwgef96BMZxK6iPxj6CtCCNE9rWkcxz71wzAkSZIyQkLIYDBIkqRYLILPuWMNWJYtFouVSgW4OJ7ni8Xi9evXwSTmySefzOVyOzs7cRzPzs6+8sorP/3TP/2Vr3xlOBx+5StfyWaz0JacOnUKHDc2NjbOnz///PPPv/jii8eOHauW5ampKUEQBoPB5JeNogjEk+CGlMlk2u02kFEpI8MyfLPZvOuuu9Y2N7PZXLfbLRTLAPExLA9eYFEURYQyvGA5PqU0CILhcCwpOqK42xsGQaDrumvbmUzGGo+zht7v97P5oizJU4WsYRgsMx4MBkDEgSAOXEwn0FqhUFhcXNzd3YWxqdXVVYZhjhw58hu/8Rv/7b/9f+v1+tbWRq934tixY3ES/P7v//5P/9Q/ee75pxuNmihrYBEfx7Gqqjs7O4VCYWpqKp1Ow+RELpe7ffv2aDQChvDo0aO2bX/2s581jPTJE6fuvffeVCoDLDbGmOdZjmM4nmVZ7Hlev993TB/YAlC0wwaEidceqJ2q1Sos/cxms+B1FIUBJtSyxiSJDMOA758Q2gzDwEPZnhsmcTAeZVPpubkFTVO2traGg0EulylXK1Hox3EchEESx0EQ2LbdarUIEsHwG7AZgB5gqVu/3xcEoVwuC4LQ7XZhsMF1bdhrAsvhGrs7iiqlUnqpVIrjsNcbRIHHTAgTuCX7K+y/d6ghRL/PH0Lp9/yDD7R28DfcJmzkwQIVfqvveXtjBMJt4roPNi1wfCKEUqmU4zjdbpdSCjO7nU7n0qVL169f7/V6oihCoQJzIYPBQJKkRqNh2zaUsul0OpVKXb9+/ad/+qfX1tba7bau667rttttcOOzbfvOnTv1en08Hs/MzKyvry8sLFQqFUopOJRIkgQguO/7d999N/TuSZL0er12u82ybDqdhuzEcRzPC6KiqKoqy7IsqXCJ9AejbreLMcvwQm84ajabjuPU6m3Xi2TF4AWl2xv1+mPbcnPZQiaVVmVF4HgWMzQhSRSjOGIpAc8ymI0ihMCGKVmWJ9lGVVVVVZeXl2dmZgByJITUajUwtD169PCf/ukfv+1tb+n1ek8//fTi4uLKygpJ6JNPPv2Od7wzjgkgFjs7O71er1KpaJoGwjE4cLe3twEygfWPCKGXX35Z1/W3ve1toij6vr+6ugrAI/ArcAUqilIoFFKp1B4Jwe0tWoGFH2CYnclk4L+8fVNWjLGqqtlsdk87NhzCp8yy7GAwaDQaLMtC5QxpKtm30Od5XtE1SVVYlqeUIox5nk+lUpRgkHlxomgYKUBcyuUyDGHDUCtAsvV6HRLy7OwsXCQgW7t27dorr7xSKpXuvuecJEm9XgdgWHCXRoiBF8+BehMsFSZw6D+S2f5nMyE94OGNEMTa3tTFngnifkkJqXIiVqb7S03f+JiTCEQIkYRAeMMlJXAcSRAhxDRNSnAmkykVCoEfbm5uBkFgGEa5XISTu9PpQJsUJ8lwOCyXyzdXb9dqtVKptLq6WiwWFxcXl5aWMKYvvPCcJEkTXfVTTz1x//33y7J44cIqpfTYsWMvvfTC5ub6Aw88sLu73etsnjhxwvMCUZQNI2VZDiGIUkwpVhRtPLYEQarVGocOHWIYjmHQcDgaDod6OlWv1w09Xa/XFU11HI/lBNd1CUGypnueV6814IrRUsWg00+Hie0GQehTiglBDMYIMYIgDLq9YrE4Hg3K+RwlSUpVhsOhIAi52TxMuIVhOBh04K2gFMOCIZBGbGxsrK+vT83PXrt2LZPJVavV1dU7b3rzw7/9n353cXHxyJFDtmPevn17YWFhNOrduXNne+v4I4+86cJ3rgNHBzRgsVicNEL5fB5yIEIICkXLsmB4GhZrt/iW43hTUzNhGAKVHwQewyBFlQWBkySJ45hqtQq29teuXYPyARbI8DwP+6HgzFUUxbbtOI71lOE4jm1psixLkiSKPMyXDofDbC4ny7JpuVa7PbYd6OiSmDMKBsuyOzs7PM+zLJ9KpQjFzWaz3W67tiPJAsdxDM/pvJHL5TlRdl03CAIgKrPZrCzLEIqwvNnzPMdxQBMWx3F1qkJo0mw2u902x3HpbLpYLCqKVK/XQbyZz1QZcCKBKhlk+5CRvm8m/Ed7vzfeXmc3ivadvA+GEwip4Jbsj/9OforZX839PeMQhjsnTSbk8yiKTNPEGKdSKUVRLMtqt9twVjEMp2lGqVjJZvKU4OFg7Puhquq+Hz766FsXF5cNI/2D73nfwvyS74Xnzt7z2GOPLSwsAEp+7Ngxy7IWFxfDMNzc3BwMBtPT0/fee28QBIcOHWIYBnYh2FAWWlaj0dB1nRCytbWVSqXgHQD6aHLk9YfjMCaypFqmE8exF4SEoMFgYNsuQgwvShgxruPZth3GEcPxHC+32v16s2Watut4hpFKp9OSqIgcjwmlhPAcoymqoWuKLPEc2+l04jieXLXj8RievdfrNZtNGBQE6nVra4sQsr21kyTJ5uampiuLi4tXrlz5uX/yc6+88vJHPvKR7e3tqakpURTb7e6jj7716tXrU9W5SqUSBIGmaalUqlarwR1BEFqtFs/zsLnJMIwTJ07kcrnl5eVqtdput5vNJrARSZLYtg1XsCAIuq5jjLvd7vb2Nqyd2N3drdfrvu8DTGIYRqVSWVxcjPcduyH8IJ9D6wtoja7rULczDKPrOrAacNzYtg2QgaZpURLHCXEdDySvlMEIMa7rmqYdhiHFyPdCeKPWtzZb3Q7LstBZQKSJopjL5cD2oVar9ft9WHkCg3v5fH5+fr7f7167dmU8HqdSKeDGUqnU2tpaq9UKgoBlWQ7oO9gSDqAcTK8BG/7GG/k+AjXy/TJhQiaZEGNM99drcyxLD1COE76LvMGZG2OM9nVy+A36OEVRkiRJYhoEQRRFiJA4InEcrywvi4IcBMHu7q5pmlB8+r7/4P33bmxs3F5d1XW9VCoRQgLL5Hl+Zmbmm9/85rlz5yRJarValNK3vOUtn/rUpxBhbty4cfbsWUmS7ty5Ax//9PR0v9+fmZnRNO3VV1+9cePG0tLSaDSqVqsnj8+3Wi1A+brd7vT0NELIMIxSqbS2tpbL5VzXnZmZcRwnlUpBEZXPF0VR5gTRcV3DMKIwSSjxPIfheMMwOE6IEsLygq4osiw7XgijzxiRbtc2NK1YLC0tLASe06gH5XI59IOpSrnVasmybA4HwKr1ej04bR3HWV4+BGjecDj2fT+fz1um4/s+vGxZ17e3t48dO/H8888vLa4MBj2IgW8//tjHPvax//7f/yafz73jHe9YX78zHJjf/Oa33vLo27/61a+2222e503TBPeT8Xi8vb2tqirLsuvr6yzLZjKZWq2WSqXOnTt3/frN0Wi0srJiW47r+uAr5zjO1NRUsZiXZTFOIteNZVlOpw0aU9gZjDF2HKder8MehGKxCP1YEATgVgqOWITBgIRTRZVlURQ4QgjYMsRx7PoBZIUoiliWzeVy3rAD3WahUMimM45rm6bJUCQIRNdTsiwihDzPkwQRbNQ0LdtsNuEFm6ZpWRYcIkmSpNNpaHwIIaIoIoQ6nU775nVRFGdnZ23bxBiJosjzrOu6umaompJKZSRJYqANAGAKMilwnRASE5UdSPhBb4335TITRAcdMKR5HfQCPwVSXShB9+3rE2iH4CvwzaAURwfQ1NdlxYPPC18EWhxsmuCVZLPZlZUVhBDP8/BSM5kMaDJt2waaAboX27b7/T5YfZqmOTc3B93a5ubmO97xjj/6oz/ieb5YLNx333lCkkIhPz8/5/ve2bNnwjB4/PFvLyzMLy0tvvTSi7qu3XXXqfvvv6/b7Vy7eiPwo0p56tjRE/lcMfAjluE5VnAdX5ZURdYowQzmlhZXzLHNMvyRY8eH45HtOppqYMQ2W51mu53N5hiOkyTFcbz+cIARSwm2LRdRBsqWsTl0XVdRJI7j0oaBSAzvjyLteQtompbSNZEXqtUqy7KmaVJKOY47c+YMCHemp6ePHj1arVZBLQgrZY4dO5bL5ebn51VVDYJA05VUykiSaGFh7uLFC3NzMwAtqqq+traxtLTCMsLnPve5H/3RH22324VC4cEHH7x48eLq6qogCDBr1mg0isViNpvt9/vHjh2bm5uDdjEIgq2trd3d3Wazee3atbW1NcuyRFGs1+vgr/O2t72N47jDhw9nMhlonIrFIqQv+E0Hg0Gv17t16xYE4UsvvQTX1UsvvTQYDBYWFhBCOzs7/X7fsqyFhQXYMgJ8A7AUuq4HQRDHxLZdyI2dXtf3AkVRZE1FCGVzuZu3VkHeOL+4sLxyuNcfbm1tlUql+fn5Uql06NAhOMcVRaGUguJ8MBjU63XgXTDGsHc9CDx45RPkH8qTRqNx5846BwnE8zyw44dCEYKBHoBVDpIKZH+SEL4f79u0TWLvdZkKIQQLrhFCKNkzp0n2K15CCED2EKLfO89CVkTfYwcGtLmdTgdjLMtyOpUy9LQoiqPh0PfCJElUWdZUHTp427anysuO7S2tTAFq6vvhoUNHKKW2bbdb3fvuu880zZ/4iZ966aWXgyDa2tpJovj+++8Pw3B3dxfeh5s3b169enV5eRkwgGKxePTo0Xa7PRwOWZYle+u4Atg3Au8tnItgz16pVKDesyzrkUce2djYkCVVFOThcNgbDAihURRt79YhScYJBfyAMizGOEwIolTgMc/LkiAQksiiGCfhaDQKfJfnWVmWTWscx2I2m5ZleWdr++jRI+l0uqf3wzCczO9kMhlVVWGX+Pb2tq6lYAvA3NzcPzz2mG3b5XK1Wi0DrzUej13XEQTh61//+vz8nCiKzUb77rvvvXjxIkDzn/vc537lV37lC1/4wtbWlizLKysrjUajVCqxLBtFEYjUQNwzHo+NlCgIQr/fZxjO9wLLsmR5T4O2u7t7+PBKGPpRHNbrdVmWV1dXdSUNvRbGuFKptFqtdDoNkqNOp8MwzPz8/OHDhweDwe3bt69fvz47Owu7soHYWJifNQxjPB47jieIIsMwYFOmaZrvB61Wa2VuGt7qJKF7xENCMINAqK0oSq8/PHbsRK/X6Xa7mqbdvHEHFshSSkulEsdxpVLp1KlTL7zwQq1Wu3PnDsdxkBLgF49YDiG0F4E8g/GeFzjHcZQS3/d9x+VA1g3pCMpCiDEYxp2E3yQYGI7FGCOMKaEUI8wyCMFWi/36cxKKeC94IHHB64Awg8echBP09HuN3/cpdyEI33iDQwSOeSgCoQsKgwCuLU3TGMwAGpZKpXzfr9frLM+xLNtutyGBW5YVRdHS0pJlWXNzc5TSy5cvA845NzMLGCxUQZDSp6enC4XCqVOnYMhYFEXQl/d6vbnpAqV0Y2MDnhGKGZjDkiQJIdRoNFRVBfB2Z2fHcVxZVjiOb7bb7XZbllSCmGazSQgSJYnjBM/zhuY4iiKe58MgEnlGV0SYiEMIqZLIUDI2+yIPSkVeiiRF12SBZ1mWIApshOu6lUpFVVXgkeEKg8sUpAUwfdNutw8fPnzlypWtrS3f93d2dgwjvXJoKZ3K3rlz5/r1a2fPns3n841GY2Fh4cknntZUI47jx5549vz580eOHPnSl770lre85fnnn19cXLx9+/aJEyfu3LnTarXuuuuufr9/8eJFwzAOH7lHFOU4jiVJsS1nOBxLkmLb9uLiYq/XKxQKQeCNzdHm5mYul2s2G+qCkclkEELD4XDCT0y2VhiGsb6+XqvVXNc1TbNSqWCeW1xcbDbq5nB06tQJw9BAE+d5nqKqhUKBUGZ7tz4ajRiGTafT9MCuFJ5lESIMw/AsF4ZhfzSemZnp9/ulUmlra4fn+VRKh1UZkwJwZ2fHNE1JkuBVQUqEOQe4VFzPhlIOKGvAxnzfL5VKe85dUciA9T+cRhPyAHKgJO1NOaD9Bb2Ag4GwBgQuEwXpweyEXkshQEUKrm8QM8neZOl3d4lC3QgZ4/sG4Rt6RYwxFJPwOgEEGwwG4/EYXhuoGQBtB3hwa2trPB5fuHABpqenpqZgqQDAbouLi9ls9ktf+lIulxuNRqdPn+4Pus89/0ychKLEExpLslCr7zSatfvuv7fX71y6/MrxE0c5nrl2/cqdtdv5QpZluSQhvV7fdb1isZTL5QmhSUJs2wnDqN3u3L69Cv8VhtEzzzzrBX4Qhbbj9XvD8cgK4ihJqO+H3V7Pdf0gCMa2ZZl2EhOeEziOUyVOUwRdFQ1NSulyylBVReRZNp/PMQxmWLZUKRaLeYJREEeSqoCJo2VZzWYTAAxIRLBPAiGk6zp4TzSbzdu3b0N97vsuTCclSYQx7vU7mq7ANvlisaQo6vr6xqFDh0ejMULonjMn/v7v/17TtPe///2bm5uQKMbjMQiaofVKpVIAijz77LNra2vdbleW5XK5XCgUyuUyyNa3tnZeffXVXq9HKZ0Qj5ZlVSqVYrHYarVg5IVS2ul0SqUS2IXA3L0sy/AsIASH2srzvNFoBA0LlLWe50FdCtbvuVwujvfHnCklCGHMchzHi4LAi6PRaGp2jlJ64cIFx3V1wxgMx9DJTyQKiqJEUbS+vg7g6vT0dCaTiePYdV0QRUOkQZcI7wYMlEwwPN8POJAdQBRNRnUppaD/gnUue+N/DCNJUpTEE2QPHXBkmoQHPbBoDU3I9EkiZRj4ItnXKDAsi/aL2+8Xgf/IDU5HIFrgn7AVWVNVx/ZGo1E+m52ZnpVl+fbt291u1zGtYrFouw7PC1NT07pujOv1dDodBtHi8oogiHGcJAm5c2ftLW95y/Xr16empkqlUjabvXr16szMzF133eX7/pEjRyBv9Pv9s2fPXrp0CQ6U0WgUOAz4iGqatri4OB6PG40GnFxra2sAaguCcPPmzZs3byqK4nshALmj0WhsOwQzcRxHMfG8gFLKcDxGDKWUZVlZ1QzDSPFBkiSyxEkSzzCMqkosL7AcVjU5IVEqrXMcJylyGEcJQaJiI4SWl5dZhltdXfU8b3FxUZaF3d3dMAxd14dPCs7BJElkWd7c3HRdFxa1m6YtSVKSRLXaTiaTYznc6XQAg7l9+87Zs2c7nV4YWblczvf9mzdvVqvVBx98sNFoDIdDhNBjjz0Gk6xPPfVUuVyen5/f3d3t9oaqqg+HQ9u2VUWDaQOO4zRNO3r08Hg8FgQum8sAsRkEnsDKuq6DjAbkDdBTAXjW6XSKxSKldDgcFgoF8Mt48cUXc9nM1NTUnTt3Dh9aPnv2bLfbLRbzt27fbm9uKapBCIVytN/vl3IZlucwhQsVrDlpHMdCSkSONR6PeUn2fR/qI4TQ8ePH4akNw4CZr0KhMBgMQAsOXY+iKNls1nXdbrfLgq2TwKuqIssSJhQhBEQAFJ4izzOAhU5oBvgPSE2QqSck/uQ+2AQAYDPpEics/Ot4eXxAU3bw/p4V8X7gJUkC2fL7BdtBLuQgNcLzPFSzGGPDMDKZDMdx4F8IuRoYKqCMwXEA9GudTofjuMuXL49GI9/3M5mMKIq7u7urq6tQm4HV74ULF+C0k2UZIvDVV1+1LOuZZ54B57Vut3vr1q25ublqtbqxsQHKDwAAAO4CxgwUKrlcrlqt9vv9GzdumKYJJ3er1Wq1Wp4XhGE4Go2GgzF8ojGhECGCIPGiJEmSoij5jGaokq5KmZSWNlRZ5FVFyOdScRzm89lsLheEoeO6upHOFvKIZcBPVZKkkydPTk9Pdzqder0+kYkNh0NVVaGNgZN7cXERWp3xeOx5jm2blUrl2LFjSRKNRiOWZa9evYoQk81mLdOZnZmP43htbQ00Ky+//DL0QhjjZrO5ubkJEjPf92GmwTCMI0eO5PN52HELmC20XpIkPfDAA8ViEa6chYUFQRCAlAemvlKppFIpjLHv+2AGeezYsePHj+dyuV6vB7MdCwsLmUwG9AkQJ0BOCIIAfCnP8zDTwHGcqqqQMH3fD6JwcmH7YQg6R5bhrl+/Tgg5duxEqVTZ2dkRRTGTyST7znfdbhe6tn6/r+s6MHxQf004TCgHYMkUqA5YlgHgc1JCctCoQCKC4hCCagJpwlNCYnUchxV4iEiIWLpffzL7lANEzOTvSfV88G+gKKDEBfH2JJjx99HqQPzRN9zCIICjBarNIAhsy4UlM+VSFTJ5rVaD6jqVSvXaHdM0OYFvNBrgiXT4yBGEUKVSeeGFF5aWlp599tluv3fixIk/+ZM/KZfLLEMxxnfu3AGt2bVr16ampkCjdOPGDYBkFxYWer0ex3FLS0uBY8JusDiOYQ0YQmg4HEIPBt5EzWZT1/VKpTIcDgllbdsVRZHhOUVRMGIp8jmOS6XScBQqisZwPFw0iqKk9YTDSNOUbCEbRYnrewylvCQRSiVJ4gU2SRIvDBhekHmO5bgkSdbX11mGu+uuu1iWrdVqluXk83lBEPL5oud55XLZ90JVVXu9HkJoJpsHqQqlFCaJYB3AhJTb2Ng8dOjI4UNHn3322ePHT8IvNRqNADP81Kc+9cu//Mvf/OY3BUEoFAoLCwsAJ05NTa2vr58/f363tgb1fxi2NFUH1jSXy21ubnIc1+/3NU3hLJZlMdTJQH9blgWBBO8q7KPfc0vhuHK5HMcxsIsLK8uHDh2q7e4Mh8MjRw5RSu/cuVOtVuv1piTLxWIxTlCz3Y0pMoyUoamjfhf6TEEQWMxQmvA8jzELrAb8V5jEw+GQEEQI2djYgKMf5EeapvE8n8lkQFQMM+hhGAKRKIoilsUkSTRNg3YRkoFhGJqi7knE4piDoVtAESBLEkIAj4JClNnfiABYKIcFfEA8DaFMD3iuTcJm75/7ghioSzGh8M1kz5U7EURuwrazLBuR8B8Jwu+ZEnme51gBTjUWY1lSK5VK4PsATrpBQFUE4nqe50kUNxqN/nAA2eDo0aNnz57FGN+4cSObzb744ouu6545c+YLX/hCtVrtdDqiwELbOTs7e/PmzVar9ZGPfORrX/savKpUKrW1taVpGihFs9nsnU4Teg/QoKVSqUajsba2Njc312g0ms0mIGPT09OtVuv69euipAFiKbCMIAgCLzmeixBSFBWzrCBIHMdRjFiWVVVV11KKbEYxr2pyNm04XuB5XpyELOIzmYzr+mEYCrKEMe72esAL67pMCGk129evXwdameOETqdTKBSq1elGowFw/9TUFHzKgADBwKSiKNvb24DrgMIWChaGYQzdWF/fnJqaATQBdF4XL148c+bMl7/85dnZ2TiOa7UabC5ZXV0FTAuwrvHYymazSULBANY0bYTQ0tISGOqk02nPd69du1YoFFiWrdfrPM+PRiOGYer1uqqqhJDhcAg6xFQqBfjq7OxsvV5HCI1GI9BwYk3r9/tR6IP+RBAE0zSDOJFlOZPJ9IajIAhUQ5+MXOwDkHvXJ8gzlheP+4F38+ZNXVEXFhYGg4E56ubzeaDmARyBTYnXrl2D6hQkPkAhIoSMtErI3sYxSGxg7gZyc8dxPNvjACjhedEwZHjLRqOR74eqqrIszzCwihC6VsQwXOhHlFIGs4gimiBCKUzThn4E6RXtV9aQ9CRRRAjQ14RhGMSwhJAoTliWZQQREeJTiqIImsMEY4bhv4u+7IcZhC6hFFOGQSxlecqyCUUkIUmEKEGUp5jhOZ5HDI4Z1o1oJl++ub5RLlZK+UKhVB4NBoVctZDLf2f88uKpo47jPPfcc8tLS+ff/FCn03nh2efy+fz2lUvj4SiXyzXr9ely2bXt6elpJ2ZGdiRpua/8w+NhEOi6vrZZ833iuNb09LTvRa7jj4bmsN9fWVm5cukKIwiBFxSL1WazoTZ73SvXS6VSwrKXrl8PEVnb2WQYhuXw4NowSRIlo8KHbdumruuGJkVRpIiI4xhNx5IkmOYgpaY4jsNMXCgYtdotU65WlpZrtVqW5Ub+iFFEXdd3dnY4jsvlclHs6hrX7/cX52cty+p6fXXq5Hg8jmLkuGF/sK2qaq8/DgKSL+jTM/NBQFOp1KVL1xHm2p3hPfcsl/LGhjloN3YRZRYWFk6dOJ5Op/P54vbuTqfb96M4ZujXv/UP73vfD9398Lnd3k5WVAlJWMRjgo+sHFlbW2U5rKtCNiMHvsJgt1SSZTlIkj4hvdXVF0msRn4UB3EYhnEQMgzIU8xuuwYkBE3iwPV0RRc5kec1XpQ4jvP39nCozXZ7bm5OlOWYkFQmE4ZhbzBA+3i7IIoiSiRMsMT3HTOJWdcLlg4d5nl+p9n1YzQc2wRhQRJTuuF4rj0eqxzruq4XBFA6chyH4sSx7Ewmk0llMWYZzJeK1TAMR6bDi0q1OkUpDYJAVlWEUKfXa66vb+7uMhybsAxFtNbvIoTCOOR0FSGUuFE6nVYEmYmIyolUVuM4tkdjYG4xQpqu7OGckBkB6wcABh1wfDoIS9LXTkJAEZvsOzLhA7Js+B4oMidfwfujSUDO4H0xDd2XrfLMa55iklTRa1mKyddlWZ6IeBiGoRjFcRwGcafTyWWzhmHU6/UkileWlqanpzutdhTFCKF2u/PAAw/OTE/duHEDlteCsAsg3NFoNDMz0263ESIcx+3u7pZKpWarHgVhKnX4O995MQoClmU7nfbM1HQURQgTVZUbjdpw2I8o1jTN9z0g/W3bjOJAURTTHCX7y8M933ddV5KkQiFHwphlsSAKsiIKgsCyDMOIkiSJEo8QyeVy6XQKqkGGRaIoRmGCKGNbriQqnhsUCoU4jkVBxhgH/t5ugl53IIkKy7ICL0H5BH0XxwkwkwFLOJrN5nA4hLmHQqGgaUYURZ1OR1LkdDaTxHRjazMMo51afX5+EZACmWOXl88Konjx4sV77r77U3/yx49+8MOdTocQkkqlbt26kUrrQeA///zz5+4+I8ui67rlUlVVVde1l5ZWEEKrt+qg8gWwHthUkFLMzMyAswHGOIoiMIPVjD2eSdf1dDqtKAqoT/HeykoPIQRN5h6CGLqpTJphmGazGUZJThCCIGi1Wmh/QgAAkCgOQMsFgQcVH3RuwP3AvNJEVUcphSlE2+ahHICUA1VkGIaO58IgIsGIZVkY2en3+yk9tbCwkMvlYCEHIKIw5k/2bSu+q9sGIBXvr1Iib3CO2Ss4Dyi86WuB0El4TNLg5D488oTln/wsVLMHgxBsoPABpdtr4vAAjQgPwnFcRBJCCIMxyzIUoyiKoigRRRE0fnCgdLtdSZBJnICkUJKklaXlmdnpb33rW51OZ2lpaTDoqYpUKhc4jknpGV2Xo8iI4xijxPO8x594NW2kaMIcPXboiSeeYBHO57P1Wl0UuG63LbCcKIqtVst1XVlP+763vb0JA3u8wI5GI9s2NU2jlMRxjBmaJCLLYlEU8/n8sDPSdBlaeYxpkrCSJBkprVKpdDqtyRuoaVoURcVCmVIKkkXbtqF6BHAYYABA7W/evLmzswNTGlCb7fXP+709GGfBABFM/c7MzDAMF8ex6w42NjZs266Up6ampk6dumt9c1NVdVlV0qnst598Yjy2Tp46dfny5aNHjz765h8YDvscxzSbbccZO45z/MRRSollVUvFShB6rmsLAg+UPUJ7py2QCjAMAQWqqqqrq6sYY4C1QBzDcdzCwsKVa9dBKANsIUJoz7MvSUCmDzpnVVVBN8LQvYEEhBjgMwBukSSJECS6bhQlDKCGLIJtaoDQwk9BEwHCVLCTDIIAEBfY1SNwDEheoygCHkIUxXQ6zZljWMFdKJcopbVaLQzDdDrdaDTS6TQEpO/74Get63q/3wfIKooiDmJvgpccTEEH895Bag4dWOv7xqw1yX7wU/H+GsOD7eLr8uTrfnbyLAx+fcjBC9kT1hAKAFKCKORIAgMFGMOg0PbWFou5w8srkiA2m03X9iqlciqd2dnZev/7fiiKoueeey6TyRSyGd/3eQFHQXj06Eq/3z+0vPTKK69wPBPFYRSxsiy5flCtiMdPHBkO++Vitlar6caMZfLjcT+K/SRCmCGKKooSJ6qqZVkJiUgYa5rGcVBoJAyDOZ6P7CCOIsMwJEnwPG88HqqaBAwSLJyK41jVRMPQUil9OOxLkjQej8MwIoSCMaksa47jVKszzWZnenp6a2urUqlYlitJqmk6sqyVSqVMJh9FkesGk7FXGFcfDsdwYQEWWigUkphOT0/funWrWCyGYQyCzJSRKeRLXuC32+2NjQ3X88rl6k5tt15rwkVpjseGob/88nd+8Affe/XlF+fn5zudVqvdmJqumKZpmuMTJ05Y1ng0Hhw5cmRra8tx3HJpCiaGAckAJg3ADCBICoWCoihwbiKEANj0fb9arcLFDSPae6OVUZROpwHlt20b1JSyLKfT6SR0tre3GYYTRNEwjChKgiDQVJ1ihDGOknSSJAzD7YnpRc5znAl9B04zEBuCIMCAL8wlA8zOcZwVeCBPg/9yXVdV1ampqVQm3Wg09mYgHafRaMmyCB65vu8DNi7LMsxeAJ4EhLnv+1xyYICQ7pN19A1q7O8ZhAcjCh/w6p5Up5Oget134v11vwelp1AYJ2EM/wsxt/dEdE9qAzKcPYkcgdM9Yfg9/yiQvQPLAnyApuhhGEZBmMvleFZot9t2y3vzm99crU5/7WtfiaNk4dBcp9MKI19TZKqI6YweeDbLUZZLWBbxCRYQxjh55KFzzWZzZrby9a989dixo9VqKZdNGbrU6/Wyad20RoQks3NVz/P8gJJYmqqUu91uIZ/p9/vzc7OKIq2treVLZUUSx+OxoasIoQFNKCGKIsqyrKqwi5fyPKMosixLu7u7lmXBpQZiJkIoy2KW4eOIiCl5FJs8Jwq8xHOiJCo8JzbqrWajvbKywrGCqujj8dixPVg6e+AUQ5IkZTKZq1evchzHsQIA+js7O7Kslstlc2wXSsVjx45du3ZtY2PzuRdeyGazsqz2er2nnnrq6NGjkqr0+31FlvO5nCxLS8sLhJBcPkNotLCwcOnSJZCh1uu7iqKwDE8JZhkxmykSgmDPJEQyNMOQzWCeExhtz/OGw+FDDz2UzWaffPLJytQ0HKmGYciybJrm9vb2aDQ66GEBVQBsNfYdFCc08t29/MYSjDFJiB8GlFJJEBiOgyGjJIp5nhdUFUga4NtgYA0OL7gBfIgxDoLAcRxEYphLBE1yQikMyHd63dnZWcdxVtfXhsNhNptOpVK2bS9OzeD9AUUgfvr9Pmg5AZjxfZ+baD4nMQOB8T1T4sHK8/uFKFSVk4RG91esvS5c0f44xYTtwG/Ie9+dzKB7gc3iPRRr0hdO2MiEkCQhFKMJY5lOpWRRkQXRGps8zzOIQQgVCiVFUR577DHbtqcq5bW1VUkWpirldqdVrU4FnqPpUrvdOHpkpd/v+76YYtVut5vP5wSB73Wap8+cdBzn1Mmj4/G4WMrSJNB1PYrdMAhkiQ98p1wpt9tt3VD8QJmdnRZFvlIpMQwSRb5SqcRJ2G4LkiTB+E82m0UkkCReEBmEEGYEhmEUVeJ53vP2WiNJkhiGY1m+WCwLgtBsdCnFo5GZTmeHw3GpVEkSks8X4zhOp7OQ7rrdPlzf5XIVPl4Yb6V0z7oSnDI2NjYWF5Z7vR7IhqrV6TiOR+ZYVtXx2NQ0/dTp09vb24IgbW1tzc7PHTl6iGGYbqtdKJeazfr995+nhJw5c/prX/saw+BcLscwjKIohw8fZhjGcbzp6dlOpyeKsqrqpmmXS9O1Wg3IGzhWgIGwLGs8HmuaVqvVZmdnT506BaPYkLFBZAs6FZiEgDxJCIFhImDhEELAuCaRDxew5wWZPEmn05lMptftA6tBKRWQhDFGiDAM4hisp9PAHwIpALOCwBTE+z6A7D79RgjJpFKUUhiCyefziGHa7fba2lpv0IcNNrBSBQTiPM+7vg98NScIcRyPTNPzPEGSEEIJpQQhijEHsMpE0gk9A7yIN8bM98VLDiRJdKDr20to+3cmPwhp8I2Pf7A0nXw/hONeikbfPQWYPbUHm1AaRRFFCGNGEAVBEHhOgEYIIZTJZGzTGo1GumrMzc1Vlme/9a3HZVm87577nn7mSde13v9D7+1229lsWpVFa9xPZ/TQ5yqVih/YLEdZOR1G7mjcO3H02Pb29r333n3l1Uuarlj2iGeYSqWMGSqJfCGf5TjOMLRyOR+GLsa0VCoUi3lVlSVJ9H0/k02xHHa9EM5soD0qlYrrdDmOYzBLKWVZDGq7hETFYjGKYkIIy/IIJXFEctkCxth1dwDq4nnBth2O4z3PSxLS7w8qlYphpDDGpmmBlnVqatrzbADPgMICkg0hDFMOMEoCLwk6GdcPJEVpdTqAkfh+yLJ8r9crlktHjhwBsqGYyzeN1Kg/uHLp4nz1raoqE0Isy+p223Dx9fv9fD7veb7juCzLBkEYBCEh1Bw77U57coGBJAtS1uLiIkKoWCwuLCyIothut0EDDM2FIAhQ+4FZ4OzsLOQrqF0RQmEYAlgyHI1Gls3zPGIZvG+8AMkKwsz3XSZkQt/HlAocN1kxAHwDNJzgAQNT/JNrG65hoAEhg8my3On1oP3TDH1nZ0fTtHQuKwgCkIRHjhyxBiPQS8DqX3hMMDqZpBAO7atV6D5bckDl/QbS77Xhhw6AMcxrNwpOMuf3S55wpOF9/hDt94fca2vXvRimrzkC9jId2ntJe6o6llUUFSAsBnOtVqtYKCiSCv4uiiRlMpmpqalv/P++8cADDxSL+e985zuzs7OVcmFjY2Pl0KI6Erq9JkJUlZWlhflWuxEEXhgGkhBpspLPZAWBO3r08LWrry4uLtTr9WIur6hSJpMZD0eGoR09egQUxmPTK+QyGONUKkWTiGdxEgUpXc0cO04pHfb6uqJOV6c4zLiuOzc9U28G8BHEEdk/nmLfT/K5IsaM4zj5fD4M4uFwGIbhcDhmMOe4Vqqc6XUHqqrWa02e54fDYafT1VQjiqJ8Pn/k8DHbtm/evNnvDcPIgcwAOw/hpIvjGGY7RqORoiitVmtxcVHTDN/3VUPN54sXLlyYm5sDQzRZlkF5t7a2Zprm/Pz8hVe+c/jwYUWVdnd3vvjFLwJvBnlDUZSdnZ0oinTduHLlSqVScWyPYZhSqXRndT2XKxQKBbiIofWChlDX9atXry4tLaXT6dXVVcuyNE0D2QogosVicWK+JkkSKJ+AexwOh81m0/M8QRCKxaIsy8Bk5rJ5gRfbrU4cJbIsFwoFsCYALCQMfUIIwyAoNwghE5UYnA6T9ZtJksDlBiprkFhDyMApwHFcOp12fU9RFIwxWGal0+lqtVoqlUgQEUJAb2AYRqFQGA6HMMQMyY9Syk2OyQlGAo3j5KKn+1bce4Xfa123mQMzfnsXUxwn390hwUymePEBZzS6b6t6MG73ytc4+u4/J3KcPWkfwhShfQAJMyzDMK7rKLqm63oUx1EU27ADIEyAWZZleWNjo9FovOdd7zq8cuTXfuVXT993zrIsjGmlUomjwLZtTdM827Ftu1QoDoZ9MP81x8NqpUII2W1bMJ7PMAxGKJVKpVIpEsWGYaiKxPN83bY1TYvCUJYkeKNqtVqlUikUChMVMshlgiDIZrOAts3MzDSbTdd1YcXsVHXGdd0giDiO6/UGmXQuiiKGYaMoMU2TJKjX67VanSRJ7r/vIXCaAhwCBuRv3759+vRpkDgPBoNcLifL8tTUFAwKViqVTCYzGo1qtcbhw4cFQbBtR5blbDbbbLRXV1dlWR4MBmEYp1IpNa15gZ8vFlieI2EwHo/7w0Gz3QIkc3F+IZPJnDpxkud5nuUW5uZfefn5EydOFAqFo0eP3rhxa2Fh4cUXX+Q4vt/vq4rOsQLLhjzPO443Gpm6nuJ4zvM8sCmo1Wqj0ejkyZOrq6scx2WzWfDC6Ha7hw4dAsxjt94AaX4URZZlgTdxv98HZrzf7zMMA6PS4M3F8lwqnQmCIEpi1/cUTQ2ikCAqy7JtWqqs8Cw3GAyWFheKxeJwONxY34aJwU6nw/N8pVIZDAYXL16emqpM+jIIEKgNAUEFNQtCSPI8mF3mRQFaTdt0QUQOczPrd+40m81isZjJZUHR1h8OEkpEWZqoPrmD2QwfgENfUw0eiJZJz8rsD0BABTXZWU8P+HbT15KKBzPkpFJ9Y2pFb7jBccWyLMdyUDZMXmShUPCj0HGcOEkkSVY0led5kiCMsSLLt27dknjh53/+523T/PVf//UHHnjAJYHneRy7t2zD9VyUxEkU6JpsWU6r2S7kM7MzM3R62rLGV69e5dVCFEW+68VhlMlkioXy3PRsxkj3e90wjEVRLuTyoW6AvVoURYIgwTaLCxculMtlx3EGg8GhQ4d0PdXpdFRVJwSZpk0ptW3XspyxOc5kcq7rc5yAMR+GYcrIQMWYzeYHg4FtueVymWX5MPSKxeLNmzeTJIFuCopA0IuEYWgYBvhwN5tNjuPAz+Lc4jmQXGezWUlSJjsFwPEJlD2gaEWIGY1GyRjZtr2wsLC5uanreqvVAvkYgzBYg3qeQ2mSzRZUVW42m7lcYXt7d3n5UBTFjuPs7tbS6Qyg9o7t9ft9WK6CELPfX6GJ0hI6uunp6UajEYbh1tYWyI9g4pFSCm9ppVLpdDrdbrdarYK4FOb34VxDCGUymYkquN3tybKKMTscDsHzBiHU6/V6ne7c/OxgMJBluVDMAz+0ubm5vLwMCieQlUHufeSRhzY3N+Eih1IWFqIwDAM6GFCTMwxjuy6UpnrKmHAhuVwO7PwAHYUxRXD6msxIgGo3n8/ncjluEn7Qp33PGDh4m5BOUEBCKsf7HOAkSU6+n933U5mgNXRftobeANVM7h98BLTvZcjsewTvFbqIYow9z4spYRiGZxiouVVV1VSD5/lbN28KnPhPf/pn4jD6+7//+2w6JwiCbbvmeBiFviQJlJLxYOj7rudr1ljgBZZnBUJw4Cc8z8YBiQOipSWRlyml3e4wDGOGop3txmDYy6YztmVxDKdpKUqJ53kkIiInekHY7/Y0RU2lUlEQirwgavLudm1+nuu2e7OzsyzmXNdlGCbyiTVyZSUl8MpwMK5WqzyHBv1xJpOJIrK9vc1xQqPewhjD0btnGToaRFEEck2QhmUyGZZldV1nGGaCrQPiBxUUDK1PGGRZlg0jxTBMr9cDYTpwGHFMNjc3WUXp9TqZfK7RbmU8z3MDeDrXdiilYehHURQFwXg4TKKIxFGpVL506VK/PyiVSo7jYsTxPI8RyzJ8KiVsbGyUSiWMcRQFkiRgTEejMcMwpmnW63Xo2ZrNJhR4pVJpdnaW4zjwdMrn88vLy5/7/Bfife8vKJ7B9wmsQ8DWudvtQo3tOI5p26qqIoZBDMdxgqqqQPQnUTwxblMlmedZz7ZK+Vyr0wfBAAwWg0g1k8koigLPC9tjwZ8OITTodSbkPqgOgNXA+6N/iGVABrSxsbG5uZlNpYH/oPuegFCntFotCCWWZV8ThAfz1esgTbRvYMFz3CTXTUpQdMAl8WAjCzf6P2DidjDe0GvzMEII0dc4ke6/Howxdl2XMhhOQUKoF/imafpeSAi59957Txw7+cILLziWffTo0bXV9U6no2ZVOCw4jsOEgg+ayEuj0bBULGSz+SiK+r2RpkgYceXSlEcYSVQ5jrFMW+JlhmGiKMGE4znZc/oMclVVRYgEbsiyQqlU3tzdIoTAmFy73YVf//btO/l8sVFv5bKFOI6HAzOTyWQy2Xy+kMqqg8HA9yPPCxmGCQMShbRYKK/d2RgNzSiKGIbrdHqwGqHRaFiWPR6Pg9Cr1Wrz8/OixJcrRYRQGIZh5AehJysiZqhuqJIkwUQvAOJhGEbRuFqtEkIYhh0Oh51ORxIVnucBAlFV3XVdFmNF0ba2dliW7w0HYG3GZtjl5eUkiSRJEnneskzf9xkGHzl0uFbvZDPFSxev/vAPL4uCyvPieDwEWoXn+f6gm82lM9lUq9USJd6yx45jr6ys3L59u9FonDx5stPpbGxsgGfk1tYWz/OKorTbbZD4yLJcKpVgTl9V1e985zuqqq6srHQ6HYQQqDTBkBKQSVEUp2bkKAhBM81xnOcFgFjKorRb20mn083arq7rqqpeuHDhrW95S73ZS6VSYDkHF61lWRsbG6COoJTClFyyv1MMIgda0Em5QSkdDofgUMzwHBAwMPWyubVlmibQZjzi/SicjHqGSdxot/qjITeJwEmCoq81R5uUlBBRYRRNIu3gOG+0//XJ33vTum+wEj4Ipb4xJv/vwt6zSY4syw5877lWoUVGSiQSKABV1VVd09NLLm2bazPkrPiD/BHD5bddcse4NNoOh5zp7mlZ1SUgMpEytHTt/sR+OBGOAFDDDSuDZWVGRkRG+H333nPPOXc/CN/9qwheIeabeGqqbemzSZ6hF7VtRzcNzrng6uDg4J//83/+y7//1fDhoVlvoM6ZzWbd4y5jxDZ02zAJVZZl6Yw26i3LsnRNJ0pKwT0/6HX7URTFUS5yahquZWiyJLwkjJE4TAnRNM1k1JCCFgUXRZkkBaNmLWgdH/NHjx5PJpOrq+t6vZ5lxXg8bjbaumbmeTGbzYVQk8lM181Wq31wcDhfzuKocJ36/d1E07RGo8W5VIoeHh4LIY6OTgzDAIXK85yrqzf1egtHMmMM6lIooeGf32w2MXCL4xiiHsNgtVpts9lYlvX27Q0E7JRS1Mm1QAZBkKYpAI96vb7Oi5OTY2wLFsJYhZtWvWEYhu/7eZ5mSbIpcimlKPl6uaJErpbp4/Onv/nNb8bj6fHx6dXVFdzzNE1LsxhOH4PBwdu3V4PBACYmEKGT3QJ2qIGOj4+vr6+TJAEzBjSM+XzeaLUfHh4wi68k7eAw4I8qyxLsE6T0lO/Sg2CEcGVZwDjCMJRSMrUt35I4ztLYMLQnT57AcBH5EP0nak62076ynQKBMea7NgJV7pw7gafEaYJfVIwiVtG04wiQUlbkUFgfDQYD8GTey4T7RebHubEKwn0Xtv3f+qCrpDsmhNpx1vaxzSrsP45D+v5MsvoXVx6yrr5zy0FYQnISxXEYRo7n9vv9Rr31+PHjf/Nv/s2nzz87PDz8w+9+H3ie67rddieNQkJIQmlZlrqhpXFSFMVisXIdC6cmEYQojSgtS0sldVNjZVbkiRyPZkRpusYUViApZtu+bdtE8LwsiNKklFlaHh+fYjEQISwMY8t0BgdHZcmjKLYsRwhVCxpxlMZR6tjlaDj7/uUPrVaj2+2+/OFS07TTk0ebzebm+r7d7sAKDRNexojjOPP5XAje63WbzaYQvCwLTWNhuJFS9vu9+Xzuug5jrNfr2rbtOPZ6vXry5DF2DD158kQIAYn98fGJ7/sAEjAGwKW5O+Otbrf33Xffdtrty8vX56dnjNFer2dZxu319fX12267c3xyuF6vb25u5jPe7w9OTx/94fff/OxnX/23//bfnj9/7jgWYyRO4DIqazXfsoz+QVupFqPmarWCdeJwOCyKAk0jIFPLsvI8H4/HWZY9e/as1Wq9vbl1HOfNmzeEkE6nszWeCYJmswl8FXJN1NW6rk+u7nr9jlJqOp1qlLXbbctyVqtVFG5++tOfLmfT09NTQ9cXi9mLFy/u7+/9em8+ny8WC0AvWP8MXQj6QDSEpmnuUhFFAoR0Tu2szDzPw7Fo2BbCcjQa3d7eVgbTkASCZ88YgzjT87xGo6Hvx8+7Cfs/XTSCZYfeD8cS6l1AumqP5oZgo7scu59OqzS7X9Zug20X+WrP/JeSvXHFDqqiCudlZjo23g4otaAF+du//dtPPvlkuVzOihJ/6tOnTzerdVFm5e7mB15ZltiedXgwKIqiWasnSZqlPE3KKIosU5eShWGqlCKEtdtd27TSLO73+2UhNWZoSouStOTCMKyiKDarMGjaV2/emqbZ7/am07ltWZ129/r6erMKPcdVgrSbHSXI7e1tHMab1WYynjYbrShMlWSlFLPZSimBq9DzHSF2K98MA7vchsNhq9XIssT3XSnlyckJpRQ80vv7W0qbZZl3u+2yLB3Hur6+evz4ERwA+v0+pm1gY+FqgAsBxuWaZkgpTdvCQR5F0dHh4cXFhW3blKnr6+ter7MlfNgO53yz2QzvH5LEXi7Ck5Ozv/u7v/v888891+ech2FRlnkYrYXgjJEwDJlGlFKHhwe2VXv9+jVUrW/evDFNs9PpYKgwn89RtmFXdq/XS9O01+udnJxcXl5Op9NWq4W1MHme53kOOAevBNt+arXa+cXjdrOVZnEcx1mSxnEsynKzWTm2VZblYrHgnJcajaKo2+1OxxNBLEoppgtkJ2rHEBLmVJjjQxcuhHDtLdcCjHZML9M0pRqD3jJo1G3bBpYLD3iYdHDOwVwDGIudGWDzvpcJ9/NVFRsffIFNRtVN29ud9EHNSd6HW/eDs+oVq8Rb/W7FTkD/VwUhbnSnvzQMQ5QcHMI0y8IwtB3ns88+O310dnd39+tf/eYv/uIvfv+7362Xm4tH55+9+DRcr7///vuzk1PHqYEuZJi67/tUESysB3WD5wUWGGqaFkcR8f3u4HATroqicBzP8wJR8ulkXhbi5PhQSpXnBerAVssjkuZ5OZnMfN+fTqd5noPIcnV1ZVmOZakgqK9WK6yCieOk2+3puu55dcb08XhaqzWKInvz5k2v15OSfPPNNxdPzsuylPKdA93x8bHrugcHB5hJ6rr+/PlzrEa5vr5eLBZPnz4FYDOdTrEUAcWP4zhJkgCTbDQaSIme5/V7A0rpdDrFBKLX6zlCWI4dh5HjOIZp/tVf/a9v37zWdDqdTk1TZ4w26w0gq2Ve9Hq9V6+Ws9ms1WoJIWaz2ePHj2/vbsJwbRiakKUQXNO08WQYx/H9/W273by/exNFEezlcRwsl0s4AhNC0MgBb4uiaLlcekGNMYadSrPZrN1uHx8fv3r1qpJrSilvbm5Wq5Vpmp7nbfL8YTSUXKAlBnmlXg/yLP3222+zOCry3HEs0zSzJD05OXl9PcRKXXhbVTJiLC+B3wKYt3BIytMY7DmkH3CPpJS+7zmOQymFpBs1s+d5wJ/Vzj20Cpkvv/wS7HnMxt91aHJPOaHtrF/IXn9ICNGZodSWUbYtO3dw5XbSKGHGxpQgQknNMqiUGmF0B5wKriSVmkbVbljJyFY6KKUUrrZT0BNFKN3iMqRRb6Bf94M604wwSiiljWYrz/M0589ffP7ll18+PDz8H3/971zb+d/+6n/57W9/65l297zlOPZ8MSWEtPpNachws4zjWCmla2o2Lcqy9AO71a7VW36UrheLhWmazKbrdGV7tuEbV5d/fPr0aRzzZt3UWS5IuViMGOOue7FaLe/v723bef78+cPDg1JqvV63eMM0vSgaep53eXl3dHS0WsetljUaTwGpfffDt57nFTwTqrRt+/io57mWFM7V1RUMLxazOS9Eu9m7fTvq9/tJnNu27Tudg+6jxWLRbvcuL6+fPn2KtHZ3N2SM9fuH63V0dmbf3Nw/fvx4s9nUavy3v/3D8fHxbLmazBdCiG+++/6TTz755rtvYZAzW85N01yGC8/zjh8dwVC03W7HcUgpdxpmr+WEy4fbt1YUhY7jXDx5dHV1tV5tbNt+9OSJ67qvLq/DMHZ9pWjy8vUfWm3vT9/+/he/+MXvfv+Pz58/H4/HR72Tu7u70XD5ySeftJvp/e3o4pyEm2Q6XazXa9j1Usvkko4m86wUpaLdwVFeluXdwzrNv7982263R6MJEO/1OrQsqyzFmzdXpmmHYbjZRJSOKaVK0SCoC6Fub+91x9hsNv1+P/Dt0WgUOL0g8K6vr03dKMuy1WrpjOGSzrIsSkulFMbu6OKAZAJHLcsSNpYIIaQBzlSeRkqn6yyOeU4ZpaZm6vYmDpM85ZwfHR2dnp6A+zadTmku83XChOJJbhOjzMrrHy4PDg5kUtrE0HSS57t12eTH5gQfNH67MpXu32H/nj/6nYqlUZEBKNm616gde1vt5UmAUVUyZFujDHX59u2Tx48ty7q5eksphb0vFOtffvmlZZr/9b/+1zAMP/nkE8eyv//++6IovJqDAaa7VwP4jgllDQabOC8x5WOMtVotVCB43xeLheOam3C5XC6Xy3m9Hriu22rVgsBbrqYlzylTSRqORsP7+zuUhZqp+v2+41pZniyWM9PSh6P7+WIahiFEVZZlFWXmerYiIssTSrTZbBLHcRiuKSW2bZZlrpRcLueu6/q+m+epEKVp6kkSTSajg6PPANyNx+N6vf6P//iPYRjCNRTGnoyxy8vLp0+f5nn+u9/97nmRYRlLvV5XShmGgcMb5RDZ8Y3QH87n8263PRqNgKm22+16vZmm+Xg8tiyr2+32ewfoIWezGWrgNMpRa8VxfH5+/vLly1qthkwSBEG1GhEjk8Vi4bouMg+KQMO2CCFQ/SA9sp2zPdwrw+Wq2+2apnl4eLhcLi8vL4+PjweDwc3NzXq9Bq4LA2w8Jnow6BVN00zTdDabZVlmBSZEEtbOVRATiG63ixeAtIlhia7rMDKEZAmFKH693qmz3VZpuqN5Sil/9rOfgVyOp4OXXKPRuPruDcIHbBsYGsHTiDEGexS9ipaqINwvSvf7wyqE9uOtusn3FUnVYwrYtwn5LpIJoZAj7TCb6oGUUlyW+79eVbjnZ2dXV1dCqfPjk1qtBu/08/PzTqcjpZxMJnB5qSp4WAzAuZkxhrUQx8fHpsYty1osFmBvwrgFvwVcDsYzSqlms9lqtYKamSQJ1STR+GI94arBVbEKZ6XIKaVZEa1W64KnsI0pimI0Kut13zBYUaRFkWZZnOdJHG9M06zVvDRNXdcSgjcagRAFjIxm8yEhxLRovV4/GLSn0ylT6vCou1qtuEgfX5wQQkajUZaHp2eD+/v7zWZDCAHxGrpYuBhiYZBt27e3t48ePcLU+9WrV5RSz/Ngb6OUSpKkXq8/e/YMC70wtQPgvlqt0jQGCbPRaOm6Ph6PHceBveejs3MU7cPhcD6fT6fTfr9fFJnnOUKo8/OzJ08e397etloNzstGo0cIcV3bsixCpGUZvu8mSeR5XczWtr5eGiOE5Hnu+h5MFl3fxyGoadpyubR1A+NQWGmt1+ujoyNYVKxWK1zxIOUBGsGkAaS8drvt2U69Xn/8+PF0PCl2y94RdSgvqWYAnYI/Nb7Wdf3s7Ay8cKxwYrBKSZKUp+CyAqGsxtevX7+GpxMGQnhti8XipHuEPITjHtQfpdRsNsMzGoah70fgx3G4D5kgzBhl++H3cQB/8MU2j4t3kw9V9Yq7e7L9lS/vr0yjuy0U0+m0Xq/DNwkd/OHh4cnJyWq1evPmTZ5lEKRtNpssSWG0yvMCHycMPxqNxsnJyXxyC9QLD4tGAkqTOI6hcENx32w2B4MB0xOp8v5BS5EiDOMoXpY8SRJeFInruiVP02xj2azSE+Z5GidrqQrT0jrdRrMVcNHGWOX45GCxWHielyRJr9fNsiyMlorkRRnV63XXa3a73VarESdLxhg4K5QK22GMMdthQqaO6y1WOWbBwN/wlq5Wq3q9jvXu0L+u12sp5enpKZeyLEvTsGzLWS5WvhfMpvPNOvz000/hHJMkSZpkRFGN6VEYF4WBOQekepPJ5Pnz50KI1XJNKR2Px+PxGKo/4HuiFEDCBoNBkiT9fh9eZsA20AXN53POOcAYvMIkScD1ofo2a5Vl2e/3cTICe4N70tnpWRRFeDQImpCmcGrgsgSVbLvvqeaCUApaQhJGSZKcnZ2B2qbrumUYSikEYZIkab71pEYFhLGBrus4sJCN8VCoqjRbp5QJIZUq1dagUIciCnvXwGjVdQPzWCiw8RTYeAFhJPI20ux7mbCqS6sESN8PCSkl0Bu2J3v/OIA/eMx9wKa6A2OM7IwS93kwTN8ybLZSQ7VNsMBgNE1Lk7Tdbl9cXFBKsfAM8HRF5VFKodw6PDhgjI3HY16Wp6eng8FgOp2uFgs8IBjAsFfFuGk0GsVxjIXGOLHSNFVsxUXWbDal7JRlzhhtNuuAxfzA0w1NN2i71XEcq1YL4jjudpumqXNeeJ5Tq/m2bSslkGSCwCuKzHFsTaOdTivLsjgOLVtvtmr1es0wjFrNMy2t3WlYluX59ld/9sVoNBqN7gkhjWZjuVz+/g+/se02qizTNL/++mus4xwOh0+fPp3NZm/fvr25ucHsC06HXhBg1TYGVo8ePfrDH/6wWCwAmuOUxMS52WzCQlfTtMVisVwu2+22bTmtZns8mjx79mwwGIBvBT2ulHKz2Xi2h9HC69ev8zw/Pz9XSnmeB94sGipgUfgaK4crv6Mt0E1pWZYgi8+XSxyOIMegigNfdDgcttvtbfRSCu5Yde1xzouiCFo1dMsYr2dpgimIbVqoM/chD1CoMUureJTQJSIU8VPbtuFsr5RahEs8EXKgZVnYWzidTkFbw0GD+lYpxbnwfb9izMPzW0p5fHxciX3fy4T7QfJB5FR9HaGyiqL9+3xwf/JBq/kRGQ5VKHl/aKGUEuTHVcWHh4fYtvX5i0+Pj483mw1WDiA9WqYJel4URUQqePvDgpYQ0mq1sCBtOp06pgKUXNHhweXnnNdqNSyvAniIQBVSosdwXAvFm67rQVCHSQznknNeMTmVUhcXF2AJ4ngmhOCJMMXCB2NZFhoSIQSeER85qql+v49xWb1ex3Cl+kRN0xRCgKHW6XTm8/njx4+BEyLMTNMcjUZYKA9e1WQyAf3ScRwEAwbfOPtxt16vB27XYDBAZwHk/dNPP6WUWpaFMx5ygVartVwukW9RvIVh+NOf/vS7777D4pdarYZJAKxx8GfiAgUiCoC92WzWajWqa8gGs9EckzcEuRACDhebzQbTlNlsBufSJEnw+VJKTdPMdzfM/W9ubur1OiEE4KSp6ajgHh4eoGNQQqCeRPAouiUMgDNAKcXRgLJos9lAYITGD68QoGhlgYEmCFQHQggeDW1qGIY1J5CUlFJkZZHzUhClG7pGaVrkcZqsozAMw3fAzH4m/CAg94NQEbE/fvg4YD6I4S13lLxTD6IF3C90q6dWO8UGqSaNO73SYrGAL60oym+++Qaek7AAQtmDiiWKIkPTYcuNhanHx8eB78NUE4d91TpiC5W2cx8PgoAQAhCGUroFaXSfc6mIrul2s91RSsVRKiRpd/pKqTRNNd0yLVdRRjVmux5jpmVpppmnaWrb1LIs163lec6YyTmxbR/igCQpGGN5LvK87Pf7i8XCNLdiv9PTWpJky+VS102laL8/WC6XWVYwpn/55VfTafTkyZPZbGYYxvn5Od5e7AbGZjz0eGhxDw8PuZTNZvPZs2fwAr++vp7P5+jKHMdZrVaoJEGb7Pf7cN02TVsIZRjWZrOZTGau67969QOuKgx4TNPs9jrT6TQJ46Dm+YH76WfPm83mN998o+n05vbedV2mkZLnuHaxRACnTFmWkC/5vh8mMfyUCCFXV1e+7wul0By+ffu21Wppiui6PhwOUbngOMOYju3tBcMoQtf1QpXgciRJslwuRVFCxGybFmaA6I/AKbVtO0qWbLeTE2HJGIPSBd0mwkzX9SrxIH/iCEBhqZQCI3S1WgF6QXWGF4w3DY8AHm+73R4Oh6iHi6L4kUz4cQTuJzelZJW4Pw6/DxAdpRRjKC/fEXEQhFJuTdLUBzf6jiVHCNG07QP+7Ks/Wy6Xb9++zeIExxgaCciu8yy7vb3dYdDaeDxGjY7VnCBegoaLfLXVzgqBK0DTNDjSwlqr1Wo9fvxY07TxeNzsOESVZSE1Znfa9bIsw/V9EpetphvHaRQWnHON2UlcBkHQbPjz2cZ1XaKMJN74HqnXAkN31+v1bLq2rcxxHKIM32smSeI6ruc2RsNpq9lN4tzQ7Tzj08miXmuNx+PZbHZ0eOrY2mw2u357V6vVNGZqzIThClySWq3WYrHA5Yj1w0VRnJ2dIc5x1pSlmM3GpmnHcex5wcXFU8fxxHYdHU2SjDHmOF6aput1WK83oyiJ49T3/STJlKKTyeyHH77DzobJZAJZY1mWrXbz4OBgs9lQSUDv/OKLL/Da8PIAuSGn9ft9IMO2bTOqAzuxLAv1Ht7/zz///PLy0nEc1/dxuk0mS8/zbN2AAzpQqLu7u0ajUZYlRAwY1kFXAeMMpW9Nn5BRDcdFCapRBpp7tNkgaHEZAHRBAsSyKogqdF3HX4qhPN+thxBcSUEo0XRdd2zPtlyNGVKoq5trrAPYNorMYFSnRENKL4oC+g+U8Shizd2KoXdBWMGbdNf1iT0DGBTulFKNvaOtbaNmj3Czn8QwlEzLnBBC1XtJdRvAO6iG7bFkdEOXUqJvVkrV/aDf77fbbRALkyTB0ILthFRSyvl8zssSLMqyLCUVkIRW6mTPdcFXZIyVZY6AhLESYO5vv/325OQEJGBs+cCFIoQQXCtynme5bdt5JotCdNoDwzDms8i27U570G53b29va0FnuVw2Gg3XcjRmM1qulvHhwImjQggRbjJe0rdX9+122zCMX//q951O5+TE63YOpzM5n22WiyiOijRNn1y8GA1Ho9GMEPLyh6sgCGazZZGrOCps2758c3t4eIhF8P1+v9FoYCvQxcXFs2fPvv/++1//+tfPnz/HdoSiKLAQt9/vYy53fX2NYtgwDCz6Mgzj5z//uRDij3/8IwyzdW24XC4p0TrtXriJ4zg+P7+Yz6etVgs7QDVNcz1HKQU8NlxtcBS+fv0aBx/nHP/2er3b21u0bZizTyaTPCsNw3j69Cnn/Pz8fPTLCVI3fDHKsuRSQkF/eNidTqd1z4/jmBDiOM5kMmm1Wu12+/7+/urqCppDuMWhiXh4eKi168irqGtEUTYajTiO18sVrnv0csvlEvW8ZVmz2ezRo0eYsoBJc3d3B12Y4zhIjABslFLAC+hOdYTeEjLI2Wzm+z5aCcTeer0mUjqOYzl2vdkwbYtqrNVux2mim0YYRxBkvQNm6Pvyv/3MRveat/3v/P/efjTB4sYYo3tVKNlR4fI4xRHVbrdrtZptmEIIbGvYWg9YdqVjlDunRrXTEFNKGWWU0na77ZgW6Py8LDGcNQwjixdgFcODAH0a8LrFYhEEASp7bE3Tdd20g+U64ZwbZlByuVqlrusdHB6G8ds0l0kSpbmcTNeEMM9vtdr9IkmTuIijnChdcKqkzLI8CjPBqeB0uQgPDw+LXNqWz0timZ6u2etVrDFrPlt1Oh3TcDvtAyW1JEke7ieWtXZd17EDJZXgVNdsYBJRFAFuAevi9evXp6eneZ5jbzF26y6Xy3q9LpTGGOt2u8PhsNFojUaTbrcbx/Mvv/zKtu3lcvny5et2u/3s2YskSf7tv/13n336xWq11jSdMe3+/l7XzE6nXREdTVOXUgrJlVKo08x2K4oiz3OlFPf3d6j0iiKXUobh5uCgH8dxrRas16s4juI4yjPOOUfSE0JUBvtAvwkh+KNAswQU1+l00GHGcRyGoRDC87zxeHx3dwfTGtiWojFDV4IMWRSFLDkOdMwe1+v14eGhUmoymaCLZroJ5o1t29fX1wBIa7Xa27dv8fJwVFW+UpbjGYal66XjuLVaw7bdOI5Xq81u/sEcZzvZLwqe52XgOWznsIoFYd1uF4U9qOdKKX2/qvwgwOieFp7u1vcQ9d6yiA+KUrWnCZb/XXUi0FHyUTmKjwRWJaZpllmOJVKmbpimqWmaZ291A8BFP0jgVbmLzAxqvLbL5HDIwk/BVkP3WA1kUaUAQEfyTC5vUNAzuuGch2FclnIymZWF4rwMN6kULE1z27Y5F0lSEM7RNYFyAXMNFCGcczwUcuxkMjFNM8/L9Xr9+eef397eP3nyyXodNptNIdTRkXN5eSmEME1b100URZgQ4l3FNhLMpkejUavVsm370aNHzWYTmm7UbOPpEo0QCj+M2nVdn81mlmV99tlnl5eX8/n89va21WodHx9DamxZDiFsPJ4MBgPX8RljlGq1Ws333SzLsjxVSrmuyxhxTAfjnzRNHx4eut2u3FlCZFmGRYX4mKAh7rT7Ukq0r1j/hFeFfSFFUYxGI4QN2vJ6vQ5vC4gScFifnJzc3d3NZrPNJnJd29/dgiC4G99jsy/+ZCK2rtbMMOH1cnhwgF1RWIxr2w6AFjDgwOFWSoFWRXb7nrePRohm2bVaDa8EPtG2bR8eHoL1ho9YKVXJaNnOJ0pKCZ+OWq0GgTI460mS6B8P2fcrzP0acssRVXsE638i46k90OWfCkIpJdkT4LMdmch2HcBTIA3FmxCzJkyBGGOWYeJNwbB1axb+EYaU57lkHNcuNYyqfA03S0xE8GahQcewFbDNdDqFcQiQxkLQZrPJmHZ9fS2EAFY5mUwM3cLHgJmkaZoYnTUDF0CRrmtRtAEYqJRwXUeI0rIMSlWjUQvDNT7mxXI6GAzCMHzx4sWf/vSnn/zkJ69evTo6OrJtu9fr4XLMsqzRaKDvD8MQi9rhHo8KEAEA6g98hNBH0d3Wx+FwqGkalntiDkEp/earxkMAADSASURBVPbbb3u9nq7r2P4nhDBN03ebgquyEIwxw7DKsry7u8uz0vMUUk1lDgRLVUIIIBagWbiIISMACo2cjKGfYRhxHPu+f3p6aprmw8MD/Lan06lQcr1e76jkGorGWq2GdYVYxgpd8u3tbZ7n/X5/OBw2GjUEKmBYlAl5nmPeyxjT6VaOFIahpmmY3MxmM6DNRVH4tQasa6o+DdaGMIACUKx2mnK8EsMw2u32zggrA0gDO2PEv9qtBDVNk0vpmqZNiGYYhDEu5WK1enN1BdnkaDJJ01QXe4a/+4BnlVcQb/LHVLn731E78LOKq+33K/bnu197LwgZY4puxU34Oa48/D1ESDi3F1m+fVW7lTI40fdfzPZJ5a64JXR7xey0+ZqmlVmEAMNprZRC3QJvvCAIqnkx7lOkWV5meZ5PZ1NKqW7qpSjjOGZMI4QkcUY1ommaYTUKnq43YZ7MMQSr1WrrzYwQQhn1fLPdbkiVe54XRotG07u9vcXBX5TJJly0O/XFcuG4xmw+0nSVpJtNuNhqi7I4juPFkjVUw3GNakaHTw2RVqvVxuNxJagROxfJPM8p0aQgUhDLNAO/XgsalmkrSV3PO+gf+l5tPJr6vn90eJKm6Tdff/uXf/G/C6Fub681TcPIYTi8DwK/KNws02zbrDyEgIjEcdjptHSdFUXW73dd1767u8MvYl5qmvr19egnP/lsNBo5jvVwPwU8OJ/PdV1fzFcnJyfdbrcUHFc/AIhqhIOFVpAgdzoddFzAxgkhGN8DyNlsNug+it3ePsaYVFtHIvwvIhkcNOhuAZkCjwGpqHLmrdAKXGk4u4VQaRqZpt1stlutDhakPTw81Ot1xnRCSkqZELwsha4rQhikmBiTwoe7LMuHhwesfMOcZhuEVbxV4cT2RL2oZHYX+4/vpt/vJ98LTvYu8PaDUO2USlW4boEfTQMAAxDJdk2EJQZxZLdRAHMFBKHaU/orpQACJUliMG07jTAMvttz2mvXtZ1BCIp+PHu73ea7ZWb4SPAeFWRSlLFlm59+9pQxbbPZxPFSERrUPF7KzWY5md4LIYQ8KnnGNJFkK8aYwRzN4KvN3Pd923FsWzNtZVhSM/h4PD48PCxF1Gg0TG74tQPGWJpt8iI6GPTRzcZxLJUgvBSCO65OqDmZ3sfJ6uTkZBMmhmFgLAZeVXVq4DQpiqLT6eBszrKMUl0IcX5+nqYpBsSGYQyHQ9u2sTsByvrZbOa6brPZvLx8m2XJ3d2DrrN6/VNoZ7vdTp7nnBeUKlyLZZmj1OecY/EtmjdcbUiMKOxRJ8NkLc9zKTSMT0H7xDr7R48eXV2/xbJUdGVoDWAJ43nexcUFsMRqhn57ezuZTNBcoDaGFBD7xfTdSm2MAaB+BrCEq//u7g7jB+zcBtiTpulkMoFfKJrSqnfA0YNhMnx6wG7FcwGIIoSgGMHpCdS60ajXajUQlbG1Al3Sy5cvqwnHNggRcvsZbD8mxc7QjlJKiUb+u64w1f9uUyi+uReE+Bq9m/roBmSJEILhtZQSosl2s7UFYNS7cWL1CuW+kzdlIHYJul23yPb+wHLnbwcYwNyt3QOYAbwL7y/OV2ZI29Db7eZgMBBCkHtRcsziqes6RennebFabUoe2w7z/EBTOt5PzzNn88KymWlRRYo4WVk2o4xruiS0bLZ837fxgp8/f/6f//N//pf/8l/+8pe//PLLL1erlWVrhDLPD8A7qTfcOFlF8ZKyAV75er0uyxKzKYyq0QOj5Ts+Pp7P5xhgWLYLClhV5KMoaLfbUsp//Md/rK57gGGj4Qy1XByn6OK63a7neUkaxXGm63qr1fA8LwwxjOAw8Ib+ixCS5zmWH4LFBgVdp9NBLcoYs+1cKfXdd9+9efOGc76Jt4UJnAKbzWaSZdh81mw25/O5pkiSJOijgAVEUfT27dvpdLrZhK1WixACpeVisRiNRjiesCt3vV6DtsY5bzWalNIKjcNvlWW5DuNarYYzGgw1jDH1nXB8H/ZTSimxbXaw4RSHHQgMEP6CIAr0wfM8QjggVvw6WpVyZ0OBzKxXhc3+nKBq0sjOkxsVCKbn5CPg9Edve0nuRzLhvlRK7Q1I5tMF6ml8ohkXwGmASaDKxxtEd/IrKWWl36/4N47jUKn274M/AQvu8OtorNEb1Ov1wWAALwkMADRNm0wmrmcFQWDb1mw+zrOCMdXtNoVQNzc3R0cn9UaglGKaajYb0MK4ZolBGQa8lqUxJrMsk9IAaH542HNdp1ZzkZkJYZwXf/EX//Pd3d2f/dlP/+Zv/uYv//IvX79+zTk/PT0VopxOJ91ut9GobTabNI2VssE+JYQ0m816vQ6EBmoA27b7/f7FxcUPP/zguu7bt29X6ywIgtVq5fs+xt/oyhBa8G5DtKB/xu7hZrN+d3cHpOH4+BDvHg5y0zQJ9bIswTdrtRr6Xsdx0EFgoHd2dvbNN9+AV+R53v39PYg+yJ/T6bRKC69evbJtu+Blu90+ODiYzudSykajAQu5Ikkx4J5Opxi1qZ2boOPYqGgw+gcFDNyAer0OQs9iOkN1KoTANP/t27fw9oVZVl5wQgj0n+v1utForFYrSikip6q6wcw2DCMtJQ7xwWDg+37VgsIAju/cij3POzg46PV6b9++3jKuHAf0LFBE0I1rMOemTN8mJUIxusN/QpIkTZBVFWFCEqUIZQwe2Eh2iKh35evu+5RSyii6JiJKQray3G1kEkIIKbiglFKNUko5kVwWSihCCAAYHNhVrGLMQLeuNu97CmNvzM4IhFIqiVJKKkqURhljSmNSZ8TUNSwJlaZhGGmqhMg0jRal0jTt+OTM8xw/cBljtmtacyPLEqF4t9+kVLeo7ZuB23Axk8RGsW6jX3frGByfDk5hoPTmzRu33YuiqEjWqzBpt09gsdVstDRNc23nzZs3FxcXUkqDabyM7h8eXvz0RZbzMkqTlM/mm6dPf6LrtfPzz/7jf/x/wg0pCm6a9eWCHB6e5+nEd08vr/84HN3863/9r//2//27NI0ajWCzWf7iF//Tcrn84x+/Nk0TC7fvbh9OT0//2f/wP/6f/+H/3oQrz/Nsx2x3mo5jnz8+e3h4MC19Mh09f/HJr3/9a8Mw6o3g/uHW853lctpoNOJ0ZNp5p+fEqa1I1u32nFj7Z//sZ5eXl9gV4Thev98PguCHb38Yj8effvrp7e3tfD4/PDy8vRk9efIkz6Tr1A/6x8PhkChD1xzfa87nc8t1er1eccUn81m73QZi2Wq1yrIM15vlfNFvd7KT0ySKV7P5oNu7ebh3a8EqCm3fe/32CkdzmqYHwcHJ+SPLcx3X1W3r8uZ6PB4vNuvBwdHt7W1ZiE6753u1rOCbKHn69GmSps1WW1BW7/byPJe6YXj+aac7HA7vRsOiKBaLxTrcSKJQPaJu1zSNKaJTZmq6bZimac7ml5Zech4nIfXsrpCyzLK6b1MpeMCKgjiObts+ISQJRw/5QuVespK6ro2miyjeaBrlqSBcdZsN13MYI0WRvdcTvldGSokyd39KQfesCj+oQquEtn9n+j49bf8L9f7aw+pHbO9lVJFeJerqR9XL0N7fpbGfVPefC4+gaRr2EKLWNQwLXU0cxwcHvSxPtN2ChIeHO6VUu90WYrso7u7ubj6fQ/NWlmWn0wEHEt2CZVmDweCzzz67vrtJkkxK4vs1TdOIkEopypjreLZtHwyODMNar9fKskzHffLs+Xq9Pjw8pjRnjCVxBl+J29vbp0+f+l4tiqKi4CjI0yxJ0hin+Hw+Pzo6Wq1Wd3d3jx8/uby8fPLkyaNHjzqdznfffXd/N9R1/c2bN2i3lssl1Iz4q0Evhgk0pq/41IBG9Pt9xhg8MxeLBdjtjx49+v3vf39/f48ar3o3Hj9+3Gg0xuPxaDQCmFmVu0hNQDLhVaHrOp4OboKMsYeHh88//xyvStd1rOZ9eHgYj8foxDabjb9bz0gprdoEYNHghQkh4FUD8ucf//jHR48eWZb1y1/+siiKVqslpfz6668/+eQTYDlgaGRZNhgMgA/h+9AlwmijXq+LkmNVliy3lA+UVM+ffwrIlBCi63oYhvi4PS+YTGY3N/dKkV6vDfg0TXNGnCjagO/JebFahWVZ1uv14XAY1Hxd17NsN6L4IAj3o6jCIZGIYLf6QfjtR291xbM9Pf5+MOzfuQqe/X/fv9uHs0dF2f5zsY/CuGoOqzgXuxvnnCtdiIKQjBBiGBqjmiACnI8sywxTw5wRHGsppRRMSbpZR1eX15PJxHXdTqfjed6/+Bf/YjgcrtdroqTGWBylD/ejPM9zWaRZTil1Pd82rSzLiqIwNM2wLKFUvd5I0nQ8mTqWbVnW+fl5PIlXyw1wAkpps9mO4zjLcl3XhVO6rmvbUtd1368pJQ4OequX96enp9PJHIzhly9fnp2dL5fLP/zhD0dHxy9fvuy0e+v1er1eP3nyZDwed/oH6NmwTQXgId5hXddh0wIUfgcJepvNptc7sCzn6uracZwwjO/uHrrd/tu3b4+OjuI4ffTocbO5hi0NlZxS+vDwMBgMANIWRfHw8IA+EMxJQJf9fr9er89Xy++++84wjPF4fHZ2tl6vj4+PUaweHR1JKV+/fg34VCkVRdFqter1erAtw+tHfdTr9Q4ODprNJoYEUkoQ2SmlQGtA3MMEC1u1q/oZzGFMRDabDcgxsLqRnKNotM2tHxyVquLicc4nb4bYn4GDAycyCs56vX50JBhj/X4fMtfNZjMdrymlYRhiS2xR5IoIz3POz88bjZrtWGka/4ioFze526akdtgjmlT2vgXbxwHw0Y8+TIDk/Zt6fyz5rmrdaymVUrB1opRKKqsuWe75cXxwHLCdpxuyN/AeTdOoCeZkXpalaeoVD/jhYRQEHqFyNBoLwXXdlFKORqNwU2Bv3nK5Ngzr5OTs/Pzcdd2XL19vNhuxE3THcRqG8WKx6J0c5aVSShoZL/LtkVkL7LuHyXqxDIIA1vS+KylNO92s0+nd3Nx8/fWfTk5OTNM0jEgIifYDJ3FRFIZhFEUShuvZbEIUOzk+u7y8nM1mL168mE7nd3d3R0dHWZaDqXz+6KLb7f72t79N0/T+/j5KM9u2W60WRqCAfB3HQT5J0xRYa61Wa7VarVaLUrVcLh3HGY/Hy+Xy8ePHaZoOh8Pj42NCCFw8oC2CfIxoOhzHB4MB8Eyc1MvlUt85iwF1NAyj2+3qun59fY3dwEdHR9DBgGwNdBpMSwCBuq4zZmiaBlkZ0hSG8tXyerClwQ2IoqjX6wFah8B/Pp8LIcDGVruJOXjVUBULIaDPIITYtq1RyjmPokhYHF9QqTRNA6mDEOK6frPZhABFCJEkWZrmUZTM50vf9weDI9xZKVoUvCi4rrNer6frdDIZapp2eHRAKc2yBFemrhmu6+v7AAluCDMUDFUaqbLKvmyPvF927sdnFRhVEqPve+xXd64eYYsg7U3w9x9N7qAXqlPMdvCeit0S0g8OBcQGngVBiKkDNzLUP4QQXTctyxSSa5pByNZHvCiKsixw6SyXy+kkAiul0Wg1m83T0zPLcjab6OXL14PB4PBwEMfxZDLhXPq+X683ozgNoyRN09lsgbQTBIEQ6ocfXt7d3MKpodtum7ZTFMU6jOxSZ9So1xu+HyRJslqtCSFgSyF5Z1mqaSxNk5JnRZli8fJgMBiPx0Ko4+Pjm5ubo6Njzvnf//3ff/GTn37//feDweD4+Pj+fnh0dPL9q5cHB/jgs/l8jkEiIWQ43J7osPfu9XrdbrfRaDw83KVpqpRChQnC12azubq6ajQat7e39XodyYoxNp/PfcdttVpwFgMh3nEcVBa46bp+cnICet319bXv+ycnJ9Pp9Oc///nbt2+/+uqrLMsODg4WiwVKfaVUURTT6RSzkEfnj2BAOhwOkyRtNhvNZrPb7YLqgLEH4BDkydlsBgtM1JbQvy8Wi263C6kkCn78yVBOr1YrcN+FEI1aDWgnL8qiKKIo0inDUhqcXJodBLWaECKPIiEkZRrT9KLkQeCbli0ViTbhYrkC9SrLMqr09WaRZrFuMMex2+22bZs4elB6UEp1bc/djOyVhXBWpJSCvVrFD/Lh/hVP90xE2UfOa1s54ftuolXcVk/HdoRvuWd8Wv06Ie/hsZj+KchYsIN893RVGYwivsrkqCiEELnU4NeANQCGoeV5zjSq6yzPM0II3prJZILTsdnuQPOCqaui7Pb+4f7+/umz52DrJlluWLbjeYZlx2k2WazBUUziGEms3SwFJ3kp80JEcdZ1g3qzk+e5UGw6X91ev/7iiy++/OJnQpR5NiSEuJ6NIieOI8uykiRqt5uaVnMcCz4rX3/99RdffGFZzuXlZa1Wq9Xqs9ns9vaWl9KyrCzL8rwkhJydndVqtQspCCGgYqH9gw/feDxGQQtaAiGk8sYsigLJQSn1m9/8BoM+KSWiC1wT4Jyz2Sx0Xdtz11F4dXONqToMrW3bprqW87Ioinqr6fieJR0cbZiqwTYC6Q5L2tBjdzod5BmkO13X4YVBCHFdB+a8GNxjBcV6vQagHQRBEATD4ZDueELAq1Eu4joHQwOnDxIsShV8Z71eY1hvGIbpGtvnpQRTRxBuJsNxHKdJkqxWK7iSel7gecHx8TGkLZpWSkmKgqdpmiSJZZh3dzemafZ6HU3ThCgtK8DT4TopiuKdoHj/WkfGQwrGT6uwqQZ0VetVpcHqd8lusKGUAk/74zjcj1X6/o3tbcWgO1gVH4xS22ExBoDV3aqCGeHH9gh71Y/gXxA49VazU6v7ux0MinNuMD3LCiEEY0RImec559uRzOnpaTWuhJsgLtYwDK+vr4uiqLT5YRje3t4mQuV5HkUJVvMFvgtvON+v9QaHtm03m23dsC6vrh3HSdJ8NlvEcRoE9eUyMQyrLPPAr0u5bDYbm81G11mWMdPSizIryiyMllGUcC6//fZ7vJM3N7dHR0cvX74scn5+fj6bzZ49e6Hr+tdff/306dOrq6s/+/mfAx2BIhmwBJrA2WzW7XZXqxVAdlTX9Xq93x8sl2vX9U3TnkwmcZzatlur1ZbLtW27nEuMCjabTavV0XWGQR/Ga47jAFdUSsF/5P7+Ho1co9EYDAb/8A//ALwEHSbAHlhoA2vB+I4xBifbyiQSgQ1b/jiOoYTG1QvcH4c4YwysQ9ScOHcajQZqXcijq9k69ltRStEWWpbFiwKUzlajicISlA8ACmma2pbrezVG9fUqLHJu6BI4X5HzshCM6rblCh4nWcZLaZnOQb/NeXF8fHx2djadTgkhvV5PKQU+g6YZWZa96wk/uGHgCDCtovBUmGTV++3DoWxvTZrYmawR8iNL7ck/AdLsp+L9tIkEhTtru2lEdf+9BnI7VK2IDghCsnNcZ4wFfuC6LlFss9kkSaKUYIz4vp9mSbfbNgxtPp/ned5sNoWorVZL23azLNtsInhDYQh7eHg8nc7AgW63u6ZpL5frh4eH+/uh3WpKJYnGYOkHVDCJYi4EnJdMx4aVxtHRkWmaF4+fZGk+HA7DMHRdO0kylCio5TzPUUToul6W+WazEqKMo/Lp06f/6T/9p6Ojo08+efbLX/5yMBgsF+snT55Mp1Pbds/OzoQQnU7v/n6ICQqiCxplAIkAaWDVA55KRUi6ubmBI3273ca2pru7O0JIlmWwloIrJFLrkydP1utlURR4h5GmhsMhSsTz83NMvdFcgVuLqMiy7MWLF8jb8N3BpY8PFIxcYCeW51awGWaA+m6zJ5b+Yh0qIgRJDyU3RrWYeEGSbxgGki02i0HQAONguLA6jsMcB50LWFmu61q6gak1MOosy+qtpuU6buCXZWk6NgwN0iKnlJqOLSnJ16tC8Have3p6moSLI3L4/MWzWq0Gyr5tm8PhOIqiNM23xAD2Tyh0q7lq9Zcg8KrSFLfq4q5iUu7WZf9oq7Zf+lYISjV8V7t9GBVBZ7++rQbxamc3UCVkRF1VGFdKQmNHWEPZY5omY3rFzzYMo1Zr+r5r26ams7u7u9lsYpo63EQsy3r06HyxXEKryjRNRzI1zZLz6WxWlmXJ+ZvLS1wBmqa1O52UKGAMvXYHezl1XYcIUEnZ7XYtyxoNR0dHRzjLeZE+PDycnB6jPoEUkGnEcWy4d2n6tlPFNFzXbaXUJ598outGkiTPnj0zdGswGPR6vWZzuw5+NJr4vo+MPR6PNU07Pj7mnB8eHqLRHY/HFxcXEA21Wq0gCO7u7hhjZ2dnYRh+++23X3311a9+9Stwu9FxgVr5i1/84r/8l/+CGq/dbj99+vQffvX3P7x6KYQ4OBzc3t4u16v+4GA4HD5+/Hi+XHz7/XcwtJdE3dzccCkqwAxLCBuNxtdffw1COUrlKIrg6YLrYbVaYWZQXWOO4ziO0+12R6MR0jigF+BMkF/gbrDrLori4OAABHfDMDDJABseFF/M8S3LWi6XkvPqaux0OrZtx5sQ8NXBwYHneS+vr4HxgIQAnhC2hYL2be6WTzw8PHiel4QL7DyE3x+OOayHkZLgk9Wrsm0fayG7RZxqbz1TFXXVpU/3tE4fcFCrUlDX31lC7d8qygvbGT0h0ioLtu2P6DYIcfjt/6LaOW1VQV7FIdmtcEPxjKkreuU0Te3dqleiWJIknBeGoUFU1u93fd9XSiyXy9l8slgsmOGnWbwFZhWXigpZckGYRhzDUkqlaQJALwgCz3eSTagRZdmWptGiyJSUghdECccGPT8URV4UmWVZjmMVRZGXSiohhLAsQ9cZITLPc6m4aRqEENe1a0EDiqeiKG3btq26aVr1egMDZSWpruvn5+dxnMKJdDKZIUv4vg/8EHDFeDx2XReNVr/fv7+/f/LkCdIFpdR1Xfg49XuDNMk77V6R80a9dXf7UBYCUn3H9r75+ttGvRVFUeDXKdHubh8ANn7//feAW3BSwEgbgQSSDQR7nHOdaVtfiSjK8/zy8tLzvDzPgyDYLzvhkorsJ3d0PHNnKC6lHA6HcGEDlQekeSklDPhQtgghcH/gLmzPnQxtBQorPEKe55vNRgkBRBqyj8ViUfeDFy9edDqd6XT6/fffp1JCuQJADj4jjuM8f/4cPCdMPtCkbDYbKqRju2EYhpvIHwRxHE8ms1arZZqmECUyjY46hO7tY8INLWyVFfczpNqDOvfjs4rJ/TaSvg+T7j8O28mXqiJWSkn2EiBjTGdbK0Q0gWoPVsXHY+jvrdPY/wJ1PNktkMHfqBlarVYLgiDP09lsRpk6PDzsdvtRtGk2m7quj8fD29trSPXare5wugR+QCkFXoLXg4sGY1/MiMCnSXgOL1NdI4LnmqbZlm5bupIlkazI4jTeaJT5Xt2xDcHzdrtZlnkUbRgjnHPTNKXihmE4jpskcRjGyOqr5cZxnIP+sWW5o9EI3iT1WhOoBucSAzHOpWVZ4/H45PjUMu0oipIixmu+uLiADljTtE8//fSHH36QUqLAhr5OKfXzn//8P/z7vwmC4Pr6+uTkZDabHRwcYFMSfnE4HGLzKex97+/vj04PwjBEhun3+5jZ4AM9ODjAe4L3DR+H57iAQFEn393dffHFF1jHjSUTuHzb7Taq0DLcgBzLGAPfAL9br9fR5UZRBLkTKjKgKZD8wamNcw6QtkIT0MVBSwEjRry8oigYIYwxz/NmsxmMZ0VRXl9fj0YjUAKn0ZrLMk4jRZVuaqUo1uGKUvry9Q+LxeLg4IBznpf505MnMKFsB+2rq2vP8zwvuLt7aLVaoAdjc6iUklLyDkKsqj7y0U3uqW/3I6qKq/1Wbf/R9mLwwyCs/pftyTXkznimyrraLgirmlnuiarU+zPG/adA74H7VHItTdMcxyWE5Hme5yWl1DQsnIWmaUqhcpEzxgaDI0pVHMej0aikWiFKYAA5L1Spcl6UZen7fpqlWZlLqryaDxXMcrM6OOxiKJfGCXzB6nXfcxwlcs/yAPB4ttPtNDRN40XCGNE0WpSJpmlZlqVZjEQNvB4qONM04zilVMuygjEjiiLH9uq1JhSx0+ncNNNut0uplqbpxcXFv/+//sPp6enDwwNyICEExAAU+dPpFPzm2WzW6XRev36dpulXX3318uXLn/3sZ9hDdnt7OxgMLi8vUaRh9LxYLD7//HNM4WazGei1myhah2G7210ul5999tnvfve7TqeTJEkpxJNaTQjBpVyu10VRCKUYY8vlErAKOrfDw0O4GIK9SfaafDB70Inx3RK4CqTAnQG9oItG1gUj6uTkBCxZpRREjNVcChAdzvTqmK44DLphQFjo2g6K4SLNMJxEVeV4VpIkcRzqus55IUSZprEQYrXa9Pvds7OT9XpNCGm3m2VZClESwqIo0TTDcTzDsGzb9TwvjmNwoTWN6jgP9svRqiLVdosH99OO2iN279/UbnXZfqzuEto7ff0HcYi7VUNIPAXbC6r9+Kx4HkwjVeZUO6Lcx48ssfltZyXEOQdIIIRYrdaaxjzPA2oSx/E334xqtdrZ2Umr3aSULhaz7Uei63GWCQGwDuXi9lxaLGa+73e77aIopORZlpimfnDQcx3DdUzDYJEs8jQqsrjI4jmlWZa16g1d1xkRTFN5FqORKHgphPB9H0pzsEySJLm9vYVSybIspYjjuFlaXl3eCCHKsgRhoCiKxWIVx3G3203THOif4BISHpRYEEys1+vf//73Uspnz55Np9M//elP8GI7PT29urqSUvZ6vT/84Q9//dd/Xa83wjAMgtpyuQqC2suXr0zTyrK80+k+PDw0Gs3FYkEpOzgYcC7a7c5o/sAYg6k7shN8lnADalJuV6b4EONiv5Jt22/evHn69Onr16/xV4MPTSlF8GCwCcEbgAnozvjOhx+5AT0hCmBkNnz6i8VivV6juEWWQ39eYaoIPLzCrRs3pYhMPN3Wlp9S2J/iTHS1gItSCJEX2Wq1WiwXSik/8A1T/+LLnxiGMRoPXdedziaz2SwIgvls9eyTT29urocP45/97GeT6ehv/uY/npwcpWlKqaKU6oam890u4v3g2W8FP7iy99PX/lhivw/c7y1xl4/jtoq6Kly3ma36YrcTuzoXEW8aofs6pqps3u8VKaUVQRQHKt2hpgCpHcfRNLbZRFJyTaemaaNdnE75cDicTEaaprVajU6ns7m/1TRq21ajUUdvqfa8DxDe1ahNCFHka1GmjHDLoLXAwTdFWWpUGCY1NGqZZuDbVAleprqmJGGe55imaVkGIbamab7vr9cb7EKIo1RoSgrSaraB7DHGCSFY/cMYm80WSZJMp/Plcn1/f1+vNW5v7uI4/u1vf1u5qkkpMXBP0xTerZBKdLvdy8tLmI7e3t4eHBy8evXq6PC0Xq/f3d1Bjlh13d1uN4qi4XAI1OHTTz/F2gnginCnHw6Hg8FAKeV5HkibWFmFwg+DuLrnNxqNXq8npYQrB8brKEaAl8IgGPQxw7ExpvM8D9TfsixBJ8IRg5as3W4TQvCeYFwppbQsCzxVDAP3QT6xkwgi9tATmaZJdzjCcrmEV28SRuCyttvto6OjSMZZlsAhBqsXESxxXGRZsl7nWZa0280sS1arxdHRIDC6lLI4zjgvZrM5ti9j3CpEKZXgvNCrUrOKjf2Scr/LYjsK2C66WDUZp7uWshoP0t0KKOxV2o9Dtev65O5W9Zn7ob6NaiU/yNJq74by9YOKVO3kF/tfsz32D3qDLEvX6zUhstvttlotXWdYcmJaxtOnTz3PWywW9/cPzVbdtHQwv+D9WhQFZQqe0GEY2natVqtRSqF/PRz0MBqp1zyn33EtG8+ra5rneWVZapShjISt7XSxwI4h09Q1TfM8Wq830zQ9OztjVC/ykWnaUsKOhwVBrdWqgRht23at1iCE4MxOkuTu7i5uJLBCiqKk0+mAbw0/MiBbr1696vf7x8fH4BALIU5PTzVN+/bbbx8/fnx6evr61dXJycloNOp2u5PJBLp1nDIQKDYajXq9Dihhs9ngcgfoP51Oge4sFgsArQB1N5sN+iVCyOvXr1ut1mq1siyr1Woxxo6Pjw3DuLq6QrQj9hD8mqYhSeD4wyxRSokDAkGodoRKCDJ0XUdaVkrBOA85E3g4alqMMQnZgpOgByL7Sc4p7BiZttls1ut1meUYGBZFMZ/P7Y5LGFWUaIberNWwgCQMwxeffSqEIIxePH3S6/UmkwnTtYKX3UH/V7/6VRyl9UZwfX3t+95PPv+y5HmjQUteFEUhRKmrvSZwP7lVViIfRKDck+3t94E4gcT71r2apknJP+4G9zPtByXlfqRJKSnZPqn2fhn8QVjul69qx06uCqFqyJnnucHMyWSilHJdeGyZnPO3b99KyZvNervdlkpMJhOUT48fPxaGBPcCcjgkZCjoGGNYU053JCld1y3LmM3WWKnVbjZZs2nbtqGzfr/HCF0ul5pGHceiSmlUBUEgKfU8z3EsZGNeSinJbDYbDA6VpIZhtVqtNM2jKJpN52XBV6sZDAsty1ou16vVqtlsUqoVRQEkxvO8yWTiuj6qvk6ng9IUVOl6vV6r1ZbL5U9+8pObm5uzs7PVaoXtOghXaO1fvHjBGPvzP//zt2/fws+bEALwSQhxcnIyHo/hcQZ+DMpLLFGB5ngymUDdZ9v2er0ej8fYREC46Ha7eK9OTk4g2AUbDgwkDKWra6bYbelCqKOVgk9hdZ6iV6w+esQ29O+YBoOOj6MEJylGi+ghAfYg0kRZSikNw1gvV2C9ebaDswAfcU3W6vX68fEh4C7Hsdrt5uHhwcXFxT/8wz/EcdhsNpUShEjXtYUor69vs6woioJR/fDwiBCZpqlusG63W5R5nqecFzoj78AVolS1kpIxRhQhUqndltzthIARxoihM01jlJKdA7LEGSaFYJQwDcinFFwwppSs0uzWk5BSvchzzrkopVKEMqoIQfwKUe7uLCmlbHcQGKYuJaRVOqWalIpSzbIMy9yipsjA1UvFdVNhsFXQztVCM5iu66lJJRdaqlFFiFKddotoWpJnBtN8r24aBo7M2+HIcWxDmb4VNBq1m5u3i8nE85xwOTdNkxIpi8IwDCaUo9u1Vi1M1GK5iSKv223rVmuxTDebKef8aCEoVWkad9ttL+Cz2SwMw4uL8/WsEJlp2y0iCSFEqnK1Xraa/X7v6OWb10meTRdzTdc55ylPf/PH3xz3HjebTV4aSZxYVhmGcRynus4opVmeUkrjWClF1+sw8Os31w/CzAkhPI5qtdomDLE4xTTN2XjyyflFFEUqLx3NiONsdvuA6mtYlFLKFy9eOKbVbjTLLBdCUKk0QmXJN8vV6P7h7Oxstlq/ffv24snjMisF5SUvDMls3aKCdJutgpc13725ueFKJkl8MOj+8PLber3++PTpfLECNfzw9Ozq6mqxCYlp6q4rDH2RxIs44qbBTDNK00WWbjZZu9VKBVmPp4zIZrfNebFYz4LA8wOPMb0sSyXKOAw5557j8FKu16HneZLQyWxONaZpmm7bm8WCJKlhWAYzsjxdTGdpnDBKbc2hnOlUI4LkccYY0zQ9S1JCiG2bcRzXW/X1ZmV5ZpSFTmAfN05N0zRNXZmiLAuRlkUeG6Y2vrqxOLOFxtf5fJktZ0uSsEW2SuwsTRbPPnlEKT0adF5fXWJJa5rxu7vher02bOs9G/wPUg39CDVVSmm69kHKIu/jqx/c5HvqPlkVjR/fk+4ATLKfgfe+L/eIcrhpO2M5+b4/AH1/Vdv+ayt4YRoGChK05kQqqsibN28Gg8HR4NC1bJD62Y45lWVZksRhtGaMRNFGKkEIwQ7XsizzvIiiaLMOy1JQSq/v5rA5gTGmKHPPc9vt9uvXr13X1nVGlZJSYCqdJFEey16v5/n+ZrOZzWZ5nhNGCSGz2YxSCvQlC0Nt56mz2Wy4KBaLhRClplEuSs9zOJeO43ieo+umFCrLCkIk0wjnfJ2sALe6rqvT7e4NSilXPNqEYRhmaco51y1TpyxNU6y2XywWIGSjkNN1/U9/+hMMPuDF5HkeiuowDFer1fPnz9EXzefz0WhUFNlP/+yrzWal6zolClgoHg0FC+d8Op0KotbrNYhEURTphYGGsBRC7LyYXMdJkiSM1qZptJt113XDsExTURQbnWmdTs+2bZ1qQqg4jrMsq9eabGe2r5Siu4uz1WrVg1qz3hBFORmP5/O5qRuNej3KC0IIVsphz4phGAAlgcNVHanc0ULyPF+vl3mRCsF1ndm2bVr67e1tkXPGGCGsLDlKMCHE/f09hkCe511eXo7Go/V6XavVNF0H0qPiSK8u2Y+jaL9Pq26apn8cXeTHVLy7R94vGj8Ecj74dUJI9fjboPqon9wHfiilSr3jzexHb1UY/+gNVSVThDFmaLppGKfHx41Gox7UiNgqXNHWu0wry5xSWpZFlke9Xq9/0PN9dz6fw1xos4miMM6yjHMZx3Gz2Tw9PY3j+Pr6Stf1i/OzIPDjOA58F4WoZRiEkHa73el0kiRq1dxGo0EYXa7KNIuLogBFcj6faqbhunaWJZtwBT69ptMo3mR5VJa5rjMuSl1nvu+GYShE6bq279eEQENueJ6nFA2oAyJ+u9EsyxJiBc/zDEODwQTwCeibZuMJDAthG0F3usosy+r1Okhb0CL88MMP6/X69PQ0Lyz4poEvJimZz+eWZaBN5ZxrpkHIdgkPBjloW2azWc7LrQu9xsqy5HIbeJQxfbevU0k9iqKSSwAn6HEcRzMMjRACyC0XinOJk/FwcFwIjh5V13XdNHCsO45T5sVisSBCSik9z9Moy7KMUJZlWavV8jwP86Fms1mWOfg0OGcJISAhQv+dJEkYrtMsJkRBDacbrCxLx/YsyypLAfQI12ezWU/TdDR66Pf76FaiaJNlie1aeZEWZSYJeaeikHvaPPpjZOvqKlfVhqb3udcfRCP5qHOrMuGPRu8O5Hx/pr/7AueKlFLf80fEUfcBZrOfqPcjEzfH8ZSQZSEIEZZu2Jbte55j2Y16Swo5ny+JkIwxx/ZM06REwwXturYQXEtou93EygF4eOMlwZU4z0shRK1Zm8/n0KQ/efLk5GiQpsnDw8PpydFms2o2641aLU0TsPLv729FTlar1SYKF4uFEMLzPKZpgM5BQFU7U0ac7ppmMEZqdQ8+vJZldLqtkueGodfrQbvV4Vyapm6atu8FRVFIs+k4DlOk3+8XRcHLMssypogeMEkUdPpJHNdqNThDj6fbZbpKKczicNmBsYCxPiZ7Sqmjo6PLqzcwaA2CoN1u2p776aefFkX26tUry9oaLidJjGbBdd3VbOuqzDnXiUKGzNJE13WhJCZD2m6Up2nachHZtu35jmkaZVnmeapptN/v27Ypyh28KYlSFNm+3W7PV0tQW7XdjVLqed48SZWQpqa7rlv3A0boerVq1htYCgScSQgB1CeOY6UEZBaKSJgjcs43m1jtRiZSCil5nudpxo+Ojjw3UErN50sEuev6pmkahgZyDzgPGlGw1apU/PhL33k9qT2zQ7mnJ9q/oNXeHELtaWc/ntTv56UPHqHSN+GLHccCheg/qf2tIrZ60h8Nsx99AdW/uq6XosAnVLXpQojb21vLsmpBUPcDx3E0uh0KB41GnuemaQjBhLQJYbPp/P7hDodFnpe6rteCOiFsPp9HUTKbzf70pz+ZpvlXf/WvPv/882izyrL0k08+MQ1ts1mBdbFabVf8LZfL5XRTlmWUxOBV27bNNA0Tv8V6ZVlWrVbbLj/NMkJIo1lHv+04FtOk73uNRhBFHmh3zVatKErOC00zdEPjgkZh6Fq2tUN316sVCDegpzRqddM0geADAe72DyGJwOZnOBTZtv369WssbPj+++8B8FiWNZ/PgyD46quv7u7uiqJYrVbpZMwYg3oY9bzjeXmelWUZx/FyudTY1qW3LEuLUThE5GWBTAjyJytLzJMQA4ZhGAw7DAspuWW5QRAQInWmGYbluq7BdIxJYQ8HcaOiW6ojHgrvj2PZiotRkuimddDrd9ptzXaw/oBSCm6NpmmrFbSgGSFktVoZpg7YJs/zohCQL2k6LcsiyxLOuSJC7NkimqZZrzVbrY5lWff3t6Zpnp+fg/h+d/0Af7rZbIZ0bQIWr/LeB6lp/ztV37VfuO5f3PvX/X4mrNo2pKvqV7YVKduOK8AoIoRIyX80m+E+lFJEDtkxCgR/xwncZ7Ttv7zq9VBK8zynioCJjbMt5lwJWfeDIAi63a5nO2VZlnmBzifw67oeoyINgsC2zSSJyrJUSt+y4DXD87yyFErRsiyHk9nTp08vLi5OTk5gvwXyVFnmURTd3ZVpHE8m4zzPYeFcUSsxSatgOtu26YZSSn3fN20Lf5RhGI3ANwxjE65KnmkaMQzNMLVa3WeMUaaUErpOLVsnhOg6sW29VA74N3e3t0EQVPF2dXWFckujjDEWJ8l8sciLoh7UFSHM0AVROS8Vo4Iow7YGx0eW60hKxEyto3ATR77vx1l6fDg4Pz9Hoo6izc3dLfDYVqcNnga86vI8x96IVs1HUk3TlGisMghkjIlSglEkd7Y3ZVlSaggh8lwqJT3Hct26bZu6ruV5qtFttlBU4SQFkTCKIs6547mWZUmiIO/gnM9mM40ynbIsy3rtTrvdXq9WScnBnmWMBUGA2nu3zaLcSrQMDZm/KAoiNSkl51wqUu78RXXD2mw2vJR0T91eOa+BhwBHvNF0QghZLBb4TMEl+JFNvep9+UKVx9ie8pB+dCM/dkPe2itK6Xs/2aKldL9sALxRvQz6PjIEEgww5eqVkL1JoNwZNLI9tu7+Lc9zyzAxscBRZJmmqRtYHhSGYbhaZ1lGFcGmWOygTrPYMLRefytTwN4VpEqN6egGcW1JKQ8PD1ut1u3tbRRFrm0qJVerlefaq9VyvSZ3NzeTydhxnMPDQzQJtm0pSgjBDpOMy7Isy3ojIBqBkGIdbhzHwqTLNs1ms0moKIrMD1yAPY5jWpYlpSKU27bjc1dKAtM+O/dbrdZ1koxGI2h2EBjQlUopDcuoNepCiIeHB875YhMppeB1DbMWMJIZY3d3d47jgJSH+rPZbN7d3ZmmuVgsIOFrNpuu615cXMyXC6UEpdSyLNu2MF7HS8LUIcuynJdZloVh6AU+AgaHI9/NnKWUGqOmaeIiIoRYlmUYuuCllNKyTEJIFEVMUV03cW0gfmzbBrM0ThMM2+bz+fX1tUZZs1a3TBO7D5bLZcpFxSVA9wiZhVKqLEs0GsCNQBAnAh6+GWVKKckYMQwDLw8jE8ZYWRZhGCpFXdetNwKpOBeFH7iWbfT73eVyKURpWb6UEhY2Otnr3HD8VHXmfrrbuyn60W0/sD6oJBnb92WqQrBafL/NYFVB+67Zw6Gye0AB26UdAKMq55vdC2B73HH5Y7todq+NUaoRwrIsL9JM13XP8VutNiEsjtMkSm3TdF3XsdADyDhPUBcZhmZbrlRcCGVbTpLGnHPBpdBFlmVRlOR5rjHj/Pz85ubm+vq63W7quj56uIvjSNd12zK63Xa3286SRNc1ZF2lxFTMCCEFL/Xd4vWCl2EYmqbZ7XZt28bmUwzWpJSM0UajLmUpZNnptCglJc8NUzs6HsRxbJmOZdlKqTTJGaO6rsWLzcHBQb1eL9LMcZwsTjCIOzo6wjkCSU4YhqPRKMuyVkeDDblSCqRwpRS0WoZhtFotCGHBWU/T9OjoCL5SUsp6PSiK4rvvvqvVfMKo41igXEdRiHIagcEYg+kLdPd5ntca9eoz0jQNkyV87kmcOo5j2UaSxGEYahr1PEdJURR5zQ9c18+yTJZC103GWDW+r1RskEowxsAp7Xd7zVo93GygQSnLkmk6ukHOueNaUso4jqXcajLozsZB7HbpaNVSRJ0ahg4DTugYqxCQUirJKaWe52V5hM2thBCsKpBSDgYD04YHQr5er/8/mJNilez1C2gAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "My5Z6p7pQ3UC" + }, + "source": [ + "### Support new dataset\n", + "\n", + "We have two methods to support a new dataset in MMClassification.\n", + "\n", + "The simplest method is to re-organize the new dataset as the format of a dataset supported officially (like ImageNet). And the other method is to create a new dataset class, and more details are in [the docs](https://mmclassification.readthedocs.io/en/latest/tutorials/new_dataset.html#an-example-of-customized-dataset).\n", + "\n", + "In this tutorial, for convenience, we have re-organized the cats & dogs dataset as the format of ImageNet." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P335gKt9Q5U-" + }, + "source": [ + "Besides image files, it also includes the following files:\n", + "\n", + "1. A class list file, and every line is a class.\n", + " ```\n", + " cats\n", + " dogs\n", + " ```\n", + "2. Training / Validation / Test annotation files. And every line includes an file path and the corresponding label.\n", + "\n", + " ```\n", + " ...\n", + " cats/cat.3769.jpg 0\n", + " cats/cat.882.jpg 0\n", + " ...\n", + " dogs/dog.3881.jpg 1\n", + " dogs/dog.3377.jpg 1\n", + " ...\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BafQ7ijBQ8N_" + }, + "source": [ + "## Train and test model with shell commands\n", + "\n", + "You can use shell commands provided by MMClassification to do the following task:\n", + "\n", + "1. Train a model\n", + "2. Fine-tune a model\n", + "3. Test a model\n", + "4. Inference with a model\n", + "\n", + "The procedure to train and fine-tune a model is almost the same. And we have introduced how to do these tasks with Python API. In the following, we will introduce how to do them with shell commands. More details are in [the docs](https://mmclassification.readthedocs.io/en/latest/getting_started.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Aj5cGMihURrZ" + }, + "source": [ + "### Fine-tune a model\n", + "\n", + "The steps to fine-tune a model are as below:\n", + "\n", + "1. Prepare the custom dataset.\n", + "2. Create a new config file of the task.\n", + "3. Start training task by shell commands.\n", + "\n", + "We have finished the first step, and then we will introduce the next two steps.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WBBV3aG79ZH5" + }, + "source": [ + "#### Create a new config file\n", + "\n", + "To reuse the common parts of different config files, we support inheriting multiple base config files. For example, to fine-tune a MobileNetV2 model, the new config file can create the model's basic structure by inheriting `configs/_base_/models/mobilenet_v2_1x.py`.\n", + "\n", + "According to the common practice, we usually split whole configs into four parts: model, dataset, learning rate schedule, and runtime. Configs of each part are saved into one file in the `configs/_base_` folder. \n", + "\n", + "And then, when creating a new config file, we can select some parts to inherit and only override some different configs.\n", + "\n", + "The head of the final config file should look like:\n", + "\n", + "```python\n", + "_base_ = [\n", + " '../_base_/models/mobilenet_v2_1x.py',\n", + " '../_base_/schedules/imagenet_bs256_epochstep.py',\n", + " '../_base_/default_runtime.py'\n", + "]\n", + "```\n", + "\n", + "Here, because the dataset configs are almost brand new, we don't need to inherit any dataset config file.\n", + "\n", + "Of course, you can also create an entire config file without inheritance, like `configs/mnist/lenet5.py`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_UV3oBhLRG8B" + }, + "source": [ + "After that, we only need to set the part of configs we want to modify, because the inherited configs will be merged to the final configs." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8QfM4qBeWIQh", + "outputId": "a826f0cf-2633-4a9a-e49b-4be7eca5e3a0" + }, + "source": [ + "%%writefile configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py\n", + "_base_ = [\n", + " '../_base_/models/mobilenet_v2_1x.py',\n", + " '../_base_/schedules/imagenet_bs256_epochstep.py',\n", + " '../_base_/default_runtime.py'\n", + "]\n", + "\n", + "# ---- Model configs ----\n", + "# Here we use init_cfg to load pre-trained model.\n", + "# In this way, only the weights of backbone will be loaded.\n", + "# And modify the num_classes to match our dataset.\n", + "\n", + "model = dict(\n", + " backbone=dict(\n", + " init_cfg = dict(\n", + " type='Pretrained', \n", + " checkpoint='https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth', \n", + " prefix='backbone')\n", + " ),\n", + " head=dict(\n", + " num_classes=2,\n", + " topk = (1, )\n", + " ))\n", + "\n", + "# ---- Dataset configs ----\n", + "# We re-organized the dataset as ImageNet format.\n", + "dataset_type = 'ImageNet'\n", + "img_norm_cfg = dict(\n", + " mean=[124.508, 116.050, 106.438],\n", + " std=[58.577, 57.310, 57.437],\n", + " to_rgb=True)\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='RandomResizedCrop', size=224, backend='pillow'),\n", + " dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n", + " dict(type='Normalize', **img_norm_cfg),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='ToTensor', keys=['gt_label']),\n", + " dict(type='Collect', keys=['img', 'gt_label'])\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(type='Normalize', **img_norm_cfg),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + "]\n", + "data = dict(\n", + " # Specify the batch size and number of workers in each GPU.\n", + " # Please configure it according to your hardware.\n", + " samples_per_gpu=32,\n", + " workers_per_gpu=2,\n", + " # Specify the training dataset type and path\n", + " train=dict(\n", + " type=dataset_type,\n", + " data_prefix='data/cats_dogs_dataset/training_set/training_set',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=train_pipeline),\n", + " # Specify the validation dataset type and path\n", + " val=dict(\n", + " type=dataset_type,\n", + " data_prefix='data/cats_dogs_dataset/val_set/val_set',\n", + " ann_file='data/cats_dogs_dataset/val.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=test_pipeline),\n", + " # Specify the test dataset type and path\n", + " test=dict(\n", + " type=dataset_type,\n", + " data_prefix='data/cats_dogs_dataset/test_set/test_set',\n", + " ann_file='data/cats_dogs_dataset/test.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=test_pipeline))\n", + "\n", + "# Specify evaluation metric\n", + "evaluation = dict(metric='accuracy', metric_options={'topk': (1, )})\n", + "\n", + "# ---- Schedule configs ----\n", + "# Usually in fine-tuning, we need a smaller learning rate and less training epochs.\n", + "# Specify the learning rate\n", + "optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n", + "optimizer_config = dict(grad_clip=None)\n", + "# Set the learning rate scheduler\n", + "lr_config = dict(policy='step', step=1, gamma=0.1)\n", + "runner = dict(type='EpochBasedRunner', max_epochs=2)\n", + "\n", + "# ---- Runtime configs ----\n", + "# Output training log every 10 iterations.\n", + "log_config = dict(interval=10)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Writing configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "chLX7bL3RP2F" + }, + "source": [ + "#### Use shell command to start fine-tuning\n", + "\n", + "We use `tools/train.py` to fine-tune a model:\n", + "\n", + "```shell\n", + "python tools/train.py ${CONFIG_FILE} [optional arguments]\n", + "```\n", + "\n", + "And if you want to specify another folder to save log files and checkpoints, use the argument `--work_dir ${YOUR_WORK_DIR}`.\n", + "\n", + "If you want to ensure reproducibility, use the argument `--seed ${SEED}` to set a random seed. And the argument `--deterministic` can enable the deterministic option in cuDNN to further ensure reproducibility, but it may reduce the training speed.\n", + "\n", + "Here we use the `MobileNetV2` model and cats & dogs dataset as an example:\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gbFGR4SBRUYN", + "outputId": "3412752c-433f-43c5-82a9-3495d1cd797a" + }, + "source": [ + "!python tools/train.py \\\n", + " configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py \\\n", + " --work-dir work_dirs/mobilenet_v2_1x_cats_dogs \\\n", + " --seed 0 \\\n", + " --deterministic" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n", + "2021-10-21 02:48:20,030 - mmcls - INFO - Environment info:\n", + "------------------------------------------------------------\n", + "sys.platform: linux\n", + "Python: 3.7.12 (default, Sep 10 2021, 00:21:48) [GCC 7.5.0]\n", + "CUDA available: True\n", + "GPU 0: Tesla K80\n", + "CUDA_HOME: /usr/local/cuda\n", + "NVCC: Build cuda_11.1.TC455_06.29190527_0\n", + "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "PyTorch: 1.9.0+cu111\n", + "PyTorch compiling details: PyTorch built with:\n", + " - GCC 7.3\n", + " - C++ Version: 201402\n", + " - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n", + " - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n", + " - OpenMP 201511 (a.k.a. OpenMP 4.5)\n", + " - NNPACK is enabled\n", + " - CPU capability usage: AVX2\n", + " - CUDA Runtime 11.1\n", + " - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n", + " - CuDNN 8.0.5\n", + " - Magma 2.5.2\n", + " - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n", + "\n", + "TorchVision: 0.10.0+cu111\n", + "OpenCV: 4.1.2\n", + "MMCV: 1.3.15\n", + "MMCV Compiler: n/a\n", + "MMCV CUDA Compiler: n/a\n", + "MMClassification: 0.16.0+77a3834\n", + "------------------------------------------------------------\n", + "\n", + "2021-10-21 02:48:20,030 - mmcls - INFO - Distributed training: False\n", + "2021-10-21 02:48:20,688 - mmcls - INFO - Config:\n", + "model = dict(\n", + " type='ImageClassifier',\n", + " backbone=dict(\n", + " type='MobileNetV2',\n", + " widen_factor=1.0,\n", + " init_cfg=dict(\n", + " type='Pretrained',\n", + " checkpoint=\n", + " 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth',\n", + " prefix='backbone')),\n", + " neck=dict(type='GlobalAveragePooling'),\n", + " head=dict(\n", + " type='LinearClsHead',\n", + " num_classes=2,\n", + " in_channels=1280,\n", + " loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n", + " topk=(1, )))\n", + "optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n", + "optimizer_config = dict(grad_clip=None)\n", + "lr_config = dict(policy='step', gamma=0.1, step=1)\n", + "runner = dict(type='EpochBasedRunner', max_epochs=2)\n", + "checkpoint_config = dict(interval=1)\n", + "log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])\n", + "dist_params = dict(backend='nccl')\n", + "log_level = 'INFO'\n", + "load_from = None\n", + "resume_from = None\n", + "workflow = [('train', 1)]\n", + "dataset_type = 'ImageNet'\n", + "img_norm_cfg = dict(\n", + " mean=[124.508, 116.05, 106.438], std=[58.577, 57.31, 57.437], to_rgb=True)\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='RandomResizedCrop', size=224, backend='pillow'),\n", + " dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='ToTensor', keys=['gt_label']),\n", + " dict(type='Collect', keys=['img', 'gt_label'])\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + "]\n", + "data = dict(\n", + " samples_per_gpu=32,\n", + " workers_per_gpu=2,\n", + " train=dict(\n", + " type='ImageNet',\n", + " data_prefix='data/cats_dogs_dataset/training_set/training_set',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='RandomResizedCrop', size=224, backend='pillow'),\n", + " dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='ToTensor', keys=['gt_label']),\n", + " dict(type='Collect', keys=['img', 'gt_label'])\n", + " ]),\n", + " val=dict(\n", + " type='ImageNet',\n", + " data_prefix='data/cats_dogs_dataset/val_set/val_set',\n", + " ann_file='data/cats_dogs_dataset/val.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ]),\n", + " test=dict(\n", + " type='ImageNet',\n", + " data_prefix='data/cats_dogs_dataset/test_set/test_set',\n", + " ann_file='data/cats_dogs_dataset/test.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ]))\n", + "evaluation = dict(metric='accuracy', metric_options=dict(topk=(1, )))\n", + "work_dir = 'work_dirs/mobilenet_v2_1x_cats_dogs'\n", + "gpu_ids = range(0, 1)\n", + "\n", + "2021-10-21 02:48:20,689 - mmcls - INFO - Set random seed to 0, deterministic: True\n", + "2021-10-21 02:48:20,854 - mmcls - INFO - initialize MobileNetV2 with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth', 'prefix': 'backbone'}\n", + "2021-10-21 02:48:20,855 - mmcv - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n", + "Use load_from_http loader\n", + "Downloading: \"https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n", + "100% 13.5M/13.5M [00:01<00:00, 9.54MB/s]\n", + "2021-10-21 02:48:23,564 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n", + "2021-10-21 02:48:38,767 - mmcls - INFO - Start running, host: root@992cc7e7be60, work_dir: /content/mmclassification/work_dirs/mobilenet_v2_1x_cats_dogs\n", + "2021-10-21 02:48:38,767 - mmcls - INFO - Hooks will be executed in the following order:\n", + "before_run:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_epoch:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_iter:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + " -------------------- \n", + "after_train_iter:\n", + "(ABOVE_NORMAL) OptimizerHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "after_train_epoch:\n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_epoch:\n", + "(LOW ) IterTimerHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_epoch:\n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "2021-10-21 02:48:38,768 - mmcls - INFO - workflow: [('train', 1)], max: 2 epochs\n", + "2021-10-21 02:48:44,261 - mmcls - INFO - Epoch [1][10/201]\tlr: 5.000e-03, eta: 0:03:29, time: 0.533, data_time: 0.257, memory: 1709, loss: 0.3917\n", + "2021-10-21 02:48:46,950 - mmcls - INFO - Epoch [1][20/201]\tlr: 5.000e-03, eta: 0:02:33, time: 0.269, data_time: 0.019, memory: 1709, loss: 0.3508\n", + "2021-10-21 02:48:49,618 - mmcls - INFO - Epoch [1][30/201]\tlr: 5.000e-03, eta: 0:02:12, time: 0.266, data_time: 0.021, memory: 1709, loss: 0.3955\n", + "2021-10-21 02:48:52,271 - mmcls - INFO - Epoch [1][40/201]\tlr: 5.000e-03, eta: 0:02:00, time: 0.266, data_time: 0.018, memory: 1709, loss: 0.2485\n", + "2021-10-21 02:48:54,984 - mmcls - INFO - Epoch [1][50/201]\tlr: 5.000e-03, eta: 0:01:53, time: 0.272, data_time: 0.019, memory: 1709, loss: 0.4196\n", + "2021-10-21 02:48:57,661 - mmcls - INFO - Epoch [1][60/201]\tlr: 5.000e-03, eta: 0:01:46, time: 0.266, data_time: 0.019, memory: 1709, loss: 0.4994\n", + "2021-10-21 02:49:00,341 - mmcls - INFO - Epoch [1][70/201]\tlr: 5.000e-03, eta: 0:01:41, time: 0.268, data_time: 0.018, memory: 1709, loss: 0.4372\n", + "2021-10-21 02:49:03,035 - mmcls - INFO - Epoch [1][80/201]\tlr: 5.000e-03, eta: 0:01:37, time: 0.270, data_time: 0.019, memory: 1709, loss: 0.3179\n", + "2021-10-21 02:49:05,731 - mmcls - INFO - Epoch [1][90/201]\tlr: 5.000e-03, eta: 0:01:32, time: 0.269, data_time: 0.020, memory: 1709, loss: 0.3175\n", + "2021-10-21 02:49:08,404 - mmcls - INFO - Epoch [1][100/201]\tlr: 5.000e-03, eta: 0:01:29, time: 0.268, data_time: 0.019, memory: 1709, loss: 0.3412\n", + "2021-10-21 02:49:11,106 - mmcls - INFO - Epoch [1][110/201]\tlr: 5.000e-03, eta: 0:01:25, time: 0.270, data_time: 0.016, memory: 1709, loss: 0.2985\n", + "2021-10-21 02:49:13,776 - mmcls - INFO - Epoch [1][120/201]\tlr: 5.000e-03, eta: 0:01:21, time: 0.267, data_time: 0.018, memory: 1709, loss: 0.2778\n", + "2021-10-21 02:49:16,478 - mmcls - INFO - Epoch [1][130/201]\tlr: 5.000e-03, eta: 0:01:18, time: 0.270, data_time: 0.021, memory: 1709, loss: 0.2229\n", + "2021-10-21 02:49:19,130 - mmcls - INFO - Epoch [1][140/201]\tlr: 5.000e-03, eta: 0:01:15, time: 0.266, data_time: 0.018, memory: 1709, loss: 0.2318\n", + "2021-10-21 02:49:21,812 - mmcls - INFO - Epoch [1][150/201]\tlr: 5.000e-03, eta: 0:01:12, time: 0.268, data_time: 0.019, memory: 1709, loss: 0.2333\n", + "2021-10-21 02:49:24,514 - mmcls - INFO - Epoch [1][160/201]\tlr: 5.000e-03, eta: 0:01:08, time: 0.270, data_time: 0.017, memory: 1709, loss: 0.2783\n", + "2021-10-21 02:49:27,184 - mmcls - INFO - Epoch [1][170/201]\tlr: 5.000e-03, eta: 0:01:05, time: 0.267, data_time: 0.017, memory: 1709, loss: 0.2132\n", + "2021-10-21 02:49:29,875 - mmcls - INFO - Epoch [1][180/201]\tlr: 5.000e-03, eta: 0:01:02, time: 0.269, data_time: 0.021, memory: 1709, loss: 0.2096\n", + "2021-10-21 02:49:32,546 - mmcls - INFO - Epoch [1][190/201]\tlr: 5.000e-03, eta: 0:00:59, time: 0.267, data_time: 0.019, memory: 1709, loss: 0.1729\n", + "2021-10-21 02:49:35,200 - mmcls - INFO - Epoch [1][200/201]\tlr: 5.000e-03, eta: 0:00:56, time: 0.265, data_time: 0.017, memory: 1709, loss: 0.1969\n", + "2021-10-21 02:49:35,247 - mmcls - INFO - Saving checkpoint at 1 epochs\n", + "[ ] 0/1601, elapsed: 0s, ETA:[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n", + "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n", + "[>>] 1601/1601, 173.2 task/s, elapsed: 9s, ETA: 0s2021-10-21 02:49:44,587 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", + "2021-10-21 02:49:49,625 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:55, time: 0.488, data_time: 0.237, memory: 1709, loss: 0.1764\n", + "2021-10-21 02:49:52,305 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:52, time: 0.270, data_time: 0.018, memory: 1709, loss: 0.1514\n", + "2021-10-21 02:49:55,060 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:49, time: 0.275, data_time: 0.016, memory: 1709, loss: 0.1395\n", + "2021-10-21 02:49:57,696 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:46, time: 0.262, data_time: 0.016, memory: 1709, loss: 0.1508\n", + "2021-10-21 02:50:00,430 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:43, time: 0.273, data_time: 0.018, memory: 1709, loss: 0.1771\n", + "2021-10-21 02:50:03,099 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:40, time: 0.268, data_time: 0.020, memory: 1709, loss: 0.1438\n", + "2021-10-21 02:50:05,745 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:37, time: 0.264, data_time: 0.018, memory: 1709, loss: 0.1321\n", + "2021-10-21 02:50:08,385 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:34, time: 0.264, data_time: 0.020, memory: 1709, loss: 0.1629\n", + "2021-10-21 02:50:11,025 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:31, time: 0.264, data_time: 0.019, memory: 1709, loss: 0.1574\n", + "2021-10-21 02:50:13,685 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:28, time: 0.266, data_time: 0.019, memory: 1709, loss: 0.1220\n", + "2021-10-21 02:50:16,329 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:25, time: 0.264, data_time: 0.021, memory: 1709, loss: 0.2550\n", + "2021-10-21 02:50:19,007 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:22, time: 0.268, data_time: 0.020, memory: 1709, loss: 0.1528\n", + "2021-10-21 02:50:21,750 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:20, time: 0.275, data_time: 0.021, memory: 1709, loss: 0.1223\n", + "2021-10-21 02:50:24,392 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:17, time: 0.264, data_time: 0.017, memory: 1709, loss: 0.1734\n", + "2021-10-21 02:50:27,049 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:14, time: 0.265, data_time: 0.020, memory: 1709, loss: 0.1527\n", + "2021-10-21 02:50:29,681 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:11, time: 0.265, data_time: 0.019, memory: 1709, loss: 0.1910\n", + "2021-10-21 02:50:32,318 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:08, time: 0.262, data_time: 0.017, memory: 1709, loss: 0.1922\n", + "2021-10-21 02:50:34,955 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:05, time: 0.264, data_time: 0.021, memory: 1709, loss: 0.1760\n", + "2021-10-21 02:50:37,681 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.273, data_time: 0.019, memory: 1709, loss: 0.1739\n", + "2021-10-21 02:50:40,408 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.272, data_time: 0.018, memory: 1709, loss: 0.1654\n", + "2021-10-21 02:50:40,443 - mmcls - INFO - Saving checkpoint at 2 epochs\n", + "[>>] 1601/1601, 170.9 task/s, elapsed: 9s, ETA: 0s2021-10-21 02:50:49,905 - mmcls - INFO - Epoch(val) [2][51]\taccuracy_top-1: 97.5016\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m_ZSkwB5Rflb" + }, + "source": [ + "### Test a model\n", + "\n", + "We use `tools/test.py` to test a model:\n", + "\n", + "```\n", + "python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]\n", + "```\n", + "\n", + "Here are some optional arguments:\n", + "\n", + "- `--metrics`: The evaluation metrics. The available choices are defined in the dataset class. Usually, you can specify \"accuracy\" to metric a single-label classification task.\n", + "- `--metric-options`: The extra options passed to metrics. For example, by specifying \"topk=1\", the \"accuracy\" metric will calculate top-1 accuracy.\n", + "\n", + "More details are in the help docs of `tools/test.py`.\n", + "\n", + "Here we still use the `MobileNetV2` model we fine-tuned." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zd4EM00QRtyc", + "outputId": "8788264f-83df-4419-9748-822c20538aa7" + }, + "source": [ + "!python tools/test.py configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py work_dirs/mobilenet_v2_1x_cats_dogs/latest.pth --metrics accuracy --metric-options topk=1" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n", + "Use load_from_local loader\n", + "[>>] 2023/2023, 168.4 task/s, elapsed: 12s, ETA: 0s\n", + "accuracy : 97.38\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IwThQkjaRwF7" + }, + "source": [ + "### Inference with a model\n", + "\n", + "Sometimes we want to save the inference results on a dataset, just use the command below.\n", + "\n", + "```shell\n", + "python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}]\n", + "```\n", + "\n", + "Arguments：\n", + "\n", + "- `--out`: The output filename. If not specified, the inference results won't be saved. It supports json, pkl and yml.\n", + "- `--out-items`: What items will be saved. You can choose some of \"class_scores\", \"pred_score\", \"pred_label\" and \"pred_class\", or use \"all\" to select all of them.\n", + "\n", + "These items mean:\n", + "- `class_scores`: The score of every class for each sample.\n", + "- `pred_score`: The score of predict class for each sample.\n", + "- `pred_label`: The label of predict class for each sample. It will read the label string of each class from the model, if the label strings are not saved, it will use ImageNet labels.\n", + "- `pred_class`: The id of predict class for each sample. It's a group of integers. \n", + "- `all`: Save all items above.\n", + "- `none`: Don't save any items above. Because the output file will save the metric besides inference results. If you want to save only metrics, you can use this option to reduce the output file size.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6GVKloPHR0Fn", + "outputId": "4f4cd414-1be6-4e17-985f-6449b8a3d9e8" + }, + "source": [ + "!python tools/test.py configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py work_dirs/mobilenet_v2_1x_cats_dogs/latest.pth --out results.json --out-items all" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n", + "Use load_from_local loader\n", + "[>>] 2023/2023, 170.6 task/s, elapsed: 12s, ETA: 0s\n", + "dumping results to results.json\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G0NJI1s6e3FD" + }, + "source": [ + "All inference results are saved in the output json file, and you can read it." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 370 + }, + "id": "HJdJeLUafFhX", + "outputId": "7614d546-7c2f-4bfd-ce63-4c2a5228620f" + }, + "source": [ + "import json\n", + "\n", + "with open(\"./results.json\", 'r') as f:\n", + " results = json.load(f)\n", + "\n", + "# Show the inference result of the first image.\n", + "print('class_scores:', results['class_scores'][0])\n", + "print('pred_class:', results['pred_class'][0])\n", + "print('pred_label:', results['pred_label'][0])\n", + "print('pred_score:', results['pred_score'][0])\n", + "Image.open('data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg')" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "class_scores: [1.0, 5.184615757547473e-13]\n", + "pred_class: cats\n", + "pred_label: 0\n", + "pred_score: 1.0\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "image/png": 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fKKwXY4g+1HVdliVymk6nHzx+GPo+BC+QMkvPn399c3PVdc3sYHzx5uXJ6WGemf/8N/+xblZHx7PQN5NR8dGTR7/77a+Cbwn59avnTx4/9H3TtQ0Bhr7zfZtnVlK6vb5ZLm4NQe6yIssIxfdtW29C7xHEWLSOjLVElERSSiHGEEJdr9fr9XK5nK/m8/n8dn59e3t7u1wMDpuxuIsPO0U1jNmhgH2j/vxqtQKALMum0+l0Os3zTLXDpl4horWkf82yDFGcM9bavm36tvFdrxGawW1smTnHzE2z8V2v77KOBNLbt2/X67VzTjezWjPd5JPJJMuyrus05NZ4SiN5NbaDX9Z1nbqsek7btgMU5JxzRW7IKXZojHMuR0ZDzhpjAFFkcNNokMB9+6PH4DFaa7uuUxX44MEDNd/OuTt37uhbFJO01up1VFX1ve99786dOxrOqgevTrmKlt6DSvtgAP/wUIR6ODRm0AMM6X/qNuDO+UyAaipZUASZgRlEsKiqdV0XVeVj/Og7n9g8Ozo9sXk2OZjdLhfj2TQri6vbm1W9+dN/9hO05uTk5NWrV9/5znfOz88vLi5++MMfnpyc6FV57w8PD4uiUP/wt7/97cuXL//xH//x9va2KIrZbKaPqmma29vb3/72t4vFYjabVVUFAOPxWCMTl5lqVLRd3fXNerO8ur5EEmMRgcsia5vN4cF0ubh1lgyBcJzP5xqBiPBmtT44nOaFOz06/MlP/jQvXAy9c/b09CT6vl4v1svbm5urosgeP35EBASSO/v0q99H3x/OpkXmzk+OFzfX7Wb9wYP7yffL25sQeiIQSEQwHo+so029WixuQ+jJQF44a01KofdtjB4GvBoSAACKSNI9swvIt3uJmVmiCLddHZMnAxrpEMHOSw9bwCb2KqXOuTzPVZdZa8qyqKoqz3PryFrbdY2i9+r3xRjbtl0ul7rXU0pkMLPbXAincHBwsFotrt9eEdF4UnH0vu0Kl63Xa0Q8OTm5d+/eeDweHDcAUIezaRqVKI3xUkoqhAPerkZF10FxpqZpFNExxhhnsywja3ST6+5VxzAz1lpryWzFTz9XDaC6ixqeWWvH43Ge53pxAKB46Xw+1/iwqqrXr1+/ePFCjaR+AgAcHx//5je/2Ww2RVE45/RD6rper9d93zdNo8pG10ghx6Io9BtVC2RZpiC1Yll6q3onapb1nxpDF0VRVVVZVU3TZFmWlZVzzthMAej5fEHGhpgOj45Diud375zdOe98/3/8X//ng0cPL6/edn0vAOWoCilOZtO79++9fP1qNBk/evzBZ599Nh6Pf/Ob30wmkxcvXnzzzTfq1Olz+sUvfqH48KtXr87Pz3//+99/9NFHZVl++umnZ2dnuhSImOf5V199dXx8/Mtf/hIATk5OVqvVJ598olFQ3/ez2Wy9XnddN5lM1DXS9wLAcrlURHS5XFprD2cH6+UKRRWT2azWfdt99fWX8+ubTz75KMssp74s86LIvv76933fssS2q1+9fhGTn87GL14+G0+q3rdX15fXN2/bzVpiiH3XNzUJHx/M2ratmzUixhgXiwURHR0dKDaY0hb922LxBgZrYMlYS5mxItJ3zaZeT6fTssrzLBuX1fHJ0Z07Z4eHh7l1VVXps3PO5blTxA6AQwjWUVFuN7e1xhgS4aoo+rZt64Zjij6AJGcssBAgsFRF3tZN9CF3WdvU41FlLLEkH3oOERGJIEXfNM3Fxaury7cicnJ6NCrK29vb+Xyu9uPo6EizoLe3t8+ePVPtUJbl8+fPnXP3799X4+acSykVRQbAKQVEEUld1zTNxvuu79s8dyH08/lNCH1KIUY/m02Yeb1eA7CGuCIphP7wcDYZV+PxuMwLYwwNDqe8n6AbPEZFhBQUUo9xOp2GEN68eXNxcaEJrsvLS5UuPV/zM+pfPX369ObmRmGMIRuxb131ngeveECi1B/eoRdOA9+iKPq+L4qirmt9Wl3X9n2fhPvgF8vbLMu64K21LitijHXdANJ0dti2PaLp+/CTn/zk5ORkvV5XVaUZBSIajUbX19eHh4eLxaJpGiIqiuJv//Zv792790d/9EfMfHNzs1gsUkp/+Zd/qeqQiMqyfP36dYzx+Ph4sVhsNpu+71erVQjh6dOnimDleX5wcFCWZdd1mqW4uLjQ/G9d18457/2ACetT2OpXQGCRxJIYWHTbWTIaKRFRkeW5y1KKIXiO6eunv0+hn89v8txNJ2VV5p9+7zsHh9OUYowhxpBSJAACkJRSCNH7FAJw4hii70PfpeCB08F07AhD16bgncFxWYyrYlTmKAklISfgCJwgReSEkjgGjoFDhMQAkFlbuKxwWb1ZJR+MMWWRFVnujEUBBN45WSmloNspy22e58agKmUVv8E7G2DzEPvEYReA6OfwLkoRpG1g0tVN6HqJiZlFEiE658oy36zWVVWdnh1PR+Msy0ajMsts1zUhhNlsNh6P5/P5L3/5y+l0ul6vDw4OFK3RdMAQWCkmR0RqG3Qn617VZ0dEapk0mdQ0jXPGWHSZzXNnLaWUhKOztm3btt40m3W9WtKQlhjQkSESVbOjO7Isy0G01Ka9ePHi9evXCl71fT9cxAByKtz6/PlzTdnroQs9JItUzDTqHdB/VbT6NFTX6mld16niVJtZluVoNGq6thyVWZbNZjMRKUYVACmQBYAhxjwv79+/H0JC6y6vb44ODo8PjzimB/furxbL3GUoIMxnp6dd007Hk+jDqxcv7925+4uf/fzDx0/UNKl38fjx46dPn+olaf7m+vo6hPD48WO1FXme39zcvHnzRm9EobajoyOVHMXfXr9+rVn7V69eGWM630YOAIpJJJFEBJk1qm45eP1PYkBOFoEAUMQ5d3BwMJvNcpdZa0ej6sWL519//dXHHz55ffHq9cWrH/7x5yAxc+QyQwYA2RAYi2SAJYa+TaFHSQRCIChMIJk1ZZ4hCiICSgh9Xa839SqEYC2Nx1VVVdYRAHvfNW29Xq+Xy7mkFL3vu8b3vXAkAINkkKIPwGxAJHHsve86jh5YmCNzHJQsIA/wIzOL8ADux+RZorUWkGPyQ8CieQqVQGYW4P19q/Kw3cJbdQm5y4iorHIDeH19/ebNa9/3usOIqGmau3fv/sM//IPqSn2UxpjDw8MQgtJllOalSXl1d9VfVauufqb6hppn0qxy3/dZZgUZgF1mjaXEIQTPHB0hGQRg4bgVwn2kZIgJY4xd130LUGmaRhHh29vbm5sbvRoF69WHUYd20O6avdAIFXYZc13NQc4HRYCIm81G8564lz9UKUVEBWY1Lq3rWgHihw8fZpl7/Pjx4fGxc246nW7qtu/7LM+Losrz8vT0vKjGs+nhcrH+zW9+o67vvXv3UkqvXr2azWZ1XSuGmef5d7/7Xc2YP3r06OnTp4vFQnXe27dvz87OTk9P27a11qqKQcTb29uDg4O7d++KSFVVRVG8fPny/Px8Pp+rlFZVNZ1O9QllWdb3/cXFRdd1mr1Q/bKfZbFksiwDAEgcYxRmYU4xgojR3AVHg5LlNi+cdZQ5MxqXhHJ9/db7DlJcr5fX129fv37ZdU1RZFlmM2s092AMIqcYve/bGHqWSMAEbFAyS6M829RrlpRl1lrbNM3bt29vb68VnFQIepcqiokjS0IBSZxCTMGLjxxTip6jz52xRBxiW9eL+c1yfts1LSRWy7/NMO0g0BB67733vUqmYjPqPaEkSIwsJGCRLAIkTqFXNS8pShxOYORkLWWZzTNLiIr6pBBFJMstsrRtu1ot6s1Gcwm6/k3TXF9f931/fn6uN6jpQWVfXlxcPHr0SDfAkydPUgptW282q75vEUUZkESwg+gTEThnjEHm2PctOWKOPnTMUdWLD13bbVyGuTWZM84Z2reBg/iptCgSpYGpopcas+nmUxF9/fr17e2tAkqDN6Uepoh47zUS1bSP7NKsKpYauw/uqMZ+aiSVQaIGTe07Ik6n077vJ5PJcrmsqmo+n6uLeHx8vKo3eVmWZbnZbE5OT0XE+1CWozzP27btfDw+Pn748IOTk7OLV68OZzMCCH1fuOzy9cX3v/tpmeVvXr3+3ne+29XNw3v3/+xHP754+eqjx0/+z7/839+8eXP37t2yLH/5y1/e3t5+97vfnc/neZ5r0uz09FRd8fPzc9XB0+l0uVxqQL9arbqu22w29+7dIyIlH45Go4uLCyLSuPpb6m/AyQqXGWMMbQEni2QAM2N1GUMIoetVB6nWK8vy5Pj4//u3//nxk0enx4f/7//X/7Oqiq5rUgocg+4PADYoZMAQeN8l33PwMfQx9MF3vm+7trZkJDEKVGV+eDCdjCoQburNzfXVYn7bbNYpemuwKsrpeDKbTDNrCmty6xwSsEBMEhPHxDFJYkicQuA+BO+TD7zTXMbgAMkwb32iAZBPvHWLBiWle8aYrW3w3ouoalJ2VFRASFFQ986HikqpA2SLFGME4MODg5OTk7IsU4p93y2Xy/F4/Pd///d/8id/ogp3vV4fHh6ORiNjjKbEx+OxhkWz2Uz5cX3fa/6jKArl0zRNo3jMZrNZr9dqS7quMxYBJKXA4q2DIndE0Pdt7zsfuph64R13VB//gMfssgUppcSSiIAIALd025TSeDy21k4mk/V6rRen1qksy4ODA2WNrlarGCNmOHgIuOPEqEwOmY99yVe4YrFYNM1GBVt3YUrddFKuVpuiKEKIjx49IrLe+zos1019fX395s2bpmnmy8VHWVZV1XK1NsYgmtvFyl1cHB+fnp+fA8DbN78rsnyzWndNm1kXen/3/M6ds/Pr6+u2ae6cn//i5z8/P7t7dnL6i5/93JL52c9+dufOnclkotv90aNHX//ut6EzKlH3799/8fTr29vbs5PjLMvatu2bPqX0+9///uOPP95sNnnuXr16dXh4qNB23/cnJ7OLi4vRaKJ2nnbMISAgwMFByLIMAaJzFqlwmbr6lkxZZoMHgbx1zrqmjpyOjo7unt9Rnsfx8eHRwfTNmzdNu2XbEpElEiKDRFlGAsYYQjTGAAsKhN4H8LPZtG3brm8Oq8N79+50XfPm8kKNkjHGbqM1gyTqP1siY50lI1HUxAEZFOPbLoJDkCLLghj2sWNIwYNRdU8iggCIAkAxBjIIrNe5TVYrQKraX3dgSluUVcVywCQHDHbA22XHQ1a7YkBZ/q1qAQBAhMzYLMvmu/S6eqF1XaszCQBv375FxPPz89VqxcwPHjy4vb2dzWa6jXU/a3ilnBtVqcqXUt2R5zlzsg5ALKRABGWZIzuQFPoOLSBHRKFB++qLAZgZMgHqDQ8KW/Njmp0fjUaKT04mk4GHpXmL6XSqkOY2Zef9AMmoXA3wxi7Pg+rgTafT8Xisfp0ybJQ+PgikgoeHh4f379+fTCaj0ej169dEdHFxoV7TYrHQ98YYwRAiXl/fImII4YPHTxDx+vq667r1eq3a9+uvvy7L8vz8/He/+50CZUpMf/v27dHR0fPnzxeLhTHm6OjIe399fX16eppS0rsry1KBzaurq4ODA72A8Xj8q1/96t69ez/84Q+rqmLmZ8+e6bPR3aO+9MHBQYxR3csQ+hi9ujQx+tC3gKzhk+71FH3wXYg9wZb2oKsxLqsss+o7LJfLzz///uXlxYsXz773ve9tF00iICMJS4zRh9CrXySQhKNIMghZZrPMWgLgqMrxnXkB9W4kz12e51nunDOIol7cer1u6ybGaACJiABRwAA65wgQZWsVt+moFHkXCirYrlDC4IKJvMtwqBnIc6ffrn/VN+6CoyGdtkUitgQpgF3A6bfMctmGPGqylsvl7e0NJFbc3lo7n88//vjjL7/8Urfuo0ePbm5u6rpWL0xdQt2lr1+/HtIn+gjirsJDYYI8z/W1ZjJGo1Fdr40xZZkLQggeCVxmicAYdNZkWVbm7r+ZIRygywEgUeHUr8mybLPZxBjX67X3frVaDUkVBQkHVTF4XCrVGsUOf8rzXNOGKjZE9PTp04uLi9VqS9VVudUiKb2qt2/f6jemlBaLxQBsKmxTluXNzY3q0YH+qr7N9c1tlmVv31w+/+aZJVNkue/7tmn+y9/+re/6zWqdQnz14uX/8G//+8l4/PLFiz/70z99+eLFwcGBItQi8uzZs7/5m79R1RO3Kem6LMumaV69ejUajbqu+/DDD8/Ozl6+fDmfzx8/frxcLu/cuaPMJg2PVe8qPNu2rZq+fXhsx2/eHvpdmsno+36z2SinVF0yDd5ijFVVdV33/Pnz8/Pzo6OjL7/8crPZqE4cXKZ3JlREKwbatt1ua9waYV1YY4z3/vLy8uXLl8vlUlXk4M4MWhsAmvXGt12MUdKOSAhgCcejkTNWVU+7qX3bcYiay+77XsOcATMfLNiA1WvegraS/c5BUIB0gFDxD+jEg2cxwA0q8FdXV87aw8PDqqo0A6EKHXcpQe/9YrH4i7/4C2vts2fPLi4u7t27Z629vb1VRsrt7a3m2BSe2HeMrbUDCUF/PyiL9WYJwM455qgR1iBuxqB15JwhMVkQamPsUoiYxAlbjuSpgOqgooK8BDboRRabdr5ummYlEvq+nc0mGpuqsW7bNkWxJntz8fbe3YfrVdPUPYKtu5SVU3JV6yWKZbStT00f123XxXS7Wv/5v/wX1XSSEPoUPafFKgiVqzoKlU0PJhv1EUfT4/HsKAh5Rsoyz/Ly8uLt7Q1YO6tm/+Zf/ZsMs9RHi1lh8na9aTZ1ledXby6OpiNH8cGd48cPzn/9i39Y3l7c//BhE/2ybT2Qq6bgRid3PnpzU883sfZ47/GHXz57Fk367/6Hf/XXf/9Xk9MiXne0kcrOTk4fZrOztbjrjt3ouKyOTmYn9fUybVqHPJ1k8/XbYmp/9et/StyenR39zd/8ddNsfvSjH725eHtyfJZn5aiajMfjEPqiyMbjIsR6dlCaLhYJ8wjY+LhusAuZYIamcnmZ5dPR2DnXtm1iNtb23i831ybj6awwNq3W14vltaR+XGXs20mZZYSxaTAEJ2KZnYgVxMjiI4SkJREi0ocwnk5NniWEgOKB6xRaSdFS4l4gWEshhOVyzQmLfMTJ5NnYmlyYmJXDxGQYKdE485br1NXcBytSuGig4fD87cVNvWlBAoInis5GlwUwI8s5+ALDNMdRBlYCpd5RwuQJIgGjJOaYUlBvxSdfjArKsIstYDKGUvIxdCCJJBpIBsRwgNClrvb1qmtDDGLAVcUkyyqJEtoUfTo5OkXG2MfC5IXJmk2zXqySj8eZbS4v4vzmBx9+8IMPP7h5+c2r3//mbDbC2D7/6jeWosFAGA4Pqq5dFjm2c5hmp7P8rJ1zO+fj0b2HZx9O8+OSJqnB1CB6wy1QsOjN+ro+KU6y4Py8cR6PyknOGNdNJuBAJETugwS2A2VMHVy1IUR0e3ur8WiMsSzLoihU38TYISLAFkce3Nc8zxG2/rEa4sGNfN/f2Lq1CqLc3Ny8evXq/v37P/jBD7744ouvv/7amhIARqNR33eIqKFmCOHg4ECkSUkyl2/LF3xKKV1dXX322We///3v9bSu6+7du/f02YuBF/bJJ588f/ZSWazffPPNarUqyxKA6roGgNlsVlWFiKzX6yyzbduen59+9PHj29vru3fvapS7XC6b6GcnR6enp/Pb67quHz9+3Ly90uWKMcQUWZBRdB1ub2/v3bv3u9/97ptvvlkul8aYuq5VTW6x0B1BbyC+D5CVrs8+WqZRB+8IGQMdAnaFY/qZqvXVYvCW7gsiYmkbTQwxhT4LDcsHaFofunNOJO7jc8O71BPZ2aitdTLGgGEAQAEEQjSIhGCI7EcffXSzap1P0+MiUN4LMJksL5//7p9gR8dXbE+vX9NO+5tEz9EsubFb+5ZSMgadtYPjoC7sYELRGBCWrX8RgBkAGMUYs80qEjKIOmtE1PZdSNFm7ujoqKjK1Wq1rmtF4Bnh5OSERbZMtOBDiqFfbWpnjMkLhwgCse+7ELo8dyGEmDoyGSKyBBZBsogGjLHGQbJExCAAQGQSgzAjRBHZAjODd7vLsbxb/WF/6J/UNVVGGO6x24hIQzje5ihdlmVN02h+dvAThvM15z6dTp8/f350dHR0dNT3fZ7nIEbLL66uLvXzFa/fppUAlElDZBEiIq1WN2/fvh3SNVojOx6Pl8vl4eHR119//fjx46Zpnj9/PhqN3r5969lnWUZkEXpni5RSXdd3794PIfR9cXt7O5tNHj38YLmc3717d7Vaee99DHXoJ0cHZVm+DWE+nz84Poq5HYprYgwCQM4YY8hkSjguiuKnP/3pv/t3/+7ly5dd16l2U19F10oXITdGwzCidxUtvKtiGVZVAa2UEjrUDbTrS+B1M/GuUlbPHNBsZ+ywWYf119M0IlA3VU/Q/Bj8QR2Zao0BAJe9hPCe2LBBQdziS5vN5vXry8v5WrLCk1s2XUI7Gk8fnZ0MW04dRfW9YVfXpqpBP1m5oAMwkbYLYlRiCWSLJsp+ols0fbiVSRHljTNICGn7+YJtUMl3LDiZHliXL5bruFiGEIzLsqK8Xa271h8cHgPAYrUUkbIcgbHzNzd1tymKLK8sInahafyq67p8lPt1E2IkAUMUJQBLgozQEJIxVtiBoRQ5cBIN01iAhVOyWhk5RCOqQTUStdaqa6vPVTl1k0kJu+rgfb3lvdfiWY0lFIQYlPc+8DNsCAV8r6+vLy8v27Z9+vTpyclJvfFaLFtV1XK5HI8rxRWbpjHGGrPVx0WRgVDf+9PT01//+tdKBEPEk5OTb7755t6DR+v1ej6fHx4efvHFF/P54t69e7//4vc//vGPw3rjW49opqMxkX3+/OXyZvnR4yeTquybenk7f2Xo8vVFmZUWLYqs1+vJbCrONE1jcqsG/M2bN2OirUEgIiEAJgJj3HrTKOnx7Ozspz/96TfffFNVlYhoQkLdBNxh0SIylBUMcRHs2hToybQrXdumUolUCDWEVuMwRE2D/Mgu9k4hiAiIbEHHnVfSd53G/EPFatu2XddV5RgRQQB3ZWXMggASE4hovg6UzSOJkRModiPILGCMSSAIQOf3HyRy2Xi16tO6iz1DBIMkX3311QBAyC4OFJGDg4Mh4Bx0BO5VvRljtP59wEJpKAZIMmgHRAEQFXNEERJNhrR9TwAMeZ7nQEaQ0JDN8rprZ7OZyTOtUytHo7wsg/Cde3cjp01Ti8h8tayqajYeReG8IsaeEfMiF+G2W3ddl1KoxidoBCSSY2MpQWJIUQxGQyTWIBExQxd870NIHJPWuKaU0rtSpm8dbdsOyk+XRsml+td9G4i4LTVC2C7K27dvNbi31qb4zhHaX2U1fUqUcc5tdvnTtm1VkouiePPmzWQy0hJYpdID0Gq5Xq/X1mYx8nq9/vjJ9xSeKctSAIeLF5G/+Iu/+Puf/gMzf/rpp0+fPj06Onr58uVkWoTQp+Q9hXv3Hty9e/err756+fIlACCJiFxdXf3DP/zD5z/4bL1et02vd0E2C6AsmfL27eXr1+nju3eVTumIDCBuZVJ+9KMf/fKXv1Sq0Gefffazn/3sRz/60d/93d9pC5NBcoYQQP5rbEG1ErqxhjTGcAoIg4BwEhAQJgRD6KzZuSrvaboYw/DI9qtljCHlsgFIWZZa5qdyKvLeT70MVcGD+hiEB40dPh9xdy8sX331e6E8pbRY3C7qPtkMbVbX4Xg82jv/3aG0Kg15UkoxBACQZIuigF1hgKokDRcRty1qmFlSGKIqJ1vpFQOAuNOTGAMzAHJCTgmBMsfMEaT1fRY8WDM7PDR51jQNgwjCJx8+eXnxet3UzJyYBaBp26vr60nlUkpJApADkQSeMZDDBN5kAMaQBTRinGGJfWwFMhRGJGROzD6ktvNtn+bLlSTPKcQY7a7MXoabBAC1PIOVG57/sGm0bcT+7jHGcAIi0mhQ/as8z5vYyc6hVX2mLzabzWazSSlpNkKTE4N3VBRFSkYLYfWXTdOMx1Nrs5RSSn0IwZpMKX8ff/zxixcvrLU+pM1m8yd/8ie//fKrzWajqY6UkoLOeVn87ne/m84ejcdT7/311c3HH1ff/e53U0qvXr84PDxMKRVF1nXd119//cHjRylJ13WT0WHTbKjIEkEm+Xg8Xt5c13Wd5zkipJSM0VgupQQMcOfs5Ory6Je//GVVVZNRObc0KvOzkyNmRpTQt2qXfBdUnwuqchIkQRAkAREkAWQWiUkTzRYQkIQAkohyjwaO0YAhx105GO6BmcLvMTEGVFOLPwa+oTo+RVFs6yH2NYIwAIS4rY9BJERAEBBmThYdAFi0iIigKDomgOhDORtVRG5hyfSuyMjl0IeUwhC1yh5Tcthp72ucXTG3JGOMEsS998H7PM+1TGlwH7YheuqNUdwR0zb1bZAImYksEIYUBSjLyz6GNsTj05OmaTZNff/hg2lmF6vlKM8Ojo+avgshtG1LxoxGozzPlbJiTMbMiUNM26xbnmvTsFYh28RBIBlrkE0IwWAQjiZYSwKEzBAj+5CQQTiyMCSm4c6H4qDBBRpeD5pvoHoO1n9YO+34gIiHh4dKUsM9LFt2xfuDy6QtrtRgdl23XC5ns1kIQX3R4+NjRPzoo49SSrPZTJdAO7jpJlND/eDBg7dv3ypTnJm7rqvr+uTk5Pj4+OTk5H/9X//XP/7jP14ul7/5zW/+/b//903TPH78uO9DURTnZ3estX3fzmaz4+NDIlByCRE5Z+q6/s2vf3t0dORcruvgnFssFpt69fHHH3/22Wd5kZ2eHk8mEzVTuyAnppT++q//ejabnZ+ff/LJJ1999ZX3/vnz5//8n//zDz74QNnnGrnxjoe97yAM/KH9/g6Kd6f0LoGm7cZSSpp93SviBtiVfeKOG2h2x951bg/nXFVV+uBUF9PgY79fUzYo3OHRDxec4raHDxFtjakIS5zOxkSEzFlmtVhWhK0j/aLhdoYb3F8Hs3foJoGdgz1ETMPGG96orrX3XYxRgJFgl0vcfhEaAIAueCGsJmNN4drMuTyr20aJX8WoOjg+MsY8e/ZMCZsiorlxETk9PdXLAyEtjSciLXHue2+MtdZ1ra83rTAacpwASZJsOdjO5WQzQStC5ArryiyvsmL0Tvb22Rv6gIfFGvaHBpDfEr/BEurJVVUpJU1XbT/a2cfWtBJPmT6qj6uqurq6stZqn6imac7OzpqmUS7bkJ3Teo7VaqUdNKy1T58+VR9PE18vXrw4OTnJ8/zTTz/96U9/enp6ent7+/Lly//wH/4DIq4Wq67pZtPpdDKZ394+++br9XJ1MJ2hAMcUfKfX+ptf//r0+HRcjYno9PT09PR0tVp0XffDH/7gz//8J7PZTDvKqBYkA7Rl8XNK6eLiou/7L7/88u7du2dnZ1dXV7/+9a8fPnw4Go1UDQ3aTUEX2KGgeuw/jkF5DSoMOeXWZIaQEwdvQHJrHKGBLXnSgOjrLZfSoP5HKIQCkkCScOza2hBMJ6OD2STPLKfg+zb4ThPuKECA+lp/WnpX2KEMG2ds7jKOkRXy3WX4tK9E37Rv31y8fv16tVr4rms2q3qzijH0bRN9r5WNVZGPyqIq8qrIo++j71PwWpYhKep/cddeSaEs9bC0h1W/O/b3qsYIzPE9cw5pvz8akozGpXaXefbsaZbZqirqep1SuHPnbDodf/PN13W9zjJbljkC+75dLee+bw8PptbkzhbW5JwwJRE2nMj3MQYgskS272PbemYxxgKgMQYFNOfpnHMuI7KAzlBGrnC2zPKRVX9mXzzMrtRotVqNx2Mt7jg+Pv7oo4+ePn2q4ZbITvkZY4zDHYxJRH3fi4AGeOPxWBibpjG7ekWtu48xLpdLEdFWUSrDNzc3GigulvPb21vNvE8mE2PMYrE4Pj72PhJR13XWZsfHx1mW/frXvy6cVVWq1ZnW2ouLC6CrO3fuNE2j4UTTNIvFIqT45MkTjun2ZlEUb/6n/+l/+l/+l//l6dOnxuJqvsrzvKzy1Wo1Go20edazb1589zvfe/P8ddNubt6+Oj09Lcry6dOnn3/63R//+E/++q//+l/86Iez2axvlr0PKXljzXK9zh12bWMIq7LYrFciQgiXby4OZtOubTJnASDFkBTJjASipQNGy8ri1usz3nsRVl2uh4hyuaXve+U5DICKcgn1KWgdwNZMpRT3ukjum46Ukmp6RT4066Nu4uD+hBCHOFOZDztojfWTVDWDVk4Y48gxgomCDLPJgQdadp4AkIQEwaAlODg7GxzjfUs7pLv0RoawBRGLokACZlbZ8z7kRdF1nbW77MsuctZABgBiZMRIWu+fOMY4nY1jYEA+mIyrqoLEHH2RWeB8vV4agylxjD50bbNeFc5uNhv2/aiqjHDXNrlz4/FYgp9fLZ1zWW4NmeQ1IpDM5FmVYzQppdKOwDGxjR1DpOX81jqDPhUWDdq27b//vc/HVebbNqaOY0B6v8fMoJX1cZ6enmrZnppjbb+3c+Jxf/kQseu6ELbdINOOLN/3PcC2y5PZFoamAePBHe2Ad2VQzrnRwUHT1tqv7dmzZ1o66JwLIXgfjo9PM5cb4xaLFaF1zhWF0dimKAqXFQDQtq2PbK3Nsvz29vbTTz/drJv/8l/+y2c/+DzGeH529vsvv7x7544z9uH9By9evDg8PDCIoe+vr68//PCj5XKJAqfHZ4v5/L/7V/+qX7c8F9c1Mcj19dt//Md/JE5N0zx58sFms7m8eTut8sm0jLFHIwcHBxzfsfz2j9vbW6VfDjHMgBVraPeH79rT5O8dg084OBf7zuq+J4mIKcRviZ++yF0GAByTkLHOWWsiYOAgIPp0RYR2SIs2z3r31He/BO39wxIALFKwmTGGgQBwuZyvN+uurb2PLARkARmAm2az20Lv/czzbTcjZhZR/U5E1Ie4vw78fo5kf/du/4RGTURktjsKCwFGH5gZEgSQThgAfNdx6KzRloPCMabQ+Q5TSpJ8ZtECE8eMgDJrrbHAEvrC5tZYh4aQxFhGZo4ShQyEtheR3NgYY7tqnHOjvBofFQAMHKvMlWU5LsfT0WwyLuf+GgAEDHN8J4S8l4RVRTiZTHTrpF1nYg0bREQJuMO7hueiSlQ16DaGTDDkKrTtpHL2dDvKLv8xCOHp6en1DTRNE6NXOyYimizRMOb4+DiENJ8vu64zxmo0qBWPdbMlxT969Oji4uLJkw8/++yzGKP6t1p+VdhiOjm4urr6T//pP//Zn/345ub6+vr67PxE/V7FWqzNmHkxX11f3T558mTT1G3bUm6stW8uX//TF6m+vvl//Ot/nXOKm+UWXiFBRAEevKZ9SQOAq6urIevDu654zAx/IIT7Dv8AaA1SJxw5SQIWEU5JRPSfnKIwIwhoty4RBCAUNIql6ecP2hY1uZxS8J4RxRgTow/BE2X7KmAQ6W9FicPWz2zOW27A1jkiskAm9pF2smQAkwinlCjwnr7YnU9q7lJKnJJoxk+2tObth7MwM/KWPmr3QlzFZId1I+N0WzFLSgyAJICEMUYEABYW6aMXkRgjpoQYSEBEiAP7GJERsSRASalv+uQJ0REZgdRFn5ITR0lrhQ0RJhGfJCbvKO99MERllvcM63ppUirK8vryjXOuKnN0Lvp0efF2M58jp5gCSmCOKUUre3Ed77IRiKgFjog4VDG+efNmUD/b9i3bnbEtjLDO7ZoFy1A9mOK2eGxnJLc0drNr9pjeb1cBAN772Wz2/Pk35+fnV1eX2iKp7/s8L+q6fvjgUdsu7t27d311G0I4mEzU7RQRbZwxnU5PTk7UT/vTn/zZf/7P/znG+Omnn15cvhGR5XJ9enq6Xq+/+OKL73zn45OTE+0sjoiPPnjw4vkrAJzNRqvlpiiKv/u7f/jxDz8PoTcGq9HI5FlTr+fz+fLq8vnz53/83Y+rR49Wt5dt2wqIIbPZbCq7SwCqAdmlBdbrJjOEYCQGFMmc1bWFbUF9Gnb5nrliFRnmbTKDmWVPYvchCt7rjcB7CcPBr5FdRmT4Pe11lYYdFlAUTvEXAEAUIv18VkxH3qv8Vli1ZGaJCREtGTKGyCSk6WzsAVph6byEGCNHjhykIDNc8L54D1TPwX9O24p1YWZAdbi22MyWijCkWHeOq4gAGr1XSQIgJAIGDRICATIgCAeJzBwNoiVsfEAkQ6SNok3iLMuwqqxq0r5HJJvnRi/ABxOFiAwmZ8GREySK3Idkk1datqVEhtkRJ9+vF2KjBVtYVxWlA1ovlldN3debyXRkichAkmhlh3QNClhFsaqq29tbhfiJqKqqi4uLg4OD21st5YJhB6gKxL3ksnqwupTaUk6ldyiY0EUcdsCAqqeULi8vQwhaJzHgE0P6/vr6On2cmqb5sz/7Z79Mv765uf3kk0++/vrr6+trtYez2SzG+PLly3v37n3xxc8Ojg5DCKenp/rkZrMZFDCfz++cnR3OJv/H//aX53fOPvv0e8+ePd3Um+ODw8PZbL3acIiT0cjZvN3UP/3pT0eT8cnJiVjazOsQwmw0Oj8///1XX96ZjWd5posQUyT3bhm/pdcQceDjD+yF4a+yo5IN0jJsTXmfljR4lcPD0m9Je/zB4YTtY43v3LnhJwAoOc1pjLBzN/KiUCuq56g7qo2ctsqXRa8SEYUFQGKMqG6gAANLEC8xCIyzDAA0sDOgrSgQgEXe9ZseLph2hD7YFbKKOroANq+stYACAGYXIOsWSgj7cK6+TmAFBCAKCidmYENEgAJMIAiJOUpMBtgQGTJgyRkgAuNIEjvk3EBmTWXHSpQ3iBmBIUAkJudRjAHnqCxNnjsA8Bl7TyGEvNhG6ZVz03K6Xq8Xi+uDR/dFUgo+9H1wOTprwYgtQ8eQYUbWorHqB6oIfUsVad8hremoqkpLrXY8xndjJBDf8dZ3rd23n5lSgoEastNVKg/69oFBMnhcl5eXxydHbds+fPjw7du3h4eHfd8rUmJ2pZMppcPDw4ODg6Zp5/M5ACgpHtAolKqklrIsf/WrXznn8ry8vr7+5JNPnj17Bv0W0z88vHNx8WqxuL1z5+QnP/nJX/3VX3Vd8+d//pMv/ukXFxeX9+8/vLmeHx4eC3ej0Wher7VqhAhTCr7rzo6PF4tFw+FoOqqqar6ouy5ZS5hwX2x2QY4ob+adbOyA/uFM2mOBKYI6iOX+MUSS+4L6LYu37wkP5++bQdkVpuj66++13qLetPsfPlwS7eo/9w+VJWSBrQuscs6SZLFYLNfr9bpuQgxAyIAIZCzBUE84FJGqMZOBCQKACkGJiPY6ARQV5gFD7vte5B11ZDiECZCRHKQEAMwCCdACsBAkbeQBJAaBCCxBUY220puMehkZgTNorTMpOmBEtCha5GWdHbltC2wlkJCBlHS8Sqc9rELoVVPM55ZS+/bqrYiMq2rscimZkdknDly3tbGoUNe2JTbtVfQOUO/Z2VlZltrNQWuC6rq228r6fRSHiCiEwCnsdNK29CmEEGHbutvspk0MmeU8z4cpNgNRq+887AiQfd9rq3OteAohFEWxWq2I6NWrVwo6f/nll7PZbDKZaAcATbIx0OvXr4+PTwRB/dKUUlmWIQRfx8PD2e3tbQj9/Qf3NpvVN9988/3PPj05PWqb/uDg4OGjB5eXV1ptmFK6f+8ug6zfrNvYlVVpDfV9v1ksvvvggXMOeu1Mp2wpZGbkd1zZfediu18RtQWQuvqKaorsLyZ+a2N9S9gMwZ7carsVARAEJNWGwsJRREAIwRhjB29lPyZEVLsESrhDBJGkcfhwPYOd+dY10C73KyKOTJJ3aXdjjDXOAc3XdfTBex9iCkBREMiISSgWAREJcPdFAsJsrdsqd5ZtgoS22c7hfvn9lRzUFsi7SqiIQEgGELYXGEVEkmTGkggIWyDrjDO0raatCjXjAA4AaIf0SkyFNTnliJhCDCEQ2sJmZNg5Kgp0jo0JiJg5wMxgNTZW21Jti2AlZvb+yepiISJVUU4mk+lkQgJtEsZY5hVLTCFEZDssK+3R+UQkz/OPP/5Yy7TVvTw4OLi4uChLx8wAabBvtBuKMFg55959GoBkWab9KbSoQs/vum40Gk0mE96lJTWNIZXVFoBv374tiuLm5kr7fFZVdX19qz2wqmr8u9/9jhNog0O1kMvl0mXF0dFRjPHi8mo8Hj979uzj73wiIutVfXFx8bOf/ezw8NCM3eXlZe9bY1AgqgH/j//xP5ZlaUz86quvjo5ODg4OLi4uHz54slwuq9JkRU5E4/EYne3axhlzenr6zTff3PvjH47H465rfEjOubwq1vVKb0Rxf7VmqlO08GxoM6OnDUOs9j3YP3RQh9/DLnje/82eS/Jt1rXswuxv+aIiot3r0l57Ic25OVsOH7j/Ff+tQ3YJdxQYTKs1bjSCJsa8D530feSUYmIBlC7G/c8c9JROaOFdUy/ctfTcpvKBY4wkoDxH2v11q+n2yhHFCIAB0HZViKyOGGSZwSQgkBvKHVmDBCiSIhILE24vHgWIyJHpu84ao01Bfds10lhriyzvwwIBQEgYtEPX1psz0LXBOVdVZUqp3iyD7yfj8tGjab3eSExd067sKicLzLnLXWV737ZtijFa4h5iFDHIbCRaSAwMSSZFUS9WVVFMy9F6XberJrYhQ+ebNoQQ+sgpGSJrjTOOiGxeKo5NgBjZCFQuL4zrYtJ844MHD6y1V1dXcdcFeLVaaS3z1dXV8fHx69evi6KgSblpm/rVsyKzjrBw1K1uoa8f3H/ULRZN2374/c+7yE+/eTk7OBTnkPnm5mYymdy9ezcGdsZm1pUum1YjOpb55VWe5xnRw/O70IV2vuq7W0tQzAprsGmS75OzJFDkxcGL118FeUtFNjsfz9u38+7F5Hy26dvYbLRCP8VYWMddl3p/UFTduq4mJSFaaxPKstmgpcwCIhNEFEZAAnZGDIL2ESOIvvMpxtwhEUTfZNbsQBwBiAhoDGbWisRdCzbRdg+KHLJx+4KmLxCBCDVGQARrDcC2GALJ6LnMPETvCNArpxS3BEvZ/gPEcoqRWQNCIrSMLkHsO6/uInOPws4Zm5El8cEzsikwyzJjsWmXb19dLxaLruusy9u2596v3t6mKE+efGTYvKHDIs9VuhAxt5aZ+75f1w0RIRnjUBR3QQIylFKGWFWTvu/X66UBmY7GRNuuakhiHSWyMfrESUASrhMPlcfIiAKYQFJiC2gFYwhtH4iZhBHFWnLOCVHfbNsjiUid0ng8rpt6E4JW7UwORjHGNjYA0nRtH/zQhFtRfe/9eDxmz7fzt8aYyWRSVrOmae5Wo6uu71LKDCDyslutNss+Bj0HTVqsVnZQZoNl0ye8XC6JrHY3bNtewRJEbLtaHU6zK7cfyBzGuK1Fhb1OaizGmPl8/oMf/EBd0GfPng2emJIwlTiiI7smh0dlnqXgtcslAgAnrTGv26btUxJWVpcirjqobOjemVJyzo3H4w8++GC1Wi2XKy0eTykxWwCwWY6IeVYaY0JiFyIYUldA/VVt02StjTH0TQtBABEzq/xnIlK0feuw7aRh61wJ7E1b2AHoIog6tBQHt21A2AdD9IcWZvjTkOfAve4m8H4stO/9Dif/V79C3s8lfuvYdZt+l4IzFomydlUfHEzzPNvUi7bbiKQYIcQ+chyPxyL8zdMvv3r6+67r7tw9v//o4ZuLy4PD8b37d8ajg5ub29cXl9533zx7WTz5M82Xpr3iD94l9IYrl11kKwmGdpiyQ01T2kISsHM6hze+t4AAoD63sO/7wmrOI6a+hRgdaeqLNfWl66yyrdMptNbxWx0hQPodB80Nj1Ur67Ub/T4B0Hs/Pbq/Wq0SyGiigyizyJULYbVa9FtbDnZ42O9DxsjM2h9aZFe1hViWZdstjTGGnOwBANbapmkQeZf94303Q9u/P3v27Ec/+tEXX3xxeHg4FEbUda39aYhIh54Cc5ZZJuzbOnhvAKwxArhcr4EskVzd3BblpCgrtK7btKcPzxTI1baLIfbW2rt3z3VMD/OWf2etJQLN5xhjjMvQGPQRjGWGvu+bts+ybLNZ31xd51VWlqXWEkIAmzmLxhhjEcHaRJR2vd9FRDNy260g/xXiJe4Ym/sCM/iHw7J/SzZ0a8oOsBlcL+97+IOIEfcqgPSj9uKL9wR7P7T7rx5DvICImnwi2gLUyuTUcorRaGQMth0eHJy+vHj98uXzlNInn3xwfHzo8izG+IP/8b8TxvW6RrJFaY+OZtZm9+6efHH17ZpV3UL76zMIp4gQGM1VKLPXbJt0JGMMIO/QnS3KJdtSJlDhZBESYAEBDSMtEYEgx5RCT9Y4Mpu2Tikpdq3fq/kw7YKxTx4EAGutvCPEvbuFIcdjjKmqSvt3aCBwc33ddZ11LoVoEI0ydV0PMDUWLZkin1nciwNhANNYk6eiXM0sM8ysA4ZCnCBiirIbcGGKwunAakTULwZB3BFqxVgFBl+8ePGDH/xAu6NqCTwRDYMju67TvvHL5XwyGjuDHFPf92WWj0ajqhwtFqvxZOayeHl1nRfd9ODYutzaTmlK67VhjsrzFkiz2eznP/+5cy4lSSkaQ3meAWDTNJ4zS2AASIQFEzOHVDddiFdFmanqcdksd5khAkmKyLldffpWGFAAeAdOsQgCAgnIH+TlcC9sG3wN2iHGgxb/Q6nYV/D6pX9owYb34q4rwrcO3BUZ/qEc/rcOZ60h7YUHacsyT8aYMs/apu66xloajSfWbGdUfPX110gwnRQHB9Pj48NyPAKAkOLt9YVzOQtOqrwsRmFSJIbrGxos3rDpdavoNhgiveFqdzIQAECjrxD6lHCr9N5fPcR31VQqhCAALAicOWcI1e8mIiBi5hCCoq8DRE97fN1BHIY5MESEe43S95WpiGjhkfakh53aTYElMViRmDiGzOaZM0i2Kg+bpo7JF1n+bvDi8LmIyABEpKCl5up41yTb7Goq425ksYqic455V/8uqIOmiqJ4/fbq4cOHX3755QcffPD3f//3n3/++c9//vM8z3Wmmq6+RgUHBwchhL5ryiLLbEEGRCSBlNXo9OxO04YuRLCYfIwsPgbK8icff7RcvlXKpQhro38AbtqNy4xzhqLE3TRibQhPMZGxrGNDBQQpcep8X9f1eXaa57lOJmFRgJsdkUFi5tB1AIIAkN7LcaEAIpIAoiC82z3f2tw0tDbcQxe/dfL/jYF693XvS/Xwe2XqDP/8loTvn/x/L4ckoN3yQUkCwigKmsfk+8zSZDIS4VcvXlxcvGrbdnSYHx8fn54eF1UeQttcL40xNssuLy8ns1melVVhgSwSoDCIR3S8x80aFoTfJ2zpb5iZU0xp4M/B0PcJcJtyHIbS7BZHbzltH44IQCIRp+MEQzQpkEFkE30ffffg0aOhKe4gVzFGnTlJu+JPHMpcCAlQPdhB8Wl6sO/7tm6AJcsyZyxTSill1gJnhMQpcYwAzlpCsoDYdnW9XhVlZoe7hf2c0p47FEJANPpivV4nbq21hrbtfZWkol4l7Lx54XdNfjWc03Y1Oo+BiNq21czeeDwe2hlqIgESh67tYevUNk2z2jTTg1hNptcvXorgdHZcVKNN3Qv2f/qnH//8P33JzCjJEqTQaVP3xe3VpCpTSol70NGFQRCxzE2P6JAYQQQYAY0zwAAQOaWU8rzQftAsgUC8D5kTZubAffICkBEyR0hsUVEodUC1heYWFv/WXtefQ58Y3qOzDHI17KFvOZmyxw3U9xr4tvRuf2rlu+wq3wUgsbDA+wTL4ev+W3Ko+SlELQ7czuchQiLI85wlrtfrm5vrq+tLY+jBgwc//LPvzec38/m8blaj0ags8xhjs1l9+NFjBLNYLK+uLkVwMpkdHZ7cu3Mmr1aaYgCwzMi7kkjtlyiS5H2Xvu+3TEnmLb0OAJCE+Z2wicjW+3r/nkh4C0Vtqw3FMFprM2PR2YjoA2SWrEFEUJDVEokAGpyOK93AzGy3mUNbVZWzOEwC1bAQ9iIy9e/yPB+Px+qU+s6AdUja8D4hgCUSIESxRpBYYrT7fs47Db2rbBhyMgqE9H0PGEW2LfJUVaj16/vemG0VXIoMAErS12TD8fHxer2+f/++pvWU4anW7/Ly0lqroAgAOEtt0yQdI+5svWqur6+tzWyWA4B1+RaPstTVm2a9Ikwh9taAiHRda60tclOvV7vJctGQqrdEaLIia2JKwpET8DYHjYCAnFnXdZ11VebcZtOSkVEx4hA5Re3EM3h9aAwbRERLGomhJsWRUQnO+9H18EZr3/XUgvexlj8UiWEXDqF1Gub27DCh4S24o33ty/AQYpBz/39ZQoO0deO2zxdEmFOKAgjSbNZvry7btj2cHdy7d+fw8PD1ywvmCIy99zqnZHowOTo6atYNWRcjg5Ag9l24vr559uyZMSeD4dqJFoqIpk/3HezhancrEPSFy4wj1/e9SNrl6/c8CFQlhMiyKzVAQokxGkg6OqogQINioIzZarUaRrvAziZr5/jh6WhKSTEYzswwpFnhHL1arfIbCNLj8Vgly5GxGQCRIwRgiSFG7kOXldloNLLWoIT3aBm4A9mIcGjppc9eCym895NpzrueQrJjXROR9z7PjZJRIiWN+rz3J3fuzufz+Xx+fHw8n8/VuI1GI21We+fOnYEMoFWFRZGv15sQwmg0Go+mXes7H1abGqEdjSZZll3fzIloOpldX19//btfZyhd6Lf+QL1y42lGxWqzzMwUUp9brMoxETVNE2OPmGLElGwKKIIpBZRtTwxjsOu6IrdIkFLKXVYUed+3yYslAmssMBFkWSbMYq0ytpGEGBGBEAnf8dr3PUN9kKrRBhH91rIPz/IPj8F4bsk3u5Kl/ZgKERWyG97yTgj/W9L23zi0DUeMmiAB2XUequt1URRNW7dtOx5XT548Kcvy9nb+9MWLoijGk6qqptZa50zmcmGLIJzI9zoP1BgqQCgGGjwCtWm81zzlW8W+sq0eBO2Zv9VWJDvGTLv1muUdTxARaadqgDR3AwhCIpvVqspcljtAjswkbIlcTovVUvGOYQUUmJnNZvtCMQSrOo8gCUBioB3VHmDdtHmeI0vyIZKhEVhjc+tYCGg7sx0FQgh99E1Xu2SsNUWRhSjbTl4AYK1NiZumQcSyGGVZttk0tCtLSynpqmm3D2bRFzokNaWkkyQ0Ka+sF4Uly7LUpL/S35RWNjCttaC+bdt3A1CNVFUZQqzrOs/K4+Pjum4vLy+Pjo5EBFiOZtPMke/WBOm3v/5Fe/nryWSiTdYcudRvbppl2/bEARGzrODQJBGIyYgYwYPJRFhS6JgBGAxuya5d22ora4NSZnkIvu87YxwYk1ICBIvAAL5vIXiDpIN+OCbmmBkQ3A7rMiZLu8YT+7EN7MovB6EaktG0O4YQYDhZa754x4D/Nq6o+1VEH98OVNum4AmRjAnbru/vObrD3sJd8b7sKITMuFrXZGA2mzHzfH7jnLtz53z+5c1vf/7bxEFHL15dX79584aZZ8dHxhgRC5xbk1nnCK0w5WUlDOORrZtOUuZ7WS4XvhdEyTILAEQ6FVN3V+z7ranfqRJgTlqbO1ybD526XTrTN6XIvJUQ3LWqMwZj3HZ8tGQAEZhjilmW5YUziH3fBo5Vnim9qCoLAOjaRvNnVVU5awihqTdbxhyANQTOikiKQex2Wq7Z9YLhHbklyzIVCgVKtr9hA8Axhab1NrfFuEBLXeo361ooaXNxq7tfRdEYN51OU0q+D1VVFcUWrQ4hKdUQERWG4fROcxuznRmg4R+9T2LW7nQqk4PhNsZoenD3CWbgyoHwsEcnk8l4PO37fj5fAkvTNBG76XjifbdY3EYfRlX1Z//inw1CKIJauLTZNNbaruu6tu/qOoRAZEej0XRatn1iYWBwZMlaYQzBhzYaY5BFUmLablDVsgQsKQoy4xadEyLZoqMMYLboKChSAPtCsi8z74MH2xMGs7lvOQcFvJ+i2LdUQ6C472fyXqpDD33XPmq6b6WH7T6YHdzt/dPTUx20WFXF/fv33rx587/95f+n7/t79+7duXNH1ejR0RERXV9fI2TWZC4rsjzPstxZZzJjjPE+JuGUDEKBJgdyLF1i2/luX8vAu+qNAne1vO/fguCOcWB4O6s0pW0/KwAQSCwMu4o5JJKUgBnRAACBUpsNCKMAkhChAUJJzNtWUYNKgj0XRvYycPuqc3go+6uq8cLQZ0Sb02iCMfap8z5KLPMqQZwvbiNIgkTWCCq1Ba2GmLDthyk6I7betJvNJoSkGTzEd22aWLRv+buGJeoVqBCqM73Ph3z69KmurCI3tIOGBwap3r+6u2ppNUvp++1IDSKq15uubogTSAgNO0PHk3w2Pjk6OPzux8daPyU67rzzmeGM5IMPPggh9H1Yr9c3Nzc3Nzf16qbdzO3hnZQSImUG87wAwYa4TRFAOMUArNMqCS0gJwm4I50DonVkjAXC+C7bzpoVlsQi2u3vnRgMUoe7STiwxzuT9ysJ9yVQdvn9ISAf7NguE/seYAN7Xu6+SA/vkvePQS98S1mIyGx2dDu/9t5XVdHF8Ox3v7m8vOhif3R6dHLntBiXN7e3bduPRiMim1Uj68q8KMvRqCxzZa2RRWNMv+kDp95DAiIwiclH6QN/ywnXK/wWODl4BIgIyID7Dvz2T9YqLLHN16ekk8kEaRh0lVAMEBMSQRoSSoaIIAGApJQ4kXtXSER7lMBBx+n6w676PMWIAIa2FF7Q2hDm4L3oOBaiPsa+68qicNYai6kNgkyORGRTNz1Hl1ubOQYEBkTZNurCXTSo3Ss0hRDj1iJpabxKl3K+hXEQJ5EteWfQavsR9sXFxXg81lntuBuolnblhd573PEAt7cagoYHbeeX65W1mfbCsCCzUSEhhHYzPTr8/qefnB4eeO8dhNj6br0drxVjCiEQx8XVhXOuKkeT08ODUX40KZumAaDny42RXdUZApDNiEzp2s4z67RZq85/gsSccmcjs7AXABFLIAwASaEZ1PZnIEPi/tv5Bt7V76Y9QGU/bBu07H4QOMjGALeYXU9eiTu2pwiKwBYOBQRE0Yp4UIDIkjFkmvRecnn4in1XindV14jYB399c5NSQnPw+vWLX/zyZwcH07/4iz/XgbUvXj1nBmbo5rd5Vo7HY6HcFkVRlnlRkAElsANgAI5JkjALJAHfh7rp6qYz06kumAZ7qGE1Q+JgjAEEEY4p6MUMgpG2JRFxZ7KEaNtqUrYlB2lno5JaCEQUSMg0GDqRBEy715xS5JicMyxsQAiBhCUGANAOPZAipN0kKkOQIHCKvI0jhhhhsIQxRrNrESQiurfROJvbJJxSMmhdnvmem7ZHvSMAALDaOcIYUxSF99si9Lt37x4eHs7nyy1DD2XwgHc7YEi+JZGtKTPmXcf14cyjoyO1hIr06HUrFroDALYToLYZSCKXZS4rEm/6PszncxTg6F2eEbKzcng4efLg7P7pzIH0i3lEUTdYRFDIgpCRjGy7mW9SmgMNnsPBuCzL8rrZIBgRDLFrVm0SYygzrsAtSxM5JjZGSPW0yYwRFuMxgkjihNpoNhKhQVXDiRlFy7Pl27jLvmYdfrm/PrjjbeFewn3fZH1LRFN8Vzo4/HL4/H2TqDtYOxjum53h7UPlmiIiehmXt2tErJv66Yuvmqa+++DukycfFONysVxe3rxNkc/Pz7OsWC7WImjzLDAKgRgBC2CRwGhzKOscYEwExCQgXeia0HapD3uTpb8VVg3XP+xvVSUi4n033Jpzdmc/cQfgx2FBCDQjvzMJIAKIBIYIOSUI1hgSBE779h92ePXgsOCeo067ahLZVZlpMK9SN6yqloYT0dArNYTgysrlLgXfdI04Gh1MI+HN25X0yCDCKAq+D1w4haacc9qX/vnzl5eXlwrIqkpWl09ECN+NTCMy6tDuXfe7/TFYAJ27pC6rao7Bs1KLui2rZ0+0lcmUYtd1lrDIHIqs57cns9H3Pvn4g3tn3Dfr1cLElPrAkR1RURZqq30fUkqz0VTHNbZ+O+RIRKy1ZydnxjhhU7d+uWrqto2QmJnIERBvnU/GbaWkRUSLFIh01ntCIP52Da6IZvATsRnEabBp+1L3LaM0/NR12A//BudC9oKT/bfsX8OwzvJ+Q+FBsId1HmRbt4iujO4nVYvz2qeUFsvbvq8fffDws8+/m2X2xcuXV9eXzuZFUWyamjd1ilKWo8RCFsGkJFEw2iwnIgCOwlnlTDSUOEXxPvbcBe5xx5L8Q4gY9jzAQSSYmSzIXpf7YfhSiD2JttV8j7gn70Zq75hrBlGIDEpSi7ol+oqIMpf2JW3AqAaZVCLx8ASHWHowIYqVKOqrYqmx1bZxhMGyLAW57RvIbCkTMBRYfO8ZMKUUGWzadddSdajPrGmaoigGyZa9Y/tE4V3DBUV41CXY3oy8W5R10wxokgbfQ1cL1Rm4NzdbRFigaZo8z53Ls4x81yeBPHPsO2fkYDI+Ppw65Pnqln1/dDhbxcgpEYo14iwAAyEDSdusfd8agumkJBq1bbtZN33fQ+iJKMttnlWFy5Z1X7fBR06SCBHQ6HQRECFrDRnmLX0J49ZWGMQhSwuwNYHvjBez2auy2d9YfyiHg1wNdmz/YQ+PU4VE/Zw/tKWw52TCnuUcXgyfMHQqYWZtf64/dT/pc1lHXC4XZZV/+tn3P3j8IEl6c3XZ9U1WFEVRANBivQo+jUaTSZ7ZPCsnFREah2TBWkKLKYGEBJTQgjEklELX+9BG9mjA7LVRU6ALd3nOwTXY4bS8j2ztqycRUVRZZ/furSeEMDQuEI3XEC0RWKLILJIYAczA9SPZC4kHoBjfr3G39h27E1i0GSTHFH0wSGjBkhFjRUQSi0YGzNonUiRFiUREzvoUV5t1BKiqql+tE0NkiilZZtZEn6a2EVGHSVxcXNR1S7s04LB22/IF2AJrMUbmbVofBwWDZlBmx8fHOuTg9vZWa9jUqOr4F8VUNDjU3TAuy9b3jJDnuYikECVFAhGC89Oz87OTvqlf31zGZjmucodyOJ1sNpsQAqQoSCgps0SU5c4RcN9tk6qOCFksweWbl1U5ns6Oq/FsMq6AsiRN2HTAghYNQhLYGnMjiBhDxAGIE0ER5ZHQtiRINdI2Rh+YxIN07RulQU5x79jfQ/siKrt2r2mv5/Jwzh/+1EzXvrnbZjXUgoegY2q6rtNRFnE3qTPuzfELIdw04ejo8PMffP+zz74rEJ8//6bruvF4rOSHGPn4+DhzRZYVo9FkPJrkoyKlgIjkCJ0gQgyhD10IXsiAEHMKyXe+jckj8f4iDC9g17eW9+jjaa9nx55DuAUdYorGGOZ35cUADLCNvfUz6V2967YrsbCS3QBA64Zp/xpgzxoPOnQf4BDZUaMAZFf+PiiU4QENiJpzLlgQETJYVVWfuK5rdG40Hq/qRhBYkAQsGmRgESFril242dY1CkBMAmAI8zyjCBDT1JXXkBAzQIwA6CjLjDD3HIpi20MlxpRlWOYFMzdN0/k1GnDW9mHjY304neWZvb6+nlQlokhsY2gBwIoYEkHYdGvnLJi07JeJGY1khiRxFvCDo/sHWLroesB1CMYdzu2Me4/ZyBSEBAlALKNI4pgwJPKRWEBSClGSLbPZqDg/P3v58uXtm2/idDaZHpyOZgelu856L3C7Xq67hK40tgiMEcRY22IGkgCIrKEo0ncpJeAYebxsVliVxjruehAuyKFAKqMzCJB8vy1pQWs0lRclAYDNHOzCG2utj7hcb2KMVVWhlb7vACDPs6ZpEnCm7eUdEEkS38VQGmTmwDFFVtsYY2z6zqTtwOembQGoKArLKbUNJdpsNiFJjHFdbzwnBlm3Xd21XfA+BCYDhIHTum/btv3OlH78Rw8+/6MPEndfP3t+fXNlrAsR13UDQNFzntOonFow/YYNBxqfkhMEhsjcobMmiz71YtHVdW+yPImpN2HVpkBFdE5iop27DtpoUGTrAao9T0kArHNIlEIQ7t/BM3HLq0YAwyA+KkxigABsTJB8mhRjBawTSBTmxH1sNpKmVYEZYuA21Chm7BywD21D+di5HLaYolhyAACMEsWAscZmYCmCCJuEObqeODA4Z/JqlAATi48hz3NIrOADQSLaNUKLMWbImKx1gMYaIUDGKLGd5M772MeYhO0gygCIhCKoVfMuy5iBmWNKFAICEZFB3OIPahZY0q5HXdt3uSusddaStRYIY0wxRi9bzaR6XafGxhhDrw2jWEmAg/YKIQiCMUS7Rg64y0keHByMrK3Xi8jh4OBgMpm0bTseF9sP3zY810dgRCwBZ1kWow/9dviWMcbm2Wg6cb7YMveB8moym01uFptRVYLl1kvnvRBLhLbeGAskQCgICCDaBQCjz3KbZZnNnEmJOXFSshc3bZsza4SQQAREQtAaSGZOOl1ExKctIrVarNfrZUpp1PdK3UKS3Od932VZlnNuzHvueqJ3LBZjjMnylFLf+5Ral2UiqMWfQzHO7XJRb1pmjpzW9SYyC0LdtDbPYtsg4nhU1k2nI8EfP3z0b378nclkcnl5efn2mpHu3r1XN82zZ89Oz+7ArkWA9z4rq9FofHJysgF9vqAt5y1u5+uGtM2zxyB9CNtG/vzOO9j3CGhHWhgcbLMb2MZ7qbx952I/lh72j5qBbXkTbkNiBCQgUTKmvDtwz6Xfd0n0i1RLGsC0ayKRUpLELnfMyfvtQGwkAOUMAgCAQRTZYxQKpJTAkCFnMpdYQuKQxIe0c0AkJbHDRRAZ7e6onm1pDFIS5iTsUyQgi1aHrWl3Ld6dLIwikFgSJSKKBBCYk7qHVnZjD9XR1yY54/F4GRbqFEncBl1DJCA78ioRgTAkBgH1iFCEiDKblWWW5a5PnhkQAVGSiAFBJCVmx5gyV2RZlpKO9UD12ep1jUhlWbLgarXa1O0osHVFVRQ5ZbaLPqyT74Qy6wCMNeTIgEFjkAVsTFGAWSSl5LeCseOhKqpnMAoIS2TxIWnHjz74siy74AeI2Mft2Oe2hrquvfebpiUi7zvF38oyz7IsLzLFuzWoY47ljuafUjLbelNUfkbb+T74EIIhR8Y1bQ9t33a+7Tt1SjvvGUQIIwe/CZAYCNtN3XbtqMjvPbj/0UcfffDBvYuLi5vbhTGGBd6+fQtoHjz8wLncGNP3Xu+1Cz4LQfcmOGMMWUJryRpKbKy1ne91k4SQutb7PjLS4G7vQ01q6OKO2bPvYyMip3ftZAZHXXasN9nLtQ54iWYoYPcuQjIoKXoSpm0XDEhE2p5+P0AgQG3tiEMCc1eerhK19Wt2v7FkdH5OCtG590rJEBEFgFADycxmZVm1IUJMSXwInYYA6u5aEUFBAiJAwXcKxseYQIRQCBmBgQEZQXLrtpC6XqsQE4sIIXICzzrgKVhrHdk8L8TIUGisSUIiUshHVzC9j0flmSXlRyRmBNqS5UUbrgXm6WSSFa5uVyGEsix3HTs1ZjDacAnRel9bS7k1iJhlKcaYQkwp9T7GJLm1jsjarO26erUMsrr/8EkfORiZFC543/QNEY4q5zlSQiJRIM2KCBm2KYTgU98G4xAERNEoFiRbMFDwqW37rut8itq7JSua7dQEYQ3ANGbg6JS1p3bAh06Xazodq85S9G8ITgraRh0pimJduoPvPXywWS5Xq5W19vBw5IpC5+TkWdmnXYNJTiLCvB22Xo1GrQ8319d5UXz3O9/58MMPR6PR119/s1qvjTHVdFY3jV+vJ9ODR48evXp1kecFkVHPpm1b9uycy04mFo2z5Cw5g0SgfVmEEYA4Qdv7vg8xCjqjlSZDlMW7JOqQ4pIdf2OwbHGv6H4/iv6vGsb9SHKQQ8JtRgK3USEnkESEtGXADJI/fObWt8KdVLOICOmov/e+MXFiEbFbT1rj0l2zEKOqwSCDc3lRVH2oEdnaDLhBNERICGCSJdgHvklAe9NhHzwzEBE4BDTMnBCIhBT90ZQMKKEBRYi23GXU1STasr+MmAE9DyFo4aP2UPiWR6EvxmUVhQNLSL0IEqBhVA5NSokkIlZ57pIUZG2C5FyOiFuaC2k9H8vO/ds0Xehb5TqJCAc+ODndLFcA4JybzqgoyxBSaFqOXd94g+b8aFoV+eX13KfOcZ6SERHRShdOBIzOkMnAAAAETglYUiQAnV4SIjNz23XDxMU+buswO9+HEBi2WXjded0GYowC7yZAAIAALFeN7LqYkXnXiWujpaIpKdWpTEnZQk3b+RDJWJflAti0bV3XddMEgbpvVYabrmVmIGzbNssyTglSPJiM7969+8mTx7PppGmay1dvjk6OyZrb29uirD77/g+S8IsXL9br2vtARAQmcwUAiNliUWTYOnSWLKlHshUDYfACfR98zwzWYf4H5Lx3x+AH4Q7O1Rf7juK+qHzrGM4hIiCEXWEFM4NyDBHhHXosKalfL8wRUQxZRGSdTrd9Aru6YU4sUZSSuOvUaMkgiffsvbdkjMtEBCSJCAoIyja+Q7QmY+YsKzJXxLhOgbMsI7TWEkAERqZt3lMLzQd9YBAlCoM2ijQGtn3VGQg5RLXX246uAsIiSRgAALPMTSaTIq+0ikIzs1vvGVFEdDvGGM22AGg7OHowjBllMXGKPih7BW0SEpa+rauqsJzqek1WxtMx5fbN1ZtqZIkIGBFZBLeeoSQBYo5d55umNSg6BdEY44ppSNzWTRQgorIsi5wRcT2f+5iq0exgXExHFUm8vpmHzU1enQKqk8CIWrwkBq33fiPcdW2MPoaemTnEGL1PkFLquq5uGpW0JBxCsNb2MWyZH7vUjmPqg+ogirBt4AkAkIIxJsawTVvZd6rNsFL/gBlBKCeHrjBEN4ultbaaTK21rQ+NfrvLWt+3fjviS31gFNQs1nK5nExG3//edx8/+iDLMt82BuTw+CjLchbJs8K5vPN914e27cvxyJBNIQBSlmXO2jzPq2kFJMZQbo2zhMASZeetUeTUe982wUdBsEg60utdr/EhGQh7Gc6BeqL4pNnmG95J4JDVwPdR6MGxlD2JBQAWBmCrBBsmQB3XyYzbTomwI83hNtcPAMAh6mVt/T5tJ8OCZQksQqwpAI4pGQbIUJJoIIcCu17JBtCgBeIir7Ks8H3wMbisRERLBgjBqkiTfd8iEaKgNRnaJIwEIsKg8TEBCSniZwjRCFIU7ll0k6bEkNiaLM9zRNP3ISUv8G7teI8Cq5lGay3gO8J3Sin0fYghxsAsYChBSIw+bqdYgt+RPCRlxtrMKUmOSJiREEWSSEKRtu2cswp1MEdD23GLddf1IbEgpBT6AMDOWmtwuV7neY7Jt6t5WY3Pj6YQ+1evLrL8YFC3hKzWKUq6rZfGGBHu+ib2fpeKi8wEu1Q47wrefQxlWcbIUcQYIyBBGAGEBY01O8Jk3JXMMcc8p5BSVOeKDTEAQmJOQAYBkISQyZB1Li+std53USAmMdvCbQMoaLDrW5+2Dp4YskhElKXgvS/z7O75nQ/uPRiX1Xxxq1hOVY3Wm41z+fHx6aZrv/rqqQCc3rlbFAUn6TpfuOzwaOZsDlsoQawR68gQYsLA2+SHiMTAbeubrg9BwGQINkWvvsqQcoC9rADuEV+HWJE4DUIoeyTPtGtkTjteGw8jHxEGUQQt6dUWISBIxLtIklHofWhHY0IRIYG4az+s7zWAIJA4lZkJTc+BVZA0USkpCYEkHUIvoA0KjEo5EphRURV5mVLyPpbMzO/lriyhRe08AGrhtkuQZVnkxMwC2whSa2UMiEGwRMbYKGwEQSRItNbVofNd37dt7pzmP40xWnugYPqg81QIt0uD24SMon++awNHFRNCHUnLEMPR0VH0PrTNqMizzK7X64p4Oh13rRJNlPEgKCiCIknb5ufjsXNuuVwu1qubRUop2XLW9T5zziK2bRv7jp2TxIagLLKU/M3lcnZweHb3XpyMLyH6zRJ2BCsRSaxMg+BjIMKUUtc1uudYYkgJowEABUKMMcAcUup7b4siioA+ZtAAUoC5770GfiICZDSGIXFdCImFAXXbJUSFVS2RJpcAUBCZCKwlayfl4XK5XK5XU5xOp9OsyNfLVdM0jQ99H8hs8cZk0JAg4ng8fnD/3uOHj6wzby/fzOc3ItLXG4MgCIK8WteBU1mMQorL5VILtZnBTfLJdGrJaS+jHMkSGQTa9uDdTYNNECP7PvZdjImNNQyUmIDSt3xIeR9cUZFTJiMisu/3fdFBPtOuB8zwOe9SqSqE9K26LVQhlL2ErQgIJBFEMQSoHWkIdKC9oqJskZzVMd0ppe3An+iDM9uaJo4xRp9Zt20nxYKonTc027ntiZhlGQlxTKLde0CYBVlEwPKWhC1qKBInffdms6mqKsvyzvcxBCIS5q5tpzGVWZ65LMbku9b76EPsvC+KKiND1qbgu6ZFQwbQIkUkLTrue51jTDtmzDbrqvxj2SF+ZW4cGBbxiX1sDdoiyybF1FkKoS+yPEbfNDw6mCCi9z4mgpSIyBp0zjhnsyyzBvM8P5xNYozz+fzt22uRdHp6enh4+PPfPs1yW5aj8biajMfJ98JRUry9ZkvALM5SDL6v10cH43/24z/+T3/3m6qq8sxpqts5h4Rd15I1b99etb4vy5xBdDjUaDSCd8OPrSBFQAY0LgM048mImVf1RuuyAaHpehKnII1WxO58LY4xWpMxKTEqL8ttr84UkYwBgKLMTo6OFcxwed40tTEGskKT8syszXK0brMsy2dPvzLGTEbjFy9eHB0clmXxL//lv3Rk/tNf/V/GmO9+8slicfuLX/zi7OyOdh5JSZq28zFMDw/OTs+/+PnPsix7/OjJZ599dnxwuFwudd9nFlMK3ps8c87aFCMz+z72PgHA9e18uawnk6MmyGq1mc6O27CQPZIK7UYwDCJEuyrHbVlcfEdAlx2cM+xYld6dD6LMUkfWZFlm8wwQvfdNvW7aBnNnEZx6IhFjjAbRWScMW4OlwpkSKCMlRBFBFsZtAE+AlozvWo5hQMskBREmsmQQmEhAKGk0qMQag7Sp6+Vi8ejJY20lUde1mkoAYSIRtrtoEAC0/eY2SwNaIBeTA0BjAMABOGNOp+PpdJqVRdf5xZI22GSElXNgbIicUpIQJQuIGUhKHBjfURBU9WxVEG51vArhAN4k30cWhm1HdMaoEK4iaXnhCKzNyTkDBMCQZYX+NTFDRCJOiRHN3bv3r64uL169atv6yYcfTqfTzXr9+vUbk7nlYlUVpUi5WCxKZ8ej8vrt5ePHjxeLRXc97/tWhyWdnZnDw6NPP/7wzZs3r14+Ozk5+eTjJ6/fXNzc3Nx/9PDizRu0iEGSsHG2Go9CSmLICDGzziVDQ1u/SMtHgldhE9z2ZcuKHMM2JNaqU9qWhPY2c1mWqTSKSOTkDNnM+eBB8OTkJPownk6Wy+XBdKLEmsy5osySD13XpBQz58aj0fV6vVgstMckIjZNM5vN7tw5/7f/9t+G0H/51ddNuymK4sWLZ1VV/It/8c/rTRhNJ3meb7ouAnDXtk33+s3FRx995H2s2+bnP//5wcHB0exgPBk55wLXzlhCJEFh5iTIW1b6el2v12sfxIKklHxIre/fG3G4J10Dc1r2yJy0BcphL1x6j2/9LfO4r82l70TNY0rGmBACEFoSQ6RET2vRWpti0GopAgbgpKMBmELotaaGiBBQXRTQ4lGl+CBqfIuIhMDMkFhprjo8BhABIYRQ5oX3vsxyBFbGUt+1LssQwZDANnggFBbeVnMYInDWFVnuuy7FkDtXWBdjNGRGeXZcFAcH07IsV5s69R1EF41jIgbs+tCGKJyi95ZFOGFkcPsJUmEZOjRu6020cxHvampC9FFYkNTLkJiYEjnWTGNRFMagdWgzl4iBPVGmFBZmFmHEhBhE+KuvnlZVcXx6upzb5XLVtt1oVD14+LDY9KOPPmrWm03dnpycBN8tl8vDw+NN07RNj9YcHBwpivv27VViOTs76bpG5yePqqIsMuZYVdXp6anJ3PXtTd02ITIYQoS+D9mu1dU2ttnB630MUTgrcpdnGEk7RGZloelWJDF2W9KlQC4ROWeIHDN3fZOYRMQ5V4zGOpo3xqjjeqy1y/V6Mpls1svlqrEIVVWNR2XbtvP5zXaydNMeHBxs1kvvuz/6/PNPPvpouVy+vbxoNquPv/PJ8eHB1dVVu6lD9F8/e+6cK8pyNJ2cn58fnRwbl4cU58tllmWZDskqysJl683m9ubmk+88BmBOECFK4r5pvdeaDFiu1pvNRkwFAFE4CseUjPmvVE4O0jXAE0Og6Oy7+knYA2D2iemDWCqWo50afYqsWWVnMudSHwGQiAwYEmtSwl37Q9y6hTt5VlKbgDquQ05/QDRo+1h3k60JcVtilkREYRuRXXFCiOPDg/V6rbig9x3H0PftQKHVwVAW2SFHEBIRBDRgCOBoOruNIXCqbGYM9iKGqDTOSjTRYyDLMUeIBiMBI4J1zpALzidOwftdh4XB+x9+7uszRER493u9T2RBw4Ysx5g47vSNNnsnY4CMIYcMhBG3DwuRyOqixSQisayqEMJ6vVpvNpnNRqORMabr+izLvvrq6fHBzFr7xRc/m00n9++cP3vxLPTeWjsaTfKi6PvQ+dVytal7f36Gp2dHH3/y5Pmrl7/5za+q8ejo+ODq6vLo5BitScKd77vWW8qMMUmYZNvHdptU2AlhTFvKi7ZP7/s+gZTWtrFGRBRKIfodO8Qa1M4sAEwoBncfxDw7nHVdN5/PtRxsPB5772+vr/K7513XNetVkbuqzEGga5vlYk7FdFxW9WYTQ+i6bjaZ/OAHPzg7OfmnL34aQg9Em83KOSpHJRlcN/WP/uRP1+v1arNumu5Xv/rNum0EIc/z73z66e3tbd/3uXNnJ6d37tw5PT09Pz+X5GOMiZOghBCaTdM0nfeRWbTlkSscIDKzMegyk+I7IRxEbvBC6Q9my+n+GcTyWwL8h0IYY1TMTwOo4UOMMdYaa8GqwVO6LIu1dnDWAEAjQ2TZVpnv1/gmrdwHRNEWHCJiCEXIAMYdOgrIoIkGFkRABGvNcj4HlKoqueEMnettSkpAN4RgrXUppaD9a4UEOKVEKFVR9nnRM5TOWjIUGQUyQG5rT8i+jyFYiaUlz+JZMmOKLK8A2t43bd+FiIjWZmk3RWgLKMN7oqgrtL+y2VBkbLSrR9RhD9p+N6WECMYZIiJIiBhjQkTnMiI0qN4fg8jp6fF8frtaLazNxtPxaDxeL5dv3rx5dnnz8OHDzWbTtfWd+/fY+5cvX84Ojha38/F4bG1WN33XdSbLKaMY4/X88v69h5999r0udL/+9W8fPnp0en7+1dOnaKgcjU5OTgRhvaoZQS8mttsGWZr4gl2ePQrrMx7kE0ALoOKOrxxSAmN1RNmYSIOfRISjUaXNZrQWwlrquu7u3fPr6+vj48N1V1tLF69fl5k7OT4kQt93m3UnMR1OJ1ervigK7z2HOB2NP//8s/Pz85fPn4vIZDKJyS+Xi7przk6OjDXz5XJx82VIsSgKk2e9X61Wm/O7Z9/77LOrqysdv3P3/Pzk7DTPc2PMaDRq1qsEzJxEMPnUdb7v+5TE+xhCIGvzPI8cIkfjLFmM4V1ufZClQVT2LR7sWOzv9PUfvEv+4AghoCFjjDNkt9Are+8tvPuW3Wdtn8XWZDHT3hB49T4HydxOQBZJuzJrEQEZulduc/RqUrd1ppiYJcsLSaHrm75tjg6mQOJTJILOt7oNLJEV3BYeASECYhIB4SgQU06WXGYEjUBBhgBsEkoeghEQEsgBjLOGxbAAgXMWXaaFEa7tAydEQmTaNTnfFzbeVZQleOeKEJElDJQUUVZo2FpblaVzjhBTCsZYYzHLMuCACJo32nUsRxRhjszx+vp6PB4/+fDjxfxmPp+/fv3aew9kHz165L3/+MMPJ+Pq+bOno+Pj6WTy9PdfHhwdTyYTYfC8Ikbn8igc6hYxzRdXjz/8+NNPv/P6zcWmXk26KQDU9Xo0mZweH00mk+ub+XK53LRtSh609MkaIiAC1p6vMRoEMATIMXoR2TU4ClohSqTwOGZZNhqVZVl67/u+875PKWW5LcpMRBKH1WpljHEuOzk5+fJ3v5mORwbw/8fafwVJlqXpgdj/n3OudC1D60gtK0tXV1frnumeQcNmsRhgbWcNtnhZmHEfCBiMLyQf8cAXgHzh8oGEGWwAcgcYLGZmZ3pqplpWl+ysqqzUkZkRGTrCPVyrK4/gw3H39IysbgIkr4WFRXi4X79+4/znV9//fdlsttNuUoaUEkbRQMOgikexEIIRIuKYKHBc5/Kli9evXgs8b2Njw7SYTzGXy8zOz/mDfhBFhGp2Quz3+z1vUCyVSqWSm0path1F/JVXXjk4ONBCHf1+H6TKZDLT09P7QSxjKQiCEqCIrosiEk10QBnCkF5IMNPUn/2URelHdG9wPGI6dk1jB3jKPeoawfhP43QGxvPTbCipHUdxFIcWRQpKIJEg6XClMYNQQlAIDSuVGjZGlMZmiDF2kqKuFaGUOIhjwzC0eKoc4UuHmwVwlDqJAqWEjkhNk4Wh7zhWGPn5fDaIA8KJ7bD+gOJIt4TFeqoa1Gg2EkEpVGowGICUBhDgAlAZenhSKAPBZtS0TA0NUUJalDJGQiERpEHRchKu63pB2B/4YRgOSVieDSKOlCWfL9GMjRBAaLAoGKBvgWmarmtreigdVziO4ziOisaOdDj8MYw+pFBKuk4iDEPPG/iep4UQe72O53nJjDs9PR0EQafdTCQSDEmtVqOm5Vq2BARC3EQKjTgK4yiKJKBpms1OO9k4KU+VX3nlxubWdrvdpmyYfhBA23IdJ+j1+iCezTExypg5FAzR4OVxkyOOIsqYZVkaDG0aVEqpJDcMw3Vd17W1k4mjEJRSkmuGIgIolQApGDN5GNkZBxEtyxKSI6pBvzc7Xfb6/TDwJBLbMU2k/qDf7XZNlh0MBulkanZ2dmVlBRGfPNkElIVC7uSk0u1iOpPqewMp+dTU1NT09KDBAGm91Ww0Wplc2jRNz/Pu3btXq9UMgxYKhdLUVC6TYYQGQbC7t82kCUBAciSoFAiuhBBAqQClhhAKLoSyLIuYpucHDJ+TKz0VWI7tc2xg/Hlpt0lXeeoMurbnOE7E9Qy6bqRRSankE20MkCglGXfnCSGj9s/IrSnQ/Ktk+MNkVzOOY0YoMJCjUWOdBTJCNY3NMCIdKdVoUuDpuVkh4nQmeXyilIiTmbTWltRnYJrbYIxYRaQAAhH8/sBilKJSsRAomWEwJFJKXf13HcsLIi+UQnBC0KBmzCPFBUhlMiNp26Yd69hAied2tbF2I6gRqQE8SxoJIYJHQojx1BchRGO+dTNNt1y0pmwkY333uRBSoi70AyiiZUlTjPNI4zZt29Tk0I7jSGY2Go3I98LAAyXmpmcymUwQBG4q2W51+p6PQCMhB56vgCSSSaH6cRydnJwUisULFy54fvjw4UMBKITo9XoAhJkGj2JCiG3bjuOEg6Gco2EYSMmIaIREUURHlESUUk3vGYYhIZpBTHNGWslkEhG5iA3DkJJzbgkhGCOUIqWGlDKVyB3t7wnhDvrdbDZLKY2i8ODgYKpUSCQSBiOB12/WG74/EFGsQColAs8vFmZLxSIhZHt7+8mTJ+tnVjX4wTCZrtZyKSIeD3zPdacvXbrU972Do8OB7znJRKFYFKCYaSSTrms7mog9k0pblqH3PsGVlEAVSCkF53Eck2cUjMM6p+VYwMxOt0ufzUM/d5ARgh8ngLIAwONo8mmT/nDCbGDcZ04kEuB7Y4C7/l+A5AaB4TYohBACheCgkCiDWc9dhgLdzbdMc7g+1dDxDucNxHPjhUSBntMnhIAiUmoU57MrJCA5jwqFnN40OedRFDI2HNceRkNggGEYCFKKKOQxRbANajBK0ANEStBMAiqJyhM8lko6juPYaFlGECsujTBmiC5jqUHkZdL5dD4nQAipyqVSKpkJw0fFLGk0Wopzkxp6rsIwzCgIkSAIqUBSRgjKOI6E4AAykozZyVhBJJAL5ftxwo8JtSSSMBIGEgIUJPJQEMR0OjNoj/lFqByOnQAimlai2eq46aykuL3ztBjmC6X8Xu0AY5pIJLKZDGazeuqq1R0MwvCzn/40m0rNzc0pEMsL0wcHB77v9Zq1lDmdYSXRj092TtbWV65fOCfDwaNHj1yWQNELej6CEQlJubARFChuYq1WLRaLCpVBTcMy6s2Gm0qGPK4326Wp8szslO+HQRQRYgAxJaURjyWQGNVhrXZ1ZqbTagSeBwAEoVTInZycpJNJilAsFsMw7HfYfHEBQA5aPQDZjTxC5OrakuWaALLrd4hrd1oNK5Hsx23GLOTe8tqcZVnpbOLoYD+KIsdyMMbNp09npqeZYvWDtm2n1ubOhmG493DPnBLJKKmUAgsybrZcLudyOYL4k/d+lnCcbDY7Pz/v5u36cdM0zbm5OcP0wshLJ5M7OzuO5QouAahrpsNwAMLkgW8Y1KZG6EUCedKyMAp0vYoorXqiuJJKSlBKU8XHnKNWzlMyiiKLTjAbAZCJFjyA5GJoopQhZQwAvD4KaTJCJUoAJRQoUMSgIoqEjONIMURLMgMojSRR0BFd0zQN05RSxoorVJGIwjBMpVL6TRGRoF50SlJUxI4NhpRQpJKgjoYIxZ7gqAAIEUopgaAkBSSIQRiFlAnLvPd0q+N1uypyStmuCjiNJEoBsULBlAAkChUSZIxRg5GEZTkGIVIYCqgWu5QghUCJKDGKRSwk4TLkIop4LMEmjBpGrpB3k0nbtoGCk3AWFuYME4ul/Kdbt30/9DxPSR0wEFTPxqhhJF2hYRYAkiqqw1ZtS2QELxzVdSiORxPRBUrEcFRcMcYIVTxWSilK6WAwqFardsKemi5Kudjvd9utlmO76VwmCILBYKCU0nt5s944qVWuX7+eTiRs2z46Omq1Wp7nMcay2WzUE4VCYTDo1ev1bC49uzA9MzOzvb2tlDIYSyRTBnMGQTiAgFIjkUxS0xiqiIVcckG0MoHjMsa8QaCENE0zCKJBr0cpNSjV8xPT09PddkepeH9nlzIUMUeEhGsrpISQbrebSaU1JYxUaNu2aTJAPhj0giCwLEop7XQ6ALLf71NK41gQEmt/4vf9qamp9fX1brdrMqNWq6XTaSnlwsKCVo9Lp9PNZvOjjz4ql8uXL1++tbMlpdQBfxRFR0dHnXY7mUx+7e03DWLoRlEcx5zHpmkAqFQq5TiONwhs2263O4yxQbOF1NI54RALBUIIEKiEksZwxEfXu4c4ax0L6aoaGfYIhkUsJb8iIXyusPf8EfNQSikUBwqIlBCChIGCKI4kgAQUSsZKEY20BkUNkxomZYyMY11CFRI55NNHDbIBAAlKapQSoNDsUWQ444IgRcyJFjgBlFr1VaFSGIahYVuI9Lhy1Op2qEGYZXQa3Vw+g6h0h44RhShAZx2IaAAzgBpg2BZBwYFTGYeCc8WFkkoJqVwWxahQBCEPhQA0kVFmmsx2mGmYtl2aKi6vLK2tLXERJRLORn2n3/dEFEeR5rEbMl8ITRen2zI4zq2FpsyTSkqgSIY8RRoIrltq2okDAGPMMRyZJDGP4jhGNBCJUpFSSCnd3NxcWFhYXl7sDrrHx8dRxA3DSDjJhw8fzszMzExPR1G0u7t9cnKScNy1tbV6vR75PiGk0WiUivl0Og0AWmAUUWmS/Hq9nswk0+nMxYsXP//yFqEskfJcJ+lHca/bt22XGYZFiRJy4PmaYdEwTQLACHVMK+m4ANButnq9HiqwDDPiscGsOI6z2Wy33bFtt9frJZPJOIp0wc515fTUbKfTieP45KSulEo4icFg0G4HhomWZSSTySjyarVaOp3EkTSdaWr4LjJmFtKF8+fPr62tPXnyRA9/6c5hrVZLJBJWKkUpLZfLmUym22p/+OGH2ZXFRCJhGUYYhqZpJnNpg7Eoio6Pj9OJpGVZmVS2XC5rNcwgCAKLaeYLABgMBvl8gdbbBGkQRVIpRKqGfmz4X4bni5y6SgkAZGJGaSxnT5GMZzImLfC3GKFSQik54vBHpYYrmzJGQKEY9hC4BKQKFYQ8VERx9SwnFIoIoCIetQFHLBhcgBAqVoRzGcnYZGpYVAPJJUpigC7voAQKAFSTOiqFUgIgNQyLIOMxD4Mojnm301cwZGpkSdNWSikhCSiGYFFqokEVsZmlFAqpdzBUiqBCQhiyRCRp4Md+LKnhWGbCTqUN2wJGy9PTFy5dnJ2dNh2mEAaeF/GYkSFoeHjrhwPBkiGJR4mxbuzAqMxFCEopJShKh0odQ5E2BZLKOI6jMGZRxKiBjCYd2+uRiIBuJCJIQMIYOXdmPZvPBUHQajTS6XS5XGy320+fbv7gBz84OTlpt1pKqWKx6DhO4Pn9fp8QMjc3p2El3W5bCKEzTw/CZrOplHBd1/f9jY2NQin/+uuvH59Um612v98HRYhhas1TAGBIMslUwIyE7QAQXS2zTavT6+ZzGaWwWasLIc6cOZNMph8/fpzO57qdTuhHOo20UmkAKTifmZlq1Oq+H87PzLdanV7fC8MwnUoN5MDz+mHom5wZRtJ2TCFCz4+DIEA9jKbQdV3TNB0nQSlN2YlCoVCv103TrFaruVyu2WzqLcx13W6v19rdXVlZef3116MounXrllMobGxsDAaD5eXlYrHY67ZN05yamrp47vxgMDg+Pq7VqtlsemqqpP8v3W5Nc4L1BgNEFAqR0SAKIy4kEsKQUoODIkgQqe6gDf/XI0scVwSelekmquV6avZUGea3HIxIQRQqlKik5FIolIhKSlBEylAoTRLMpGQKKWLPC41YjqeohsWz+Jm6DpWoWfc5V5xLrlApCSAYU6aJBtVeIR7SAYNSQkmJAEAZUARBoNfqxDGfm11wEqmtvad+GCWT6YHf1+UkUIo51FBKEQYGoZbJEqbpGMzSwH6FwgKBRHGToDIoEkBFjTCGMOaxok4ykcwUrERaUQMYXVhaPHv+HGO48ejB4eFuFAdRFHmep5mFUI8x6lm4KNbJLoJUBJXmvudCFxKUrtngEFWolAqCQHAFQ2VEzfYtFCMI0mJoWsyKKUUQSr8LJYTMzs42Ws1er0eQ2YbV7w84l/Pzi/V6vdvt6mE/3x9wzpNuolgsZjIZxzQPDw+bzSajWCqV9DRWKpXs9/tRFCTTSQDodDq2a9m2c+XK1Y1Hj/YPj+I4zqTSqWRaSpCKM8B8OhMlEoSQKIq6nb4fBq1+3TQtygwASDquk3BXV1Yopft7O8lEQgoRhmEqlRl0e+l8odfrJRKJ2dl5iqzbbXf7g06nWyoVCwVLSlk9qudyuUIhF8WB5/XDyLdtpgNmXR7QwNQo4oaBSqluv1c5qQaeH0VRo9HQaFLXdeeXFuMgjKIomUrpcDSfz8/OztYD7/z58/1+v9vt9ru95eVlx3H6ne7BwV46nZ6bnXYcZ25+xrKN6nHl5ORkupQMw9CyjV5vwEyzXq+3O71u3yeGLaRCyhRBJbQPHJU0dLCmRwdx7P1QKQVCAR3NhIIidEhCObZA9Tyn44uHVLEeNwcEBVKzAgLBOJKcc+CCcE6FYqCYBnYKZSiFOCY71/YGenwPh6OquqKmhADK2BBfDcCJpIJIqYcohhAcJaRUHACYZJSiohDGXCpkpmOYvpIoOCogrpMcMosrxUTgEUIMZjgGS9pmyrFtw2REqShCYiqCymAUkFE0mUEpPWkNhJIcKDJqOik7mWG2LQGtRPK4Wvnww1/Fkh8e7XZbTSHjMAy6YXc42zacJgHFh3PlUghERQjgsN4rpRIMNWuY0HUmHbjqMyhiUkoZM7UlA0cEajCwLENKHkcKhKQUGaOUkP39/WQyOT8/32zWd3arOu9ijP3VX/w555xRmkqlUqkUIoqY1+t1IUQcBJq0f252emZmplqt9vv9lGsDyFYrHAwG6Uwym80KoR4/3rQtN5XKWFYjjngQBGiTOI6DKFRh7Lou59yyXRAyDn0Z8X63Vy47rU7HsqxyMZ/JFWzTEkKkkynGWNJxlVKmabYbTSEUj+JEIvHk8VYi6czMzPm+n87mL1+9ls/nNx486LXDZNK1bIP3AkTUPX3PCzXjE2PM933DsMIwAIVCiFKhYBhGebl89+7dVCrVaDSKxaJtWu12GwByudzy8nKn09nf26tWq8VicYDq6tWr8/Pzjzce6QAkDv1K9SiXy6VTCUTa63X2duJkMplMJq9cuVSr7GoYXRCFScPs9gfdwaDVG1gOBBEnBuFCxlxIQNDyZlrFTvP8g8JRf5yMBiMoosRhFYA9rzM1jpV+i1eMooAQApSgREUVIZQxhoxKKSSlCrkEImE4fScJAQJSgyU0WQ4xAEAB0+y4AECkolJbPpWAgpgaECIQASjXieuQj0+3yJRUDBG1oAKAzKRzyWQ6CKKe5xumQw0SRLqDioiKIjCXAaKymHIYWKhQxEJEQgkDEQQHJQxUjFDLoJbBGGPESlBEpjBWwIH6YUgUcFCDOKw+qnqhZ1qUMWIZKATvdDoh5XEYCc6RGdrudRnGGOm/0RGOnlKKSmMH9C0XiAgoBedaxckGUK6rgy4hpIwFKkkFt0ytpTEQIjaZyUyGqNKZrFJKUzxlMpmlpaUg8H/yk590Op2LFy+ur615nre19aTZbKYSyWIp7/v+/MxMLpcDgNpJ5eDgoN/v53K5fqNvmMx2LM/3hRDUMAI/Ojw8nJqZKZenTctpNpteGAEq0zIIowyoyYw4jFAKg7JUIpkoJ3K5XBiGWgZIciF5xJCk08nFuflBLGr9gWb+z+VyjFDTNMMwbLfbQmQXF5Yty6OUJhKpmZm5Wq0x6Aau63IRSSkt23AcKwz9TqeTz2flkFQfDcMIg4gxphSm0+np6elkMqkZbNPptGVZvX5f92l1hJJIJM6dP4+IjLHPH9z/8V/+ValUunjxvBDi1uc3oyhaXFy0TSubzZRKJUqI7td3um09AMl5NPA9wzD8KFaUeWFEmOnHsQREMuQyH/sxAc/q9/C8Hqic1KUR+tUAEx35ye9kQpJl8lAyVshGAS4qpSQCQ2SWJWPOla6pSKEAKdUCMYpSqRTX0qiEDQ3SpHLEXCiR0hE5Oh+GyQYiSpAEkDKDUiJijogU1RDshgoJRUoJCKRATcsGyxr0FRKuAAAHXoCIlAAhwKaKGaUERa3MLhQP/SiWUegYDJSkSkokwAgFk2CMwAwnb5qmAtIPwiCKgl4fiKcQeoO+H/mOYxNC+v1uX3LbthzH6QUdOTpASAUElAKldEeIETQMhqhAClRKCFRiSESiS9aIKJWK43gwGCSobqkpIYTkClCCJIxxy7IAjL6SGkVhECoActn0/uGBZeeXFhcPDvaOj4/q9fr+/v7Zs2fjOL5582a326UU8/l8KpHknF+5cqXbaj19+tR1XdsydD+g2+026r1sNmuaJhIihGg2mwohnU43m+2pmelsLmea5u7BYRRFlmU5ju0q0zGtdrs9XC5SJpNJwzCazXa5WBRCdFttKeW1K1eXlpYYxf1K7WBvl4AKgmBxcREAENXR0dHly5drtZrOmqNYPHq8mcsXi6Wpva3ddCZpmowQ1em2dIMxl8sNBv04jhkz4zjW+RZjplJxrdFYjqKjzc3BYNBoNC5evFirnuzv7y8uLvba3TAMbdsOw7BWq8VxnE6nOedLSwvFYpFSOj87e+XSpXa7tb+7l0i4fjA4Pgp1n1aJGKRAZboJJxa8XTmxE26t3kJCewPfspNSoSTUZAYhDKlEBIKUiOcsatKhqdEBz0oDZPI5OGJAkxOq0i8ejDHCKBAiQQkllFCKDxuJMZcR19xiQBANpQhRAFQoopSKJQKAQRARhURm2Aq51AyLGotECAB4XmAySoEioFJAkCAalNHQj4aDxEM/D5QqCsoistvs9gf+3OJCpMTG5uMwjrP5DGMGDikMgQne15+RAJGSICgETgj3A89mVF+mUkwqFfpBL4p61E5nbSDoBYEXBMyxlCADvz8IfEQQMmIeEAIGIyMAK2o1L4bEdMwo4iKWOpWnSAijACCiOAg0wZH+B3DGGIDyfZ8ApQpCHu7v71vz88V0OooiwtA2HUQS8ZiYpNvtSylt2zUNNxZSCMGY2W6352fnfH9wfHxICPZ7nWajdunieTeRiOM44bq+77uuvba21m62tp4+yWaztmHMzMxowLuu3ff7/cXFeanH5qWKAiElSFDtdncQ+IZpnz1/LpFI9bzBo0ePUqnU+sKZ6tbhYNBjmgQIFIA6PNiTCqempnZ399fPnjk+Pl5bXX/48OHCwoKUUshYKh5GPmPG/v5+JpPRsOzj42POuU7VBoOB53m9Xu+f//N//sufvLdkLezubvf7Pce1bJvZjqVAmibzPE9Hhr1ejxDGOU+lMlJGWkjQtO2FhYUoiphpaEbmmbnZdDJ1dHTU6/XKhWKhULBte+n8mVKpdHBwEHjezs5Ov9+bmZpOpRPJlJvPZpRStm2LOAy5KBbzhJBBv6fp83jX84OIS4yFpABomI5BFRkKJAohIhEJKU3TmbQ3HOG2fc8zDMO0rCHIUbudOB4r4E6+amyKMFHXGXoqGUMUSyBAEDSXklScc6mQS8GlUkjBAIJMAAgA27SH56cAABwIJZQyM+ZcEcaFxqYRykwuZRiGhDFgTGgpQUREGkkVDnykLIpjgso0TQTgYaBAUmYEQYiUmo49NT0tQCTTWRfEIBjosTVKiUGRKRlLKWPJoxBMxgxKTEKpRTlSQslI4UVKyaWUkYgCwc0oBIJ+FPhhQCSXSvW9PuccGBhEa8eAjFUUAgXkJJKcy2FSCGPAge7SCCEoDrkJdBkbUQ/djxJxEEoRKdWYN0mNaD8oEEpZMPCEEEoCUN2QUswwGSPBoI+oDMNIJtyB35eSMwaIYBjG5ubm3OzsjRs3Wq1Gr9dDxGKx2Gw2s6lUGIbFYpFRTCQSnU4HEU2Dca61uJg0QICSgvNY5LL5/f39ge+trKwsLi56ntdsNg8P9w0cj9gIBQpRK5+SSqVy5sza1tOnP/rRjx4/fpxMJra2tmq1mmUaBiWuYzuO0+v1gtAjhOQL2UqlQik1LdbpdXd3d3//93//5s2bf/Pe37766suEEMsyFxbPISohYs8f6MjTshylAh6LKI4BYkoNznkUBc1m0/f9ZrNpmmbSdZVSGuVTKBSWl5fn5ub0Teh2u41WKyYKpMym05zzRMKdLpdPahXP86LAV2I+m0unk66UVq1Wq9dr3W53ZnoaEahhIKGU0kgoRRk1TDQsociQmkGjkyklE9DQsQPUKZ+eKZ9M/+AU0P/5Y9IIJz0qIQxGAEl4xmzKQElESoimUBzOQehdQPvlIfGPlFwIHZnrkwz/NPLdzBjOfEqpWw+EAlFIheJKKc15QBCRGQpAIjFMI/a5pro0HduwzFjGFtoAGu0qpFRsjKlVUnHOlQCgjFGUSlGtdqPZhygBBQoJECVBaAAgoNDEmjEPCSEU0WQGMwhFApKLmMeCBxgM+7aoGIHhdMioUUFAciAaDQRSaSIPADKsjqHSA1YAONItkXHEKWNxLMCgiiACoYQCAaUw4jHnQg+CZjKZMAqoYyRsJ4r7yZSTzizZtj0YiK2trSgMv/71rxsG9X3f6w8454Zh9Pt9RIzjOOGm6vV6u90Ow9DKOUJwIUbcwcqIueCc7+zszC3Mu26iVqt5oee6NkC27w1EGFFKkTIFoKRSiiM1CWIQBPVmo1jMNxoNpPTe7dvdbjeZTF44t/Z0+8lg0A9Cv9frcc5jLmzbbrfbqVSKC5VMJl3XtizDde1bd27/j//4v//www8qlcrA6/X7XUIgl88iomHYmsswDKIgCKQEBIMSw06wMI78cCjNGUQRATBNs9VqPXr0qFarpVIpRiilNJ1OT01NPdne0swgX355Swl5/aWr8/PzP/zd3zmpVWzbDj2v3WkIIXgcJpOuaWQ8r0+ZCbpkLZQXRlKBQMIIlRI5KC64lFKNJC75hLo1Uc/MTJNZDJe6HOq9kd9skzqbnTS/oVFRJqXU8w0AgIoQoIQQPxa6+KdGMaN+iRiRf8oJqpvJY7zp40g3e/wqgKHyCKAUXOgWHIwmpzTdkUTlR0G31wvjiDEGFPTcj+bOAK6k5CyMhGEYJjN0uVLDMogCRjAmhChAkCbVVV/KlZIghIwVokIJFAgqQE5QUAKMMtOgtmEySkUUBrGQQgY8EEIqISUCl8NcdNiNFVLvHkqOlcqVkEIqLpDoyS39yQmgazsEWRRFvu8jReYYRCghOAMNmDaVUiYFzbloW9bZc2d2d7f10HQYeqDidCrtum42m3rnnXdMw6hUKo8fbyilVpdXXnvttc3NzXajkUgkKpVKMuHs7e3phTXwepxzHksglKAWBkEhVD5fVAqPj4/jOEplU/l8ljJstOoYEwMMgyAo1Kp0RApCjYWl+cPD4z/8b/7h559/ns8VT05OYsG//vWvnz23fuvLz5v1EyFEIpUqlRYHg0Gz1SEEgygYDLz1M6ulmdLP3//l9PR0q9X4kz/5nyuVyrnzZ1KphO8Pjo6OXNd98uTJ6uoqY4wSRqnmQFSIGEUxs4aex0m4tmkFQeAFgSPEK6+8cnh4WKvVTNNUplk5qZaLpXQ6ffnyZc/zlJCvvfJqIpEAlLdvffHZzY+vXbtWzOUMk+bzWQLo+d1Wu1av1wv5KcO0FbJuv+eHQa8fhLEwhRJccAlCgQaO6dE+JEoOSXcBETWSWFsL0xetFIjhikdEVCAmFDjGdqi+ijNbP6LtV3N3a4dJlAKBMuaSc8G5EAIpIYoQZADDnVI+ryc3aYSjuGZonFJwRERQhCAAUgJIlJKAcsjPj6BluVApikoSShRAEPmRiAyLUUo5j2CksSRkLLlgUUyRGIyaoBSPleCUAiOEcsGlQJAKJMRUCV2lFdgXXc4jRVQQhZxzhcilUErEMQcpBEFJUCmQXIlQhH4kkOs6gZQSUBqUoa53CSm0rLEiI1C3ktobAwhUcjh7hwSBAEkmk4QQ3/cpAjNZcliwIoNWn1JqWJJSQylkyBg1GWMbDx7uH+5ZNi2WcgpixkABD8Lep59+kU6nGaXb29vJZDKbzSql2u22lJIx1u/3bdv2PE8Py+ZyubAfyBEp+LCfKaWUkEtnKyfHCuXMzLRS6uHDh4ByemaqXekhIh/q/gmlRzYJC+KIWWx/fz+ZSu0d7Nqu9fTp05WVlf297W63FQQDzsXUVOnCxTOBH20+3TJMWq2dKCUT6SSltNtruwkbKXEcZ3p6+vr16z/96XutVqPX73734nd93x8MBjpYIIS4rkuJQSkDgEajoSsKtm2PQcOxEIyxqampZDK5srKiyVpiwR9vPtHBOedxPp8HlNXjyrlz54qlfDAYKBCDgWdQYlrMMIxk0nUc6/CgZpi25ST7/X7EhR8GElAoFccCCAUAwhgq1E1UNam1OKxfIpkcMR1GeaAFTwGfCzsnQ9Nx2eaUwWgNU83YggIkAckVEI4KUA19rJIKQCmqzQYB9GzueIBDW5kkCJSgQD1NKEEBKBkGoZZIYhQJISbVjG0xGYrpAUihkV5aaMO0DTdhIyLnUcJJGhaTIAgQAVKBkCiBKMYFgwjjiHPORRQzStLJpJNIRH6AACKKYxEQgVIRooBzCNGTKlaIseAAEhklqAyGQkgCSnIuCRcERcxFHIuQc8Y1q6KGnSmiEIGo4UATQQV0dCul0qTjCoiiw5heKaEm/kmc8yjiccx1OZ4xSixHSskjFcogiiKpeBzHUvHDw4MgHhQKWSlTrmvH3Ds5OfKDQavVtm17EEWI+PLLLxcKhY8//OiTTz5ZXV0tFAp7e3tra2sn1eO5uTltpchBCCm4EgoEf5bPHBwc2K6VL+YYY4eVw4OD/WTKzeQy6XQ6DMPBYBBEoZSSMUNTJtdqNcMy//LH/+v5cxcrtZNOv7d6ZrXZae7t7SDIYiHXbLfa7ValchzHvF4/iaK41+tkMrlOpwVAvv+7379///65c+dq+5V0JqnBPQDged7+/v6DB/ey2TwhRArNo8mIOUyNQh4TLReNmEgk3GTCcRzXdZ/u7MzNzCilarWabdvpdLpUKmlITRiGT548VkrNzk2bJut0W1tPHuXzuUIx51imSgshVLfbFnHUaDQALSGBWk4QRZRSjZhRoAMZQgihBJFQEEQqopTy4yHdPZKRsqAa5nhCKzMoRYeGqTEbz6FGxynlqdrp2FtybYFAQCuHSVRcKSJMw0A9ykS4ZgDSuFXDGColD7dZeKYGqcc8KdUAHjFCTMYGQYKMIBAikRCQCpUyhkBzIblUCITBqOihCEMv9Kq1k6yMhgxGFA2DAiqiCEFgng+MAVEqigSPhcXANNF2DMMyQUglIn2XlGICQCEjZCAlF6CU5Eh1JkgoNVCaSkoKKIUQAiXnPBIy5gKHeFGpZwjp0OUN4+whTzdSQKGUlEPeSDWBYhNKcAljympEDMPQ9307mbBMo5CfGgwG/X4/9KMwDgDAMDjnfGFhznaY6TDLogp4t6d6/U4YBr/zO7/TaDSajcZgMNjY2BBCDHr9tbW1MAz1jFmz2RRC+L6vUeaMMUqVoDLmIAVXEglhpgnEYKZltZqd/qBjOua582fa7davf/3rly69HMVcIWgaC4mgZckyubxpmgpxEPjpdJoQ8s43v9FsNs+eXUdU/X5/Z2+3Vmvs7++GYdxsNcIgSqUSs7PTlUrFNM2FpXnDMFKpVI3v1uvBxsYD0zSz2Wwi6Uop8/m8lGBZFgLxvCAIAh5L23YYG85Yj1vPhmEAVQCQTqeTyWSn0xn4XqfTUUotLi5qnHc2mzVNo9vtHh8eSSmYwRYXF5aWFwiBZqNWq9U0L2CpVMrn81tPD3SoJoSghgkAgJQrSRGlUhIUKgSlKCGUGADgcT6emyNIYFSCGyL4R434ob1JOZJrPd2dHz+iRlNy+lepNO0ZQUQKqAVbpATLoEAkJwACpVIwhqQqAUozbymAZwYvhSDaw1DNNKN5rrlBkVAALWUmhh6ZgKSMooKYx0IKJpGAoUCikn4YCSE63dbu7vYgKHe73TAMqElNO4F6mgEo8wahbaNhGACUi1gKafQjAoNcJgtaf4LaRAEzTAIoiFC8K+UQ8IogKEWlh8gZihilkLGIpRRxGMVBGIahZHJsTqg0EkKpZ6ovEwEAgFKKMqoQpB7i0gLVCnSlWIs6EUDO+WAwcFJJalFmuQAwisSY41j5fDaTz1SrR6m0I6UIoyibTS2vLM7OTaXTyY8+3P6Lv/iLOIoWFxcRlZ4toJQ2Gg2/308mk59//vn1a1f29/dnZ2dBC+5M7AgAQ8lyg2DMuVLKsiypZLvd9n1fQ1IIIaZpJxIJYrAgCJq9VqvTzuTypm1/7fr1drt7/+GDVCbdbrcJIQuLc8eVQ8/v61Q+8LyIc8YY2LC2fiaTL3zyyScJN/Xnf/7njuM8fMi+8/bbX3755e3bt/VgRzqTOjo6Yoxls/lUKgUKa7VGFDU5F9rkZCTHA+ae52ksnoi5ZVm9TieKoitXrvgDb2Njo16vr6+vb25uJhKJdDpVq9WymdTa2srR8QEPw9u3b+cyaSQqk0kxZsRh0Ov1NDxFjlwZ0qGn4rGEEdGRQKREKYrmaDoWxuNIBHXYqUbsvSAlRZ16IygllRqDuV+0Q3iBZkYppWmvh05M5zmgtf+U0hSGUkoQIIfg1UhGL4a1iKgZ93BEezN+L4sZlFClJBccERkhehyeaVuNQQkuKdHESFobiRCIoqjVahmOEfFYaRJ8AogUNT1Tww0MEluGySwaM8GDMFJCgFIxWkgJVWBIVKAgirmI49hESwhBpFQEqaJEUJSoADSZhSIgpezLIJRhSENuSzUs2VAlUAghFScAkkhiAiqCiDFILlAxAwAVZ0P2MaUMBQwJGfI3QsxDI2lxk2ZS6ZNKZffexqtOaqY4MwgqMfcUDEwmHCeRdC2I/Ppep1mrYugDU0HkH1LpR8FJvdoddNdnX3rj6kthGFqWEYahi4QQIv1gZXbG9/1z587UasuWbTgW7XRajDEnXdIZVxAEIY8VoOmYhmlGUdw8qYdBnMlkTMceDAbKdNNJM4o7UspYhhgrEUnfD6mJ03NTnX57Zn768y9vrq6snT27qlR878Htc+fO3bn/pDi1sLVzlEjl+GGVGvbmxj3LMouFstf1ZCSDjmcqtjq7kMlktra29maevvXWa/v7+7nc2ffff991Enu7+7lcDoEc7B9ms1nP6wPw2blpKaVtk8qBX3ByKJWbzkZR1Ot2FhYWhBB60LnRaDze2Dg4OFhcXDxzZv3BgwdvvHI5DEPGWDHr7O3tdVv1TqMxMzNTyOdN09zf3+9141TKHnjQ6/cBIGW7hmmFvkg66d1KM5LScGxghkA1gnAKANDVQAWYNp1hECT0fAGICYV6ZFQhxiBRKUBAg47y8FEtlKBCIqUMeDzkaEXUJU/tXB0eAAAqBDFkk0dESUFJ1N4PJRJFgRJGGaWUD6ujihCkhOKQz1tSZgqphgzoQJQCRGKYTMSEK0REYAoQQ83ARYArSUChRSg1CUWpqzLIlOkKEfcDLE5lGLUdaszM5I6PD00CBBVIJSVnRI3GiHAYZGvahZCGQBlVILlgCoExRACCfFgAVqjhsXq70o4ClJIqHivLgtIDY6fKWZPpNbwAkNdFAiklyOHQPQISRNu2O50ORTI/O2cyFgXhYDDodDqFrG0yI47j6lG11Wim01mTMs/zas2aAhGLuD1ouUnHck0pIQ7iVqsVRZFpMsZYtVq1LGN9fX3g9S3L0FQOepoJAHTXvuedSClBEcMwtHQHYyYzjMHAY4xFJBoMBoPA17z3ngdCeolEwrZtpVQYxFLKRCKRzuYfP358clJ7urU9VZ4ulUr5fPGoUm23u4jqz//8z4Mg+M53vhOG4f7+fiaTTiaTURjt7Ow4jgMAmUxGTw8JIZ4+fbqwsJDP5/WU4BdffJHL5TRoZqzFa5qmZkPsdDr5fF6HCdq0MpmM5tGYn5/d2trKZNJhGL7zzju6ZmOYtNvt6t5MPp+/ePGi4ziJRKLVaiWTyX6/L4QwDOPp06d62LLf79vUVEBj+az3oBSAUoKPWmugBAqFIBEIKGoZ45EaHA3R6wx/vDYmazDP6WHqFHJysP2Zgq86ta5e9Jn6h1Nn+MpXTSafX+V8n6vHjuJ8hkoqMYyNpRAoRRzHxDEJMaTig8EgnXE5571BP45DiRaj1LQppa52/MMLopQCpVIpL/ANQgVlRAEKSQAtUAyJRD3aqIbOXLfxcWiBQiohRDysC3KlFBAE8WwsGl+oL0/eDngB/SClxFH2rpeRrq0VCgXJhWEYjUbDpMyyLIoQR4E3CEzGhGEMvL5jWVEURTwSsTw+rFRODhvtFjNZzQ2+9rU3TdPM5/Nf+9rXKpWjo6Ojp0+fLi7OE0Lq9bplWb3uIJNN6aKo53mGYTCGoBSPoijiQnSFkgSZ49iI2Ov1okDLmqsgiAgF3w/jWDDGTNtKJtJKqVarxZhx69YtZljZbP7mzc/jOHZdd2tru1Y5OHfu3Jkz5/b29m7duqWLlvV6PZvNtnZblNJCIaflPnU7u91ub25uuq57/vz5hYWF//gf/3J1dS6RSEgpXdcFgH6/r0ag0CiKHMNtt9uGYQCA41oas55IOHEcnz9/fmFhXncLhRD9QbdYLEZhjEAs087nCvNzC1EUxRH3Bv7hwREhJJvJFYvFzSdbiUQik85aps3DiAv0hYpCTVmNKJXUdJ2jf7dUoMFDkgCdaK/jaEJicq2f+lnh6U18WMWbYIWZNIZnrx1jw0eHHKNtEEZzR89Fs6fW3lcaIQ6fpvSQudJRMwiltMqT7n4rRYYam0HgOa4lJQz8vmnOJNJJLoJkJikUF0hjUEJJxiiFMRUfpcQweBQHUWQZkdQkcFygAg7KoFQpJRVyQD1nBEgkICjgursghRAi5oJzTZyjG7Ry8iZqKxrDYU8ZIQDoWFxKqYQkCpDptUd7vd5UseS6brfbtQxjdXVVKVWr1aKgL3kgeZxKJW3DNCiTXKCCfC4fC14qlWbmZir16vsf/NKwnRs3blT2W81m03XdXq/3H//jf7As67vf+07MI9/3DYNKCXEcm6aJQDOZDCKGnFuWOaRwRilEHARBGHPHcdxEghDieQMQyjCZkhCGoSYy1/zblBiIpN/vNdqtXLZAiTE1M21Zzu3bd13XLZSKqVTqpWsXFhcX6/X6zZufuq5brZ5wzpeXV3K5XBjE2Ww2nU53u/3BYJDNZovFot9ra97RTqdzfHy8uDilDe/o6Gh6erpQKBiGsbOzU6lUtHShpnzWk5PJlJtMJrWHZ4xNTZU/+uijdrvd6bZ++MMf+r7vuu7du3dc152bmwOA3d1d27ZbrdbMzIxhGLlcjjFWKpX29vbCMKzX6/V6fX5mMYy4F8ZBEPKR96OAxDSlnqlVAqQkIBEVUaMOxATv49gNvmiHv8UjfeWD4/KMmrA/OS60vhCCfeXJxyY9Xpww6T9RV1Oe1YqUUgpUHId6uAIkB0IZAjJGAPt8YLKEYVIE7rhmKu10Or5tm1JFiMAl5xFnupA8pMgkqBhDISTnoeBIiSb5UkLGsaKCAIBBUKtAoxadGkEKtPgv5zyWQg9c6Q80NjwYhR9f6QnH38VI5FEpBYCEENM0HdOKwlD/yfO8MJl0XVdzqDE47rTaMQ/TSVdYdrfTC/2IEmLbtvC9KOS9bn/Q8/xB0O13T45OEM1MJnNycuK69je+8Y16vb63t6er9gBSCNFs1h3H8X2/kC8FQcBVqOnPhDDthEtKRAgRhHG1WiUUDKCWZQgQqDV1UCmJmXQ2mUz6vl9vNDwvYIxZphOG8fe//7u9QX/76W4+V+ScV45P/u7f/bvZrCWEcF338uXLmUzmZz/7hZRydnbWNM1sLm0apmmaUvJEwikWi4h46+Yn2Ww2juNWq/XZZ5+9/vrrt2/fbjabcRzrMFVKWa1WdcGTc86U4bpuvpBljOmAQjN8P3782DDY/fv3F5fmV1dXLctqt9sbGxuri3NBECAlQRTWarWFhYWt7adra2u26+QK+W63G/H44uVL9Xr90aNHzXZrtrzo+0FvEAQR17V9RKTUoIRqIxwiREGhkkCGGrfjiFRXvMlIqfeUHSqlJt3Zi1v2i5HUUAVswgKHdDQTbnDMNqaeT4VOLcWvPLQPRNSjvajGqqAIBNRIcEwAGNqxJSyWSdjZXJrzMGkbQcIZdJQEnk6nhIijKJKSM30j2LCUq6cYKackFpwqxgiVBLiUsYypIkopm1Ht2obTgRp5M2QclAJ0p0oB6h3omd7VJFsMvBCaT+5Do/uCDIfcaq7tpFMpPWmacFxKqe/7QohyudxvVYLA8we9hJ1GRCUFYySdzj68/8B2HS1wmS/m3nrrbSEEEMwVih9//LFhGMlMcmtnmxk0mUzuHx0uLi4iEiF4z/MTSPq+V56ZBkqIZemxa87RMaxEIgEAnh9IyYWScSwy2RTzWRBEQsSJhGMwWwjVbnd93+92+57nWaaTzBiUklqt4QfB9vaOUkAIZcwIw+j48GRpaWV5cbHX6dTr9QvnzqwsrymlqtUTxUWjXY3DkBGyvLhYKBQoqjiO9/f3NfeEBqweHx+7rouIvu+3Wi3DMIrFolJKY9+ogY6bUEpFUSSECENfCNFoNFZWlqenp7/7vW/rucqbN2/Ozs5OT5cvXry4sbFxcnKiuQX0Z9/e3kbEVCplGEaz2eScN5tNx3GWlpY6/X670xv4MScUkVFKpUICugg3ZtYTQxEuAAXPEDB6sZGRAsxXrn5EeM7njEK+cX6oU0o1noGaOIEcmxZqYMyzQaoxKHRc/Jw0wvEqVS+EwRpPrWsiSikgCkEiISajqKQAKcWwrA9EKiESFiYoLk2VOQ/KuUw2YaUdFovY8/pcxpHBAkqY4oJQpusxSkiiaW0IiQVnUhBGBUUpUSklQCpQko/kYxUhisCowzMckFdSIShyOsqf9OnjD3bK4+vjGRGGJj0ekeFls9lapRoEgSYRlFI2m01E7DUroe8pJYWMURLDMKjFXNd97bU3ylNTfW9wcHyQyqSn56b9MKhWq9Vq9cyZMysrK5XqcbfX0fNNb7/9dqVSKRbzQohMJhOGISEsCCLfD/PFnBBCy8ozgDhmnMtur1ssFaonNc4jx7GQ0jBsGAZNpVJhAJqs2rbdZCJNiQapqHK5/OEHH6WzmXq93un0CCGFYvHosPIP/uvvPXr06Cc/+dtWq5NIJBhjCkSpNLW9vZ1IJNrtdrvdTCRSmkV7MBg4jqMT1zAM19bWdnd3c7mclFKPIwVBMDc3l8/nNRY0lUo1TxrJZDIIAsdxEglHr7mTk+r58+e+uPXZ7u6ulPLatWtB4BkGTSRy29u79Xqz1WpxLl966aW9vYPV1fWDg4N8Pm8Y1uzs7ObmZqVysrHxeHp6ut/3+u2o2x/ECu1ExrAMU0rOhZRcCiLgWb1EgKSAEsAwhkp4MAr8yGiQYtIRjfdoTa82GR/pHV9nuXKC83d4ktEz5cgf6tB0bIE6QRq/1+T7jteknNDoPuUeNP+FVEJKhagIaOJ3hQSI0MMHEpFQVBQpEGVEPvp+OZlQyio6iUQpv1DORlGwtb1JDMYYAaRMfzxUoIQcf1SdCxlSMgCFQyQaaDj5KO7XDNNqJIY+Cut1QqrUBK/2ZAj6lY7+xahgeH/lSOxOSkTUG3O/39cI42az+fjxY4c1TWradooAiQOp6Xx83//yyztLy8tA8LByHIvIuu/EIvJ9f2554f6Dewpkr9cbDAaFQmFra+uzzz6bm5srFEqUGrOz80+fPiWENJvNTqdTni5JKTnnYegLpQzDCIKg0+kkEgnPG/R6/UwmAyg1gXQqlapWDoVQmUwul8tFcSyEQsR8qVgslI6PKp1O5/iktjg3ryPGfD7/8ccfr62tcc41ZuXLL+/85V/+5Y0bNxBxaqocBEG73fa8frfbjmNRrVYdx5mfn7dtW88EahP6kz/5E7036fTPNM10Or2wsFAul282f51MJoPAy+ezU1NThJAwDI6PjzYePWi1Wrlcbm5u5uzZ9ZdffunRo0c7OzuXL166cu1as9m0LKs0NfWXP/7xH/zBH+wfHi4sLdWbTTeZ9MPQcpx8sTgzN/dkayvoq74XUMOw3LTFjEjEhEshuVIUFMhngSFq1D+ZoJ2ftMYXw8JxxDR2euMXjgOr8XnGT9NObXyucXlGDhEycGr9jU8++f0rF+TwDSgOC01KKAVIFAClSGTMFQwLGZSCQRmjlFFqBT0a+ElABYQFfirpJm3XR1goFFOpVL5YzBXyzGQGSBXL+NmUECihJGGUSxGLoS6v1MpVBMUQ1QcKlVBC89cZ1NAyQ2PwK4x2qTAMNMxlTETNRjP1pzY/bWymYegXMsYUF7pfIiw7CALGWBiGhVxeKVWpVHTfGVmQKKQSdiL0QwnCMAzBYTAYJJNJy7LmFubPXbzQ6bUr9Vq313YTiUIpf9G40Gw3opCvrq0RQoIwNE0zm8sxw8jmcpzzMIrCiB8dHSmltra2lpaWNDtg7fg4lUppcSjd5u50uoyx7qAvuGKMHR0d5XI5x3Fu3bqlgammab/88st7hwfz8/Obm5ux4EnH3Ts8KGRzuXy+2+1+42tvSCkf3n+Qy+Webm51262pUrl6XHnzza/dvHmz1+3EYTQ3N5fP5hBR8rjf7dRqtWQyWSwWC4WCdo+zs7OtVuvkpAYAtVrt8PDw8uXL+/v7zWZzZWUpnU5vb29r4M7GxkPHcV5//fVur23bdrvdHAwG77333vT0tG2b8/Ozu7u7hUJBA1Dv3Lmzurp6eHjIGPvlL39548aNzc1N7Yf1uKNpmiHjEhQjJJlMGo7VHQyiwEfDAqJ1FIfsLABACCWEcB6ON2K9u+kKzTieHKc2oxXyjKl6aAUjLnN9Er2c1KigYE5KHbKhsLbQmB4lY8GFbvNRggQVQBzHmtVBozJ0GVCfajJMG+8IABq7+Ez0WykhJDimRZXk2olqaCqAAignk8Vc0avWssX0oFrvVY8VUYRCp9v27Vb3pH7i6hbFRGMAEWEiVdP3YrwJAUAshh+eTmwPoAC0HAs+M0KtPCh4NDknppTiL8g+njrGD46dp5Sy3++blElmBEHQ7/cNOmwnmrYDQPwo7HZ6/b5HwKCESQmpdFohHFaOJYhUNrO4NH9cYQ8ebex+tGua5szMjOM4A68XRVEunzl/7uJ7772nlAqCwLKsfD4fxzwMo9nZ2e2dxzs7O77vSwnnzp3jnLtustVpM8a63V61WjUMQ8/Laf/T6wa+P7h27YplOSe1WiqVOjo62NvbY4x1Op3eoI+ocunM9PR0Kp3O5XJPnz7NZDKOa62sLh0eHk5NTW1vb6fT2R//+C8RaTabLZUs3ZozTTOO47Nnz56cnOhKxvz8PCHkV7/6lW3bP/zhD//Df/gPnufdvn377bffbjabFy5caDQat27d+r3f+735+fmDg/16vaYj3jDyCSFRFLRaLULI9HT50qULT548effdd7/zre/HsVCK/+3f/uT69euLi8uffvop5zyKol/96sNr1661Wh0p5RtvvPXBBx8YhpHJJk3LMUwrlU4wK8EVUMsTinb6fVBICRUAOKSSHaoyTq6u8RobpyHjHG/8tMnlNz5OvWq86Ws6DAnPXJkcucSxb4SJ7sUYJ6BPKCfULOD5MHXiykeQN5QwbHaAklyqcayIksRc24vf7ymoxGGjwoSIQh4qEEjBdR2khBiMPVMjmVBoGQ4R6TMKPeSnUD1ru+unCS06P7oR4/muYYgPwyvWbzH+AOMRaT00Of54k592+OCERI4uvJqJJCIGQeB5XiaV1ucMY8GCUErZHXj9vmdS03UThmml02lmmhGPu91+q9+2XZsrmc1np2YLx8fHSolEIqs33Vqt9knrk2vXrsVxPD09PT09s7+/L6XMZrNCiNdff9M0Te0NkBqtVmt3d7fZas/NzcWCx7FApBqaYBjW9HTq+OhOqTRFKTVN1ut3hIwvX768f3TIOWcGDcMgCMJUyoiioNuTVtOYLk7/u3/37374wx8+fvy4VC406q1arUYIyWazr7zyCiHs6dOnOslMJBKBH1YqlV6vt7q6ure3FwTBW2+9VSqVPM978uTJzMzM1tbT1dUVfbWPHj06Pj5++eWX4zjiPC6Xy1EUOY5Tq1cNY35j40GhULh06ZLjWJ1OZ3t7u9lsGobhBf7Tp0/7/f73vve9hYWFf/kv/6WUcmVlJZ/Pc865FLbrlMvljz/+uFgu7e7uEoiEkhxUq9M0LD+MZBgM+kGExCTAFQIhRElUChVIPVMytsBTRjWZhk16ocnvL+7dk1atzW/MKayUUs8HlS+2LtTIbZ4ywnHYDM8fQxFCAKDaLCUgEqWpuBVolI1SHBGFBJC2aVAlY9+LQ+A8kpKjoRhjzW5PgoZGaxaA8dfEgYg6FEClKCLTGk6EEEZ1PKDrJVqM3jAMRqkeb3wmWCO0fvdzNRj8DVpzkzd3sg8LEzID2tQ9zxuSrykVx3HMVawAqWVajuW4yJhQGAtRrdX2Dvbbva6TsIUS2wc7zXatPFu0E66dcPOlYnmmXJouLa+uLiwtWa5zcHwElGzv7X1x+0tmmZWT+mdffBnG4t7dB81Ge3dnP5vJVyrVYrGUyWTffPNrrVYnDONMOpdJ5xgzlVJSQjKZvnjxolLi4sXztVp1MOjlcrm/fe9dpcSjRw/b7SYXUSLhZjJp0zQIwSgKH9y9C0K8/sqrv/e7P3jr9Tc2H2/MzcwsLSysLC3kM1mv1/X7A5Rq0O15vT4BVS6XlVJTU1O5XO6TTz7Z3d1dXFyklH7xxRfnzp2bmZm+fPlyu93OZDL9fr/f76+sLH/55Ze7u7vpdDoIgkazVq1Wq9XjqampXq/XaNQ453qoP4yCZCqRTqevXbu2sLCwvb398ccf37hx45/9s3/26NGjR48eaSkFwzB+9rOfTU1N6XBUobRt23EtxqhhUDdhJl3boIDAAQVBhagQFRAgFBV5NkQ76eLGv45Tu8mN+5TVvWiHX/mgUkqOSjIS1Fda4IsvP+WBX3xQ52uKDLWHQNdTRpaJRKECxQXnXPBIcO6Hnhf4/cGg3+/7vh9FkYwlCBnHMY9iHnEZxWycmD5ngUoZlFIkQ93tkUKbAhUCII4/7bArTYDoiUSlNwPts4etwmcfY3IG7Ld/VP1C/TydOTi2rZSKoogD6gY0pTSKIsclgNSyXWY4zPSCQRBFkR8G7V631+vZrrW4tlyem0qXM7GIFMqjo4NSqXT9+vU4jh8/fsyoeebMmWKxvLe3t7OzUy6X0+l05fik1+tNTU0dHx8nEknGjCAIT05q29vbYRC3Wi2pqFIYBjGjNueSEGbbbuCHnXb3pFZdXll+uPGAMnL9+rVKpcp5FIQegMzlMrlCNplMAwDn3LCMKA5+57vfHAwG/9P/9H/9gz/4g2r1pFwuf+tb39ra2vY878GDBxpAp3Owk5OT2dnZl199rdVqdbtdDVe4devW6upqKpX6+te/ns1mL168WK1WX3311W63++qrr968efPhw4eVSuXq1auIeHi0Pzc3p2n8X3755Vqt2u22wzBMphJKKc3e/2jjSbVa/Z3f+R0p5c2bN4+Pjzvt3o2XXgGA2knDYNY3v/nN7333d/7Fv/gXf/RHf8Q5/9M/+wvbti3LMkzDtE0kVEgZxGbPjxA4IAUAJIgKFCGEYORF8FyKNTx0G3OyoADDwd/nGlovms2p3sZw7WmdUKXUhGOctMCxzx1XMSayUJh0g6feV4JC1GzVz10YSoGEEiSCgE7DJFBUwKlCjexUSohYKo6xIv5wbSMiAmUSQZ2yfl0XIVQTglIClKBWMJVK4uiTK6VQKkEBFQBRRM+FaaiMeBbWa7nsyQ/82+/pZAygE1SlFOdcgzxQAUUCAHEcE0Icx2l26iFXsUTXdCzblQq5kkqK5ZWVmIe9Qa/dbZkJY2Fplhqs3W4tLy8jYqVy1Gi0jo6OTNMcDAZhGO7u7jqOW61WNx4+LpfLu7u7tVpteXn1zNpKv+/Ztru3d1AqToVhnM8XgyBwnWSz3YrjjpTSdt1UMl2v1yuVE6XE0dGB4zjb20ez8zMvzVz/27/92zfeeD2K4lqt1un2pZQHBweEkHOL55XCL7/8Us/pPnjwoFarr66uPn78+MmTrenp6Ww2u5Zd29jY8H3/zJk1zwsMw/jkk0/CMPz88887nc6rr766s7ODqIdg4M6dO7qo67ru/fv3HcfR25brusmUi0TNzc2VSiVK8enTTc0ymkwmDw4O9H04f/58IpH44P2by8vLvu9nMplvf/vbBwcHn3/+udYDTSaTV69e/fzzz3u93j/5J//kww8/DIJAKREEXhj6hBmG71FKvTAOQ58RVIRJkJqJHgklFJEimTC2SV832bQY/wkm5hjG1nLK3U3+ipqzdIRYBt0h1M8ZmZ18Hs42aXjjtx6Xdk6tRqWUBKEpEIdXq7mShk+SOhHTrwSpCKAwKTEoMSmlhEkqJVeSA0AUhwQoAhClmPbaI2zZkDVRCYlIpZIUUQkJSCghUilQQCYUP8c2o/eaYf9FKqUlowARUVI6uaPgC63CF7+f/tgjVSpBqMkMRlkcx/1+nzGWSqWqTT8II2/gu27SNkzJVcwlF4oxli/mcrJwUj/2Q88LvFyykCvm0m7m4cOHW1tbSql0OhuGYeX4SbvdPjw8brc7uVxOcHVwcCClXF8/q6PuZDK5ML9Ur9dNx261WoSwQd+jlBFCbdvu9zwpwbZdQlgcB5ls4sKFCw8ePEink2EY1mqbC4tzrVZramqqVqsxRhgzCYFCoXDjxg3f9xMk+uyzzwghn3/++fz8/DvvvPOf/tN/ImQIZC+Xy/fv3xdCvPTSS/1+PwzjT359M5PJcM47nc7s7KxSam5u7vHjx7rDWSgU3n777SdPnly6dMl13StXrlQPDxNJ5+DgwDCM5eXlWq3mOFY2m7VtEwBs287mMtlchnNuWZbneUEULi4v7ezs6Lgxl8tlclnGmCYpD6Lw4OhwfX395uefuclE5aRaLBUGfW/gexAJLiUhRAJathHFQqDej4lmTyAEKGPMNMcWOF5y6oXyzOQaGNvk5AuHFjWRWOrXak4mNS40nPZjz2/6XxWUjs37RSMHAAXjEsazqgwAcM4ZstGAMpdINOlTs983DGpajDFKUAFIPbyOgERJUASVGDYJxDODenbgyKehVCA1Rb3USSCllGl6rSEGcOS+pQINHRzJzY1F5ya3txfd4Fc6xsm/djqdfr8fhmEURb1er9FoaAEJN5VExN7Aq9fr1WrtpF47qdfqzcYHH334+Ze3Or32zPzcysqSbdtRFCAqKWUqlZqdnV1aWtLdtoODg2QyKaXM5/MahmLbNgDxfZ8xdv/ewzCIEbFUKnU6HUSqaQVbrZZpmrMz8/l8XodSjJnJZLpSqVQqFe1ker2Onk6o108AwLLM+YW5a9eura6uFot5w6CVylGn0/n617++sbHxwx/+EBGbzWYymVxdXb148eLu7m6tXi0Wi6lUamdn51e/+tXu7vb58+ebzebU1NTa2prulywuLq6srGxsbBwcHPi+3+v1Dg8PLcuKoqjZbO7t7ywuLtq2/fTpUyllr9dJp9NvvfWWlFKL/i4uLmoqxGazGUXRtWvXHj16hIjb29ubm5s//OEPdXr59OnTSqXy/vvva2EcXRZ+4403MpkMM5kQPAxDPxj4vs85Z0z/xxWgUjDKl7T+5BjeGMdRFIVhGARBEATy+UM935Z48RhXccZeC34zGelXL6pRxngq/nrxh8lfXzRLbdrDduXzua5SAkymGOGAoRBBzP0w6Id+3/O4FLEUkeCxkMymBudccK6egV+RmEaslGb4QBnHSGzLkBLCMFaxP747hFJCEVDEikuUwEBRNXkHAcAQz+BpMOEPx7cbJrAIiCgkR4YoFZexjuwpoFRRIulQmwZy4McqwVxUYMcsibbXEUEgTNOMlWh7bde1E4lEPOhGKjYs48mTJ5mT1PnzZw3DCCM/DMOZ2ZVmo7u+vl6pVMIgRqBnzpy5devW1NSURqjk8knAeGY2XywWc7lc9fiwPJ358MMP9/cOX3rlZUpZo94ybbNcLinEhw8fUmok0qlOe+A6yZOTWtBH1yycnFQ6DS9fyLz26svbO1t37ny5Z9FsNjtTSGdT5pnF6ffee69VOajVaiDS5XIxm17kkflf/70/+vjjD4rFYr1e29i4T4h68OAeALEt9969hzPTc/VaY25u4dqVyx988MH8N75RP6lm06mdp1tn19cSjl0oFOon1YO93Rs3brRarU6r2Ww233n769lsNplMzs/OJBNuu9n68IP333777f29vVKpaDCadBN/9Vf/ayaTefTwQaFQuHDx2o2Xrjx48ODv/uiHtVrt//F//79ZltXvtdbX1w8Odvr9fiaT+fLW/ptvvnn50rlf/OIX8aA/l0/P5jI3f30LgU3PLpbKM082d6amptt9r+cHiGgaVHAZiyAOQwPpcysflCKodIRFkIMiqCdkiZJSSGmBBCWEiJWUusmhzdh1Xc0nQ4Y+UIESinNAXajQ4ocjrkAc6q2hUkNaaQCt/kkQlZRIiA7pYcipARp6CQDyuYKlIhyJ5iZVFCUSJTVxBDVYpFSoFJiOZFY0MoG5uK2UORQ+UlRKoutFQUyHjlQR9uIeA8/3EqSUOgETQoRhyMgQdjR2gJNbxW9ycS9+nzTC8QamtPa30uwWMMpch7NWpmnapouIFInv+/v7/Xa7baOpQZK+P8jlcoi00WgwgxSLRcZYOp1OJNxeb2CarN/v375z6wc/nL5z545pmk+fPg2C4M0339RYMMMwFhcX7969q0d46/U6Iu7s7IT+IAiiQqEwP7dYrdfq9cbc7EKxXP71p5+5yWQcx5zLM7PnKDEGg8HOzm4ul6vVahpLoMmplpaWSqVCPp+7ffv2wcHBzZs3y+WpK1eubW5uIuL6mTOVylEun3306FGxlB8MButnlj/44INer5tMJnO5/Orq+p3b9wBgd2+bUfPu3buGYVy9enV7e/vs2bMbGxv9fj+dTj969CibzepPsby8XKlUzp8/3+l0CoXCxsaGUuqb3/zmn/3Z/1IulxljzWbz+vXr1Wrl7t27lNJMJnflyqVCoRDH8dbWlg527t27Z1nWt771rVqt1u/3Hcf53d/93VarpVPoSqXy13/915zz6WLh448/Nk17dXXVYPaDjU0u4Oq1yzv7x6DLLZQKRKEkgCTEGOsNvuh/vvIYgiKV0msDR7yDp+Bm4zPIySI8PkOujQc1EBEmIjIppSbBUBNwGTlRySHquVKqhptP4nX0NZjGs/M/l7gqPUfCQLNFSQVKKoWUapsngMAmDeZUsD52WZMoWzGUCnzWToHny1zwvENXLzyifzjVkx1f9yj6/4pggDGmiw2SiyAIBr1Ou90uZEoLCwsS214Y5Sjxo7DTH8zPz9Zq1bnFhWvXrlqW8Ytf/mxjY2N+fr5QmvrTP/3TTqejad5v377d6/WOj48Hg0G3202lUmEYFgoFvQqnpqZardbc3EKlUuGcX7l8bWFhAQDjOK5Wq91ud2pmxrbcdrsbBAGPvTiOGaNra2vVanVubu74+HB1dfXOnTvFUl6IeGdnWzcAq9WTpaVl23ajKBr0/bt3bwdBUCjmX331VR1nci6FEOfPn9/f32+1WicnJ2EYplLJdruDiIuLi0dHR1NTU7OzszpPS6fTH3zwgeu6UkqNQygUCr1eL5vNRlF0++6dUqn0+PHjge8hZe1uR0r55ptvbm9vOY6TzmYQ0XVdpKxyUj04OLhw/qpuAuVyOb271et1TUizv79///5913VfffVVPZUPABcuXEilUo1G68njbduKzp5bz+aKBwcHQgBoqRWKCMi5Ekqh4AoovGA8z+zheYtSE90LeH4A6tSSm1hvEysKQBdG1W/OgHR/W02IW0yWiPRBX3ihmoiTx4985fUMWd50tUgB6tEjbZCjy2OT55WjyS4cVWxhDB4f9QYHcXzKnE6Z/viejj3eixc3ebyQLo7pZ8a3FWDEpazTibECrmEYvX4/jKJkKhMLPvCDMPQJpaZt+WF0+869k1rt+kvXCsVp09rlQp05e6ZR76VSmZ/85GdKqXw+L4S6ceOVTqfz/vvvV6s13w8tK6CU9vve1tb2yUn96uXLiUTi1he3Dw8P3/7GNwihW5vbJqHlcrlQKBweHBsGPTk5AQDfC7UQfCqVIgQWFxd3d3c3Nzc3t2SxmM/n89///vefPNlUCsIw3tzcLORLCTcVeCSby8zOTi8uLr7/q59HUSBk/Morr6yvr//4xz8OguDBgwe5bEEIUSwWNAHy7OzsnTt3vvvd7yYSienpaULIX/3VX/X7ng7q6vX65uamnsbqdrsEmWMnms3mzZs3v/3tb29sbHQ6rSiKpqZmFhfnnz59Wq/Xa7WaZVmpVObq1WK3MwCAZrN58eJFpZTOD7WAx+HhYT6fTyaTzWaz0WjMzMwUi8VardZotKSAbDarUbupNC8W8/VmN5ZCciEAFKEAmmRTmuw5rOKpZTPOdMZP0FTOkwtJP2EsAwzPV93hq4zhK+1k0gGMf3j2oEabvGDpGu817pCPsQdj2t9RZ214fjb6IoAKlVA4HPDi2o6Ifs5wExpbnd4ShBBDwPgEu5GcoCHAiSLyqf3gOU/4G27KZA49aYSaoQilAgCCY8eI+gPjCNNgmqZB0bKs0JPNZtt1XdOwm80moZBKJaqV2ptvfC2bS1cqR416k3NeqzWq1dr8/GIqlXrppZeEEAcHB+vr6x9//LHmz/3BD35Qq9VgNFWsFSBefvnlzz777OqV6+vr6/1+/6OPPmo22pTSkpsoFoue5+3sPj2zfk4Auq7baGwLrvr9/vz8/MbGg5dffun/9T//29dee6Xba7/zzts///nPDcOcm5uLY+G6rmMnXDcZBEFD9DmPZmZm7t2/c/Xq1Tt3vkylUoVC7uDgYGpqilJ6eHgMANVKLZ8vFosFXSy5d+/erVu3zp07Rynd29u7cuWKHmLK5/PdbvfBgwfLy8vVarVSqWTTmVar9d3vfL9YLO7vHbbb7W9969ubm48//fRTjYY/f/68RsN1u93Dw0PTcPSM0t7enpaIOTk5oZRWq9U4jsvlsmVZ6XRaA3empqbu37svpVxdWc/ni/Vao/pkM5VKlaZmB37ox5HkoQCiKANAJTiXihFj0ga+cp1M/jze/cdQfr0etBGOPRKMYyiYXJajX/Grl+I4xD3lCSbOdvqQExeMiFrQdtJXP+9RlEkoI9QgI/MhIKUCpCPnotf5RAg6WVNxHEerz47xrPrQd2TS6sbH5B2c9IST8IgXb+5kADzeDsY3YgzNIXq613G0gK5pmvofk85m+t6g1qh3ev0gCgk1FJBqrSaUsp3E9s7ehx/9mhrmN7/13StXX1KAX//612u12q1bt/7sz/7M9/1Lly4RQjKZzPHx8e3bt3d3d3V0qgd55+bmFheWEXF9ff3ixUue55mmqQX9giDo9XqpVGpubs4w6GAwUEoBylar1e22S6XSyclJKpWyLOvq1av5fLHf77/77rudTi8MQ9dJvv32O9lsFgA5jwBgd3f7s88+syzrtddec103CAK9RyDi/Py8aZqFYo4QyOUyL7/8cqfTeeWVV05OTnZ3dzOZjBaEymaza2trZ86c0ZIy2k6azeba2roQsl5vcM63t7fzuWKz2fzlL3+1tnbGMIxGo7W3d3B0dNTtdmemZ+dm5xcWFhBxdnZ2b2/v+Pi4VCodHw+zOwB49OiRhgecO3euVqvt7u7Ozs6+8fpbKysrJycnnj+4fv3q6ury1uYTQiUjoNkBFQiQHAAYQfVVx3jRv7hOxj5AE3xMgkX/i44X7fxFy3nRGvGFr7FmBk7AueI4PmXh44/Dhl/IFBpAGICBaCA4Bht/Md2PmrQrbRLjeeexkxy+8cTN0vHhqTsIL2xyk1c2dqGT/nPcGH1WLAVERErJsBdCIIoiOaLGQAKEEFRCKRVxbrtur9fzPM+yDWoayOjUzNztu3cLpVJ5ZvbguPL+Bx8xxpLJJDWsmXLfdd1yuZzNZm/evGnbtu/7nudls1kNwjRNM5PJCCFM02y326Zp3b17T0q5sLBAkAkVN5vt3d3dleU1LsXq6ioXUa/Xq9Vqi4uLcRzXq82Dg4OZmanNrce6BWKY9MmTJ9/73u98+OGH9Xq9WqlFobx69drq6hqP1Te/8e2NjQ0/GDx+/PiXv/zlxYvnw8jP5Qqu687Pz3/00UeImMsVNIupkPHZMxfff/993Vd4+PDh3/k7f8dxnI8++ujoqFoqlXRLXZdegiBwXffWl3fOnF1rNBrbu7vNdjvi/O79e7NzCzdeflkIsbTsD7xeIplutNqLyytCwe72juM4rVarUCgAQLPZTCQS+Xw+iiLDMObm5nZ2drRKlGEYlmWls/njkyoI6fuDXK6QTCVs28zlU34QIBGubVEpg1hyyRllhmEE8VdLfL74oH5EN4S0M8DRJBSMREVfLOw9t/YAlJI4JM6dOO0Ly1WdjkVPH2R8VvLc/PEp//niIyPrHY24j/i/2bAYiwAw7OZNOmV9oiiKoijSRF3jCtWkacnRyMmk7zp1nDLLU99PeT/9LuPLOAVP1VA1fUlKKS25nkwm2+22TlSklJpVxTTt69evFwqlarVaO6kzaliWk83mE4lUpXLyk5/8NIpiRPKDH/xwdnYum81dvnzl+LiSSCTn5ubjmHue3+32Go0mpaxeb3S7XcZYv+8BkLNnzyYSCcMwfvSjH+Xzec/zhBCPHj3y/QGAvHz5crlcnpoqLS0t1ev1lZWVZrN59uzZTrs3GAz29/c9z9vbPbAsp9vtPXm8NT+3KCUsLs2Hkf/aa6/atlUqlW7fvp3LFtbX1zmXlA7p1bLZdLNZZ4w0m/VHjx5p73ThwgXHccrlsl6pc3PTFy5cuHLlyhtvvPH666+fnJzoIZ1Go1GvNU+q9adbO/Nzi8VikVJ27tz5RqO1tbVtGIZtudev3Ui4qXQqe3h4eHJy8s4775imefbsWT1xn0ql9Kz95uZmuVxeXl7Wqvdnz54lhOzs7Ekp55cW19fX0+lkHIQxj2zb9LwuKuXYLGFbjCBIwRBcyz71r//KZTO5oPUY93j/1UBlc3SM/dL4tF/Zb/zKWEwfQ8D3ZIo4+pmoZ184+hpbhLbb8fqc9OfPfQSJ4y+QGsk++SVRSWaapu/7ajTnPx6cxVH2NeYg1NOAMLIQGHk2bTnPgDzPQ++EeDa4NLYuHE1djKec4NmMswAYQsBjKaSUQBkwMjMz47puIuHGcez1B0EQMAKU0kwms7+/77jWxUvn9e44GPT+5m/+5uWXX3769KlSSvfNs7lMEASlUulw58nW1lapVELEw8ND/T8GgA8++KBYLJbL5b29PQ0Z09JL8zPz3W5fi6483txcXz9bKpW2trZSyUy5XE6nU7du3fpv/tv/9vbt241GLY5DnSvq0cdz587NzMycO3fu019/XDk+mZmesyynWj15tLFhGu7Pf/7LVCrzs5/99Fvf+ub9+/f10Mo//sf/eGNj4xc/f//MmTONRmN9fX15eTmKwlQqyRgtFPL9nqeFEwuFwvT09L/+1/+6VCqFYfjtb3/76tWrpmnu7OxwzqenpweDAec8m82bpu374blz5xBVoVBAoIVCARFN06pWK2trZ/7Nv/njH/3o93d3d/O54ltvvKlXtsb3HR0d2bady+WazWY+n1dK5XK5Xq8npczn87pO+9lnn+3s7OXz2Watvrq6+uTJo0KhBEpkc8lYkUazA0oggSgMCepxgGfH2CrIV024A4DeWOM4HnfFJt3AZGFmHEaN0xwBI5AnwTgWw/MrpZ5TCFZKKQGSjOqd+i10FqfhKEopzrmIYyEEY0yHh8P0bCRPgqPGnhztA8MPJUH7OTkxi6yxCmN7GcJfTkENcKI6qibKxFJK9lUjHpPbzH/mPqeeP8aP654kQ/0uEoa1Ncjn845lE/Ksk6GUjON44IUntcra2oplGZVKZWpqampqtdVqbWw80DrP8/PzlUrl448+sSzr1VdfzWbylNJGvTWw/bnZBV3Hf3B/QyNUHMdZXzu7uLh47949wzDeevPtJ48egSJrq2ds237waKNcLqdSqV/+6v1ioXzn3t2rV6/atrWzu/3Sjevdbvfk5CRUMaX4ta99Lebh3/zNu3fv3p2ZmWk22oyxIIja7a5SYNvu/Pxit9u1TDtbSObzWc/r/+Ef/uHNmze1AMbly5c554lE4o033vj0009937vx8vXt7e3dve1SYVGb0MnJybVr13K53K1bt2CUY+uuhud5W1tbOsw+c/bq5tOdfLEc8nh9fZ0QEu7u2G7y3XffNQx648YNapjf+u53ugNPSvnw8ZP1tZUHDx7k8/lsNqttT0fpxWKx3W7rjSmVSsVxPDMzg4g7Wwf5fP7b3/52pXKUz6YZY3MzU/VmM5dPu7Y5CCPDoGnimpbwgphSRuG5mGu8GH7LOhkvPzI6xvHaGPY5PmccBuPtHhFBzwSLUywyvy2rVEppV0MAKRK94GE4y6DEb5hAOGU+zz4doUgZUkaI0iT/qKQatk+GA5Zs8l5IKfWIkM70JvFockSTTC3rK2/ZOFA+ZY2TofbIeJ79dbKyNLxmQhhjhpYnlYIx5tqOY5saKsBRaX4RwzB4FARBADJKJSx/0NvfHXDOk+7S/Ow0gZf29/efPn363t/8TalUKpVKxXy+VCqKOOr3+6urq+12W5dVNA5zfn7+5s2b58+fz+Vyh4eHug0gpXz48GEwCHQvpFQqzfa6iUSi3++7rru4uLiytlqr1XK53Pz8fL1en5qaqp58rGKyuDjf7/ePjg/OnDkDAA8fPkwmk41Go9/3qtWTTDoHALOzs4LLc+cu9P2D48rht7/zzQ8++GBra2t6ejqfz6+urk5PT3t+/8MPfzU9PRVG/k9/+tMw9PU0vRBicXFRx8nz8/OffvppoVDIZDKWZWkumTiOe70eYyyKoq2tp3qJbj/dXVs9I6Sam1vY3Hw6MzMTxzEi1fLAukV54fzFo6Oj/f39tbW1hw8fdjodwzAuXbokhDg6OiqXy8lkcmlpSatWcc6DIFg/e6byQfWLL744ONjLpBILC3OWZXARmaYlZRz6nuAhY44BjEDEowCo+1sM4DdZxbgwcSp70vHXZPc8iEJ8VnQApUBKKZQEeAYsUc/X9odvgUBG2kPjtUq1nUulkAgcmc5EW04+G8Ianhafb9dJQKlQAVGgEToEQJCh5x9eBhundkOfO3K4zyEMJmb8XizIjt8PXnCD6oUcVz0fb0we41CWDJG4ilKqyaRTSdfzPIpEh6AmM6SUmn/FMoyz6+uVSqXTap07d67X6fz8pz/N5/MEYGVpKZVImKYphOh3u65t8yiuVCpLS0tBEBweHvZ6vXa7nUwmE4mEZsUFgLm5Oa2sVK/Xu91u0knrUqdp2jMzc73eYGtr69rVl1rtxrVz1wmB3d3dpaWFv/7rv06lEufOnSHSiONYVw7X19dM06zX60EQJBIpzwsdOxEEkWFYJ9Xa3t7BwsJSp3/cbLa++c1vXrx4MZPOPX78+Oiocu/eg1/84hf1ev2b33pHiOjjTz58++23Egnn888/55x/8cUXCwsLpVLp4cOHUkqtRm7bdrfbdV1XZ0qFQqHZbBJC9vb2Ll26tLu7pwmCK5XKpUuXfn3zk9XVVcdxoigol6ebzSYAefDgASHk0427c3NzQRAopXZ3dy9fvryxsaGVNmq12oMHD2ZnZx8+fPj9738/juPl5WXHzhYKDzudzo0bN0BGs7Mz9+/f9/v9QtkRUiqQBCSAVFzwKJZSCniW9r8Yjr544ETlHF844Pn4EwA0nGO8BE/5i7EbHL+vtr1T61MpUEJqL6qE1COykxY4sTuM3+u0Yesj4AqFJuAf5pmAyBUwJMPWIoFnQbZ+5TDQff5Ek8Y2tsYXTevU41/5tFPH+MHxD5pURtdgdBZqmqZt247jaCIQz/P6/b4u0Pm+X57KWzaN4oAyTKZcypAZZHZuOoz8bq+dL2QvXb6wtr6iGV++9rWv5fNFIVSj0QrD2LKcIIhqtUa/77322hvpdDaORbFYTqeznMswjKenZ5PJZCqV0ngUSmkYhkqpZDJpGMbNmzeXl5ejKKpWq/Pz8w8ePBhDxtyEvbKy8vjx4yiKLl26JIRqNBq+75fLZdM0C/ni4eFxFPFWq+N53ve//733338/l8sVi8WpqSlE/Pf//t8rhf/oH/2jdrudy+VWV1d/8YufdXvtVrvh+z4hRLfjpZSa996yLMbYzs4OY0ynT4VCodVqcc6npmc9P2y0mmfOnbUcJ4iiIAovXb5anppZWV3N5Yul8jRSEvH40ZPNh48eLyws5HK5vb29jY0NAIii6NGjR9vb267rnjt37urVq6+++uq5c+cIIb/+9a9/8pOfvPvuu4OBXyqV5ufn0+l0r9fjIpqaLuTz2WIxXy7k8/lswnUoAYpK9zn+i45TIejE6n+OPWycECYSCcuyJlf1ZEB3alkO/Qo++1VNNOqEECLmIuZxHCsu9JA6PB+OTlZ9XjQWpVSsVCRlJGWsMAaIAWIFHNCPIz/mARd+HA8TQv1dC2jhaD5t8spwVFA59fnHzzm1sb14/KY/jT/P6ANoVCtoPiXHcXR5Wn/aMAzDMGSEOo6TSqUcxzEYebr1hKBaXJiJAt80zXNn1l3b+vt/77+6efPm4eHxg3v3TdPMpJMn1eOPPvwV57zf7wNAuVyenp7W1q5r+rlc7vj4+NNPPzVNc2VlJZVKdbtd27Bbrdbq6qqUst3rJpPJGzduVE9OVldX792/PxgMrl+//vDhw0KhQCn95JNPrl58SYMwdaw7NTUlpcxkMnt7e3HMwzDsdvsGs5XCs2fPz88tommvri1fODz/2We/tm334sWLnEs9S6Vjvw8/fH/gddbW1iqVytRUOQzDa9eu3b17lzF25syZ27dvdzqdpaWlsevzfV9XTXSTybCsZrM5VZ7xvXBzc3NhYaFeb6ytrepNwfcHu7u7nudVqkda62J/f7/b7RaLRdd1L1y4sLOzQyl1XVeTcGvU6NzcnBBienradV1GUlPTpUTCeXD/Do+8J5sbQsSU0pn5OUBDIUFq9vyo2/MAQE/eTPqx3742xstj8vt4Bb7YV6OUGsYzODQhiECklEoNSRO1J1QTWdJz16AAAKieldeoaRkLQDJUiFXk+e6alBJHKDmNByCEjONSbTgcUCAROOTdp1rpGRA1EgglIn22M403DP0e+qInyQj0E+REr+KU/ZyyzNGHe26E99RrJ1+uf9VhMENiWVYi4TqOg4hRFPX7fQIoRzVVy7JSCYcxdrC9oThfWFiYmZkdDAaDQa/fbfd6vaODg3q9XigU2s16IpO6du3Kndv32o1md9DX6RMhpN/v60omIaTZbPZ6vWazmUqlCCG9Xk/nfsCRxyKdylSqx0opz/NKpdJxpXJ8fJxKpZ48eTI1NdVut4WIL148/2d/9mfn1i5+7Wtfu/Xl5wCwtLSUyWQ++OCDMAxnZ+fiOD6pNqWUBwcH+Xz5ymWn3+9fubH8F3/xFz/43d978mQznc5KAfv7+8vLy4lE4l/9q3917vyZP/zDP3zvJ3/9y1/+fHllYXd3N2HzYrE4Oztbq9XOnDkTBEEikXBdV0fUUkrHcXQSqzPVdqejlEqn03t7e6Zprq+vSykbjeadO3cMw8hkMvfu3UPEoWnxMJVK5XI5SulgMLh//363233jjTeklBqFI6Ws1+uZTGZ7e1srGTbr/rXrVwyDVo4Pzp9dTafTntdNJpODbk8h9cNYSDAINShhBAlD+I3m9tWHnJizgVEFcrxyyAjUNV70WqJchy3EYKCQc87HpdEJOxxb7+kFqxQqoBS1aJLGUOKzOtBzNR6p5IuLf/KcQgGXigxh61Iq1KB0ZtDhmD6oZznh+NBp4fgqJ9uAOCJO/crj1O7yWxzjZCBx6i4MadIp0/5ZKRUEgS+57/uu7WiSC4pkvE3oFdnpdJ4+3dIlmHQ6c/bs2XfffXdz87FlXZmZmbly5cqFCxe2t7cNk85a8zMzM61WS8d1k9ZYKBSy2ezc3JxSynXdw8NDSqnNHC2yeXh4OLsw/+DBg8FgkEqn/WCQTCYXFhY++eSTXLGQyaRN0zxz5sze3p6ueUxPTx8c7CPi9PR0s9nc3d1LJtKtVqtQKJ5UG8lk0jTNRqMRx5lcLieEWF1dff/9Dy5euCyl3Nvb63a78/OzhJA//dM/PXtu9dvf+ea/+3f//vz55UEX9/b2Xn/99YODg263a5rmG2+8kU6nB4MBpbTX62mfHARBPp8/PDx0nIwQsW4wZLPZyvHJzOyUUuratWtCCN/3AeD4+FgjgZLJZKO6c/HixV6vl0gkbt26NT8/H8fxu+++WygUVlZWjo+Pdb6wurraarVeffXVW59vrK+vP3nyaHV11XWdTCbTatVeffXV+/fvS2BRLNFwKXUsy7JtGykTg/+ynPDUHg0TIFIcUdpOeDmIo1Abp27fgcLxEMZvWrdf8Y4KCCFKyJEVAn3GUPwcEdszp/obCq4xl5RLJFLz0BAplFJCxGGsADRtKZA46pqGUDII/R4jYJsmSpQxyBhRsnymOF2aTdgJRggBCDxvMpaQE5AimLDYF2ue8LzrG0cU8ELB1zRchqYShAi0wHTRMAQQXywWpmgYBe1O2rbnp8vFbFozRKLJ0GSl2ekLVy+jyRLZtBcHH938JAbxO7//g53Dnb3jvYPqwa8++dXm7maz1/zy9t16o7W5tX3/wUat3tx49EQBqVRr2Vzhzt37SNj9BxupdDbmkjKzUCwvr68EPPzi7peLqytuIlkslW/duUOZ1en4Cu39w3o2PwNg+z5dXr7suuVCPt/tdF575VUd7208eSxAOalkfmYqUEKY9KTbVo4ZMezwAJP28WHv0cO9Rxs7J9Xm4uKi7bBLl9cTSWY7mM44c3MzjFkEEj977/Nz6y/Vq6o4taSII9HeP6pVa+3Np7tcqtnZWd8fGCZaJpyc7B0ebnW7FX9QTyVBmBgz9Hg0CHyCiinJ+x4LedzpfvnRhwlGTg73Svn07Gzx7LmVRufkf/jf/I9WIjkIg67fNR1ycPyUWfKP/ru//0f/3d/vdRsH+7ulQjlhpB7d3dq8f9A4GlRre//n/8v/6enWxtWrl3/x/ocDjwtpv/ezjxsd7seGmSpZifxAQDsITwa9p0dHJkhDCUMJJjmTnCrBQDKQqLj+UsClivV3qeIYZAwyUkJ/xSAFAUEADMpR6QcFAUmRowol1/QWAlQkeBiGcRwjom1aJjMMpFQhSoWxIFwSLqlQTFGmdMZKlEKtdxQjDoTwQAWMhAYNLDYwSI9ClyiqJJGCKmkgmAQZpQSRIEqpxxuBS+QShSISqEI2sJNNNE4E1pXRRKuh7CY4HZquS7cu3RpP1HiCTSa1zw9bgW3b2WzWsiylRH8AnHPdwDiV48oJYYBTlga/oSOjXmhdwAuec2zP4z9ZlpVOF+YXF6QU+/v7YRzl89nLly/rJDYMw62tLc0qrcsht27d+qf/9J/W6/Uf//jH09PTL7/8MiKeWSNHR0e1Wm1qaspxnGKxWK1Ws9msUurVV1/d29t75ZVXfvKTn5w7d+7hw4cvv/zyL37+829+85uXstl33303lUpduHDx0qVLd+/dnp6a7XRae3t7qVSmenKSyWS+/PKLbDbtMJrL5bRchC5dLi8vd3s9ANREt1IBoEJE0zSTyaROIOM43tzcPHtuHQDOnDmzu7t7//79a9eucc7PnDnz8MFGoVCoVCrZbE5H5lLK69ev1+v1RqPR6bRmpsprayumxUAKyzYBIIqCQa8vZNw6juM4FlLpd9cgeCFEFITvvPNOPp///ne/92/+5H/5P/7v//nV61cR8Ve/+nBvb+fSpQuZbGp5YZEy3N3dvn/33vT0bCGff+utt9KpfLPWXFlZ2ds7uHXr1oXLF37v937v6GD/6OjozTffBICbN28uLC0KIYIgEApiSToDPwzDdDKVzxU73fA/0yP99mPSHY0fmdzNdXN/zP8yLmoMR1Wfr+hMvvwrUqqJdXsqdpsM6L7yhZHgiEjU6drSJLkhG+PRxkaoa6kanqNpiXU55CsvaNzTP5Vwj211nAScMjn5VUi3UzasxvP7hnF0dDQzXTZNc29vjzE6MzNju04chz9576fr6+ual/7C+YtBENy9cy+fz6dTmVKx/HRre3t7+7vf+d7Ozs4Hv/pwfX393t3Hq6ur3/jGN3Qb8OLFi6lUan19/csvv+z1eouLi9Vq9R/8g3/w2WefXb9+/fDwMJtNP368kc5mbMfMZrP37t31wyCdzuwf7Nqum8lkGGPZXNr3gi+//NK27aRl7u3tZbPpo6OjarWyuLK8u7u7tLzc6/UopQQUNUwA1E1ZAOh2u5ZlWZbl+/7+/r6UPAwDRNTRMiJms9l2uz09Pb2/v6+USiYTe3u7u7u7b775+v1790ql0t7eTqPRuHjxvB9wPwhMyygWi4wRg7I4jveaNSEECmnbpk4XpZRRGN145eU//uN/owd//w//u//tF5991m639/Z3pVALi/MLC0uua5+oI0Q4f/5iPltAxHarG4YRo3atVgsDXiqVlpeXj+p7m5ubUkrGOvqC8/l8s9k+f+ESECoBu4Mwijrtdjvi0rJdJM5vWuL/XxwvLv1xjDa0llEbf4yChOez0skh4MkFPF6HL3qXU+/1m4xWn4oaxvgCYMLTTK7/8TTU0AjHl6jrbHpSrtNpScUdx5m05smkEV6gMxz/iZLTO9aLHwZeMD8yum5KqUEpYUYy6Rby2TgOB4NBIuG6rouUVKvNV199tVwub25uptPplZUV7asXFhZ+/vOff+Mb31BKxXF8+/btTCbzox/96P3333/ttde63e6jR4+63W4ul7ty5Uqj0fjTP/3TpaWldrstpZyfn6eU6uwuDMN8Put5XrNZDwKv0235weCkVqvX6zMzM2EYLiwsvPfee4VSsVqtaVrOs8ur77zzTqfX/s53vnPnzu04js+cWTNMO4oiEcUAYJkMkIFS/sDrW53ATS0sLCQSiStXrrTajWw2fXh4UC6XhRC7u7v5fKFSqayvr6dSGQDodLqDwQARG41at9u1LCOfz29vbwFAs9m0HRMAeCw6nQ4iUkqTyWQqFTAkgkd67oRLLuI49AfvvvvuK6+8tre384tfvD87O/vWG28KJQkAEDY3N/fe3/5seWXJYjTm/jvvvNPr9A3DMJldOT558uTJ/s7++XMXUqnM/v5+rX0Sx4elciGRSMwOG6omYca9e/csx83k8nYiU8jmJKAfxoyZvQGH/38cL+aK8HytYZgrqmfSDCNjeL4iODH6BJMR3MjAXrTDF4+vtMahLSADRBjqdg85tAFAPA9QIafeXh+6DaU7clJKnVjrf+SYvuk3vXbypkzGrpM/fGXqOP60Y1zsuEmYz+c1IGt9fV1XBfb29gAgjuNPPvlEKZVOp//6r//6888/L5VK/X7/H/7Df/jrX//6z//8z8+ePcsY63a7U1NT3/rWtxYXF3UhsVwuSyn/+I//+MmTJ7lcDhFzuZzWCfvFL37R7/cfPXo0Pz9PKJSnismkm8vlwtBfXV2eX5jN5TKFQi6Xz5anStMzU9lsNpNJeV6/1+vU6/VPPvkklczoYk8unfnoo48OD/YAgDFmGQYhBJRUggsehZFfrVaPjo7u3r27v79v23a5XC4Wi4ZhrK+v625kv99PJBKaTK3dbm9tbeXz2Ww2u729rZTq97u1Wq3X61UqFcMwEomUlHJ7e/vevXudTs80zWI+m0g6OGIuD8MQCBq2pQemHj58+Ps/+OHS0ko2nalVT1658XIymb5w4dI3v/ntt978Wqk01Wr2OJcXL1xZWV7d3zu8f/++ZVnLy8uGYSgl5udnL125XK2dzM0urK6uPnz4qN3uXrx4eW9vz7UdKaU3CHQ7d9DrN2v14+PD32pZ/wXHi0vu1AJTEx2/UwUIMnFMwjbVuD34nEjmc0CU33Qxp2LAsc1zgTGH8VcUK/1dSDL+YmM2NCE0Y4VSapgWju3Btm3TYoSQMAzHVaxJKC28MA/2lRsVTDhAOTG5rx8Z/zBErhnGeOaQAnY6nSiKbNuM45jz2PM8REylUp1OB0aVaz1wqAljPvrooxs3bnQ6nTt37gwGg7W1tV//+teXL1++/fhhtVrVyz2VSt29e1drSDiO0+/3X3nllY2Njampqd3dXT1SqJTsdNrMNC5cOLe/v28YtFgsahZ6J5FCVH/v7/3Bk82n6+trX375peu6x3tVQumnn3565eqlmZmZIAiuXLnS6/XSqYSfz/V6vTCMfS8kjmOajBFaLucZo8VisVarLizO1Wq1TCaTSqXS6XQmk6lWq4Swg/0jRKpRAaZp53I5ANjb33ntlVc1E/b9+/eXlxejKGo3WwDSdRKO42i5mGLRDf2g2+4IyQnBZDKZTqYQZNtoV4+OX331dWaxZDL55MkT23b/7b/9f77y+tc+/eTm1HQpCKLFxSXbtr+8dUdJaZpseXnZ98NarZZyU6++9koYhre/vNMJBm+99bUgin76k59PT0/nsumnT5+eO3uh3W4PPK/T9RJBSA3bNM1SqeQ4Tq0d/Bcb3Fcd4/UzubTURINuHJeOVuZYHez5EVZAnFBHnDzGS3rSE04iV+EFz3nqQESlkWfaGU5c53PhqG7pwnAbEFKCUkgIjoY1dDNAjmnnTdMcv8Gkyx6DEiaNcLjrP98knPw+PtX4B0IoHYnj6LvDOVcKUqlUsZATIu71eoRgKpVCSnzfO7N+NpfLPX78eHt71zRtzuX+/uGNGzfm5xd3d/eiKJqfn5+bm0PEzc3NL7+883RrL5FIZLPZIAgIIcvLyycnJ4i4tLRECHn48KHWkCoUCoPBQJMapdPpYrnUbreZQROJhOXYvV4nm806iUSn0zIM4/GTjQsXLpw9t57P57kvd3d3FxYWHj16lMtl5+dnj6r+0tJSrVabKpfDIGg2WjyWtmEalEgedTodPZWjuyD1+onj2I8fP37y5Ekcx61We3FxOQxix0msrKycnNSIaUVx0GjWhBC2bV6/fv3zLz472j+IosgbBK1WK5VKZTJpPWVfrZ4QACUFKOladrFYzBeLFLHXaZuWNbMwbxhGMpn84tZtyvDi6ophGEopy7JmpucMg0khHMc1TWduZqZer3f8ZiKRWFlZQakePXr45MmWEKI4vxALhSCpYbTb7cXFxYXFZakwlUp3et2IAxqmH4Rerx/EkWe7QBP/v9je+JAThNnjR04trbEdSikBhgRlOKEdNo7G4HlfMml+MGGHAEDoc4IZpyz2xXdHYpw6J2gsJ04Y4Rj8qi9VSkCgAKB9kZ7eD4KACeI4zrh/OPl+k1c/6fr1g5pd68Xj1LWOf9Dx8ThCkFJy3eNErFarhkFX1lbn5+eUUt1+L47DDz/8UHMNEUJu3boVx7Ee+avVaqZp6r7Z+fPnf/rTn66srHiex2PQ0XW32w2C4NKlS/v7+ysrK7pDvbOzk8/n9cWsrq7atr22dqNSqUzPzrRanTNnztTrdT8IPM8HgJmZmUQioUkxwjDMZrP7+/vlcjmTyZgmk4rHcby3t5fNZm2ToVSJhFMoFASXYRhbJlOSD3qhbchisaBRL+lMslDIPX261e12k8mk53m1Wo0xc3FheTDwi8WilFJy3uv1PK+fy+UajcaZM2fW19cNQvf39+fm5rSsfL3e9DxP19l6rXav3eFhZNtmNp9xE3a9Xt8+2Hv77bf7ne7jx49TmfRrb7yue8Wm5czOL5oWe7q9mclkirlsFPJCodBstsIwHAz84+Nqt9vVknUA8pVXbqCbabdavV7HsRNB6B0dHW9tbbZarU6nC0gz+UIxn7YdHoQx+tS2nX70n2Vj/x+P8dp7MQQ75ZrGbmfkCUcrcyieeRp8MunT1Aup4FfWRH5TmIqIoVQwhJU+F9NOIvgYIcTzvCiKCKGccymBUSaEFiIdyneYpknokHVGf8fnDzVBkXjKSjXGZfyS8XNM09R4cUTUBq+U0nNWzLKEEP1+nwI6lsGF8Lo9QqBYLAoRHxwchGGwsLCg0cmpVEoLOGslCaXU7OysnrRYX19/8ODBgwcPdK1F45tff/31vb29vb29dDqt1Y7+36z9V7AkWZoeiB3l2sM9tLj65k2dpUVXy6kW0z0zmCUGoC3Apa0ZZ7kGGtb4vEvjE1/JB7yAfADNCBJY0NpADBc7GEwPMHpaVFd1VXdVV6WoyryZV8vQruURfDj3Rkbem1XTDdCtLCtuhIeHe8T5/Vff/32O42iatre3J0fRMcYSdVmr1Vqt1kcffYQxxgpRFK1SqaRp2mg2CVEODg4kVf5kMpHU19J1F0WhqiohqNlqr6wse97k6OioP0Ddbrcsy+WFXhrFEwQELaej4fXrN7M8khUUXVcRQpqmrq6uTqfTer2+v79fq9W++93vnp4Mut2FSqVy69ath08e27bZaNTW19ffeeed23dura+vO5b92WcPRqPRjevX4zgsy3IymdCSFzl99PBTXdebjdrXvvpVx3WTNIUIbVy9WnB2PB5OkygTLMjT3d39ZrNZ73bzIh6OglqtBqFgjHmet7K6dOfW7X6/f+ocS9gahFAOGWq68slnjzzP46wMw7BiGghAhLDrVhcWFoMwPu4PipKVjE+nnqabeZ4CaM0WKIQQn9fz+PlgHhVndT6ZKF0uy8ttFr7Nm9AssATnGYosziOE5BQFnxMXku/FWLlgvfK9EigyM7AZfrUontHhnGE5ZR8BPOtUZse5bNJ8bhLyGUCtmMOYz+jEhQSji6d3lHnv/J+/yY+Y2XBZUgwhApBAwDknhBiaZqqa49i+76+vr46nE8kjdnRy3Gq1To5Lw7AqFbfXW9Q0LY7jPC9939/c3BwOx3JOvNtd+NM//XPP8958800ggjzPpceo1WrtdlvKm5ZlGQSBEAIhJNlydV1njF25chUhVHEdKbV9fHSyvLqyuLjY7S6wkxNKabPZrrhOq9XinOd5fnrobW0/BoDrhvoXf/EXGxvr7U5THlzBxDRN17HdSoUxTss8Cn2AhK7rnHPf9xvN2sHBwZMnjyuVShiGb7zxxo9+9OOiKK5evQoA+qu/+qsoivM8v3nzhm3brVbrXVb+8R//seu6tmFKzhvp/23buX7dWV+/4jhOf+AnSRInicwSBYITb5pl2dif3nn11Ve+9CUhxOPHj70kspibsnKwt7m1tZWmebPZvLK6Vq3WPvroow/ZLzY2NnwvCMMwCAICgbwvk1M0nnpRFGEIsrRAAnh+mMShEGI4HBJFkwBDzIWu6wjjLMugYc8WK4RQXB4VeFYS74uRNJe3iyUGMYvRnoad8w/mSxtiDh8Gzxtvs1M9C80uAGXmPuuC2zz7FAjALBA9w9ZAeRx564EAPDNPeOEi5/2seFbI+8L1XDih+S/oaSg898YL39QMdsQ5N00TQ0iLsijLJEniOMYQYgCyLJNTFI7jMEYnk0kURYyVt27d2t/ff/DggZxk73Q6vu8Ph8NGo7G0tCQJiP7pP/2ntm2//fbb9+/fZ1TIafetra2dnZ2Dg4Pl5eWrV69+8MEHvV5PUjPFcSxTNdu2t7YeyHYIoxxBWRDGEOLBYBBFiarqiqIM+qO93QOpEb+xseG4dpZlN25cG42GnPPj4+MiyxcXexhDXdcd26q6lSwrhBBlnt24c8uyrFqtJqnya7WaBFL3+/1vfvObf//v//3RaOJUqh999DGEcGNj4+6n9z3POzg4SNN0ZWVFOoqTk5OlpaWFhYWjo6O1tbW7d+/euHHjvffeE0IstDocAsrZldUV1TIKynXbNCp2EEeKqh8eHnqBr1n6a1/5MoRwa2vrcPshgEA3UJxM//wvPr2yfvXo6KTTahdFkaeZnGnSFVKW5WQ62t/fh5Ue5wBiWDLqhVlZlkkcYozr9bpl2zhKiiyPkiRLUt1ArKTEfGqB8OmA3lOMKEZ4Zg8zM7i8PbfPfHm7sLw552cZ2fMqmTOvw8+nZ+ed3gXruPzGC0sdPC+rvOAJ5UZm38j80Wfl3HPEC4Twojk99/MuHOfyns+1z/naFEIInPvGLMviOFYw1jCxLGNpceXJk83l1RVVNQ4ODhYWFh4+/HRxYcEwjFqtFgSB5GKwbfvmzZuDwWAwGKytrb311lue50nZsKIoIMBJkty/f1+mi2tra1mW3b179+rVq+PxWCIwW62WEKIoiv39/V5vgXOua4bS0LtdRVV1RPBoOKnYrlWpSfmKvf2Dk5MTQghCOAiChd7SyemRhI8rilKU2UsvvUAIYZRqKtE0rV51OAOU8qpTURRla2vrtddeK8vytH/81ltvZln6l3/5l9evX8cY37hxI0k+efz4MSHktddeOzo6RhhoulJvLIeRT2lxdHSAEMAAGoZTFFTXzNFwoqlGmuSKommaBqnQNK0UXFGUJEnCNIGahi3DS6KNhYXbneYnd+/9+Mc/Pjw8tCzLsqyuCXu9HsbKZBx88N57mqbs7x/bpjWdTvI0H4/H4/HY1HRCUBQHeZ6TRCnL0jL1sqQInBGOSAxDrVZDiIwmXp7njDEExXysePbg0vqR5Amz4sLnGeEXOA/wjCeYfcLMxp6prEomh1kVcMYqKF3ObGh4Zp/oeRYl0zTwrNWdfeoZH6l8aW79CwbOWaDIvOVcvs4zjbdzpOnsOufd8bzhzYfFZ7udv2vey4NnXeK8Hfq+TxBCACoYywl6TdNs3VhbW1MVLKPWPM89z2t3O91u9+S4jxCCALtOLYqiMIgDP1JVtdFo6Vr85PF2p937ype/9uDBg3/5L/6Vruv379//2te+Jif3fv7zn3uel+e5pJp2XVf26Hzfz/O82WwuLS0F/jjwgzQryrJ0nGoQRIqqcg5s2y5ZGQZRpVLpdDq1WmM0Gu3v7xcpsyxreXkZQlGvVuI4rNdcSUiRxBFC2K1YhqZpmlakpWmaaZpKNc80TSV+TRaiNU2TCsHdbvff/k9/+Lu/+7+Qx3/ppZem06kUeFnsLchwenByypjY2dl57ZVX33333TfffPPg4OC1V1/VNO360npW5MeDfqtZLwColPkoDIfeJCny/8f/+C8++uTj0XgCCe52u9evbrz55pu3LF9VdM8LojBVCVpeXt16stuotW3LGY9GxyeIUsoIU1XiVKqoCrjRyLJEVRSaF3mWJElGCHIBCMOwYruSpFRAmGclVpU0zeeNRC4jcY4gma/+P9dj/OpGOGchswDtqRz3fC4373bAnIu+YBczI7wccF5Y/5f8zVNqtmdf4s8Y4YVrkKf0bMQIwbNgnwuObv7GcNlhXth/fof5T5eXTQWFEKqKqitEahLKud6yLHe2nywtLem6PpmMAQD3799/443XmvXWZDIZjUb1er3dbkMIT09PkyT58MMPV1dXGWPvv//+4eGhZIw3DEOGrJ7nLS4uvvjii91u9+TkZDqdGoaRZdnp6Wmj0eh0OlJGoixLjBXHqVbrNc65ZVZURbcqtqKoSZLkZcmY8LxA1bVuZ2FpcWVxYfn48IQQ8vrrr1cq1mcP7g4GQNeUzz777M7tWwghS9fVBlGJYpp2HMcK0WLGGo267FIsLS1tb29//PEvFxYWqtWqYRij0bgsmZQBjeN4dXU1pzljbHFx8eTkBCFkWVaj0ag57vb2ruu6YRjWarXT01PTNPf3969cuTIdDpGq5ll2cnTsZXGOoJ/GkyT59NHmz+9+nJel3aojhLws/WznSXtlcblTtFqt3kJzPPJqdfvRo89sq1qvVzudnm1ZEMKVlRVT04sik1FDobdCP+CcJklURCUNSwyFrmrNVl2CrvI0lWJehmkWRRFfWNyXVshs4aA57unL2+flihcynVlO+FxnMx9tzm8SYQuetUl5evjpeV5c4fNWMNtHiAu293TlP/24p+f3bKJ7vuvFZy4ccXau8833+X3QXKg9u/LLXnT2vGmaCsYqUVSMIIRSQKtUy93d3Sj0Fxa60+k0DMNWqzX1PYTQhx9+KE2UMSYVMw3D2NjYkL3+9fV1Xdffe++90WhUrVYxxrVabTAYfPbZZ/1+X9M0ORk8nU7zPJdxlIRxyuJNo9GIIwIAqNcaEpJ/dHQcp0me565Tk0xbnhdEcdzpdBYXFxFCuq7HcfzZZ581m/XDw8NGo1arOoqCMcaGqlmWxSjFiJimiQE0DIMnmaap/X4/z3NVI8PhsCiKer3+xhtvSNF5RdH+0T/6R3lenpycMMaBwj1vsr+/a1lWp92R+hz3Dg51XW80GlLBdzKZvPHGm48fbXba3fh02F7oOZYtiWdU16k4DtNUP4kAQablCATTggIAwjQZTidDHDSa7sLiGkKo223/hz/5s29/+3v1etVxHMAhhFBRFBUTqbmNMd6bZEmWyuEyTdMwhAqGtm23W92iKDzPi6KEcoEVYnJTURQsnh9eAjZTin+6QAEA5bnswoXtgg3Mthl88uxfMbNJNHfUi8eZDSGAZ7PQy0t3/tYw840zw7vsJwV/DvgGnLFuyx4+eD7dwOyDz1NBIQSfN5sLbvC5j794E8/mkPIxQmiuKPuU2hQAkGWZ1Ljc3t1RFPLSSy+9+vprURRI/XrGmOM4Evgix8CHw6HjOMfHx5PJRAKgZWlnf3/fsqw4jnd2diQc/MqVK/J2GEVRURSS4VvTtDAMi6JYX1sLgkDq/pUlHY1G/eFgNJxomlYwLnF8aZYdHBy9++7PptPp22+/vbK0vLW1hTHsdrsbG+vHRwerq6tJHDFS6rqexHFRFApK5aoFSTadTqXWYlEUq6urGxtXDg4O3n333c8++8wwzN/6rd+ZTqf7+4ebm5tLS8ubOw8BAJ1OZ2lpZTqeHB4e/vZv/3an2XrnnXfDMOy02kdHR6+99ppUtknTtNPpWIZ5kB2VBZR0VVGZH03Gx6enOS0rFTtJUwGh67pMiKP+qbFcOz4+KgvGGLx567pbrVSr1dPT09XVjdCPJENkgXAURUKISqXS00zdUzllRZ4mYZSkcZnnUIBarSbp3hBCpq5xIMqyTNMUW/aFdTC/HjjnApx5p3lzeu76ee7z8/BmaYTnK/ai/cHzouDMhcBnyzBCUhg+K5OIVMznZoh/lXU+/+CpEc5hrQlEDEIBoayccggBwgAhPLsDibNRYikezqFA880TCKDgZ3VUxsCMqRsAAIXAUPINX2AiPbs3yCuE51g+eRBTVRSsAEaDuMAYI9wouRLlotlZHftR4MWdzqpC8KA/zZJiMhm9euuaFEK6d+/e2mL7ypWrg9HwF+//1LAsADkmsN1t6abmxf76+kYURZXmchRFBTTDqV/SwND0NNuqWHYYeLwsrqytMz0DhglNff94U1XV7bt7jHPN1qGOBpOjadCvtbQXX9ugNOp224phT2N25BUP90eHB+PQ0D/2Dh6l48Od/esnfVDQ5fanL968rhtOo95u1Jw/+P/+fzauXL/9wmu+nx8eDUZhXnG1ovAcxwVAJGnAGKO0LGm++fihaentdvODD96L42R1ddXzR0QBb73y5Xffffev/+PffO1rX1tcXGSOePDxZ4ZhfPMbv/mzn/0sjuhCb/3e3c0XXnjh5q1X9vb2kKs9PHo8CYZra2uL9SZSTNNs3v20/8f//hc/+vmH/6f/y/9ZrSuNpv3w4ce9Tv10MhmewKsbNxE3LMPByNhYf2l/d7S8tE6palh1t8a4KBSFVEGe8mgcjcbTMwknQytME6iqNp0mUEWD6TCOU4EgxkgIXhZFliQYoSbKfd/XDUvR1IkXKIqqmZafxDrGDMJSACY4g4goaiF4URRVeO6XhFx/QIqoMM6FlLEFApwPCkEIgaCcPxNznZcYz9pgMoxlrGQMYIwxIjOPBM+A1tJYJLSFIygABAid2WdSzPUtz5NBcN7qkO+VL8mTgORchercp8r/YYzA2ZS+OCP2nXOvQPqgz7PsGSgWzqXU/NKE7vnRBL6kIzf/6lzofPYgTdOyYAAAwABCiFIqmSa2trbWVpYBAK1Wa2lxYWvrcVEUb7311ns/+qs0zSGEDMDxeKqbJ7Va7c23vvKnf/qndhAWZalpmh/FeZ5LtqWFpRsSylwUBYCiLMvxeDwZjS1Tp3k2GAwEK23TarVaAgJN07Ks+OUnHxFdfeVLLwdB8JWvfOXll68ppGA8UzUSpQWaJiEItGFsOhXFUaN4gLFi2laz3bJV3VKU3YP9z+5/dPvGxtpq7+2338ZIPT09dd1O1aktLa+leZ9SurK6dHx8VJwmhmHs7e1CCI+Pj2/fvv3yyy+//7OfD4fD733ve3fuvPjRRx8dHBx885vfLIoCIbS3t5ckme/7Jycn/+v/6r+u1+vD4QgAsLKyAiE8ONh3HCcryvF0ktMSYmBVTKxaJ9OIw/Lf/MH3d0cTiBjCOEziJEujKFpe6RGixnEchsnGFUfGF+NRIPmRdV1XFGU4GuV5lqSBEMAwDDWmqqpKnvwojGXPWla2JWk6hFBisBRFkQUw3/ctRm3oQiQEYAIwVVXiPOcICybkxCoXgnNelJTjc7qjp0b4nO1CKDh7EpznRzPm7NlME4QQYyxX+uX1OctF5x3j7NVfK/q7fHA4ByoAM8pDxhiEZ10Uzrn4vGu9ZDxgzoTmP/LCPs89lQvPz79F5h4SQi0Jj998801WFluPH2EMFxd6N27cODza/+u//uuX7tzgnGdpQZk4PjjojyfXrl3r9LpxmmQlZYwRTT0rPFI6HA7D+GxUEgqmEEVVVU0hBGHL1IGh27ZtaMrCwsLaympRFJSV/iS1bWvojUxTv3Pnzne+851Wx84yryhDiFG4fzTyp35SKJYFtcwbBy7mKcu5ggtIia7Vm4121TYJfPcnf/Vk67MvfelLluUcH41cN4vC0nXq2/ubvV5vd3fXMHRGxUcffdTptA8ODr797W/X63WZsrbbbUqpZVWuXbv20c/vjkajOI6vXLmysrJyejqQrI2MMamvWK83X331VUldRQj+bPdxWZRX1tfcei0p07JIh9NJtWH+wR/9a8WpqSZOioQIUK1WqWCMsSTOYiPpdpYQQoEf7e7ucobRKqKU2rad5+5gcOr7fpZHElAYx7FlWfM2INHF4/GYMQEhlBQ+CCFZfDKwIgTXDMO2TaSQOC8YKwnGqkIEQogAwAXCClYVDoRSMp7O4dwE5BBIsXg4K6UCB215mAABAABJREFUAKAA4pmexOXU6UJ2d2EFznsO+YDP6688b60+jfieV4mcP7pM5y5YFD+PB4GcJzxvTYLZSV6GqM82eGm2av5cL1z2F5zchXfNDkgwQRBDCME5dhSpqorU4+PjimV+9RtfR4L/0R/9Eef0lVdfwgqp1lqnp6cZY4ZdqVRrRFHCKBl/9khR9ThLi7xEWV7QkhDCudA0fTQamaZpmjqGkHNOCK5WqzXXMQ1DUKYouOq4Gxsb6+vraZwMh0PE0At3bvz0/en+/t5v/Rffwxg/ePiZYytE4UEa+2mqmFYyip/s7wYFvnL7BoiOB6dDVtICCrddc6uuW3O7Nfs7xnf/3f/0B59++qnr1peXNpr19uPNXyz21izLWlxcFIBVqy5jpedPFheXiqKwbEPX9U8+vgchfOWVV/K8/OCDD770pS9Vq26WpRjjoijSNL179+Nms/29731P1cje7gnnvNVqyN5XtVrd29s7PBkuLnRXr15hZer7U4FRkI4Vw4iSISKMKHo0ndqO1e22R8PTLMsYU5Mka7U6gkMIcOBHjUZbUkhJqFq9Xs+LhE6KsT9N09SwaoqipGkqqfoAAIqiyJ1loxIhlGdlHMfT6VQIsdBqckGLIs+LTGoWUM6IbmiWLRAWlJWM0TJlvGRCUEqJmCtbiFn1UK5pLteOEAKcq2DPFub8DR2eswbPIr5ZWw8h8txVOmuZyG1mwxdM47I5/K3bZaN9qk94Phr//MrvbJvhqmdZ7HlR6xnDm8Wo8FKzdXbGzzVCyhhCEAFYlqxIs0KnhJCKa7uu++DBvTgJf/Nb3/zG279xcnRYr9c1TTkdjTJK7YrjVmumZZeMjkajew8e2Lbt+wHEZ9T/mmkAWmJVWVnpyrYH4JwxppwT5CyvrGCEaJ5pisoEjOPYtu0OwYjziW9UHPP45NAwDD8KNdXQbaOk6XB6RAFeXr/yeBQ/3N5url3/vf/qH6w2lH/5//of7//iEy8Lsa1RAsbBBIN84+qV/8P/8X/4b/43v//b3/svNjY23n3no7/7u7/34MHDRs+5d+/etesbn376YDIZdbvdd955p9Np05K3l7t5VvZ6C4ZhaJqRJMnhwTE6J6e6d++ubVcIIdevX5XC4IZh9Pv9yWSiaZptO47jCCGKkmu6yTiY+FMBcrda1QIeJpOr1xe2T0ZJmgKeIa7GkZ+GQXNjw7Z1VdF9P6jX2lE0vX37NqWiUqnoui5XW6VSSbPqdDr2/XA6HX/rO6/leT6dTqXu1Tl5NC+KgpCz6VNFAXIinFJ6OjihJSeEACSIqiMEEACaplQdmyOcFwVI0rQoGC/OCFuwMrd0ZjO4s6xPyBUr7ZALgcHTkgnnF0mZnpsWzfYRz7KufF78eWG3L7bDC5Hg7LPmWyxPBd/OjQpjjOd95XO3C37sQjg6yzOFOE+EL4XOs1sLvFRoEkJwCDgXZVkKAUu7hBD6YXjz5s0sjbd2d9rNhmlbnud1Oq3JNDQtlxDk+/5w6mVZNplMsqzgII7TrNlsAoSyLEOQFDmVWk5lWQrGTNOoVqu2aRGEGCt93+92Oo7jVCs2QiiM02q92ak1/MGJPx22m/UCMkXFiqKsrVwL4lEeZrphjcfTx4NHx4NBc6HbaLf60/GdazeBSqChelE4CTyaJW3bchoLJaBxFPzDf/gP/+gPfyCY+spLX7l7916vu2w5jBCCIDEMs9PpKCrudruu69Tr9SiK2u12peJ8+OEvIYSNeivPc4nelLTi9XqtVqvdunXr4ODA87yl5YUgCCCEq6urEq1aluVgMLl9g5RlycuCgyyLPdOATU2/cXXxdDIIJoGtq4iV/aO+AtHtjQ2FTxYXV8qCIUR++tP3bKs6HEx0XbcsK8sy3/cluD8M4zTJTdOWA9O+75um2W4bsr6f56V0MpLQrSzOiCoRQpZONE0TAGiaYVZsh9WKsjRte+wHACPGBOQFFoxghIhGFY6edihmqxYCAATkQMilyyF8imgBc9DTeTuRswGSThqdb/J+8dxgbWYtF1zi5f7kvHU9d7t8/AtRLpmdEDzTSJKn+LlN0vnYev6eMSNcE+c6TfCckQ48z2vPF4TmD6UrhHMAITxjcRUwL4soicu8uPrWm6enx5988kmjVpUaRv3hKReqbdtpmg5GwyxLAAAY47X1jdFopJug1ekIITzPAwCkaWoYxmQ6NjW9Wq02m816vW4ZJoRQsHJw2lcUxTSMarWqa3qWJQUtSVlAxE1Ly/txiVAcx05ePT0ZJHlAQa4ZlbwcPHryeOSlrYU2V+CPfvLDn/3ojz755ce9WttwLAp4lIQaoF44BUxrVCpf/upbP/jBn967d29p4ZpttTgH0+l0fX398eNHlUpFVev7B9s3btwYjUae5z169Gh9/YplBZTSx5tbb7zxRqVSQRioGmk1W2tra48ePYqi6ODggHO+tbXV6/VUjTSbTV3XKaVpGt+9+7E/DlnBYi80VK0s0lH/ULXNTr0WZu4LGyuc7k29WEfqSq3RqNc7lqukiW1VVVXf2d7/7NNH6+sbhmHpuo4QCENfckxlWTYejyGEVzeu9fv98Xg8nU7b7bZTceWvXJas2WwmSTYcDqMoCsNY/sqUUsizaq2R53lWFpVKBUCc5BnR9HqrhTCCUBAEORYQAogFAZCx+UX/1A6Z4BCdiS6dCQc9uzIvL9HnesLnGszMU8086sxG5LjPcw3h+Rt/jtohAM8QoJFnEANn7Zm/3QjnW/PS0mQ6PjPCp/oWZQmeV7yakZ3K52fXTAVnXBCIsKIQQCjlSZJAATRFPTw5btVrnXZzOp1wWtTr1TDy949GWVH4vh9FIcY4iKMsy6pVx3YdkqnVajXPc7l0ZLw0HfmSQtNxbFqWcRLKNvrrb74RBeHp6WmSpq7rIoXoli0gXF1brLj2J589OJ1OOAOKouzvH968c3U0HaRlVKs2bt++UzzZe+/+I6aGt15uZ97IMAzHcQqac0HtimmamhCMENRo1LzheG1tzR9njx9v/db37uzvHXVWdM/zTNOMk5BzpqnGaf9YCKaq6pUrG6urq7u7+1/60pcG/VGa5kFw1G63R6PR1BvfNK5Pp9NutxsE/t7e/srKymAwcF13cak3Gg9URZcyiXWzWoTZwdbexnqz5bpZPIK0YHFY15U379yOvPgXB6eVhvPCyy+0ay0eRLV2y/fiTqfy859/WK3WVVW7c+eOpmkyBWWMqarCqIiimBBlZWVtc+tJURQQwmq1WqvWx+OxEAJCvLu7WxQ0DMM8z+UUtWmaQoiNK4vdbjdNcj8K3VpV07Q4zYmqYKJyCJIkHU8mXhjkeU6ZYIydnkbPLkB0vn7kWubnz3AAuLgk/ALOfRQhTznNZhV+earzOz81krkYdb6nhxCet4JfxbAvH/bCxxGpHyKtglEOISYEUPqUgu38bOTbhGTBgOcwvNkdQhI8AgAopUVRSA5fCKFyLm8ozjm85RtnsofgvMEqznuPEjQkmBAQqKqqqxpRFUzI6elpMJ1YtlGxLKKpJycnU2+cUZzmOUJIM8yyLDudjqZpZZlLrlvD0FhZtho1AECe50mSdFvdXq8n2RxHw2EQBAsLC2mafvjhh9VqtdlulWV5dHxsmrZuBJK2rNqovvTSS8d//cPxeHxTu7OyfOXRp4+RwoECNVVturWVRdYP0n6YRv6kaTu9ZjsK/bXOEuAMAaASJABzHPvDj37RqjZef/317/+//y1CzuMn206lUa06hmHJryUvEhky5Xkqv42yLI+Pj994/Uvr6+sLC0vvvvvu4kJ3aWmJcz4YDG7dvoEgQQg1m804ToSIkyTyfR8hJKsgo/HArOij40HrxmLqxXmYmETRFB2rRrVi+Rn71htfXm2shF7UMCvXF5fWlpZZcHLr1p1/+S/+1Q/++D/8t//tP3r/Z7/43/2jf7yzs3d6etputz3P++lPf7q5+bBarVYqlYcPHym6urKy8vjx448++qhRb8p46ujopFKpEAJee+01QggtOedcesvj45293YOJ743H45JRy7J008YYE1VhHCRJsriyXBSFomqHuztxHGtW+3zCG3HOqaAAACAQwgicEWmflWfmSy+zuHFmJDOhMXGu6TBbhxdMbj6aA+dB3Aw0oigYPGve8BzZMxtBlGtYsiRr+tM5+Hm3KQ3kGSNECHF+ZmZfbNyXM0B4PtQ478HRpSneCzWl2V1E7iZzCTFHbM6ogEIICFIAGGN6tVaWZY6hUuBRniVxSGmhKJKNDY/Hw6IoGo0GpXQymRiqstDpagq2TR1Cg1KKIJBAtsFwQhTkuLamaRDCPM8fP37s+76u64ZViePU8wLXdevNFlbIcDx68+byLz+5ZzmV2y/c8f3w3t0HVzZW6tVWyZM4DSM/CLMUFpkOBcjjcHiKPMLzrGraTcdxdN1QiKVqFdNgtKzXq4KJ034/itMkzSeer6qO5OGXGUueF3lecgYgxJqmHR4eUsoVRRmPx5ubT8qSdbu9Ws2RpKkff3x3fX1d1+FPf/reCy+8ILWZLMtyHFv+lJJh6fT4sa3h9W5NdetIEF6UhGITmWFcsIx13PrG166CEsReoCJsUNpcvf6z937x/e//6//+v/8ffvjDH373u9/9/vf/9T/4B/9gc3NTCLG1tbW9va2qapqk3jS4fv26bmt7e3vT6fTVV191ner7778fx3G1Wh8MBmEY+75fliWCRELVfd+nLJtf4rTkvDxjTqlWqxXLrujmydRPo1jDuNbtTtMZ0b2AEMwGnQAAQvDzsI4DIOaC1V97uxypfcGTF8xBPpCsKNLZzkA2GGNB2cU3CgEAwHMBKZGKKzPACgDPcGNf+NTZ85ftUHZp5+808t+ZdsWFs39aPj3Pd88OiyCngjN2JuWNiDymqmu0yKUvjaJ4MplgDFuthmCo3z9ZWFhYXV3lgrl2pVZzw8ifjieclnmWWpalKDhJkmA6Kcuy2V2SFfw8SygtVFWdTqcyoLIsy3acwWBw0u8TVau41bUrG15wZNiV6zduPtk7+uu/+tHXvsFevHMn8Ee9Vmc0hb7vGxC27cpyozGd+mNvnEDV0PSeW207bkVRFSBUCDSMCBA119nbPtjc3AyT2I/inYMDy2kR7GiapusqQkDPVdma4YKORqPV1XVK6d7uwd27dw3D0DTd9/04jnVdf+edd7rd7ssvv/yjH/3oysbawsLC9vZ2GIaGYQAAKC3KsvQ8bzQaxaF6uHe003KrNm7UdShIFpUCpu2FFRgkcUphznRFdet1BSFdUf7kB3/67/7dv/vOt793/96nN67farU6v/M7v/uTn/zEMAxJJhDHsaZpACBVVTnnH330Ua/Xe+WVV3q9XppkUq1xZWXt/v37um7K2xxEghAiKSFP+4Ner3fz5m27UmGMSf2PMAw9LxhmQw4BLUuMkKkZkEvWWUEpo+WZaDTA4NwBivm1PcsMf93tuUv68/aEc3rA84VJMIcJmwHCznBg50rVF6xgHqFKJAwCISS58sEZYOZp1+EMjH5++3nu6V5w+uBZ/w4+56Zy+bsQQsivWABAMFZUVVd0NIfxgxBSzhVFsSsVBGUNIL9x40ZZlnEYrK6upmk8GQ9ffvnlvb2dyWiYRqFOcKXqqArGiKcpiMPg+Pg49L0sK4Ig4Jz3+31CyPr6+mA0PDw+ct3q1atX6/W6LAiJMGh1e3a9bdm/2Nt7b2nlIPRix3QQBRZW67pBMGwYJgAgDkOFlYaw3EqlZ1VdRTchMiDUEVIAUAnOkuSzzz579OSxgCBMk+3d/cWVa+MxBgAoKqEyaOM8y7IsTwzDkFXHjY0NTTPSNOec37hxI038/f39lZWVOI6fPNms1+v379+vVCqyPWDblkQ4AAA8D1uWwbh2Mjz5+BNqqOLO7Y2KpVHBC5+O/J1mZ7lRreVpAblAEB7v7zx++Nnf/OyT27df8Lzg5ZdfXltbq1Qqm5ubpmlOJpNf/vKXMmQYDoftdrvTWTo8PIqiSMo2QQgXFhZ0Xf/ggw/+/M//8stf/nK1WpU/epFTibiIoqjV7uYFvXvvQRAERVHIQbBud2Fxcbnb7kgl8Pv37+9v7cjBTlmeKGkua6EIQelpEIZn6R886xqeL7BfI0Obt4rPC/Fmz8AzSbIzK0BzIsGzHWQ8jM5lVL44opw9JjIWvbDHPKj84pmBZ56fWSa6JKN99ufchP6FS5pPUp/WgdiZ8CKfUXcDAITwPM/UNQEYpUXFMhcXF4syCwLvxrWbjx8/rlRsRXHu3f3YsoyNjY2tx49u3bp5Yqj9/gktU29aAiA0jJ1Go0RGGIamaUrpIkppGCUQwp+9//6NG7ekWj3n/OjoyDRs16l13KoAEGLy1a//xnAUPnm08x9/8B//m//6f5V4I0MnDcvBCaAEL7huvNB1NVVjtq7ojm03NLNqW25FrzuGo6tlmjzZfPThhx8OBr5VqZWUnQ4HI8+3rEXLsjRdBUBGXDAIzgg48jxP4qxer+d5SYhyfHwchmEcjQEArVYny7LNzc1vfOMbUjxYVRUt04QQnjfVNB0AIHniJnHqTb008xUVlKzodFpmxdYs5+B0kFCj1uBJFKeRlwaT3ScPHz246zauJ3H+0ouvttvtdrs7Ho9dt7K9vb23tyeJXgEAiqK4bg1jZTr1v/vd7wIApFc8PDzc29vjnP/O7/zOkydP6vXmeccil3NPZVmGcVypVBqNZqfTjeM4CIKTo5OToxMAwEeMVSyz2Wwu9HrLi4uMsTzPx4OpDNdnEdYswQGAnYWgUAAA4Fk4+p9ohM99ad5U5J8zZwDOvZ9czFJXFyEkXRpCSApFKucTueCc2+IsX5sPR2fWMosM590X+JU9IUKIzammgfOAEz1vygM82/SfN3g+tydjLAMFgQgCgMQ5SoJzRdeazSZlBQA8TZONjStREKoK/s53vsWKcjA8NW17f28HQ9BuNfIk8X2/yFNs27pmUwZs25aaoWVZTjw/L4swiJeWVlzXHQyGp4PhK6+8IrOav/rh33z7a7c13QJQuXLl6mtvvLGzs/s3f/k3q63WG6/dabWbnZp7PD6ZponQxJVuu1N1Se5CCFWEK5bdrruNaoXgktHk0aOHH3zw/vbWYyGAomgCgKyko/H45KTPGLNsEyEgBBuNRoPBwPMmkqzRsqzJxEuSxDAM3/cffrb5W7/1DSmTeuPGje3t7R/+8IdXr17t9/sLCwtydv7w8LDb7cnKDcY44rlQcQrY48ODsTd13Uqr0220F3THff/De0kWZ2kYTAaJPzQUXqtZnIPl5dVvfes7JydHw8HolVdf/tGPfvRnf/Zntm0vLi5KxNyLL77suu7x8fHS0tLW1tbp6anrujdv3lxcWMqybGdnZzLxbNuWNE2yNysl3IqimEyDOErLgkmbLPICQqyqhJe0Ua0tLi4iCIus8DyvUqmYpiFbAnL1I4TgWaGSF6WUsj2rjkIIf13zmy3d2eN5nzaPjp49ObMUmb7NFvYsHZtvQs6CVThnAjNDmD8ymbUKwFMn+0WcGQB+ruMW562LeRc3M/3nxqWz/qTE6wghOORIIQRhCDGGCCGiIEww1oiiaYpCEIZAfgWWZa2srAhKgiDoLXQajUYSRrqhtpvN05Oj8Xi40G03mw2nYhm6qhHsuq5t23/yo/d93x8MBlLp8nQwTNN0MvEsq1Kr1Uy74nneZOKNx9M0TRcWFj57+OjK1Wu64RIOGvXWV9/66kc/e+8P/+0f0sRTv/b62nrP1UzGSoghxrioMK1oAQAwQpaqVyuWjsBoND493nvn3Z/cu38/yzLbUZjgEEOiqSPfT9NUCKGqKsaQsVLX9Xq9btvm3t6e7/tJkty4ccuyrOPjk1q1ceP6rTxPfvCDH3z729++f/+u69YopQ8fPnzrra+UZVmrKUmcCSFc18nzIsuySqWiWInlWCrkoMjHUeIn+TjKjX6o206YZhwwVQGs9AXNalWnt7xYc178O3/n7wjBoii5c+fWD3/4w3/2z/7Z1atX5OylJOGv1+tCCCDg4sLS3vHj27dvb2xsJEmyvb3d7/cVRel0ekIIxrgkFqlWqzJq9X1fMy1KKaUcAESIqiqKZCSheTGdTvf39sqybDdbk8mkf3I6nU4bt+9I2S94Rj9RzhdgZgtyzop+bU/4zHL9nJee+cRz5yHmhErBeUAnzguTMiiVStlfvJFZTw88Hbf9Ih89s+bLkeeFWYp5v/rc65n5dEl6L30jA0xRNE1RESIIQIwVXVE1VRVCYCAIhgTBsiynga/pzXa7vfV417T0KIqGpyeaprVbjcXFhS9/+cu7208whnmWjkIfA1Gr1bI8yYv0zp07w+Hw8PAQYyxDi4WFBdtyHj/e2tnfM3Sz2+1eu3bj8PBQMilyDpI4U1THsuyFhQXXcrIg+MW7P/wX//z/mYWjb//m13Rbg1AYWBEIlYIbik0QxggpCCoQRb6392T7088+uffxJ/vHUaWmVlTbC0pVgbqqx3Hc7/cdxzZNU1ERpQXn3DRN1634vn/jxo0kSYIg8jwPY2yZZhzHUTR4+eWXdV3f2tr61re+s7Oz8+Uvf3mWmAGQSVJjSqksOFlV21AVHSHMmSqQgnHJYJKxNEwUXSNEFaKoONWVhdWbG8uLvWav+Zbk1b969er3v//9e/fv/t7v/V6axr7vHx4elmVpmvbR0ZFcf1mWSR1lSRgnhEjTNE1TySk8nfplWZqm2Wp2FEUJw9D3fctpIYQMTbcsC0KYxkkSZ3mSFkVR5oXquqxM5SCibdudTiejdLbSGGOUczkQe7kW+sVFh8/b5nPCv3XPy4t/vtEtzkkDAQDSJRJCePH8wsz8RggHsGSgoJADLgADgmBVanHOducASKlSABCY4z6chZSzNuCs5jOrfFqKJq0RzY1Lzk56hpvheSmTwjTPVLWgigIA4JRBCDVNM1QtjmNVVTVVPZObREoYUQATQwNxNEYItdp1AMBgNDwd9B9tOqZpW1ZF0+pYMz0vmCZFl9V7vd6Cgxa7PVPTf/GLDxlj167e0HW9iPP15ZWDJ9sKUYswnp6OKpVKw3Z2Hm7S5UVdy7JkdHxwWq/X7Yp27YXrQe7/8pe//ItPHh/k6ObNm6urq7WaW9E0AIBmar7vjz0vjuPJZLK7u/vo0aOjo5PxCHBhT8YEIUyAAuNMFaGmCH9QUzYUR7dVlTBI8yROBbd0c2mxE0UTzrlhwDyH/cGx1MfOkrRZ16sV9bd+81tlQZvV2ulBX1W05eVlb+ClWdLptGge58nUsbBgQaPQVFY6FRsiHAc+wqBXdXAFj8d9XOLXXnuz013gCDY6XdO2sEKqLTVJaCHYJDhZudLLWRIm/vb27rWrN/KS5SVvd3sYK5ubm5RSRVFuvHRlaWnp8fZ2HEUY44k3zcvC96cPHtzTVdU2rSSYHoY+hFAn5LUXbwqkaJo2Go08b6CqKqMlRqDb7Z6ennIhNBMvrV4Nw1DL1FIUmqbdqLpFUYymXpDEUIA8yxhE1UYjyQoAEBAIMAYFRAArBGOMS5Ryzjk762ALhBjjjDHBhQIBRpBxSUgtIIQIQoFKAIC4hGyBT4WMoPzvrOCPz94Lz0QEzwyFi7OJLbm7AFQAIQAFc33FWU4HzqkTz4xQ1icUReEcQC4YE5TSgrIZ3f3l+8FzrfnCHWVmb7KJP/tzZoQz1MIF2IGiqUKIPM+FEIALIURRFAmIpKbXzP7l3de2bauuHh0dlSVzXdcwTMdxbctZXl5+8OAzSdq/uLh49epV3w/39vY+/fTTrJj87u/+7te+9jVFUX7845882vzs6sb1Gzdu+L4fhuHh4SFCqFKpWJaVpiml9PT0FGN89erV5ZWlVqtFCGKsFIKtrq7+7P13/+f/+Q+rVffmzZvr66u9Xq/ZbCZ5Eoah53lh5Pu+PxqNhoNxGMZu1SFYEwIWOeMMqaqpEFKW+f7+/sbGhlutQCgAEWmaJkkWx+HW9uNrN64RQo6OjtI0dR3H87yf/OSd3/nOV4+Ojr3p9PHmzvLyqoLJytIyxsSbThVF0VUtDqNHnz0cj4fedOrYlb3jsYqJ6zjLS4sV87ptGY5tQChM0+QQLK+v6aYhMLFchwlAVIXmdGdn5+4n9waDwXg81jSj1WoRQp48eSKJsGzbFgJ2Oh0IoRwKk97A87woisIwpJQeHx93u908TeXcY9VxJddzkiRPdva73a6maZJtZDweSzwqhHA6ncqCsLxHt1qtRqNx75f3HccxbKvWakKVHA+GByen/f5ptd6QSwYJDASEAHBOOecQw/l7/SwQO/sTPNW75vCMYuK56/zX3Walo/mkUVZrwFwl5XKKByTvKDwXIcMAUsplQfnzjHB2bZcrNBf+lEfmJbvwpHww60xe+JpkxfJsCBqcpbyUcZmgz2fGckg0DoMoSgEAQoAsy05PBhAO87y8c+fOw4cPP/30U1VV19fXF3qLL730wq1bN4Lo9Ec/+pu/+Zu/efXVV7/73d/85S8/3j/YlfGbaeqqSqI4HI2HktuGMZZlYnt7GwAgAJ9Op7Wa67ru7du3d3d3S/q667rT6eT09PTo6EAS3XJRyAhfCIEQxBhbhmabZlkwTTM4gyFLqWAYMlZmWVb0anVCiKropcgF55pmaJrmeWGr1UIASZ5Vx3GSOM2zouaKvb296dRbW7tSr1fX1tbiKEuSaDSaXLt2TbLIcA50Q1VUwjmXqteQi2q1euvmnZWlrmloGiGMl7ZrCwg0w0jyQiCYJNnx6YlC1CeffRaFsVTCkqoHp6enjIk4StfW1mq1epIku7u7nhc0Gg0AAPKZN51maer7/ng8lgO+0/Ek9H1d1y3HgRBKy5TJv2maiqJIpnMhhGEYEMJKpbK2tra/vz+dTiULhvzdB4OB41Qghr7vT0Kf6EZeUsexDctOs6LkDDAmECFIgQIwzigtVXyGgBEQ8fPqBhMAzFkghwiALzK+52ZPX7zN1vB8asY5n+8HXqjQzJ4neZ7Lm5mqqgQTQgAEZcn+9r6nmKvciGdHM+bDX3xpygk8heGhWUNydk5Zll3IIQkhCJ89D88rsVEU+b6PMRZ5Zhp2pVJp1FsQQsExIaTT6QwGA0VRNjbWTdM2DMMPvKIoOOclDVZXV03TVBQ8mXgQAl3XsjzBGHc6raLIfD/M83Q8KQBAjUaDYMUPvP7gNC+ysixXV5dv374NIej1ei+9/ILv+++99+4nn3wSx3FRFOPx2DAkaz5RVdW2TckmDACaTjwhIC2BpqpZWpQlZ1SYBg6C4OTkpNVqKLpCWU5ZVhSFELDV7ERxIDFoqqrmeW6a5uLiYh4PbfuMpuX09DSKAjkemaZxWZZ5nqoagVDIFfzw4fbqxutFkUnlqVqtoavKGeaEM83QAUB5XgKMfD843D/UDYuWLMuyRqNh23a93oQQJknW6XRUVV1dWY+iSPorhEClUoEQcp4NBgPf9yEXGEDXdbvtDispFKLVarUaTc6553lREMrf2jSFJICSN744jgEARVHI+4WmaVmWycQ4SZK9vT1qFKZlIYwBgkIwVuR5nlJKEVFYyShlGBKocYIUDIWAnAMoIALwbF1SfiaGIgCSNRsOEbhU87ywdP8Ttpl6CpgLAy87KvC85JPMqjKzPWSZZC7oFGdXcXaSYmYk86Yyw6xdCC+fjnvN1XNlJHPZDQIA8iyVyoRCCEoZAEBVVYUoUpWNYIwQkhM9UIAwDF1DM00TAHJ8PJCGKimG6vW6hFD2+ydhGBJCVldXr11bj5Ix57wsS8Hh8vJip9N6/Hjr008/vXbtmqoRw9TyPFdVQikNQy/PY8epCyEURSnLMgi8MKwGQUBpIesKlUrl6tVrUm17OBw+efKk0bAoLeSQq6oSRcGc0zzPq7VKFCWMlYoK8pzFiQ8EsqzKxPM2n2xbTmVpqct4UZSZXN9pmkoM7XQ6VVVV0zTLsDEkSwsLYRD5fqgrqj8Z16uugqBVqRzu766trfU6a0WRTSaTg9290JuaGgrDsMyLwI88z/erDrPNim1qmtofDvSSCoymU09AGCUpLQGxlIWFhdFodHx8jBDpdrsY435/6LruwsJCFCabm5ue50uV76WlxbIsp0F/MhxJo1JVlVOW53mZ50tLS5qmyV/Ndd2KZQdB4Hme7H9KPlLGmCTRYoxtb283Gg3LsjjnhmGsra3FcRyGoe9NDNNUFcwgABghDIBgrMyFEKygJS05JBgCRQMAAYwhnaO3ZbMSI8RACA4RhwDIkQuAZnWOeV/yn2OH89tz1/bnbWS2CSHKomBMcPYrnYeYi4Bn+8/Z3nmvjz4jFAPnkLVg7spnm6xHS3i3YE8LvrZtE0JkYUbi7CSFScVYtJ2a67ppGpdMlnjK0cSLszQIgiiKyjKngqqKatiaU3OSbCphPRCJJI2iMFFVcv361a2t7V6vt7a2CtdhmmYnJyejcToaD6bT0LIMVSUY4zRNZemSECK5qJut+sbGxu3bt2XxsNFoKLiQM41B6CkKllzURVEYhhUGcRBE06kX+gFnBcGKpgJccZIkGY/H1aoNIGe80HUCOdQUJUkSAlGv3YEQjsfTlKWGphUFK4oiDP12uyvN+Ojo5OjogDGqKNgwtLLMkzTOsowQ4rpuzgVCiFMWRZE3DRACmqYACDVNS/OCcjYeTcMkLilPo5QQdZpGCBEIseu6tVotCCKJAjk5OeFnrIQwTZN+n8pSwng4Ojk5iaKo1WppiprneRyGMpLnlOU8l+m9/NVqtZofpaPRSN7UpCcsyzLLsuXlZdu2IYRSyVSqJiOEut0uRCgMgzDNDNsyTNtxKpppHh6dAAIQwBACDDnnJQIYAsEYRAgJiAQAZxSBECP4FMsFziwQzC/U/3zD488O5cFz2Pe8LXxeiEtkNogQopRleUEpx+isJ/MFnnD2eU+NbQ4jOl8LlTetmfOcvQTmzHj+G4ECzSZH5IyixLmbpgnO+xkSdM5KWhTFg/ufTcbe8vKyW62Ypk1pIb8RBAkhxDR1IbQkiZIk2tvbo5TG0WRpaUkIsbm5CSG8du1avVHd3toVggnBGo2abdu+76uqYlnm6enpdBJRSkejEUKoKIokibIsWVxcrFaraZoGfgTPZ8yWlpZu3LiRBKcAgDRNoyhQFNxoNCzb4JxHYQIhPDnpf/LxgzLLLUPTNZMQcnRYTCaTvb09zgvLNqq1CoF85E+XV3pxHIdhUHOrCCF/6uV57tiVTqPmjb0sSQenfdu2gRAYAoxEwWj/9GTQP5WORcGIFuWwP20tLAjCMMZZlkVZZGZqGJKcTmzbnvoeAEi2SYFAURzHccJoKvWqgiAaDEZlWdZqNcdxNjc3u52FdrutaVq/3/d9jxDMOR+PjvI8B5wrGOuqmkKIEDIMA4ozYighRJ7niqJUq1VN06I4l2mIrusyQpHez7IsmRkJIbIsk0KurutOvSGCpGDUtoyFxZ5pO1ESx0lmaSrXVcHPOPMpZSUrGGMM2QAADKGY08FG5zT1f+v2n+wM4RyaR65zcd5RBM/OWzwnHOVzrBtlWTImsKrIm/cXn+uFY81GM+D5SKK0bZGXYq6JL/vy0o9d8KLwfBJKsi0ihBQ8i0vPtllOyDmnRZnned1t5Hk5Gk2EEISQvEgxhpZl7e3tWbbR63Wq1epoNNjc3Nzd3T05Pb62sXp4eGjb9q1btzjncojh+o2rQeidnJwM3ju1LadSqfR6PcexAeCGbkMIJf44SZLBYLC9va0oiiT2RAjFcSwBkLpmpEm+0FsyDEMAFoY+Y8yyDEXFlFLLspxKlRBy9+7dLI8VoikqTJI4SQrJ+KbrRFE77caqZRlxEhBCVpYWj4/h0dGRRpRuqwkhFpTlacYZbdTrURRhBMbDwVlHWJRFnsqalq7rFdvWNU1wkMYhQijN4igKk6SSOTaL/CAIHj3ePD09tStuWbKJFxBCwjDyfR9AtrKysrd3kGVZq9WSSdrDhw/7/X5RFEmSMMZ0Q7NtuyiKzc1N28ASHGMZJiEEAYgQ0hSVc+77viw3OI5TrVYRQp7nmZar6zpCSGZ9cviGMWbb9mg0AgDIzv5kMpmpNRe0zPNcFnVqbqUsy7AMTF1HCGFMKGdZliUsKYq8LEuhm9IYEARM+gN4tlbROYkgBHzmDP/W+uKvuM0a3TPsqFztn2f88+ZzNkSDMcZYUEoJUXVdT7IcnutjQCQAQjPwmjSh+dbCzCCl7YE5zdT5aFs8C02QG3+Wq4afz3fJ90IAZ/cV2eqY/TAzp1pSzgT3Ap9y5rqVSqVimjpR0GA08E8CAKFbq66uX6k16v1+fzgcAoH6p8OD4qjT6aysrPR6PTkAKTFfjuPohnpyekQUJAXrb926dXp6Oh6P5WC+FPTNskwKJzmOs7S0BACQyvL1ej3L1DwvEEIQKrquQYhoyRln7Xb78eaW3G0ymTiVaq/X8zyPAwggz7IkTsIktU5PTxEW/cEJEGWWZVHgs7IQBGOECMFFUQxP+0Wec86btVql4jSqtTAMoWCAKxgyhFHBKCtyQ9V67U4axaMwKcsSIVGxdFVDELKClsfHx2EcRVFk6BNV1bOsiMIYEgwAeu3VV588eUKwevXq0vLyMmPs6OhoNBrJezQhxPO88XgsDa/eqAKaDwaD80DdPT09VVV1eXlZiuEcHBzI8uDR0ZGu68fHx9VaCwAgoaTSknVdl89IdeQ8zw3DSJLE9/12u61oGhNn925W0iSKaV5oRDk8PLQct1qtGqrKypJTBgHXVFIAoSkEIZSXFHCGMRac5UUBIeQCQM4xAOIcniV7ZE/X2yUfNV9unN/zwlvk+pwfWZQB+YVwdOYbpR3N6iNEQpllrUIatLxgeo5UEOdsbdJsZjRVs6PLE50fZbpQpLlwYV+crcI5mJuYXfmMGh0AuRRmB8mLlDGmaZpt25VKxXVdxso4jr/yla8GgV+WpTcNBIeuWysKenR0kiRZp9Nrt9vVqsMYm078LMsAAN/59ncRQoeHh7JMV6vVIISUUlUjr7z6kjRgwzAqtithmZKLOoqi0WgimyULCwtXroAiDg3DsCzDtHTZDZb3rqPDkzRNCVGbzebKykoUJsfHxwcH+7X6jeEwjCKa53Ych4PBKVFgniZFmdMipbSAgmMIIGCcgjLPu0sNqR4DABCCY0xM04AQZFm/LHPOOYCIEFUWjdrtNkU0z1PD1BACRZF5gZem6XA8BABM/Cn0/ZXFFSGYW3WajdbS0sruwQ7G+MqVK+12+/j4+Be/+AXn/MbNa81mUyqEjsfjsiw5p6ZpIgSiKP7aV74qyTgIIcvLy7quf3r/wer6Wr1ed113dXXVsqzj4+ODg4ODgwO7UpNz+icnJ7I4J8OKsizjOK7ValEUXblyRQixsLCgadrj3SdYIVLIFQCQZQUvSgLglfV13/dZVpiuDoSYjEd5UXQ6HVYWZQExxrKJDwUEQkDBMcICinOoNwMACyC44AI9Y3i/lhucT/8u1ClnBsbn5vUuLP7Z24mkfpBtN9M0MVYkH4S8Pz3d+6lhPGNCsw+bJYEzP3m5DSg+X9/juZfHnyaN8Mz45+ZEZBdRwqZMU7csAwAQx6Gsqu3u7jqO0+12KpVKFCWKolRs98r6VdfWJ5PJ4eHRZDJ1nAqldDyejsfjPM+bjXbVre/t7zx48GB7e3ttba1ardq2Va/XHz58uL29LauCnXZPktg2Go0kyYIg6HQ6d+7cUVV1PJ5mumaaZV7ygnFdV1WVqBpWFIVyBLEWRX6SFbppJ1lRUFZtNARnWR5xoRAFaZoCIAMQGKZepElRZIJTjABGQAgGOAW8APKeKITglNGCYwEBUwhQCCqKglIOASq44BwALtyKAxXshyFRkBAsTiKAOWUMEkgp7Xa7/dPB/tHhZDi5c+fF3Z2dD95/f3XjCmNMlqaKopCS4PKLGgwGx8fHQrBer9NsNsuynE6nNcc9OTyK43h1aVnqcOi6/vrrr0+nU3mfkjFnlmXVeu1r3/g6EMRxHIyxZIWr1WqLi4tS9dU0TZkHyX7PZDLJ8xxrqqYaQhEEY1YwKigUwNQNxgSkIC9TjDHNCwIRNgzLNGMvp0VGZxUHrnDOaVkqpk0A54ALiBCCAEh2Xc7RU3qLy0WKX3GtzrvEC9Dt+ZdmJjMfGAohiJR31nWdsZQQASHiAsjZ0LP88pLdg7kbxnxGd+E2cNawvuT3fkVTFEJIiByEEIjzsREhZPSIEIIII4QYYJZlVNwKA+zo9EiWJSCEvV7PsoxOb6Hdbo/H4939Iwnnj8KYlpyWfDL2wiC2bKPb7V25cuXk5KRWqyMEEEK6Zk6mI0n+vbu7K4TgnN26dbNeb0wmkzDygyBkjCFEhBB5nodhvLOzJyv7v/3dv8NYnhfM80OiIFXFbrXiODbnAkASp1leMLda4wJRSru9xU/v72GMDEPTdaIbimnpCPI8jynLIKCqAgEACFBOM4IU09AiPyjLEnJGsIIAF6zklNIit009x7DIKaUijZM0zTkHhqZpFQdjUDDKRZkkAVGh6Vo9sxVGSa+3WKlUsjSP/KBimVEQJlF0fHzc6XQIIYPBYDqdWpbJGEvSSJZSrl+/Kr+Wk5MjCVrquXVJexNFERDCNE0kwMnJiWwRQQ3KPoQfBmEc2bbdbHRn8Zj8V5bfdnd3FUU5OTnp9Xr7+/u9Xs/3fUVRGMJZSWleIAAVpKhEwZAQpAz7J1gqiARByVi73UYIcSGqtikjZ0oZFwiCEglBECCQUQjgGff8LLbjCCnz6/PXssML3ujC47NQc07gafaWCyZARqNRURSSOjIrSowVTTV005IdHiEEhGimpSHE0wlifmlK8IKrlbGrnJuaDzJnl/p5FzbvOWeecHZkfk4ZTpAs8EQIcUoLIUSWZYwJ2VV7+PBhURRbWzvLy6uGYViW1W63a7UaKJKtra3BYIQQWllZqdfqURTt7u5yziVRN6XUdV3G2OPHj8fjcRz/Ynl5Ncuyer0ehmEQhEVReNPgjTfeaDQapmlqmkEpvX//Psb4zu0XppNYCAGRIAQAyAmBbtWq1ap5kVqWoSrGwsKS6zYODw+Pjo4o5apK6g3HMLSSFmHoGxpSCEjj0LZNTJCqEMA456wsctUilmUiATDGlCKE5YCKAEAgBBUFYayrKk+TUpZPVFV3HCcTimmpOlQBEmmRh7FPDKKbxpUrawih7kJnZWmVIJQXKQT8zTffnESxqurdbvuVV16Jomh7e2s6ncqcByGEMSSEuG6lUrFkP7lbb/e6vTSKj46OFEVpNBoAwslw5LpuEAREVer1+urqasnowcHBaDSyraoQIkkSecx+vy/rApJPMY5j2bhWVbXRaGCM9076nPMiy0zdUBXN0o08SYs01xWNEMKhUJjCgeAA5GVRpGmz3cqyTPZpKaUcCACBimQGxgUQACEAuBCCAQYAK4UKnvUlX2yH8wne/KpGl4Zyn/uuC4ZwZoQnJyeuU6vX65pmlIynaZ5n5Rn49Tz+PJ/jfWY4anYq8/Yji11wrn8oKdzmne8XnCu4dHeBF9zx+T4y3IUQVhw7z/PJNEYQ67puWTrGihDCNM1Go2UYxunJoCiKdrudZ+WTx9sapKZprq5cGY2HDx9uQiiazWa9Xi3LstGsqaoqi2xSoVT2x2TN5ujo+PT09Nat2zdu3ECQfPrpp7VagzE2HA51Xa9V6zIBGI0DxpiiQsPQiQIAoEwAxkGSRu12yzAMwyQAKpbtANifTEe6qakaUlWFiSLNYsZtSzMMQ0OQIwAR5ggBWjAhABJcxcjUrTzP00yUZV4wijBECBAMozAlhECBBKdQAE0lmqphiCjNLFO3KpbA6Lh/HEQ+A8yq2GbFzrJMlAAAvrKynETZaDDcfPjpKEpd15XoIk3TbLsShqHv+RAJVSWKolQqlm3bMlYqaWEycu/evUePHtXr9VqtNhqPDcO4fft2VhZFUWRFHgRBkqXLy8srKysrKyu7O4ey6CD7THKSCwBw9epVKRoph/cfPnyo63qSJABhDLGuIdetNmpNTVF9BrIkduyK53kCgmqjjgieTKcZE5VKBbAciVJBnGNIS0aLAiKCVZUAxhEUAkAoOEDiDLT9nKDsV1yolxf/hUU+81XzQSJ41lfJjZRlaVmWadqcA6Jqvh/2T4ccQErpzAgFhACetTtmLY15OwFzFIaSX/XCqxdO7le8tlmSiWYSxxCCc2dIIQUAEILKEkAoMEGarpqGiRDiHAwGIwk8yLIsCKIwjHd29j3Pe/XONTmyXZa5aZqtVrNarRKC87w8Oe4jDGq1Wq/XlSp/JycnlUolDMMrV67s7R1sbW1JQnjD1OQmFcIajYahmwcHB48ePer17uR5hnLAAdKBgjHiAHJANMNiAiVZLpHiaV6ougkQ0Q1EC2DZhuOYCsGMlZRi3dBYmVHGAAeIEEXFgAHKiiSNFKzO94U5ZxBiCKEMpEtKpcvSdYUzMZlMiG2bltnttgVBfuIFSSAgxyoejQYQYg2rg8Gp67orSysKJh9/+NEgiLvdbrvdPjnpHx8fCiHq9drKyoofTG/dutHr9Uajwe7uLmOs3Wk1Go3+kwNNUd2KI9M/WdLb3d2VIHin6h4dHR0cHGRZVpalpmlhGBuGIWv3sj1o27asjXme12q1ZK7heV6v11MUhXKiqSoSQFMNxkTJcyiAqZlJkhQFpbRQDV0zdFnUIESZjscQQkSIShDDsOBUAAABQVAAOREBBEKAc8EBR4DPE7LMp3C/7jYzLVlhmX/mQh532YGRIAgGgwGlp1lW2I5LKQ+CiKja3Amdy0ZJI2T8gpHMMsCzlsa55czO7/JVfYEdPnPw809AEM76kLNAlyPMGJOeStM0QpBMBizLMgyr11t89PDxcDjsdHoLC0uci+l0Gsex53mSji5N8yiKxuOxALwoCggFALzdbiuKwhhXVVUyt3e73SAIrl+/3ustxnHsedOjo6PVlfWyLH/+859HUdLr9WzbfnD/0/F4/PWvfz2KSFGwsswYYzbVDVMVAmKMEcZCiKJgYRhPJp5Ee2OMNUVJBbdts9PplEUaBZMk9lUCHdsqy5wJQDBWFEUAVpa5YBwDVQ6qqSoBkMnRASGYFIgPgwjjXNMQxkrgJ5PJZLFWM03dcRyOhUSZVutup9erVCpBEGlYBwIMh/2DvYN6tfG9733vL995//DwcGdnx3EcJKV287zb7d64eS2Oww8++ODo6MA0zSsb66Zpjsfjg4ODRqOh6zpWiGmaIEuzLJNCWpZT0QxdCCGJWCWjsVOpdzqdNE3H47FsD0odGyllMa80LBuMqZdhjHlJoyg6KakKMcHYta08zy3diFIxPO0TQ7MrFYTQZDIp81zWOFRVxec+w9BVJgCCgp8NIYkZK9Q8XcWva36Xl/G8M5TbbIrighHO+OYAACQryP7RSMafYZKfHbpIIYScMdnePFPb5RxwUSIhzoVv0HnnAAHIOccIyT2xAIBxAiHEhM1dsAACwLN/ARACyBInOPv3bJ74/HoA4EAgIRjjJQAEYwEABQJDKKeAOQScM6RZjPE057SkJQRCgyY2dJ0cb23DPNVAKQofUogx0vWs0cCawSEsq5ZzeppNJn5ZijwrHceROXCtSqbj5KPp/XrdabbqhmF+9NEn+/v7EKg3btxY6C1vbm6GQbq/f3h0dLS6uq4oyqNHj5I43djYaDabH330EcAoCILJxPvN3/zNW7evTqdTRsVw4C0sLIRBGIaeputRFNQbNUUFtboNssn6UldV9DQNypIxirOyFLpWlgaCmooxAggzhIGQrIiNXgVCGMVBFkQYobIsiixXVZUDoaqaQqGOlZSVWZZM/dHEG5cH6k3rJkIEQtZt1C0VY4zUIjt9coKIIjTr8OBUN8wvf/nrllnxPB+ohVklUZSlxdQ0zXbPtW2L8vjkdNfSjV6n7joqhsjSlTwOIt/Xm2aGi0Kl0+mQRKTd7tZrDbNmjYaT1fXV3d1dwzCvX79+fHz86suvbG5unvaHURRwzuXwsRCsLHMAQJqWrlspy7LfPxGC27ZZFFkQeJwpAAFdVXNGwziQBpbHNKQZAKBEgmFM87KkPuecl8yoVDRNo4xVHQcQUm02JQheEkBSSuU8qkKwqkAhMBUlZ4xzjgEmSMEQcQ44EwAgAZAAiAMoAOLnYDcCmezeYYxl0V5WX2bhiXybOP9PkTpLsx7jrGCDZ15KnL2ZzzFqzxyRDC/BeTZ59id6jtzx3+rZLgSis6T2Cyo0zz3UhZ3F+bXOTl46SUrpN77xjTAM/WAaJVFZ5hCAbrfrOM64fzKdenmeV6uOU3GLgnpeYJkVRVEgEq1WgxAyngym0+HJ6VFR5L1er9vtLvSWfN/f399P01Q25b/yla/Eceo4jqqqhwdHw+FQuk2A0dtvv40QopTLSkO73WaMRXFACHr55Ze3d54AAN5///3FxR7G2K3Xq9UqZ2DiTYfDcRJnlmU5lQrGGApMsBRUIRgJBRNVJWmeSU8ufzchBCKYqEpRFCWlaVGmaZrmlHIBISSaeu/evZWVFcuykjyhJVcUDRFYlvTVV1+beEEYxpVKxa5Ui6Ioco8Q8vbbbwdBcHh4ODztCyEsy9I1jVLKWNms1SGEN25eOzo41DTt/v37k8lkRjwhh5KCIJDa4zdv3D4+PvY87/S0X6lUVFV95513GGMvvPhylmUzWiQJqUEItdttGbJyzqMoGo1GUuVXjozI0Qpd103TlPjBGaO2XBWyCIkxJqpaFIXMaTnnUiBRKp9HUZQkiThn6JSVRV1RAFE455wKTnkpGIFEwbhkshUEEAAcQQgBBE9LE//JznO2PVMdnRnDfAZ5OX6d3+Bljhlw5uTPXuUXnfJTm3k2RAbPGvCvEqZ+3jMQQuk1iyJLkmhzc5OygjGWl3lRZAJBDhjGuFqtWpbVbLbKQhwfnw76Q01TllcWkyRRVdJqNSqOXW+4aRZmWVoUuRDiyZMnO9t7vV7v6tWrnHNNM770pS89fPjw5z//EADQ6/WOj06Kouh2uwght171vKmqapTSbnfh4HBimJqu641G5969Tz797F4Y+s1m/aWXXlhYWCiKLJ2eSGLcOI4Hg0ESZ61WS7Q6RVFAATiGgmPEmYIRhgBCHKWJoWolpSVniPGyLBmniGAuAGM0y/O0KPOy5AICTFRNs22Hcx6nGRdCIuMNS1cUxfdC3w/TJLcrzvLyqqroYRjV681rL93Z3d0Npt5xeQghlDYWhuGrr748GY6EED//4Geu647H44WFhW63K3XRIISWZTHGJhMvjmNJS2WaZqfTURTVcRzJf/HSSy9t7+xNJhNJ9atpmqxIG4ZRFIX0jRIkHEURhNC2bYxMCU7AGDuOY9v2DOE0W7cSRyXNksKnY98AAIlcUVVVgpyeqjOcz+Bahi2n7WhOsyxjJccIE6JwVvKzXAxAwJFAHHIAgRDsfCxYLmBZImHgIreNOGfmR/PLdWYX83A2Mn8xYC7NE3NjR/PmLj6fnnH2AZdfuuwJL9ve31qwAXNWLea8Hzh31BDAsiyldkUY+QAAXdecqttuN5FCIAaEEFamw+FwMpnqmtlstq5cueJ7AcZKvV6ltBCAp2nMWFmW5WQyGY0Gn3762dWrVyHAf/VXf8UYW1lZsazK4eHhm2+++eqrr8ZxfHR0tLu722w2r127trGxsXu4U6vVOOfv/vRnnU7n1q0buq7//Oc/z/MUAAEhvHPnVhgFlmUkSeS6bhkixliSJEmSSD9QFEWcJgghBARnQJSAl1hBUEEYIoAARwhRAbgQTIC0yPM8LyhTFEUAxIAs0UBKOeOcQ3Ttxq2spP3+cGlpYWFhZepPIBS27TzZ3tV1vVarIKw4lWoUxScnfUXRFteXLd2QtyrP8+Ioarfba2trWZwkSeK6rqSikgNWQghN06T8E8ZYdtiLopBI0S996UsPHjzQdePo6OjJkycSKGOYdlmWcnJC/qaappmm+eDBA8aYoiiWZQEApKsUQlQqFUqphIAbhiG1biTDvPRm0nmcAZWFKMtMGjw6n1YFAMi5sBkj68wIMcYZ55ADIQCCGCkaA5xzwCkjQDAgmEyJABdQcC4EBBg+053/VdbtZT90wX+Seej3BSOZf/N8mDp/eHDJE86OAP+2pudlT/sF0enls589I2bdTwGYYJAzCHi16srbSlnmeY5VBA3NNEwtDfNbt264bm17e2d3d9uyKoSojuPIoDGOQ9+fFmVWFLmmKaurq9VqYzQa5VnZ7XZl7V7y7Z+cnAwGg3v37g0Gg+WllRdffFFRlOFwqCjwwYNP6vXm8spiveH+8qNPqtXqq6++cnBwYFdM01Idx4mTIMsSiESzVQfZZBa4YowNgwAAgyBwKw4+v60xJiAAEHIkEKWlwlQIASQKBBwRzHNRMlqUDGAkIMKKAijPaBqlWRTHULHzzGCMaZqhaCTL8yiKJl7EmHDdumlVgiDK8wJCbFdct1r/+OOP5QxUq9UaD0fHx8eWZa0uLW9uPmw0GnEUvf322w8fPnzxxRefbD6WnL9SGVsIked5EAQAAEppv9/f2dk5OjqqVBxCyO3bt4UQuq5L6if52+V5Lu0kiqJGoyGBaZRSQogMK4qiGI+mYRjKvh8AQNK9yUbibEHOLLAoipIXjluhlMZJlKRxq9XSdJUL1h+MZrEepVSCOhBCll2RfGiEEJOoHIE4TYu8UFQNQ1QKToFAApSccSAEFwAjCAAEQMKpZ4Y4W5fw/En54LnmJy7oE15AeV+Am80A1uB8oWPl+TnhBTf4jJE8j0T1/3+eEHMhtcgBhJKKWQjBh8O+aZq2bcuVQQV3q9VGo+HaqsRblWWxura8ceWazEBOTw/b7XazWXerlhCcUqppSqfTOTg4Ho/He7sHe3t77Xb7pZde2tnZ+4u/+AtFUer1Zq1WW15eXl+7IiVsr1y58md//cfNVmOht7Czs5Pn6Qsv3nZd1zRNLuhw2E+S6LR/dPv2TcbKZqvuOI7a6Y69qYxPCCEQYCGEnKkjEBEsR+MEAAAhAAUUEDIBABcAQVXRhRCUA4xxGIYIKgACJkTBWZKlQRxFScpAsLy8WrHd8cQTgOm6XnHraZq2Wz3TcgWH00kQhnmr1VlYWFhcXD69eyIR6rqqLSwsUFYSiA4PDxcWFhCEw8Hg008/Pdjbl7P2lmVJi9V1Xf4cknXbMIzr127KqvLu7h6E8MaNG+dM3mMZOkp7m9UdsixzHAcAIDlmarWaoihxHEOgKgq2bVMIQWkRRaWmaYZhlGXOWAkAVxQs5VSKosjzFCBBIKKMp1nMitI2zLN2SEnP2xhkZoSEkCiImBAAAA0T2zAhhARhBWYCIy5kKCoAEwwIweWk7Rdh0L5g0V4wivn9ybyZgfNolT/LOjH/GZdjTgBm+qnPGQyZBbfzz1z2tLOjffHFXC7MyB+Rcy4gQhgRhDSVaKqiaqhSser1mqKqaZqHSSx/aUMV7XZ7cXFRJjbDUT/LMqKgvOAlzcsoB0A4joMQSNN0OBzKW9Irr7zy6quv9vt9KQb45S9/eTwej8dTCGG1Wm2325L4aHt7+/r1K9vbu8cnB0TBYehPp36WZbdu3RoO+/V6fWNjPQj9drt5cnLkeV6e523TcCmtVqv1ai1K0iKnaVHAgo6nnkoUQ1V0RREEQagQAQHEmqFDDLM854JhjCEmRNUQQrplcw5KxgtO85JmeVkwziEwbUvVtYKyaOIRQjTDMnSdUnDt+vXJZOp5Xkl5lie6HmZZsbOz0213hBBlXjDG2u12r9PO83x/f79im3GWLSws7Gw9uXLlShAE3W7X930OZrQDQZqmEuTAGNvf39/f33/llVfiOJbd1CAIMMZS/LQsSzltOJtR6vf7EuokRx/lsqxWq5p6xuUhg/aiKOS4xs7OjhyskZhnVVUl2kkQKKsyssMk1VSlk5SDCzLgl64bAKAizASDACsAISAIViqGrhGlYLQUgHAGGYWQCnYOGQP8jH7wrEzDn00Rn67NL84J53c9Y8WZDRPNnB6ao/ieRdK/lnmAOQv8Yk/4q2yX95/dIM4BdBwhrKhY0xRdVw1DK8tyPB5iogIA8pKGUTSZeFndOKOrgVASfuV5xjlrNOqe521tbUEIX3zxTr1el1xPk8nENM1r165JXJVlWZrGZWNa6goeHh6envRfe+2169evR1FkuGxpaQFjfHh47PnTWq2W56ZhGEKIWq2WZUm1Wk2SZHFxceqN6/V65g0IIfV6vdUKseePpz7PspyXcRxzTScQKYQIiBFRsKIqqqoZhqTDK/NMwhgoY0gIx6mmRc7STGSQUUEFwIiomm6apu/7qqq2Wq1qo06wOp0E4+lkbf3qyUk/zTJdN1VVrbi1oiiCKMon4cLCAiHk5OQkjWOVYNd1DU1/+PBhrVZjJW02m4qiLPZ6QRDEYVhrNQkhsgGbZVm1Wpe54mAwiOP45OSk1WrVarXpdHr9+nUJe5B4QFnykaNMskVUqVQqlUq1Wh2Px1EUAQAcxxn0p3J9SsxqkiSmacqcULYHZJf4XE9FMEbzPCWESBQUQiAIQkppterIZVMURZYlYRhwzhVF0YEMa1nBhWBcVUuMFVXBEEIEOGACACIEZ5IsigPIxVnvQgAAzqA3kAt0Po1xoUXxq6xnIhG00mXLe4n02hjj2YgUAEByHIhnqzpP3SMAiqKIWUsDPHVx7JIW79m5ztHgg2cd9DMe8rwVOfvcWbQsj1CWTCEEQoyBwBhrmoIJzPNkMhkyxoqiZIJrquHWG/VGy7btyB94XvDZZ48cxzFNHZxjfZKEm6b+0ksv5HkeBFEUJVJlZWNjAwBwenqaZZlhGJzzIIiiKGo2mxDilZWVv/t3/65pWE+ePEmSRFEUVcXj8bjIabVahRBnaa7ruu/7zWYziiKEgKZpcRyapl6vNSGEEAFaMLkQJ55/Nl3OmRdEpX42jcY5l+SZRUGJpaVpDhAyLJszltMCAUSIGmdpxXbygiFSBlEc+GGz3ZlMJr7vX716tdPp5CXNs2IcTQ6OjoMg6LQXS8qTJPM8j6i6W2s8ePAgz/MrG0tCCMuyNtbXhRCtVrPMC9kq4JTFcaxpim2alUql2WxeuXIFEJymabPZrFQqnueVJZOFTV3Xm82mLJnqun7r1q0wDBuNxvb2dp7ncq5XrhbP86bT6euvv46QbLFO4viMnkPTtIpj27ataVocx5ggRSVCiDiJIAK6qkkPgQkyTJ1xO8vTeqseBIFt20WeK4qSpWkUhoqihEHQ7/dd122326WmlbpOKQ2CIGNnxNMqJipxTV3nAGRZtrS8Mva94Xjke76fRJQxxdQty6IUnS+8clbdkTWk5xobf1boYba8nwlHJRLlPK7js5fn6Srms8R5LPVTuxKAUipnmTDGEIpZLRigzw0vn7td8Kiz5ubnhakYY4wwIUBXSM216zXXMlQI+GQy2d/fp7RstttOpZqVNAgChBAAMI6TarW2sbFRluWDB/fCMJQYjnq93mw2G41WWZZlSQkhUuekLMuyYAiher1er9fjOI2i6MGDB7/xG9+8fv16nud7u/uU0kajwRir180oilRFr9ebSZKlSSa/BzmPl6ZpWVCpXZckWZ6nNUVACHWFGIYhWQYZFXme2bYDCaZMZAXFGKsqAAgjTLJM1gWZQAxwUZZUSqJEScIZTOKMM6CqqqIoRVEUOW133c5Cp6Ss3WkdHhxhRX28uZVlmR/86euvv9nrLa5dubqzuzWeDFfXlgEAjq0Dzv3ptN/vU0rTOC7L0vf90bBvmqbjOIamFEUxHA7jOE6SRDUN2fdLkoRzLlHjhBCMlNFoVJalpukYYzlfJpm5JaeRZMeSTg9jfHJyIsejJLuMbdtCiN3d3W53SXLwJEkiR5zkjx4EgbRSybiFEHIcxzCMaTANgkDGHZL2u9/vM8YWFhYcx+l0OpJpSlZoIYQcCICgPI7jOoamZVlRluXHH3+k6ppZsddWl5M8mwZ+nCZ5mii6fZZOAQYghxBAJCASYmY7MzgK5AByhM6CzflA9EIk+NQIZ8YG5/oTGM9rMn5RAMkYA+eUFrKJfFY+fl5z/1cxxfkYevZgdjGzHQhSAOCUUoakNgBASHpm7LoV0zQNQy+KIi9Kt2q1Wm1As6tXr4Zh+Gd/9udFkb/88stS1aTdbhuGITlXwjBK4gxCqKpqliemaRKsRlF0xu0HsWEYvV5PFiHkMMFgMAAANBqNw8Ptfr9fqzYopScnJ6cn/Xa722i0IMQIEc5BmuZ5VmCkViqaadowPhGcFUVBWSG7ZIyCgjImOMsKmtMyLwC3CcIK4QhxGiaUFZALBgUQjJcUQM45BwykaZ7npYDY0E1VTfKkoJRubKxXKtZwOAYAuLWqlNzwAv/FF19eXFwsKHVcuyzL49OjSqUyGAy+9Nqrsj5p23YUhHEcc841VV1fXwcAEInkFQIKYBmmoel2/YyNQs7ixHEsw6V6rWlZFsZYtl6kqUyn0zjJKKW6rsvSyGQyybJMdiYIIbJvYVmW67ryOJVKRaoOW5ZlWZbs5TDG1tfXj46OKKW1Wg1jvLW1BQBYWlpy3cpkMpL65K5b0XU1DH1K6cnJkeM4kv9O1nIUBVuW4XsJJgRgBBBkgheMFqxgvGy26hBCQDBnOS8zAoRjGFhVgpQBAYAAGCKIAAAAI4whOpMREwIAIdm75QixuDTld9mUyOUpeHFOWjo/DzE7BJ8rtCB0bq7iqVcUQsBz+5GjGL/WdsHm5zEK4HMeCy4Y4wUoSppTVjIOAWcQilrNDYJoOp1ipLZ7C72FJdu2G+7iX/7lD9M0/upXv+xWK3fvfhJFwbVr16RYqjykqujIViGUNKy8Wq1WbFdO2QghdN1sNBqyLHF6enrz5s2VlZUoiiR1hWEYMnxijAEB6/Vmp9MzDXs4GFmWZRq2aellyQzD0jSFc753+CnnXK7Ler0uIFaVMMnyMIgppQXnQghVpQZlCmUAUEOFBBKAORRAcAEQQhBjiLGuFAUtioJyIBgQVKRprqtGo1kjCuoudMqyWFlZznP69d/4BmPs8PD43/7hHx4dHd154dZoMtQ0rSiyLEvefffdpaUlQ9OhAIwxzwuAEJVKpdHopFGcJElepARhwzBkuJ7xM55oyeobxyk8n0va2NhQVXVrazvLsoWFhUqlsru7W6vVdF2XTUXOeb1en/3usnHvuq6iKFEUKYrS7XYHg1NZ15G8dVmWpGmcJImEZCiKEgSexMRK0CTgAnIRB+FxXgjKXNd1LJtS2m40K5WKlLJREEYCpGnGOYeKQoGgZZ56uR8EukwviYxRIVEViDEhmLIyCMMyKqlSkUY1I3MBc3wuF5bxM4nVpZeeGqH8BueD1HlzuuA9GWNYUWc7Sy1VIQSUOd7Ml3JpogghVPJfieXqbzVCedu5HJcKIRCEiqLoOpaCZzXXIhh4njfoD6MoAkD0FjqLyyuUga2trYdJkcSpAODJk+1er7O0tMw5E4JtbW0ZhuW61apbd90aISpnglKu6VC2NDDGrVbLNM0sKySxRaPRStP08PBQIWq32x2NRtvb23deWRccFgUNgkhV1VqtBQTa2to2TTPLckoppazIqRAwirLpdBqGvqJomm7olm3ZVUS0JC38MDJNsywpKynEmANRMiHJIEzVkLBDwUoKEEGKvFfmeR6Eke8FaZZTAUI/KEq2sr6uaIpTdTRVj6KEUhrGgaZp3W6XqPq7P/tZXqTD8eD4+FDTtKOj/eWVxSJlg9O+PCBjjJ6XCSbDkSRAURUi6wVRFDHGpnHIGJtOpxLpImfEy7K0TF227E3TlNQn0mijKKpUKhhjSdsjYeKe51mWpaqqLOokSdLv9/GZTBCZFWaks5UeQg4fS1mLarW6uroqhDg+PlbVMwJBic6XY2hhGEoZ49kksTiXs/YlZoCLssh5STVFbdRqNbcqBM9zWhQFURUAoUawoRAgWJxn6FzCCM7J2c/Swrl6jADgc0eZnskJ5ZvlPX5WzJTOcNarkJmrRCqgeUDMJccq33hWOPrCMccv2J4pqIqngSi4lDECeV8giBBMiCzhMggFIQohCGHYbNZr9bbj1HzfP+2PwjDcfby3sNC9fv3q6tqKpilJGoRhUJal41QBAEmS5BnD2IMAcQ44B71Fh3OO4NmXwDlXFE3yjrZanWq1Gsfx5uam/BqDIPjpT3+6uLiMMR70x1lW6HoCBE6SrN3unp6e3rt3rygKyzIMwzRNI/CjhYWFsmQlZcX5byn7YEAgCFEuBKMsS3MEIGNMV1QdCkVRIOdZljFaYHjG8+X7fhgnWZJFScIAKQqqavqt6zdWVxerVWdra6fd6o1GE4zVD97/QDXMRr1FWbG4tPTyyy+/+urLtVrVC70XXry9fX8rSZKjo6MkSeSZZFnGSypXv0oUOQifpmmZ5WWWc3JWPJf6BVmWSROVReCiKGSrVmoYKooCQCYh1JIqSgJiOOej0Wh5eVnTNM/z0jSV2fhwOHzllddkLTqO4zRNEULNZpMQ0mw2kyQBAMg8YjgcysZGmsaUUtM0Za1LDoVmWSbFTPv9fr/fl7UuCfSJiwxCiACUfCGc8yTLTLOoVh2JZQWcQQRVRanYtsXNeJpKA5F3Fmk7su9/wRDOndjzPeH8M0TMlWTAnKuZTXzNyqfwfF5pdixwHoXC87nfs8IM/1Wb75e3uTuKAADg88H8eea1eVOklBEEAECU0iSJPA9jyHSNFEVRr9fVjoawNpn4h0f9OMlVRX/hzktc0DCMt7d2KMuLImu26isry61WqyxpFCbTqR/4cVkyjBWFaHfv3lUUxXVqruvKz63VGp1OZ0ZfqyjKZDyNosh1XQDAwcGR69YgxHEcm6ajKjqEqNdbpCWPwuTw4HgwGFi2cePGjbW1NVXVqlUSRUkZRvI+KLteecYmnlfQUgqG5SgXQqiqCg2c5zlgnHMeR1GZZ4qi6KqEPUFVVXXDVvWUCUCI6rjVt978Ur1VyfN8Oh3bVgVhXJbl7u7uxA++9a1v3bp1i6j4+vWrjlNRDXU0HigKls06aRhFludFnuc5BvBsqQHIGGGMZXEi+bW4RiRRmBywlHpmcvWnaTqZTHTdgBDK6ovUDJVJnWziSbI/WUlijIVhmGVnViqEIITIuFQattxTjixgjKXSweLiommae3t7CKFr165t72xOpqO8SG3b5oIGoRfFgaLi3b1t27Y9fxKEnm3buq7XiKsoSjQc5Xle5gUQnArOGIuSWMGQFrnjOK1Wq1qtljQfDIeDwSBJknZrMc/zGdYcAIDPWWXOPIecKDhHzMxXMcDndMLJbA7wQiFEfimze7Ps6lBK6fxQ75wRCiHweWEGwqeaZ7+u6M2FgUg0m+rnz+/jl2WpEYLQDFwgpCWbpmkYRpoUB4dHnheqqq7pVp6VrGTj8QQT2Ou1iIKTJJlMgOM4YRiZpmnoVrVaV4heFJxgVdf1Vlf3fZ9RzjmvVCqyspdl2cbGRhynsgAohGi1WpZlDYfDtbU1SunW1mPOxFe/eqNWbUwmvmnYh4eHmqatra2ZppnlCaVnbBqD07GASFVVy3EFIHg0HU+8GdpYToRICjzTtKrVKkpiRcGspBhjhhCBiGAVIWTbGsaKXXGCJC0oM7ygVm/cunXr0cldINDS0lJRFKpqPHnyUNf1OiaO47z8yitTb9zr9aIoPD4+pqzw/enJycm1a9dkc+9gb78/OCWESNyJpqi1Wq1WdymlIzIIwxAAIPGcMjaTLmuG6pReKE1TKTAqwaira1fkejMMQ1q7LALZtn18fDwYDOQkZ5ZlEMJarTYcDmUWAACQuG3Jzy37DTIclUVX+a7XXnsty7LpdCozbUkz1Ww2Hzx4IBmlZPUVnAt7tbud8Xg8ybIiz3heAi6Q4BBwWV5OkoQLmud5EsemadZqNVZpSx2UOI5loC5tUMbql23sgtuYZXbP5IRsjixx/mU54Cw51yThPITQMAyp4HF23BlOTkKQhBDn8SoXXEAg0Fw/Y+5fcA68lrgDcS7XxoXAkuJQmrYQQsyIwM+usAAAMCTQ2fSKiQudkIquWaapaZpODAJNgswoDOOpBwFvOU5d1+I4LmmCLDiMioojABBFEeW5KPI8oGKH7ne7XV2pVBpVmbyNx2PP84bDUW9hQSE08MeKUnS7blEUp8d9XVeXl5fjOBa0jPxJFEW0MEJ/VObR9kH21ltvLTL48/c/+L/93/+vv/3d7xmafnBw0Gv3KpZz5/oV19QsrdJzOya36q36ONVomWuEaIoCeF5zyMaKZQD19HgQ+lFRZEbdbrh1t1LXNBWXhauHSADFQpqjYahhca5koGhBkkW5h0Ta9/2GbrzywhorxrfX1v743/8AEWV1/VrBkEqUarW6vLauEG2hu9Rudp9sPkrjyLR0moCdzZ1J4D3eemKa5iuvvPx3/5e/ByH8xS8++NnPfjYZeLW6u967igkpswRVtCSaXL16VQ7j6qamGep4OomS0LQNoigIIcrLrMgVRUEMUc5sWLEqdp6k0+l0Y2Pj4OBgOBzKEd6z9l2WVyvOYDBQMeGct1qtMssnw4GgZRCFL770ku3aDzcf1Wo1igUjQABm6kTRFUGZppJOs2FZ1mDY1zQVABGGAWMMEVJxXYiVVm+BAVJrdjvLa2EY9/v9PM/1Sfj6izcCQg4oHzGQYS1J85xjU3O8HAhViCQ3SmaqpF6t8SJP0ySYnnjTKeCiZmiUIsYYLbMoiiqmJUHnBtEBYPI2SghJsSwmnxc+uQAAIclgJgAACApAZuv7spOZ7xzKZFRuT73cHNoGzJnKcxzcnO198Q7PVGWevWfIz5JLbnbXqJhmlmVRFBm62mq1HMtUdU3T9DiK0jQtyszQdALP4XgQIoRqtRo6E2aDtm0jhDgD+/v7w+FwOp0uLi7KwEnX9UajAdCZ4M50Or179y5jTFe1drsp7zuS29P3p1Iiqtls5go+Ojj8xS8+ePDg0xfv3HRdlzN6enq69fhxu9l5/bW3bt++XeRUJjBurSoEVBStyPMsDU0dq6parzUNRQWcaopXmnnFchrVlmU6mmKqqmqUSCVIU1RDIRhBwCkUAEDMIYxLmgRRnCTNduv2K69du3lHcPjhhx/WarV2Z/Hje/eJZn/1q191q48mfrC4uPjjH//44ODgK299aTrOTk6P4jC6fv36p5sPp9PpdDp95513/vAP/1AI8fWvf/Uf/+N/LIT47OGDP/mTPymKotVqfPOb38yybG9v7/bt23Ieem9vjxC10+kkSbK4sCznlSSoRQjBGJdTFEEZLC4uyo4oIUTX9cXFxf39fUmuJRnWkiSp1Woy+NzY2BiNRmmaPnz40HIqGGPP87I8F0IUZRlFUZkXrCiLPGclJYQAIqrVqmEYYRiGYZSXpRACQry+vr6zvXd4eGi7jhCQc+44zsrKypMnTzAmEMJqtUo59KM4z0skQJZlUHBGC2YaqmPrmgLkkuMcnNcdFUXRNQ1CqGmagrAkH5NjSQAALpl4FSixbkJgzrnEugkhIH8qz0aeeqpLmM+ZHQIA8Dlh8PlE6Zl9yqR8hn64bHKzg8O51t8X2OH8cS7vNisNn2OUmEwzbNt2HLvZaFQqFUltYBhGMJ1QSpGBFE1BWVYWVABOiFGtugghz/OLolAULJnmZBcrTVPGSk0z5FCcZZmDwZBS6jgO53w6nSqY9Hq9paWFo6MjuYZ83//kk3s7O1umaTabzQyw69ev/29///c3Nx9alkXLoigKKDgh5PDoqN3ai+N489FW6Ed37ry4tnGFx9ypOoFfhmGsKRYxdKTrTsVs1Nzd7Z2j/aMio9NgGoWxZVWqTq1qaQrGEIg0L2iRQyB0RcUqTNK8P54UDCyurK5fv/Xi629g3fj00aaqaMNk2hKiWmuYtiuEiKJoeXn53/ybf7O2tn779u00Tbd3dwAXZZH9+Mfv/L3/8u/pur6zs5OmqcSRHR4e/5N/8k9ardZv/873/vk//+cHBwf9fv8//IcfSJu5f//+0tJSr9djTMgxwkePHslIPssKmdepqiab7Lqu66pRa9SDIIjTJC8LytnC0mKcJlmRU86iJK7VakVRmLZVFIWAQFpys9ksKRVCuK47GAyEEIZhFBBqmlZ1XNeutFutquNSSg9PD1RVlTqtGGPbcWRG+uDBA4VonU5H0bXxeCqLOnEcB0FgmhbGCiYqhyjNizhOoyio1WpAgDzPC4I4N4UQlLGyLKGqAsGEELTMAQCQEBUTFSOCkUIQxlhXVCEEQWfCEBixubDuHFjDZ7UZhMScEV7e0Dml/rxNypwNnStjz3vCC43IeWObFTwvZJ6Xt8vlVvlAFmzm01d5nNF00m21a7WaYRh5ng/GIyEEbiBd13XLVBSl0ahhDNMolikWMJjEMcjsVQhOaVEUmeM4EMKizAbDUwiwnLjhnFoVxzCMVqslGC+yPE1Tz5skSSTLgEdHRycnJ0mSvP76m6+99lq9Xj8cHDPG+ifH4+Go025dv/5K6E0nw9G9e/dMS3/hpTtra1fCKN7aeXf67k8m3viNt16p1+uEKEAgoupYIYATw9DddgcAoCBlOp6Ox9PTUR8NBrbt0JpVr1erFQdAQBnkjArAVMwnQeiFaXtp8dU33uwsrxLdiLKcCq5pxkJvCSmqbdscgP39/TAM8Wj01a9+9f333ycEJ1G0sbEBBXj5lReTKH7v5+85jvPWW289fvz4Zz97bzgcrqysmKbZbrf/+N//yR/8wR90Op3f//3f/+3f/ju9XmdnZ+cv//IvDw8Pj49P4zi+cePG+vr6yvJavV6XJOWyhCidw9nMrm5J3icZC2RZJgnwp9OpTPlUVZUSogCAPM/zNJPLwDCMWq1mOxWE0OlggDEusiwMQ8E4BtDQ9TiMRqNRvV2ToO1+vx+GUavTOVOeUZWlxRVd15/sbO/vH0rBxiAIFhcXsaIWBWUcQKIAiCBCcmSFiVIwAIAhVzvSNGZbumlyWpZ5DuS8Py04wmVZClVVEEYIQsAxRkhTGEFCCJ2Vz67nC9VRDsDzjFBayEzmeobenrUxwLMDUfKlC23GCx2FmQX+iuHo5SNIGi95d5lx9SOEVlfW5FSb7/tFntq2XXNcVVWDwFdVXbfsar3GSko0VQbSCEPOKda0Wt0FACCEyoJVq24QBJZlCSEk/EpRlLxI+4MTJ00BAGWeMcZGo0FZllmWJEly584dz/M8z6OUdjqdWr2+f3Dw45/8pCizmzdvLvZ6t2/eSNMUcsY5f/PN169fv+p5gVt3j06P9g73GWCdTqPeaUiaas4BxAQhAgQCiGi6GUVRrdZo1ls0L0+OTjc3nxwfnnjR9P3Hn16/fv3G9WuWbnBAMlrkrIA59+K81Vu8fvvFdm+pACCeekhRm91eMBkWZfT48Van2xtPw/d+9kGl1rDK8v6Du/3h4Lvf/S7GOEuid378k62tLcPUsiLd2dmRTK0vvPDiZDKWTmwwGFy9enVzc3N1dfVf/at/ZVmWaZpXr1797/67/32e5z/5yU92dna2t7e3nuy8/fbbQohKxZ1OfcuydN2wLEvXdYnJPj09DYJgZWVF/qaGYezu7uJzjSBJZiGpKOTYhFtxCCGPHm+61apZsVGCu92uFwSu62IIoygydUPCvmlRyuUqZTB2dnYOD08o5wsLC9Vq1Q8DCfWsVquKogEARqPR/fuffvs33srLJIjikgnLqgAAMAJlWY68CeDM0DRD0yhnEELF0FVVVWsuhjBL0rIsEYCUUgwgBxAJgCBEEMqYU8rbc84NmEMIIXhqLHwWlZ5v5MKin23SCM9haGCWB+I5OkMxt80m9Oet7kII+kwD8HO2+VcvPBYznhsA8LmS6cLCQlEUEgOlaoYso3mBHwUhwQgphDN5LZhoKoRQswyJZZPHJIRwDsxcl7FlURSTyTTLMoQAY2UUBSfHfXCOX4cQNptNia15/PhxURTNZvPWrTuO41BKR6NRGMYbV1am48ng9OT2C7fzPC/yNM2Tbq+LMdZMw7SMJEuXV5c6vW6z2VpZWbE1Q1E0WuaaahRFKRjHiBcFFUJUHcexbU65pmkAQTnZEE5DAYgfxJNpIOVvVV3DhBwORm+/+MrGzduckDjNKUKM0ZKyshSj0WTryXa7szD1vcdbW29/a63Zai0t+cvLy0mWxmGUxuF/+Q//wc7W9tbWVr1ZV4h2fHy8s7MjhLBtu1KxJaZMwmgP9o9UVV1aWjo6Ovrxj3/8/e9//+tf//rGxsbf+3t/b3tr9969e6urq7u7+5LZkVIWBAFjTNfMIj/D1mCMbdv+/5H232GWp1d5KPp9v5x3jpVT5+7pMDM9WWKUJSSEQBIS0TZwbLiC4+NrAT72se8FTLA54CNfc4UIAgQCC0kocqWRJkdNT3dP566uXDvn/cvx+84fq2qrZkbisZ+7n3762V1dtfeuvX/rW2u977ve1Wq1QM7W7XZhbWgYhrquwyHreR4hxHEcFjMLCwvValXVNNd1G63mkSNH4P23xmNCSKRqmqxQSlVVzefzURR2u93xeEwISaV0SVHgyD569Kjr+LVarTvoW5aDEFJV9fjxo8PxCCFk2264P9s4Ho9c12MxE8UhIonnO67rcizmGIwQyhsqi5EgcDyLSSyGIWYQZhnEcRzPcgghEkeI7plrkDgWaDwBSClGFFECxSiDEUIUH8iE3y886Gu3mgFeOuHxD7KLB3/kjSGN3lCR/uO3g2nwdaEIumrYE0oI2dzegm6YFziO48IoanV7/X4/n8sgjKMoGQwGYeQTQgRBCigmhIgiz7IYhkfT6SxC1HXtbC4NL09VFYbZs8oTBIHFnCzLsCZaFGXAxyH+fT9sNFqm7UA3mMvlTmRPDloNSmmSRK9evmzbNiewpVKB5/mhOWQYzg8ChmEWlhd4XhRFuVyp8DETxyHLCalUmpIgSgKWZcOYpI20JCsxoZ7rIo4tV6teEFieXTx8VNf1kOLRyHQcj+M4HsUEx0auVKjOYlF2w1BSNTPwu91uQtGw1jAyueMnTzle0Gp2Tpw4cfbs2d167Z577tnbKkPi1Tu3EEJrq3emp6dfeumlpaUlSmkYxNMz1Wq1OhwOgZELw/D06dNXrlxJp9MvvfQS7HuhlK6vr1+8ePHa1RvHjh1bWVnJZHLpdPapp55SFJUQ0ul0RkNT0zTQx2iaFifJcDSCQ9S0LFGSEMYJIRzPR3FsGIbreQhjPwjwvkva1NSUrCj90dC0rVqt5vo+kBwMwyBCe72ewPNpI+V5XkTDzc3NWq1m27YoSkBL2rZru46q6LquswJPSKvb7QKuWyqkZFnlRRkWuULDFRlR6Pm2bSOaoIQkSQTEYBT4jMBalgUAIYuxyPMMw7AMTpKEIoIJjcMQISRQnmEYShIGXHs5WA2ECKYIU0IJphjhPej/u+jo68IGhBEHWzLo/SZOO/A70AME+vesbP8Ho+514Tf5WXSgHUX7e3yB24WXNx5ZmUxG0zSe5+MkimMiCoyqKqIscQwbR6HjWEkYMeweA9kf9NKZFKV0OBzCYATLCkDyAMcF4Q14j6Iord1WNpUGWpnneUri4dCFwXDTtG3XkWX59F1nzpw+q+janTt36ruNM2fuqkxVv/3tx+rthqYpR48eVlX1W48/rihacThADMdxPPVc1226vjOdnXIcS1HlbCYTx0GSuIrESxIXRJFCEcYcwwsZ3WARtl1vc3tnZ72WcnxBEMIwSjAbJtjpjnqj4S987Jdy5anOyI5IUqhmEs+3/aBQKIhVrtVqFYvV6blZ03K26/UwDgqFQq1ZazRaqiTXajWO43q9Qa3RtF3v7rvvNgwDnM6iMGk0GqIoPvTQw/V6rd1u7+zUoLYEtbc2ZbAMXylPzc9xYRhub2+3Wp1ut3vmzJl3v/s9sNHl0sVXd3d3YaQ9k8mA+ygMWCVJUq/XGYaBDhzML1RVdRxHFEXLsiRJymeyjUZDlCXdMDL5nGboN27caLRaGGNFkniedz1nPBiSJImLEcZ4dnEGvBQ4jqMUQe2WyaRN2xJFET5ZQRCq1arjOK1Wi+NQmk6EL5EoivlMmuf5Wq3mOjROYoypKIqiJBFCfIpgdlkURZ7d64k4lmUwHo1GwKwEQcBiBnY0YIwJZTDCDGL298bHkKMoQyfD8Difz74xANCB+hMhdFDJDa6sE+nMJEOC8TPGeDKXOAmkSSwdLF+Z/eVN+IAWllLKfZ9VMRPtjiRJYKYAT5TRjX3EKWEYJpMy5udnZ6anFVHoddvD4YASgkkSBEFCIo7jRE20LCuOklQqlc1mOY7z/TAIgjCMdV23bbvT7hmGMTc3Z9v2zZs3jx86miRJs9m0LIeXRNM02+0uRYhSmspkDcPI5wqHjh4xTdNxnB/+4R9ev3r9nnvO9YeDxx77xiuXX/nRD/5IdbqyvrFx/dbNr33tsamZ4g++7/2pVPrxJ566du3G/ffff8/xu3u9XqtdP3LkULmUW1qed50xpjHL4nwuo8lSGAUkThBC1tjsdrvPf/PF9fX1VCqVxKTdbntesLSy/JM//VOCosqqmmAaIyqocpwktucSRKOBOxgOr1+/furM6SCJrly7tluvlavT6+vr1WpVkpRRfzDoDSmlHMM4jjMY9AzD+OAHP0hp8sUvfjGdTi8sLFy7fuXRRx8lJAGraMsaZ7NZXdcHg8H8/DwhZDwe9/v9u+++98knn+R5PpvNtVotRdYOHTr0Az/wAxsbG48//uTFixez2ayuq7dv356dnZ2ZmUmn0zs7O7DLiVIKFSlUKLA623EcXVFTqZSRTm1tbyOWOX7yhOd5t1ZXB4MBgxDLstPVqXwm26jXPcddXl4WFB7ETN1ud3t7p9luI4Q0zdBTBkZsEARBHIVhDAvestksw9FMJjM/OwdHT+B5Yeibo3Gv1+v3+2k9nc/np6enYbUGy7KuZ8VxXCgUDE2HopfnOMuyhsMhhxlw9Tc0HY5pSZIkp78XXAyFC53lGMrgMNzzuaKYcP9IRjoYP5P4BLXOwViaZFEIS8Bv9g6JA8sGJg/L7E/xf/90+D1u3P4NcDa8zxkORyOO42RJ4nmexShM4vHYEoVOEgZh5OuqVq2UGYbpNBuDwQAhVMgXU0YaftxxnPHY4nk+m83OzhYdxynkS4dWjty6detrX/uaqmrnzp3b2dwqFIpT1erO7m6z2TQy2XPnznh+yDDcxtbm9tYOywvVapVh+eMnTjVbneWVQ41mZ319bafeePDBhxFmeE48cuTI5s728vJsJpsnURIGcSaVPnr4yOnTp48cOtZttUWRV1UdM1wYJJbp+b67tDBHqWC7set6DEWKomhGjhPlueXBwHK63f5wPNK11NnThxdXltVsMaEoxnyUhAmDSRDbrjsyRxhjqzEybWtoWnfWN+5/4IEwjvvDwclTxwuFQrPZZFnsRyHLc/lsrtVqkYS++c1v3tzc/M3f/M33vve9/+pf/etvfeubly9fXlxcvHXr1mAweNObHoEfzOVyzzzzVDqdXl9f932/WCzzvJjJZHzfVxT19u3bSUxLRyuNRuMP//CTnufdf//9jz76KMuyq+urnCg4jtPqdgbjEcaYEwWBJI7jjPs9hFA2nwOSsNls9kfDSrHUarWiJM7lcpbrXLlyxbZtUZbDMGQxTpI9WlwURd/1hsNhVa9A5QyAKmRUx3HS2YwkKoIgcGHAsiHYTCZJwgi84zjtbsfzvCD0aJxgRBGmi/NzAsfGcTIaD8BTw/E9RdFWlmbBQzEmCULI87xREPieV6lUwjAkPeJ5XkwSnGBKaRRFHCvuBRFDMcYE05ggRAjFAqyWQIhyb2znXheErwubiR09eq0GZ3IHQnTCqgNgc7B7nOTVg4/zPe8fvIGAkGGYiV4W/EgiipOEhlFECCEkdl03CSPbNleWl0G4ZFoWRiiIo1QqlU6n+2ZvMBghhMrl8tTUTLmcuK6fJMnq7bV8Pt/v1TY3twRBePvb3wGLZpeWlhiGeeWVVxzHfcc73lEolF58+TvNZrPRaOkp474HH3j00Uc/81efnZqampqa2draGUn9ZrNer9dnZuf/l3/+C1EcXLn2aqvRSsLk1KnTlXK1XK0oiha4AYe3IjeM41jRldnZ2Uw2bRhapVLSNM1xbMzKFPMMpixHoygw7SAIAtd1lUxuanEl4kUfc6WpmSOnzxqpzHaji1iGYZiYJJhFLIsH45HjWKIszU/NkVrtIz/2kwmNB6P+cGyWqhWGYbL5zK3Vm2kjA0pIjuNYljMy6WeffT6Tybz3ve995ZVXNjc3//k//+fz8/N/+7d/G8XBT/7kT966dYtSks1mNzY2yuXq6dOnwY+wVqstLCy88MILhIBDD0MJzufzrus7jjcYDC5dukQpjuO4VC08/PDDCwsLURQ9/vjjGxsb/X4fzlaO41RV9X3/xRdfNE3z5MmTH/3oR9u7ddM0eZ43DMP23GazOR6Py9WqZVlzMzMgolAleWZmppDLg85J07TRaDTp3IIgpBSbpomNPRMtmINxXc/3/enUdBAElmXxHEPjhCQxhxkW0ZnpKk2I7TqmaUVx4Eccz4vpdDpKCKVoZFocw8iynJVlkRfS6XQYhrZpea7vhxFGDKwjJRQFCDP4u3JOEHRSiliWxRO7mkIh9/2CcNLyHQRIDzaBB4OQvGb6/rtl6mQs+OD3oAPl7sFylBDC0+8dhIIg4AMS2ImsnEXsHneCCEJI5Ll0JpVLZ3LZtCzwsiIyCPue67ouQkQURT/0WJZVFA0caX3fb7e6wIal0xmWZU3TDoIAWpQkSdauvVoqlR568JFStXL58qtXr11r9/rNVufo0eNvffvbLl5+9fNf+PtHH330Iz/xkz//8z9/9913tzZrS0tLXuBOTVVKpYKR0m/dusGy+Oy504IgDMeW67qFQonBnGma6XTa9wMw+ZR4IU4ihmEcy3Rdt1It8SyHMd2bIbBM0zRt2w4pTpLEspxut2/oqZWVw4IgWZZVKBRYFlyCUETiwbAXx2E+n0/x6eu3rp88eZITuM6w2+o0wyS88MorlUplc3OzUCh5jt9utA0jpSlqLlcY9NpXrlxBCK2sLD3//POZTObjH//4cNj/0pe+1O403/ve97Isu729BZEGkxZBELTb7YcffvjatevAvK3d2WBZFobKIXN6XhBFUaVSabRrcKTed9991Wp1YWFhAmZeunRpY2MDFlcEQQAr71fmFizL0lOG5/uO70mKHARBlCS2bWNKBUFIGylD1TBCEAwDs5/JZMbj8Wg04ji+Pxx2Oh1BkBRNzeeKSZL0hoMgiOBEI4TouZTvuqmUXikWOIzjKGARJkmk63qvN6CU2o4XRTEnSAzLl8tlP3Cz2SzPciC1H4/HvuPm83nDMGzL2t3dHY/HkiACmCwIAo08juNEnuc4joVxe0IJgakDghFiKMGgwJrE0uRv5oDR0yRCDk48TWpU+gZmDyIN+kZQeE5IxYOw6vfsCb9fELL7G7xhGwGIg+M4FgSJxgnGWBRFTZUNQyvmC/lsptNu5vNZiRf6vY5pjnluT+yfyhgzMzOIMlevXl1bW1NVdWlpec84jKBUKqVpWrfT39rakiRpeXn5Zz7yI6ZpfvWrX33p5VckSbJshxXEBx96ZGFh6a8/+zeXr1750Q9+eGlp5T/8+v/7oQcf2djYmCnPTE1NCSIvivxubTOXyziW6ThWsVhkWdxqdRBChw8fLRQK1tjqdDpjx15eXj58+LAoikkYweyPLMssw4T7t8DzgPhGCK23ahhjx3bDMFZVvVSsFAolhmEYiiRZQCSJ4iD03NF4yLK4XC6uX98pT1V7g24Q+YVK/tbqzSAOnnjyyepURZFVjhPSRmZ3tz7o9o8dPaHIsjnqx3G8vb09Go3e/e531mq1xx9/4uMf/1fvfOc7//N//s+ixN+4cQOExPPzcxzHnT9//tq1a9lsThTF6enpp5566sTxU1/72tcEQTCMNKBZzWZbFEVAXyzfBF1EoVBotVqg7b7rrrviOIYJCQg/y7JAHu30R1EURUl8Z20tpuTw0SOe521sbaVSqcMrK5lMptfpupZdLBQMTbcsa2D2U6kU2HAlCbEcxzRNhuHS2Uw2k4+iqDccxDEY9oQMwwQ0iaIgl0mViwWRZVkGqaKAMbUty7E9RdPDKHH9IIhiy3T0dIbjkK7rNEng4zBNk2e5paUlQgiNk36/7zrOZEZXkiQaeSzLChzPC6zAciyDGIQYRBBNGEowQpgS7nXMwcHy8nWQ6SSbHWwUJ/ch2PCexwwGTgnodfzaMWS8v1L7dT3nPw6iwkPRfS93juNA0JQkFGEk8byqqpqmyLKKMQ7DMCGEEBSGoe06rutpmgqLtbudPiUYYxxF8czM7NTUVC5XQAjNzs4nMe12u83GerFY/tCHfqxYLAZB8Bef/rOdndpgNGQo1jTtzT/w6NTsXK8//OQnP4kZ9rd+63deuXjxDz7xf8Vx3Gg1wzjq9kZ31jeWlxcLhfyx43dREpJ8/tqVV9/5znffvn1ze7Pmum672a4UyudO321ZVmfcLRQKMzMztm333EG91kqlUplMRpG1IIgpQQwjMCyNk9APCMMwA8cOw9Aa25lMjhGVkesxlslQxrWddErnMI5CF9OEJoQiHLiBG4Qsy9u2m9Dw5e+8srmzdvTEUVmRXN/LF4qdVhtm5Fv1Zr/fr3vee971drTvQv/MM8+Nx8OHHnrwxo1bN27ceOihhy5fvux53iOPPLK+vm4YqUKhoOvG7Owcx3G1Wq3T6dR2G7qWWlhYePXVq4QgnucnRQd0VvlKDo7gUqk0MzNTr9dv3ry5vr4OLwMhZFkWxrhcLlcqFUmSzt91tt1ujy2T43k38MEvqxIEMDxRKBRInNA4SafTuqqBjRCMSgFcFEQRIWQ0GsQkiSNCKXU9N0koaCR4nmcUUZIksPAKkyRjKLlcRuD5HssKgpBKZwllXT8YmZbrh1EUxQQlIxMgD0opJ0j5bK5YqqyvrcmyrKczPC8CRkopYTmBZWOEEMUkSUiMEow4jsEsgzCBaKQYUVwqFQ5G3cF6clKLQvzsDfUemLfAB5ScE7AUggS+AplzMpUzybEY42ivi/sfRUcnywPANR0mreI4FgSFEMJxjK6okiTwHCdwLC9wgeuUisVqqcgLrGPZo9Ew8H2EEC+IkiSl0+lsNmsYac/zNjY2Nta3dF2fn58/c+bcysoKpXRjY3N1dbXb7W7fujg9PZ1QZBjGmXP3MCx35fqNXn947733yYp6c/XOyxcuXLt56/Cho2sb65Ik+S6ulIsrK0t+4BZzOcwkR48cKhbzKV3b2drudrvFfBFjrEpqtVrd3anbxGEYZmpqGiEGIbS9tZtL5zKZnGmavh9A6+v7Xrvdtk0LY9xMhpRSy3IqlSlV0k3TlnjJtR2JF3LZNEYk9l1VFjmW+q4bJ2Fte9juNBut+vKhpd3GjpKS773vnmvXr25sbS4sLLQbbVXVDS1T294NgzhJaOTZv/prv6Kq6qc+9SlYQL+zs3Xs2DHfdwuFwtve9rannn7ixRdfBGMewzByudzCwoKqqoZhbGxsiILcbrfPnj178eJl0zRd1y+XyyBhwxgvLS5nSilo2NLp9JkzZ4bD4auvvrq9vY0Qgmxp2zbM7ILXUzVbaDQaDMdatm17riCJcRwzHMey7M7W1uzs7Pl77rVG4zurq6qsLCws+LFn2za45Xe7PYbjCCFraxssz6WMjCRJmGMJQb1eD1pNIrDZbLpaLCCS+I6ZSxmlQp5jGd/3h8ORKMgRoUFELMfdrTcxZgmlgiDMzc1NT0+7rttutWAVR6VYyuVyHMOapjkej4fDIaY0nU6nNRrHcRyFJIoRSTgWiywjcIzIcSwhDCLMZIrijbfX5aVJfEIPPWkO9z0IGMuyJs0eQghKC6DaoKuBxVTotZK3/6kbxCrGGAJyIm0lhIRh7DMsQoTwPCIcwnR2dp4kked5DCtHUWSaFsvgQqHw0MOPAA6+sbH1jW88Ztv2XafO/NiP/djRo0dN0+52u88//3yv13ddVxCEYrHIx8uyLBtGmuHYa9eumZZdLlff8fa3y5r+xFNP37x5O53OLi4u1hp1gHBu3dg+cfIuyx7Xa80rV16tlAq3b946ddeJuempfq9naPr09HSr3qrVaojgeq2GVMY0zSAIZVlNGZnhcGzbfqfTL5VKju0BXxeGoWWPMca6phUVQZKU8cgyjDSJCWYZWVN5XrTNcRjGgW97luXLPIdJo1HvtBqmSfOFLHRusBdla2vrAx/4wH/87d8CYbrnBaIoFsvl+k69UilvrN783H//ux//iY9+/OMf/6u/+qs7d+6cO3eu2+1WKiXLsj772c8+/MiDnU6n3+8rinLs2DFIgIANnj59GiMWdPCNRgNcemF5PSHEc30E/suynCTJxsYGxng4HIJJ1OnTp03T7Ha7IL6HCUOMMcyyKJo6HI382q5t22EYSooCMxC6rt++fduznWw2K4vSzZs3C5V8p9MBLTHGGHRwlFJIITzPaykjSSg0nBhjPw4KhZwsy4Hn+r4/ogSRJI7CE0ePeZ4fBpHj+X5EXM/3fZ9hOF4S683GxAAuiiJd0yY6FhhlhqOEUsqL4nQ5FwaBS+MgTJIkRAnlODZKGIVjWUwZShlM8VS1iA5oryc3yHjkwBz9BFYBDgT6PfhVwzCE2daDuRQeVlRkTdMy6TQQjKZp9rs9y7I0TSNRvGfhelCVxpLXPQg8jizLE+4x2V/ymk6no8Egk8lJomJZznhk86yQzRYNPcUz/NLSEk3I2tqapitvectbThw7FsfxhevPbG1tWZazsnz47NlzqVSmXmtubW01Gg3XdcfmMEmiTCady2cwpq7rpnghn88/+9xzU1NTKysrzWbzgYcf0nX9rz771xzH3bhxI18sgLVeOp2+cOHCqZN3J0mCWfbSpcuEEITZ8lR1MBhRhHQ9dequM0mSlKemPM9Tdc0wjMR0KaWQ3uM4BoGloijD4RCUg6qqAvEFZYiSkZl9O3cGYT9wbdNyXddQNVEQwtDvd7qj0SiOIvCZdoMRz/NJTAVJEwRxMBjpWur8+fONVv3bjz/25je/6Zlnnzhz5tSPfvhH//d/+2uiyM/MnPvGN/7hRz7w/n/2z/7J9evXVu/c8l1na3tjenq6vrOrKEoYxrdv3vrAB350d3eXJEhNG/V6PZvNJkmyvn7n2OEjd911125t++knn1peXmRZtr67myRxNpvFGNu2HYTm7OzseDxeWFgwTWtra2dpaanV7Ph+yHG8KIqIMq7rg7V+GIYwrrm0sowY3B8ObdfZ3NwMk7hQKIgcLwjC4ZVDjmVhihzLzmazgsiura1FSTw1NSUK8ng8Hlum63qdTiedzWqaIcsyw7Htdrtea4ZhyIrorhMnBUHI53KB622sreeyWU3TdnZ2dF3P5nKaoXth0Ov3x+NxEEeuJ/i+H0UBy7I8z/IcIwi8KPGYklK5wPOcY4193w9DH2wdccQzDJPEcRT4CFNFFCSRZxFFScKzmOcYhhI8N1s9mPoOXvoHg2FyH0hziASWZcGZJwgCdKBpPBg8XhhgjAWeh6I/lUqldEOW5Xa77dnOeDy2bTuOIvCQ5Hne8pyDMTy5ua7reSEhSFUF2He3xxuGgeM4SZgoim7oaVGUWSxgxBTz+dHQXFhYeNMjDwmCcPXqFdd15xdmlTSTz+cVRWvUW5cuXa7Xm8VCeXl5+ebNm3EcJyQSRT6Xy+qG6vvucDi0O12GZU+ePMlx3MbGxgc/+MFOv/eZz3wmV8j3+33DMMaWOTc3NzMz8/nPf/7UqVOt5uDOnTvpbLbX6/d6vff84Ptqtdqdjc3ZubkgiDDL2Jb7zve8e2dnN5fL5fN5bzCGIw88pKHelmW53+8DvGYYBijUoe4gfAI1vCAIIi/EcWyZI9eyHcfJZlKiKA57/e2drWF/AP0zYiOGYQI/4nlREtX+aEwSNDc39453vfOLX/z8tetXfuFj/+Lf/O8fX1lZ/q3f+s1f+uX/x9raiNLkB978iKrK/9v/9i8vXX4lpWuf+uNPBkFweHnliSeeMIx0p9V+85sf/fCHP/yLv/Cxs+fvmZ6e3tzcvHHjhijyLMK6rp89d3ppYfHatSvdblfkeVmWqtUqyESNlFir1TBmjh8/Xi6X//zP/xIj1nVdjFnAioeDcRzHKysri4vLo9Go06zVarVMLivKEsNxDMdub2+bjp1KpXiGVRRlujrFYkziJPQDWZZTaW17e9vxXFmW44iMx+OROR6NxtVq1XKcJKG5XE5PGcPhsNPuEUIYgR5aWu71er1u9/iRoxzD9nu9Q4cOgRQEYSwpMuZYx3XH47Hje0Eoj0aD0WgUhiFCRBJ5VVVUTY4CP5fPFLLZdMbAGLdajeFwKAmioeQwxkkc+r6fxBEH2y3iSJElgWdlgedYFq8sz9MDwmi037Ml+47Xb+wVXyd2AaBpIuCmrwVLWYEnhJB9WJXneYHjWZadmZlJ60YmkxEEwbasWq22tbXV7XYVQ6P7yOpBSgO2hUD1CxQtRH7ZSFFKdT2la4Zleb12T5G16emZo4ePPProo/ls9ubNGyzLnj17OgiCF55/tjOqdTqdXm+AKKPrhqrqAi/tvTBB4HjYNUwSEoG13nypeuTIkcuXL2ua9sEPfvC/f/7vkiRRFGVrZ9vzPEmSKELHjx8H+2qe5zOZQhAE37lwgeO4fn9w733nL1+6stuo//N/8YtbWzuvXr3iOv5HP/rRJ55+KpPJaJo+V6yAwhjKCvDGpZQ2m00ISAjCSe/tJh6005LAi6JIkmQ47FujcX/QTaI4jqMkilzXdix7b7sYChHCvh8ymFM03XPDwcjEGH/kxz9ar+/+5V//5Y995MO3Vm+urt744Q+8//Tp07/x658gJE7icDweHj9+7Md/4iO+62Rz6du3bz/z5FMcx43HViGXt203CIL/49/9h1euXH722Wd7vV4+n7es8c7mFsMwhWKOY9iHH34QY7x+5w7DYLDcTqfTa+s3MpmMbbuDweDcuXOapm2sb7344ou6ntI0bTQaR1HEcVw2mz118vRgMOh32tvb27woyKrC8jzDscPh0AsDjuMkXiiXy2kjlTYMx7JJnMRx7AeOJEkE0eFwyGBuZmYmJsmtW7cxxs12O45JtVpVdW04HI5HFsMwksaDG2KjXl+YnSsXSzdv3ICLLY5jihDDsWESm5ZlmqYfhVEsQ8GJMeVYFiHCcazAsyyLWQ7rilKpljRNa7eb4+FIVVVDybEMQ2kShiElCYcZeHuTMBAFXhYlQeQ4dt/fHgrCyYc9SUSvowehoJ94gKP9EWNIiZO0OUE+Hd8DyTU8EXiPQ2G5E+/R7rqmlcvlRx99NJfL9UZD2DFo2zZoHQCdh/KM7lshpdMZWAPiDUZLS0sMwzYarerU1I99+KO5XKG+Uzt58mS73b5y9dV+v9vptP775/9G4JhMJlOayk5PT8/NLVim0+l0TdPMpPlCoaDruqIoHM+Y5qjTaZvWCESDLMtevnz53LlzU1NTf/hHn5ydnd3d3d2t11KplCzLlm2fPXt2c3Nzc3Pz0be99dlnn52amitVKtu7u/1+/95774UFQB/84AdHw3632+62W0srhwbDnm2OZEH0PI9NKMzOKhzLiYIgS0mSmKZVKJfgjYXXAJ9CQhKRl1jMERIzDBOFoeM45nA0Gg8cx+l1OmNzKLCcJAksx6Y0VVGU2u4Og1lEKMEEEaqqchjG/eHwS1/60i/+0seu3rj56T//zO/8zm/98Z/+8X/87U98+s/+8H3ve9/f/M1fZzMpyxr/8A//8Gc+85mP/eIvPPX0E+9617u21jcuXrzIMJxlWaIoq6p64cKFwyeOWZb12GOP9fv9xcX5yA/6/f7Kykqv0/3Sl7508uTJ+++/v9Gob2xsIIR6vd799z9s2/bFixcr5SnbcquV6a2trdnZ2X5/CFbZi4uLmUym2WzazjgI3SRJDMOQFJnluf5waDk2QoiXRMdxBJ0zDAMYY1BswlxLr9dDDC6VSiRB4/E4Jkkmk9nc3CSEpFIpsCGGN9bzPNsOrNH4wQcfPHbs2NrtVdu0oMuANVv5QoEXhf5oOByNoGuwbD9JEoZBgiByLCYkASIglysKHMMwOEkSRGhKN0ReUFU18SnGOEkwy7IMx4LmmUShGccRxUwcJzBF8T3LzklKxAcocoZhNE2DccyDIphJBL6ukkQITTgT6CElSeKMFMuyw+EQnNTiOO52u61W69KlSwghLZ3h92vXbDY/N7cAjpRgMs+yLABxDMPADgMJ4+FgPDU1dfbs3ebI+upXv37lyuckQfy7L34+m82mdBUhwrK4UM6qsswwDMxlY8zKkloqlXQ9xTI8dJiUUkCPYOkXQoRl2Tt37tx1110zMzN/98UvaJq2vr4+Go38MDAMY2yas7OzHMddfPXyAw8+sL29vby8HCXJhQsXwPR+bm725u3bhWLu/vvv//KXv9zrthmGOXHs6NVXL/f7fXCh7jXa6XQ6l8tls1myvwzLdd1cLgcZEqqSZH8tpqzKDMIMw4ehb9njYa/f63UsexyHIWZoSlMJjYPQYxiczRgzM9PDbpdlWYyYiFCBFzTDUFWdE4R6s/Unf/xnP/+//IuLr17+4z/58/f/yAcb7fZv/c7/+Tu/+XsXLnxnY/0Ox3G/+Zu/eeLksU996lMPPHjfr//6r//e7/6nz372s2trG71Od3NzO5VKlUvVzdqOJEmnTp2CYQswOwQTpPPnz9u2/corr9x//33Hjx+/ceNGv9+/fPmKpmlxTLrdvucFhw4d4XlREESMaRB4mqbJslguFwmJDUMjJLZHlq7rqq7JqhIlieXYgiBwPA+BxPM8IhTW0RiaLstyFEXVahUxuN/v+16YzWa9wN/a2gZuE8oogecqlYqmOqurq7OzU77jWpZVLBSCIGg5rTOnTzMM02q1wCRGUZQwiW1wFkc0l1Mty3Idx3UshBDGSBQ4XuQty5qdrhqGznOMLMuSJJimGQSByMrgBAn4sCiKHMdSQlhRoZQ6cURCn0MJwZTCchmoPjGilCSIUlCYUYoIoZQQhmUhbCa1KNqHOrl9A/M3tnOpVArKUagwwzCMKAK8IQ5C2H+CDgwfdpoduA8JGQgZjPHEHzaKInhDYZbih3/ofcVSeWw7f/CJ/7p6YxUmXADv8UNPQ5Kuq7zAcBjFNMAEgwubIEgYsRzH8zyvqYYgCDCjBJvlJUnkBdayxpZlPfjgg6VS6dN/+ReLi4tbW1uj0ahYLsG4TTabnZ2dffHl75w6dQowzIWFheHAAjXGu9/9bsdx2u3WRz/60Vs3rokir6rqzHR1qlr+2te/QimNwyAOA0Q4sC1jGEbXdRj2KRaLQA9AkQ9vHQyvJEmCMaKU+q49HPZ7vd5o3As8T9VkkeUZkSUk4XmGEOKFdqO9yzEsSQjDMighTmRxgsDxIsdxo9Fo/Ykn3v+BH6lWZm/dWTt85MT584+sr6//+Z//+fvf//7P/OWfy7L4wgvP12q1JApFif/xH//xX/7lX3744Yc//vGP/+kf/8mb3vQDt27devzxx7VMyjTNH/iBH3j44YdfeOG5fqfLsuzNmzfPnTl78eLFqampM3fdBVble8RgrXX16lWW5cIwlCT5c5/7u52d7cXFRWDwS+VCp9tSVEmUeMwkQegghMIwjEYjWVUMwzBti2EYN/BFUeQ4Lo5jgeNN03RdVxalQqHQatdFUWR5DiZjVFVleY7juFQqNRiNYLgxXyyUy+WUEYDAJZtK37hxo5FK8Rw3tuxerwe7ljHGvV7PcuwgjhzH6ff7Y9tSlJzveXEcYowYhmHw/ph7EnEcx7FsEPiQe6CeBN9Kx3MBnZY5RZAkjDEOAoAzwzDmDraC9ADHAKfypBwl+0uJgWyYzEzAxYFfy/XjAxT/eDxmGJj73ytuKdkDWqMgCMMQYywKAsMwYEEpicqkR40TEoV7rSmDuSSmXhwghHLZwtGjR0+dOjUzM/P0M0/evL26s10bDgY8J0J9EsfxwtK8IokUJZZjKkQoFvO8wDqO47t+kiQsG3CsYBipyVQUzDFNynIARXie5yX5xZcvzM7O3759R5blhcVlhmF2d2pLK8szMzNbuzuEoFOnTn/pS186d/fdnW5/Z3ubYdF4bB89evSv//qvFxYWTp488fTTTzWbzdFgqChKo1FLorBSqRi6bOjyaOjHcWRZJvgXh2GQzWY1TbMsC+ptoEMppbCqTeR4iEbPd3zXcT07Dvck9WDihRDhBRYh1vOcwaCnY81yHZbl4oTanh8nVJIVyx4fO3bsiaee+sQnPvELv/iL/6/f+I1/+S//n5/+9Kd/6X/9X8fj8Z07d970pjdtbKw9+uijmWzqwndeunPnDs/z73nPe55++ul2u/vjH/mo43jPP//83NycF4elUunFF19cW1t785sfETn+lVdeOXTo0Pb2drlctm37pZdeOnPmdCaTqdfrg8FAVdLT07OZTGptba1cqubz+Xvvvafb7bquTSldWlq6dOkV2zZFUWy3Q9d1EEJRFLmWl8qk4QqBqQCWZcFhqFQoxmEI7AvDMCsrK88//3xCyalTp6IweemllxzPLZcrcRybtg3uEp7n+b4vSXI+nw+Jl81mDcNAlCqiVC6WOJaVJKlcLnueF4RhEAR+FELAxHE8Gg2hIpMkWRRg2QvlGGZ2dlbXVLpPInCYSetpscDXdtuQpQD01lMZ8BYbDocEB2yCMWG4g2GDD0wbQRhMiIrJfcdxgiCA+vhgxzhh21+HahqGEUVRGASwe4DjOIHjwV8ZnojZ13MDnagoGXTAffQgVBtFka7rhw8fPnnypCRJly5d+qM/+iNVk0mCWJbN5nIs5jDGrMCn02nbtnu9Ds+hcrlUqhRVRaKUcJxhJZYoihwnUIKBEgj8vU3xoigaKQ0h4jg2pbRSqVSr1a3VjeXl5W984xv5QkE3DNd1y+UyxcgwDNXQv/Od7/zoj/7o7dVVTdehQvY8z3Xdu++5u97Y/dKXv/iff+/3/uqv/mowGABhpWmaNR5VSsViPgf9sKYpjuNQmoShPxz2HceCYSKAf6DbAUd6MBCYKlU9z3EcJwx9zFCMKcaIZ/Go3ytXirquWPZ4OBwmSSQIvKoqfMQTQhCKEcIIkSSJCE0IIZub6/fdd98rly4/88yzH/rQh37n9/7zE48/9Za3vO1T/5//srOz9bP/7J8cO3as3W59/R++urK0uLm1vr6+HvnBD/3QD62urv3sz/7shz70Y7/xG7/xXz/x39a2N2dmZrrd7u3bt5eXFx944AFVVb/z8ovHjhxtt5uwIA38s0HPOT21cPPmzSiK3vGOdwWB98yzT9XrdVVVVFX2fKdQKMzOziqKPB6Px+OxYRg0jGRZhpEFEKOBsMZxHEWUYKoo9H3Hss3R2PM8z7fL5XJMkl6vhxF76NChIAp7vT402IqiybJsu06j0SiXqtlsdmB2R6PRvffe2+t2V2/eKuYLlFJw6WYYhuU4gihEoCRJoiIjLNi27bte4LtJzAiCoMqyLIs0IaCAFTkeCxQYfJY10ul0EASYZcMwFESZ53nMMoRgQZRjxMYUEYbFhxfnXsdAkH0btdcBLVAfJvh7L7uYNIEHQxohxIlCFEVRGAKmAkU8wBUswlBtIkrhCvN9X9dyk4edlKMMw9x3330LCwuSJL366qvPPffcYDSYrk7Pzc21Os04jpMY9tLsQfyqLDIMo6qiIouKImeyeiZtUEps21QFfXNzM0noVHVG1w2OExRZE0WRUrq1tSWIXCaTkmVpfmHW85xvf/vbR5YOE0J836/Vau94xzu2drYtyzIM4z3ve++HPvShf/fv/p3tOE8//fReafrii77vX79+/bOf/ey//tf/+p577rnr1Knf+I3fOHLkSBAE+XyeZdlms3nPPfcMBoPNzc1isbiz04KtfWfOnGk0GmD4lyQJEGVQ5U5EDnEcK4IMjoCua0uSUCoXLWssSUKv38nnc5lMqtNt+b7L87znuYZhqIwex7Fp2cPh0PEDzHAMxyaUMS2nPxqLkuK4/n/9wz/8iz//zNrG+r/7d//+r/7iU//wD187d/b0f/gP/8edO6t/8ZefHg36iipls1l7bJ4/f/6uu858+7Fvra1t3H333W9+06N9c/SFL3wBYmM47BeyuRMnTmi6cvXVK0kSJUnSajQOHz6UzWbjOL506dIjD79lfX0dVr4QEucL2TAMJUk0zZHnO9PT06Y5EkVhNBqB5aFvEVEURVmyHLvb7/OioKqqH4Xdbndhdg4htLK0bGjarRs3VVmZmZkZjfvNZtMPA0EQwiAOwxAxOI6TMAyDKCIEsSyLYJERYhFCfuyU8oVcLuc6Tq/dETheVRTf94vF4mAwsB1H0dRCuSSI4mAwaHU7ju0HQQC+OwzDsHg/h1GSSqWqpXK1Ws1m0xzHhUHg+/7q+pbrumEY6ykDlLS5bKHRbi0uLouSZNv2cDjiDiac18UVs6/bpvu21hzHIfpd7fVBIHTys/i1KlAoEuBigt4GUwTVbBLFe054oqgoSjqd5jhuNHRBtcyyrGEYU1NT09PT2Wx2e3v761//+tbWFsMws7Ozx48f7/f7r776arlawJgyLGIxphRBWmBZNpXSSRJBDaApKqUUEjjP89PT0wzD8ZyI9u1MwB5b13XXsxFCMzMztVrt1q0b2WwWIWZ7e3txcfHUqdOb2zuO47qu/673vPf//L0/+PCHPjI9M/dHf/RH7Xb3nvvu/8Y3vjEcDlVJ/Lf/9t9+/etfb7fbR48e/YM/+IO5uZlMJjUajdbWVt/97ncHrsMxxDYHDz94vt1uF87e5fv+eNS3rVEYuDynciySJSkKvSj0JsUCu7/FlbAMolES+yQJkxj7rhP4LqGh6zpxnKKUAl6VK+bCKOptb88XFsIwtFzHDfwoiRhKMGUShGdmq6VqSZL1wWD0hc997r57726321/4u8+99a1vXV29tbm5+cQTT9x//32/+qu/+pm/+PNsLp3JZJ558qkXX3yx3x/ec889mmZsbW397ku/e/8jD/3SL/3Sl7/85a9+9atvetPDKU1fXV29+56zHMeZ5iiXy/3UT/0Uz3M3btyo1+vz8/Pf/OY3K5XKysoSITEsh0mS2DTNH/mRH7lz587YHG5tbYA6n+NYjDHIvnVdF2XJC4Juv9dqtSzXgTbStm3XdXVVBdEIIaTdbluWJSmypmk+G7Isq2iqqmobGxuNVqtSmUqn01s726IoplOpzc3NVE6DSXGWYeIgJPGe0NI0zZWVlWwuNxyPGu2WZdtw5Vy5chVhJCsiz7D7sAhhMM5kMjzDRlHUarWGgx7w2AzD8KLExYnrB1EUJRS7lsNwAsMwtuNQaHejkJsEzyTYDgYhfq21hCiKNI4m3/m6cnFy52BPSBFVFIVlGMuyAFBhGQZwJ1YQ9zjJOAavAT+IU3qqWCzOz8+Xy2WO4waDwZ07t/v9fqfTkWU5m00jhNrtZqNRS6VShw4tW/aQwZRhEceyHOYYhpFlRZWlbDozGPYCz4/CMI4JTRISJwIrXL58eXZ2dm5uAVHGNC3f913Hbzab4J1z/r57VlaWbt++tb2zqapqHMe3bt0C/cri8hL2mFdfffVXfu3X/uzP/mw0Gv3qv/m1X/7lX75x48aP/9RPfvWrX93Y2Hj3u999+8b1+fn5T3/60+fOndvZ3ZIkYXd3d3FxUeTZfDbNs3hufrpRq0mCkESRyPMch9zYn5kqMQyjyhVFUQaDAZA6cOKCjGEShBIvRZEsy6IkMizLihJLkcwwmGPY0WggSQKl1LRtThCSJBkMRmzSgMehlCIGY2BsKTUtvpAvmbZZyKdfufDi/ffd+5Y3P/zE409yj9z3Uz/1U7/9W7+5trb2pjc98uWvfP1nfuZn/uIvP33z5s2zZ8/WajWE0Obm5unTp6Mo4ljhzp07Tz/99L//9//+7rvvfvLJx+M4Ho/Hnud9+MMffv75Z2/dutXr9aIoBG4T7GHD0B+NBwiTOAnr9TrPc5Ikffvb36aUzsxOHTlyTFVV0zTr9d1Go5lPVaCLI4iCOkMQhIwoxHEMHxmlFLr3+m5tNBpVqkUoBXVdVxVUr9fX1tZGozF4Z8D5DlZdkx8EmYQgCKlUisQJRgg++vF43B8MLMfmREHTtG63u7q+pigKz+xJNaHZA5eLlKbDXVjcAA8oimLfDlU9ZTleGBOGYcBDQJZlczzEDI2TmKIEH56boa+9TUpKGNgDGADwTEVRzMADqAD2DaADdOIba1GEEMGoUCgIPD8YDGzbFgSBY1hIdJEfwK4fSRThwUVRXFlY8TwP+gfTNGFumlJaKBQ6nY7neZlMJp/PY4xt2/Z9X1Y5SimDOUEQeVbgOE6RNVVRcrlcp9MyhyNB5PL5TDpjyKLEsnhnp4YQ0jRDFGSEsCTtLQB1HOfYsWOYoY5jqarS63eGw/709HQcEFhCMB6PL1++/KEf+7Enn3zy6tWrf/B//ZdPfvKTq6urWsqYnZ29ceNGqVTK5XKYJOfOnfu1X/u1T/zX//L5z39+2B8oiuS7bi6XKxYLKd1wXMs0TUWSTdPM5XKDwci27XK5PFmE0u12wQ0FdJLAwQLAjBDKptLwGQVBQDCCxSkI47E53K3VpqamOI7b3NmGHp4Q4nuJ67pxHEqSJMkilPc8z6czmWPHjj/99LOHDx1vNluIMj/4g+/7yle+5pP4Pe951xOPfyudNiglb3nrD+xub1Wqpa9//esz1SmGYZ544qlTJ05SihVFabe6V2/dqFQq7Xb7rW996wc+8P5mrX7hwoWLly6wmHnzmx9RFGVzfb3RqB87diyO452dnVwu12g0ANuAYSW4oKvVsu/7kiTdvHlT07R8Pp/P5yVJGvdNx3HGlhmTxA/DVqc9GAwIRhzH3X3mbBRFiND52dlOq7166zbP8w8+dN+VK1fGllkoFDhWGA6Hpm2FYTQej6dmZhzHi+N4enYG5khTqRQrIvAQSuKYxglNCM9xMHefTqdZjrNdx49CzDBhGI4sM/RdIAYButMUVZZlDnOu6xqGAQaNsJI5jiJKacjJuVyu2aq7rsuy7Hg8xhgLHK8oUqFQ4DguDvcz4UFtCn7DlODBgvOgkAVuB7vBN96AUaCwdDqKKKUJwwLMBa4NsiynDMMwDGAgXr1yCUAIKF/321Hc7bUZFucLWU3TgtCDYfB8IRv4Y0IIxglGCaUxiWkcBXHAJWGkyVroeqY5YjHWVYMV2cAN5ubmbty4YZr2kcPHisVSFCW25WKMz58/Twi5/OrFdru5tLQoiBxCaDwee1YoSQ5J6PXr16dn52RZsSz7t3/3d1944cVr166fvOsuSZJqtdrszJykyN/69uO/9R9//RN/8F9+/Cc+sr6+HgSBLIuzs7O9TjuTSUdBmJrRa7vbKytL/X6/UimFvq8pAiICixNz1APPvyiKUrpMKeUYQlkqcIgmge95hBBJkhx7xLKsF/jwZjoOJ4pyQgnDMJSQOCYMg3hehN4hjmOCY8vzvShGfMQTXpSFbDadz+fN0bhYyBq6oqn8+XNn/u7vvtDc3Xrg3jN/+5Wvdzqd97///V/60hcJSY4fP/7tx7551+mTsizX6/XhcHj+/PmLF14RBCmfz7/7XT+YMKjdbouiOB6P/+Iv/kKTlVOnTrEcfuqJJ69cuXLq1Kn5+fn5+bnt7W3LsjzP293dVlUVLgxV1avVahzHV69ezWQyQRAyDJckNIqSfn/Y6w08zyvnS77v266jGfp0scjynGmaY9vK5/OgyR72BzzLMggDvf70008zDLO8sry4uDjoj2D/WRzHMzMzpUql2+0Ph0O4/PaG41i0Z5kZxzzDBp5PkgQW0QyHQ0mWwzjqDQeO6yqKksnnNE2D6zNJElmUYNdiEkb5fNbQdU01CCFhEDi2DU8tFqtVWdL0lB9ECGPYHmvbJqVJJqULrMRg8l2y/nWaT7o/OjgpLyd88Rtjj752MOJgMmRZ1vO8MAgADCSEJBRhjBVFUUQJBlgxQqZpbm5uWpbFYTwB5aEABrrMtu0oihzHRoiCcWgcx5ZlKhJKEKaUkDhKaIwSFAVB6PmqrCiKkktnaJywiMEEJQmNgpjhQ0mSVFUHSTGA3aVSybKsmzdvcjyzuLjYarUYFuVyGdM056eXUqnU+vr6/Pz8oaNHfv3Xf/13f/d3X3jhhb/5m785f//9u7u7Kysr6XS61WmPt8bvete7ut3uxVcv/9q/+ZVPfvKTqqpm06lOp3PmrrvM0bjZqnuOXSoVWq1WLpPieX61vptPp9VcBiGU1pROxyahzzMMSxNJkmKf5TFVVTlJEhL6BGNV5IfmUFG0MHAxxoTQJIh4no/CMIyTdDotCEK73bEtZ2ZmhlLcbrdZSSEMSiiJkyQiSUxjWRYLhdytm9dr9a181qBJyLH0xPHDG2u33/Oe95w9e3Y4HPI8XyqVHMf+7d/+7Y98+EN/+7d/+7GPfexLX/hivV5vt9vnz5+/fftOFEWf+cxn3vne93z729+em5u7fPny0tJCtVT+1Kc+9c53vf1jH/vY5csXm80mxzBzc7PwmQZBEIQWwxKeF1VNYRjGsseypKZSKc/zer2eKEqu66VSaZblzLHd6w1iPxIEwQt8lueKgpDNZovFopYyQNcO00PQ20OFefLkyeFw6DjOtWvXet2B4zjpdFrXDUVReoOBruuyLDfbLU3TFhYWarUamyBNVnieN3Q9Y6RGg6E5HiuKcvTo0WazORqNFE2dnp4OwtDzvDAMMylVFEUWM1DNCoIUer4VRpBCEN4rj7PZbDqdZll2c2DanhfEcYJoEsBeACZJkvGwn9JkFIdRHOwBM5PbRLAG7enBoILAIOg1JmsHRW0Hv3lyR1GUIAjCPWpFiuOYQZjjOMMwQs9vtVq+79P9spbjOA5j0LjhvRmlEE4UVVWhSCOEuK4L5F6SJAgjhChDMaIxJSiOEE0og9g4DKkoCoJkaDqoBVjMptMZwsZzc3NJQre2tizLzueLiwvLhmE89thji4uLCYksa5zL5Xr9DgRYv9+/fft2qVS66+yZb3/72x/96Ee7/d7LL7/8jne84+Lly3DSf/mrX0mn06IofuADH/hPv/tbP/MzP/XNb36zUCiUy8VbN26uHFq2bdu0RqlUajQaGYaxvblezGe3t7eDwDdHw5mZGdd1F+ZmMSUwOhCHQYioY5lxHIs8Bx5WSRKTOCJRKHKYSJwsq5RSzw8NTRlbNIwTVdHjOB4MBlFCoyhxHM+2XZqQmCBVN3K5lCiwCYm8MPB8t1wuDHs9TZF9z9naXOM53Gk0n3/m6XJl/oknvl0s5E6ePPn5z/9dLp8hhExPT//xH//x3WfOMgxz69bqxYsXK5WpbrdLCP3GN77x8MMP7+zswOAS7Db71re+tbm+cf/95zHGTz7+eKvVvOeeeziOe+6550rlNFjf67oahkGn08lm8gzDuK5LKc1ms5pqVCqVKIpUxTKMNCZROp3u9nsjc7y5uel4rmmaoiLD0NDU1BTPctZ4TBMCXYxmyePxmGKQauF0Os0JPDisjsdjUZShtmdZFuQ1Ed3zf0ilUuVSGVMUBgFsHzMMI5VOIwZ7YUAI4Xkec2wQBJqiQtOYJEkc78GN9VoNNr8KnAj5Ay5jQYyarc54PGQw9mw7Cv1sOqVIgu/FPIMFnkV0n6Cf1KKvQ0oPBhiEBN1XtLH7twl+M8mNB3lCGIEHQ2Vuf5sSwzAHdaF0X3bD8zzHM5ihCYnCyI/iAGEiiJymK1EcEBpHceAHbkIiXmB5gaUoQUmMSExRAgoGntszmCKEeJ4XBSHsWw6DWOSkarkCm150XRdFcWpqCsiDL3/5y/fccw+suXRd99q1a7qunzp1Coqora2te+65Z21tDWP89re//fd///fT6TQh5O6775Ykqd/vq6pqWdbZs2e/8pWv6Lr+4IMPHvTtS+vGcDis1+uyLKqqev3G1ZWVlcFgMBz006kURYkkC45rCSKHGcpymOOZKA54gUWYUJQIIqcbaiqta7oiK2IQehQlPMPK8t5wM5BJoDhxXY9SynHccDhutVoJIbbjMgxTKJUWV5bnFuZTqVScRKY5qlQq/UF3NB56ru3aZqfZmJufvfLqpWw2Wy6Xoyja3t7+4Ac/OD09ffXq1be//e2wY1gURYi34XDY7/fhE/z617/O8/xb3/rWTqfz0ksvaZp28uRJSulzzz337LPPHj9+fHl52TTNjY2NQ4cOKYoyMzOTy2WjKCSE5HI5SRY2NjbS6XShUCwUCjzPdzq9jY2t8Xis63q73Y7jGEDsbrfb7XZd1wX1WafTGY1GruuOx3uyb5CbEUJ0Xed5HuRslmXB+B8YpQP9SAjZ3t6GGQBCCIicbNseDAatVqvdbo9GI8hpEAggXRqPx6PBEC7d0WjUarXazeZw1Pd9H65hSZIgW4Dn4tbWlqjI3W630+mEYeg4DhTDiqLADE2xmK+Uy0zCMQnHUIGjAkd4lvAsfIXwbMziENMAkQCRENOIQTGLGUJxQmgUkzCiUYwTwiEsMCxLEUMo/MEJwQlBcYLixB6bPMPqqsZiJoliliCGosD1Yj+ghHAsK7IchxkWYZ5hBZYLYHKO7qnm9oKcIpEXcEIZgmRelDBH/Yj6kYhYL0AxYQnlowSFcUxQQnESEz/BfqGcNXLa2O6b9lhSRDd0X7rw8kyphKMoCdyZal4R0TNP/sPVq8/ff/+JwWCbF8PhqLGxeXN+aaoyVVzbuJPNZ9Y2t+4+f9/G9k6/P/yJn/ipr33tH04eP9VqtDfWNhmCZ6szu5s7b37gkdPHTp07ecaQtIfuObl69RXP7OUMqdfcVUTG9yxKwkIh1+12O71uOpPT0pkgIVomRzmhPzY3dnaxKL568+bAcvumY0XJyI8GXuhSxkP8eqt3u9bijIJamL6921EzRSdC6fKUG9EAseu7tZgTepbjJKQ1Ho5832dYKZ3q2BZWFYckVR1hpy0EY2yNarduFVU9r2TmKwuqoB9aOqFrWdsJsoUKLyt3NjcKU2UcD+49fajfqrmjcVrO3Lh4Z+Nm+7GvvPiLP/sr//UPPk0i7ud/9ucwE4+tBmYtxfADx4x9p9usqSL3yAP3v+mRh7Y213e3d2q1miBIa2sbrU6PE2TL8adnF+46c7cgqbyoveVt7z589PSNm5uN5jCdmVpaOdHpjR0varQ6mMNOYPqxlWCfEaITp08FSXRnfW08HuuqqooSTggTJdOFkshyta1tczQaDAYjc5wvFwuVUqZcwZLcGQwJxUZalxVeVQSS+IHviDyTzuilSllPZQaW2xmNsaKM2v3GzrY9GsaB7zgWy1FJ4cfWgONJEFq+MwzsAfKtoi4tlLIlRcgZeUNOCVg05NRUcTqlZWnMYMpUK9OnTp5OGZl6o2HZtqbrumGomhYMzJlC+dDsAvUjTGgxk2MxRgidO3cuncs6Qdgd9PfGQ6GPh7MEvdbX8P//2wTOId/Lbe1gXp2Yi05y7Pd8wNclXrTPi8CAhWEY6XQ6n8/D9+RyOdgRa5qmruvXrl2jlML/ApZt2/aNGzeuXLny0ksv1Wq1hx9+OJPJpFKpwWCwvr5+7ty5U6dOXbp06ed+7ueeeeaZIAhyudzZs2cXFhaAec/n81/4whcIIU888cSFCxeKxWKtVoPqALbt7cFompZOp+M4XlhYgMkA3/dhMHI4HhFCLNPRdR3QM4ZhoijSNAPG0scjazAYgG9CGMWCKHGcEMaJaZrd/rDV6kRR1O9044j0+31D07vdriRw1mjMIux5Hix5NwwDHt913evXrzcaje3tbVD8QqewuLi4uLi4trZWKpVYFkuS9MrFlz/+8Y97vguOLOfOnXv++ecLhdLU1MzKygrYb0IVmiTJ9evX19fXVVU9e/ZsEATHjh3L5/MnT54UBOH27dsbGxvPPPPMP/zDP5w/f75QKKytrYVhCPuAV1dXt7a2AKLjOA4akP7+LYqilZWVu+++O5PJOI4Dq4fG43GtVnMcR1XVXC535MgRSZJGo1Gz2YS+APKSpmkgSQNDCowxTPGDHykoLnO5TLFY5Hke3H4lSSkUCrBPW1VV6CEppa7rWrYNfVCSJJ7nWZZlWVYYhuCW0u/3x+PxYDCo1Wr1eh0KYIB/YEINdokXi8VMJoMx3tzcHA6HoihWq9MMv7+PHto8dn8D2T8SUf9TtzfGIcTG6/45ibrJoTCZ4Tj4UG8U5UyK4ck/IaRBcgEVJgjiZFleWlp6+OE3ZTK59fXNK1eu+X64tLSysnzY0NPn771f11NzcwvZbP7y5SutVuftb3/nO9/5blmWO53Oxz/+8T/90z89duwYOCAxDAMi1VarxTBMKpV605veND8//xM/8RP9fp9SOjc35zhOs9mE7fY3b96cyBXgjuM4AG/KqhLH8aA/goY5TGLLsjmO8/0QJnolUQnDEFjpKIoYVnTccHNrZ2e75gUJQszOzs729k69Xu/3+57jJlFI4oQmRBS4lKEpijI9PQ2zLDzP+74vCAKcDuC8RAgZDoe9Xi9Jkm63m8vlLly4cPTo0a2treeff351dTWdTj/+xLefe+65H/zBdxNCPvaxj/3Tf/pP6/V6uVxlGEaW5enp6Vwu1+/3t7e3G43GysrK3NzctWvXnn32Wdd1fd/vdDqqqsL+90984hO7u7vZbPb8+fOnT5/GGK+vryuKAhcxqIXgUAB2lFIqSRLoaSeXShiG/X4fLnRoWTHGsO8efKJgLBOMsTHG+Xx+amoKIQTDuJqmQWTCgyPEQGxjsAhLEp7n44gQQjDHyrICw+i5XK5YKkGkOY4DGQV0eaVSiVJ68+bNzc1NyAEgYJyenl5aWur3u2traxQRTdNM0xwOh4qiQPMFnRE3wWDI/u5B9AbpzGuD8PsG5/f8OsMyEzT1IH4z6Tkxwgef96BMZxK6iPxj6CtCCNE9rWkcxz71wzAkSZIyQkLIYDBIkqRYLILPuWMNWJYtFouVSgW4OJ7ni8Xi9evXwSTmySefzOVyOzs7cRzPzs6+8sorP/3TP/2Vr3xlOBx+5StfyWaz0JacOnUKHDc2NjbOnz///PPPv/jii8eOHauW5ampKUEQBoPB5JeNogjEk+CGlMlk2u02kFEpI8MyfLPZvOuuu9Y2N7PZXLfbLRTLAPExLA9eYFEURYQyvGA5PqU0CILhcCwpOqK42xsGQaDrumvbmUzGGo+zht7v97P5oizJU4WsYRgsMx4MBkDEgSAOXEwn0FqhUFhcXNzd3YWxqdXVVYZhjhw58hu/8Rv/7b/9f+v1+tbWRq934tixY3ES/P7v//5P/9Q/ee75pxuNmihrYBEfx7Gqqjs7O4VCYWpqKp1Ow+RELpe7ffv2aDQChvDo0aO2bX/2s581jPTJE6fuvffeVCoDLDbGmOdZjmM4nmVZ7Hlev993TB/YAlC0wwaEidceqJ2q1Sos/cxms+B1FIUBJtSyxiSJDMOA758Q2gzDwEPZnhsmcTAeZVPpubkFTVO2traGg0EulylXK1Hox3EchEESx0EQ2LbdarUIEsHwG7AZgB5gqVu/3xcEoVwuC4LQ7XZhsMF1bdhrAsvhGrs7iiqlUnqpVIrjsNcbRIHHTAgTuCX7K+y/d6ghRL/PH0Lp9/yDD7R28DfcJmzkwQIVfqvveXtjBMJt4roPNi1wfCKEUqmU4zjdbpdSCjO7nU7n0qVL169f7/V6oihCoQJzIYPBQJKkRqNh2zaUsul0OpVKXb9+/ad/+qfX1tba7bau667rttttcOOzbfvOnTv1en08Hs/MzKyvry8sLFQqFUopOJRIkgQguO/7d999N/TuSZL0er12u82ybDqdhuzEcRzPC6KiqKoqy7IsqXCJ9AejbreLMcvwQm84ajabjuPU6m3Xi2TF4AWl2xv1+mPbcnPZQiaVVmVF4HgWMzQhSRSjOGIpAc8ymI0ihMCGKVmWJ9lGVVVVVZeXl2dmZgByJITUajUwtD169PCf/ukfv+1tb+n1ek8//fTi4uLKygpJ6JNPPv2Od7wzjgkgFjs7O71er1KpaJoGwjE4cLe3twEygfWPCKGXX35Z1/W3ve1toij6vr+6ugrAI/ArcAUqilIoFFKp1B4Jwe0tWoGFH2CYnclk4L+8fVNWjLGqqtlsdk87NhzCp8yy7GAwaDQaLMtC5QxpKtm30Od5XtE1SVVYlqeUIox5nk+lUpRgkHlxomgYKUBcyuUyDGHDUCtAsvV6HRLy7OwsXCQgW7t27dorr7xSKpXuvuecJEm9XgdgWHCXRoiBF8+BehMsFSZw6D+S2f5nMyE94OGNEMTa3tTFngnifkkJqXIiVqb7S03f+JiTCEQIkYRAeMMlJXAcSRAhxDRNSnAmkykVCoEfbm5uBkFgGEa5XISTu9PpQJsUJ8lwOCyXyzdXb9dqtVKptLq6WiwWFxcXl5aWMKYvvPCcJEkTXfVTTz1x//33y7J44cIqpfTYsWMvvfTC5ub6Aw88sLu73etsnjhxwvMCUZQNI2VZDiGIUkwpVhRtPLYEQarVGocOHWIYjmHQcDgaDod6OlWv1w09Xa/XFU11HI/lBNd1CUGypnueV6814IrRUsWg00+Hie0GQehTiglBDMYIMYIgDLq9YrE4Hg3K+RwlSUpVhsOhIAi52TxMuIVhOBh04K2gFMOCIZBGbGxsrK+vT83PXrt2LZPJVavV1dU7b3rzw7/9n353cXHxyJFDtmPevn17YWFhNOrduXNne+v4I4+86cJ3rgNHBzRgsVicNEL5fB5yIEIICkXLsmB4GhZrt/iW43hTUzNhGAKVHwQewyBFlQWBkySJ45hqtQq29teuXYPyARbI8DwP+6HgzFUUxbbtOI71lOE4jm1psixLkiSKPMyXDofDbC4ny7JpuVa7PbYd6OiSmDMKBsuyOzs7PM+zLJ9KpQjFzWaz3W67tiPJAsdxDM/pvJHL5TlRdl03CAIgKrPZrCzLEIqwvNnzPMdxQBMWx3F1qkJo0mw2u902x3HpbLpYLCqKVK/XQbyZz1QZcCKBKhlk+5CRvm8m/Ed7vzfeXmc3ivadvA+GEwip4Jbsj/9OforZX839PeMQhjsnTSbk8yiKTNPEGKdSKUVRLMtqt9twVjEMp2lGqVjJZvKU4OFg7Puhquq+Hz766FsXF5cNI/2D73nfwvyS74Xnzt7z2GOPLSwsAEp+7Ngxy7IWFxfDMNzc3BwMBtPT0/fee28QBIcOHWIYBnYh2FAWWlaj0dB1nRCytbWVSqXgHQD6aHLk9YfjMCaypFqmE8exF4SEoMFgYNsuQgwvShgxruPZth3GEcPxHC+32v16s2Watut4hpFKp9OSqIgcjwmlhPAcoymqoWuKLPEc2+l04jieXLXj8RievdfrNZtNGBQE6nVra4sQsr21kyTJ5uampiuLi4tXrlz5uX/yc6+88vJHPvKR7e3tqakpURTb7e6jj7716tXrU9W5SqUSBIGmaalUqlarwR1BEFqtFs/zsLnJMIwTJ07kcrnl5eVqtdput5vNJrARSZLYtg1XsCAIuq5jjLvd7vb2Nqyd2N3drdfrvu8DTGIYRqVSWVxcjPcduyH8IJ9D6wtoja7rULczDKPrOrAacNzYtg2QgaZpURLHCXEdDySvlMEIMa7rmqYdhiHFyPdCeKPWtzZb3Q7LstBZQKSJopjL5cD2oVar9ft9WHkCg3v5fH5+fr7f7167dmU8HqdSKeDGUqnU2tpaq9UKgoBlWQ7oO9gSDqAcTK8BG/7GG/k+AjXy/TJhQiaZEGNM99drcyxLD1COE76LvMGZG2OM9nVy+A36OEVRkiRJYhoEQRRFiJA4InEcrywvi4IcBMHu7q5pmlB8+r7/4P33bmxs3F5d1XW9VCoRQgLL5Hl+Zmbmm9/85rlz5yRJarValNK3vOUtn/rUpxBhbty4cfbsWUmS7ty5Ax//9PR0v9+fmZnRNO3VV1+9cePG0tLSaDSqVqsnj8+3Wi1A+brd7vT0NELIMIxSqbS2tpbL5VzXnZmZcRwnlUpBEZXPF0VR5gTRcV3DMKIwSSjxPIfheMMwOE6IEsLygq4osiw7XgijzxiRbtc2NK1YLC0tLASe06gH5XI59IOpSrnVasmybA4HwKr1ej04bR3HWV4+BGjecDj2fT+fz1um4/s+vGxZ17e3t48dO/H8888vLa4MBj2IgW8//tjHPvax//7f/yafz73jHe9YX78zHJjf/Oa33vLo27/61a+2222e503TBPeT8Xi8vb2tqirLsuvr6yzLZjKZWq2WSqXOnTt3/frN0Wi0srJiW47r+uAr5zjO1NRUsZiXZTFOIteNZVlOpw0aU9gZjDF2HKder8MehGKxCP1YEATgVgqOWITBgIRTRZVlURQ4QgjYMsRx7PoBZIUoiliWzeVy3rAD3WahUMimM45rm6bJUCQIRNdTsiwihDzPkwQRbNQ0LdtsNuEFm6ZpWRYcIkmSpNNpaHwIIaIoIoQ6nU775nVRFGdnZ23bxBiJosjzrOu6umaompJKZSRJYqANAGAKMilwnRASE5UdSPhBb4335TITRAcdMKR5HfQCPwVSXShB9+3rE2iH4CvwzaAURwfQ1NdlxYPPC18EWhxsmuCVZLPZlZUVhBDP8/BSM5kMaDJt2waaAboX27b7/T5YfZqmOTc3B93a5ubmO97xjj/6oz/ieb5YLNx333lCkkIhPz8/5/ve2bNnwjB4/PFvLyzMLy0tvvTSi7qu3XXXqfvvv6/b7Vy7eiPwo0p56tjRE/lcMfAjluE5VnAdX5ZURdYowQzmlhZXzLHNMvyRY8eH45HtOppqYMQ2W51mu53N5hiOkyTFcbz+cIARSwm2LRdRBsqWsTl0XVdRJI7j0oaBSAzvjyLteQtompbSNZEXqtUqy7KmaVJKOY47c+YMCHemp6ePHj1arVZBLQgrZY4dO5bL5ebn51VVDYJA05VUykiSaGFh7uLFC3NzMwAtqqq+traxtLTCMsLnPve5H/3RH22324VC4cEHH7x48eLq6qogCDBr1mg0isViNpvt9/vHjh2bm5uDdjEIgq2trd3d3Wazee3atbW1NcuyRFGs1+vgr/O2t72N47jDhw9nMhlonIrFIqQv+E0Hg0Gv17t16xYE4UsvvQTX1UsvvTQYDBYWFhBCOzs7/X7fsqyFhQXYMgJ8A7AUuq4HQRDHxLZdyI2dXtf3AkVRZE1FCGVzuZu3VkHeOL+4sLxyuNcfbm1tlUql+fn5Uql06NAhOMcVRaGUguJ8MBjU63XgXTDGsHc9CDx45RPkH8qTRqNx5846BwnE8zyw44dCEYKBHoBVDpIKZH+SEL4f79u0TWLvdZkKIQQLrhFCKNkzp0n2K15CCED2EKLfO89CVkTfYwcGtLmdTgdjLMtyOpUy9LQoiqPh0PfCJElUWdZUHTp427anysuO7S2tTAFq6vvhoUNHKKW2bbdb3fvuu880zZ/4iZ966aWXgyDa2tpJovj+++8Pw3B3dxfeh5s3b169enV5eRkwgGKxePTo0Xa7PRwOWZYle+u4Atg3Au8tnItgz16pVKDesyzrkUce2djYkCVVFOThcNgbDAihURRt79YhScYJBfyAMizGOEwIolTgMc/LkiAQksiiGCfhaDQKfJfnWVmWTWscx2I2m5ZleWdr++jRI+l0uqf3wzCczO9kMhlVVWGX+Pb2tq6lYAvA3NzcPzz2mG3b5XK1Wi0DrzUej13XEQTh61//+vz8nCiKzUb77rvvvXjxIkDzn/vc537lV37lC1/4wtbWlizLKysrjUajVCqxLBtFEYjUQNwzHo+NlCgIQr/fZxjO9wLLsmR5T4O2u7t7+PBKGPpRHNbrdVmWV1dXdSUNvRbGuFKptFqtdDoNkqNOp8MwzPz8/OHDhweDwe3bt69fvz47Owu7soHYWJifNQxjPB47jieIIsMwYFOmaZrvB61Wa2VuGt7qJKF7xENCMINAqK0oSq8/PHbsRK/X6Xa7mqbdvHEHFshSSkulEsdxpVLp1KlTL7zwQq1Wu3PnDsdxkBLgF49YDiG0F4E8g/GeFzjHcZQS3/d9x+VA1g3pCMpCiDEYxp2E3yQYGI7FGCOMKaEUI8wyCMFWi/36cxKKeC94IHHB64Awg8echBP09HuN3/cpdyEI33iDQwSOeSgCoQsKgwCuLU3TGMwAGpZKpXzfr9frLM+xLNtutyGBW5YVRdHS0pJlWXNzc5TSy5cvA845NzMLGCxUQZDSp6enC4XCqVOnYMhYFEXQl/d6vbnpAqV0Y2MDnhGKGZjDkiQJIdRoNFRVBfB2Z2fHcVxZVjiOb7bb7XZbllSCmGazSQgSJYnjBM/zhuY4iiKe58MgEnlGV0SYiEMIqZLIUDI2+yIPSkVeiiRF12SBZ1mWIApshOu6lUpFVVXgkeEKg8sUpAUwfdNutw8fPnzlypWtrS3f93d2dgwjvXJoKZ3K3rlz5/r1a2fPns3n841GY2Fh4cknntZUI47jx5549vz580eOHPnSl770lre85fnnn19cXLx9+/aJEyfu3LnTarXuuuuufr9/8eJFwzAOH7lHFOU4jiVJsS1nOBxLkmLb9uLiYq/XKxQKQeCNzdHm5mYul2s2G+qCkclkEELD4XDCT0y2VhiGsb6+XqvVXNc1TbNSqWCeW1xcbDbq5nB06tQJw9BAE+d5nqKqhUKBUGZ7tz4ajRiGTafT9MCuFJ5lESIMw/AsF4ZhfzSemZnp9/ulUmlra4fn+VRKh1UZkwJwZ2fHNE1JkuBVQUqEOQe4VFzPhlIOKGvAxnzfL5VKe85dUciA9T+cRhPyAHKgJO1NOaD9Bb2Ag4GwBgQuEwXpweyEXkshQEUKrm8QM8neZOl3d4lC3QgZ4/sG4Rt6RYwxFJPwOgEEGwwG4/EYXhuoGQBtB3hwa2trPB5fuHABpqenpqZgqQDAbouLi9ls9ktf+lIulxuNRqdPn+4Pus89/0ychKLEExpLslCr7zSatfvuv7fX71y6/MrxE0c5nrl2/cqdtdv5QpZluSQhvV7fdb1isZTL5QmhSUJs2wnDqN3u3L69Cv8VhtEzzzzrBX4Qhbbj9XvD8cgK4ihJqO+H3V7Pdf0gCMa2ZZl2EhOeEziOUyVOUwRdFQ1NSulyylBVReRZNp/PMQxmWLZUKRaLeYJREEeSqoCJo2VZzWYTAAxIRLBPAiGk6zp4TzSbzdu3b0N97vsuTCclSYQx7vU7mq7ANvlisaQo6vr6xqFDh0ejMULonjMn/v7v/17TtPe///2bm5uQKMbjMQiaofVKpVIAijz77LNra2vdbleW5XK5XCgUyuUyyNa3tnZeffXVXq9HKZ0Qj5ZlVSqVYrHYarVg5IVS2ul0SqUS2IXA3L0sy/AsIASH2srzvNFoBA0LlLWe50FdCtbvuVwujvfHnCklCGHMchzHi4LAi6PRaGp2jlJ64cIFx3V1wxgMx9DJTyQKiqJEUbS+vg7g6vT0dCaTiePYdV0QRUOkQZcI7wYMlEwwPN8POJAdQBRNRnUppaD/gnUue+N/DCNJUpTEE2QPHXBkmoQHPbBoDU3I9EkiZRj4ItnXKDAsi/aL2+8Xgf/IDU5HIFrgn7AVWVNVx/ZGo1E+m52ZnpVl+fbt291u1zGtYrFouw7PC1NT07pujOv1dDodBtHi8oogiHGcJAm5c2ftLW95y/Xr16empkqlUjabvXr16szMzF133eX7/pEjRyBv9Pv9s2fPXrp0CQ6U0WgUOAz4iGqatri4OB6PG40GnFxra2sAaguCcPPmzZs3byqK4nshALmj0WhsOwQzcRxHMfG8gFLKcDxGDKWUZVlZ1QzDSPFBkiSyxEkSzzCMqkosL7AcVjU5IVEqrXMcJylyGEcJQaJiI4SWl5dZhltdXfU8b3FxUZaF3d3dMAxd14dPCs7BJElkWd7c3HRdFxa1m6YtSVKSRLXaTiaTYznc6XQAg7l9+87Zs2c7nV4YWblczvf9mzdvVqvVBx98sNFoDIdDhNBjjz0Gk6xPPfVUuVyen5/f3d3t9oaqqg+HQ9u2VUWDaQOO4zRNO3r08Hg8FgQum8sAsRkEnsDKuq6DjAbkDdBTAXjW6XSKxSKldDgcFgoF8Mt48cUXc9nM1NTUnTt3Dh9aPnv2bLfbLRbzt27fbm9uKapBCIVytN/vl3IZlucwhQsVrDlpHMdCSkSONR6PeUn2fR/qI4TQ8ePH4akNw4CZr0KhMBgMQAsOXY+iKNls1nXdbrfLgq2TwKuqIssSJhQhBEQAFJ4izzOAhU5oBvgPSE2QqSck/uQ+2AQAYDPpEics/Ot4eXxAU3bw/p4V8X7gJUkC2fL7BdtBLuQgNcLzPFSzGGPDMDKZDMdx4F8IuRoYKqCMwXEA9GudTofjuMuXL49GI9/3M5mMKIq7u7urq6tQm4HV74ULF+C0k2UZIvDVV1+1LOuZZ54B57Vut3vr1q25ublqtbqxsQHKDwAAAO4CxgwUKrlcrlqt9vv9GzdumKYJJ3er1Wq1Wp4XhGE4Go2GgzF8ojGhECGCIPGiJEmSoij5jGaokq5KmZSWNlRZ5FVFyOdScRzm89lsLheEoeO6upHOFvKIZcBPVZKkkydPTk9Pdzqder0+kYkNh0NVVaGNgZN7cXERWp3xeOx5jm2blUrl2LFjSRKNRiOWZa9evYoQk81mLdOZnZmP43htbQ00Ky+//DL0QhjjZrO5ubkJEjPf92GmwTCMI0eO5PN52HELmC20XpIkPfDAA8ViEa6chYUFQRCAlAemvlKppFIpjLHv+2AGeezYsePHj+dyuV6vB7MdCwsLmUwG9AkQJ0BOCIIAfCnP8zDTwHGcqqqQMH3fD6JwcmH7YQg6R5bhrl+/Tgg5duxEqVTZ2dkRRTGTyST7znfdbhe6tn6/r+s6MHxQf004TCgHYMkUqA5YlgHgc1JCctCoQCKC4hCCagJpwlNCYnUchxV4iEiIWLpffzL7lANEzOTvSfV88G+gKKDEBfH2JJjx99HqQPzRN9zCIICjBarNIAhsy4UlM+VSFTJ5rVaD6jqVSvXaHdM0OYFvNBrgiXT4yBGEUKVSeeGFF5aWlp599tluv3fixIk/+ZM/KZfLLEMxxnfu3AGt2bVr16ampkCjdOPGDYBkFxYWer0ex3FLS0uBY8JusDiOYQ0YQmg4HEIPBt5EzWZT1/VKpTIcDgllbdsVRZHhOUVRMGIp8jmOS6XScBQqisZwPFw0iqKk9YTDSNOUbCEbRYnrewylvCQRSiVJ4gU2SRIvDBhekHmO5bgkSdbX11mGu+uuu1iWrdVqluXk83lBEPL5oud55XLZ90JVVXu9HkJoJpsHqQqlFCaJYB3AhJTb2Ng8dOjI4UNHn3322ePHT8IvNRqNADP81Kc+9cu//Mvf/OY3BUEoFAoLCwsAJ05NTa2vr58/f363tgb1fxi2NFUH1jSXy21ubnIc1+/3NU3hLJZlMdTJQH9blgWBBO8q7KPfc0vhuHK5HMcxsIsLK8uHDh2q7e4Mh8MjRw5RSu/cuVOtVuv1piTLxWIxTlCz3Y0pMoyUoamjfhf6TEEQWMxQmvA8jzELrAb8V5jEw+GQEEQI2djYgKMf5EeapvE8n8lkQFQMM+hhGAKRKIoilsUkSTRNg3YRkoFhGJqi7knE4piDoVtAESBLEkIAj4JClNnfiABYKIcFfEA8DaFMD3iuTcJm75/7ghioSzGh8M1kz5U7EURuwrazLBuR8B8Jwu+ZEnme51gBTjUWY1lSK5VK4PsATrpBQFUE4nqe50kUNxqN/nAA2eDo0aNnz57FGN+4cSObzb744ouu6545c+YLX/hCtVrtdDqiwELbOTs7e/PmzVar9ZGPfORrX/savKpUKrW1taVpGihFs9nsnU4Teg/QoKVSqUajsba2Njc312g0ms0mIGPT09OtVuv69euipAFiKbCMIAgCLzmeixBSFBWzrCBIHMdRjFiWVVVV11KKbEYxr2pyNm04XuB5XpyELOIzmYzr+mEYCrKEMe72esAL67pMCGk129evXwdameOETqdTKBSq1elGowFw/9TUFHzKgADBwKSiKNvb24DrgMIWChaGYQzdWF/fnJqaATQBdF4XL148c+bMl7/85dnZ2TiOa7UabC5ZXV0FTAuwrvHYymazSULBANY0bYTQ0tISGOqk02nPd69du1YoFFiWrdfrPM+PRiOGYer1uqqqhJDhcAg6xFQqBfjq7OxsvV5HCI1GI9BwYk3r9/tR6IP+RBAE0zSDOJFlOZPJ9IajIAhUQ5+MXOwDkHvXJ8gzlheP+4F38+ZNXVEXFhYGg4E56ubzeaDmARyBTYnXrl2D6hQkPkAhIoSMtErI3sYxSGxg7gZyc8dxPNvjACjhedEwZHjLRqOR74eqqrIszzCwihC6VsQwXOhHlFIGs4gimiBCKUzThn4E6RXtV9aQ9CRRRAjQ14RhGMSwhJAoTliWZQQREeJTiqIImsMEY4bhv4u+7IcZhC6hFFOGQSxlecqyCUUkIUmEKEGUp5jhOZ5HDI4Z1o1oJl++ub5RLlZK+UKhVB4NBoVctZDLf2f88uKpo47jPPfcc8tLS+ff/FCn03nh2efy+fz2lUvj4SiXyzXr9ely2bXt6elpJ2ZGdiRpua/8w+NhEOi6vrZZ833iuNb09LTvRa7jj4bmsN9fWVm5cukKIwiBFxSL1WazoTZ73SvXS6VSwrKXrl8PEVnb2WQYhuXw4NowSRIlo8KHbdumruuGJkVRpIiI4xhNx5IkmOYgpaY4jsNMXCgYtdotU65WlpZrtVqW5Ub+iFFEXdd3dnY4jsvlclHs6hrX7/cX52cty+p6fXXq5Hg8jmLkuGF/sK2qaq8/DgKSL+jTM/NBQFOp1KVL1xHm2p3hPfcsl/LGhjloN3YRZRYWFk6dOJ5Op/P54vbuTqfb96M4ZujXv/UP73vfD9398Lnd3k5WVAlJWMRjgo+sHFlbW2U5rKtCNiMHvsJgt1SSZTlIkj4hvdXVF0msRn4UB3EYhnEQMgzIU8xuuwYkBE3iwPV0RRc5kec1XpQ4jvP39nCozXZ7bm5OlOWYkFQmE4ZhbzBA+3i7IIoiSiRMsMT3HTOJWdcLlg4d5nl+p9n1YzQc2wRhQRJTuuF4rj0eqxzruq4XBFA6chyH4sSx7Ewmk0llMWYZzJeK1TAMR6bDi0q1OkUpDYJAVlWEUKfXa66vb+7uMhybsAxFtNbvIoTCOOR0FSGUuFE6nVYEmYmIyolUVuM4tkdjYG4xQpqu7OGckBkB6wcABh1wfDoIS9LXTkJAEZvsOzLhA7Js+B4oMidfwfujSUDO4H0xDd2XrfLMa55iklTRa1mKyddlWZ6IeBiGoRjFcRwGcafTyWWzhmHU6/UkileWlqanpzutdhTFCKF2u/PAAw/OTE/duHEDlteCsAsg3NFoNDMz0263ESIcx+3u7pZKpWarHgVhKnX4O995MQoClmU7nfbM1HQURQgTVZUbjdpw2I8o1jTN9z0g/W3bjOJAURTTHCX7y8M933ddV5KkQiFHwphlsSAKsiIKgsCyDMOIkiSJEo8QyeVy6XQKqkGGRaIoRmGCKGNbriQqnhsUCoU4jkVBxhgH/t5ugl53IIkKy7ICL0H5BH0XxwkwkwFLOJrN5nA4hLmHQqGgaUYURZ1OR1LkdDaTxHRjazMMo51afX5+EZACmWOXl88Konjx4sV77r77U3/yx49+8MOdTocQkkqlbt26kUrrQeA///zz5+4+I8ui67rlUlVVVde1l5ZWEEKrt+qg8gWwHthUkFLMzMyAswHGOIoiMIPVjD2eSdf1dDqtKAqoT/HeykoPIQRN5h6CGLqpTJphmGazGUZJThCCIGi1Wmh/QgAAkCgOQMsFgQcVH3RuwP3AvNJEVUcphSlE2+ahHICUA1VkGIaO58IgIsGIZVkY2en3+yk9tbCwkMvlYCEHIKIw5k/2bSu+q9sGIBXvr1Iib3CO2Ss4Dyi86WuB0El4TNLg5D488oTln/wsVLMHgxBsoPABpdtr4vAAjQgPwnFcRBJCCIMxyzIUoyiKoigRRRE0fnCgdLtdSZBJnICkUJKklaXlmdnpb33rW51OZ2lpaTDoqYpUKhc4jknpGV2Xo8iI4xijxPO8x594NW2kaMIcPXboiSeeYBHO57P1Wl0UuG63LbCcKIqtVst1XVlP+763vb0JA3u8wI5GI9s2NU2jlMRxjBmaJCLLYlEU8/n8sDPSdBlaeYxpkrCSJBkprVKpdDqtyRuoaVoURcVCmVIKkkXbtqF6BHAYYABA7W/evLmzswNTGlCb7fXP+709GGfBABFM/c7MzDAMF8ex6w42NjZs266Up6ampk6dumt9c1NVdVlV0qnst598Yjy2Tp46dfny5aNHjz765h8YDvscxzSbbccZO45z/MRRSollVUvFShB6rmsLAg+UPUJ7py2QCjAMAQWqqqqrq6sYY4C1QBzDcdzCwsKVa9dBKANsIUJoz7MvSUCmDzpnVVVBN8LQvYEEhBjgMwBukSSJECS6bhQlDKCGLIJtaoDQwk9BEwHCVLCTDIIAEBfY1SNwDEheoygCHkIUxXQ6zZljWMFdKJcopbVaLQzDdDrdaDTS6TQEpO/74Get63q/3wfIKooiDmJvgpccTEEH895Bag4dWOv7xqw1yX7wU/H+GsOD7eLr8uTrfnbyLAx+fcjBC9kT1hAKAFKCKORIAgMFGMOg0PbWFou5w8srkiA2m03X9iqlciqd2dnZev/7fiiKoueeey6TyRSyGd/3eQFHQXj06Eq/3z+0vPTKK69wPBPFYRSxsiy5flCtiMdPHBkO++Vitlar6caMZfLjcT+K/SRCmCGKKooSJ6qqZVkJiUgYa5rGcVBoJAyDOZ6P7CCOIsMwJEnwPG88HqqaBAwSLJyK41jVRMPQUil9OOxLkjQej8MwIoSCMaksa47jVKszzWZnenp6a2urUqlYlitJqmk6sqyVSqVMJh9FkesGk7FXGFcfDsdwYQEWWigUkphOT0/funWrWCyGYQyCzJSRKeRLXuC32+2NjQ3X88rl6k5tt15rwkVpjseGob/88nd+8Affe/XlF+fn5zudVqvdmJqumKZpmuMTJ05Y1ng0Hhw5cmRra8tx3HJpCiaGAckAJg3ADCBICoWCoihwbiKEANj0fb9arcLFDSPae6OVUZROpwHlt20b1JSyLKfT6SR0tre3GYYTRNEwjChKgiDQVJ1ihDGOknSSJAzD7YnpRc5znAl9B04zEBuCIMCAL8wlA8zOcZwVeCBPg/9yXVdV1ampqVQm3Wg09mYgHafRaMmyCB65vu8DNi7LMsxeAJ4EhLnv+1xyYICQ7pN19A1q7O8ZhAcjCh/w6p5Up5Oget134v11vwelp1AYJ2EM/wsxt/dEdE9qAzKcPYkcgdM9Yfg9/yiQvQPLAnyApuhhGEZBmMvleFZot9t2y3vzm99crU5/7WtfiaNk4dBcp9MKI19TZKqI6YweeDbLUZZLWBbxCRYQxjh55KFzzWZzZrby9a989dixo9VqKZdNGbrU6/Wyad20RoQks3NVz/P8gJJYmqqUu91uIZ/p9/vzc7OKIq2treVLZUUSx+OxoasIoQFNKCGKIsqyrKqwi5fyPKMosixLu7u7lmXBpQZiJkIoy2KW4eOIiCl5FJs8Jwq8xHOiJCo8JzbqrWajvbKywrGCqujj8dixPVg6e+AUQ5IkZTKZq1evchzHsQIA+js7O7Kslstlc2wXSsVjx45du3ZtY2PzuRdeyGazsqz2er2nnnrq6NGjkqr0+31FlvO5nCxLS8sLhJBcPkNotLCwcOnSJZCh1uu7iqKwDE8JZhkxmykSgmDPJEQyNMOQzWCeExhtz/OGw+FDDz2UzWaffPLJytQ0HKmGYciybJrm9vb2aDQ66GEBVQBsNfYdFCc08t29/MYSjDFJiB8GlFJJEBiOgyGjJIp5nhdUFUga4NtgYA0OL7gBfIgxDoLAcRxEYphLBE1yQikMyHd63dnZWcdxVtfXhsNhNptOpVK2bS9OzeD9AUUgfvr9Pmg5AZjxfZ+baD4nMQOB8T1T4sHK8/uFKFSVk4RG91esvS5c0f44xYTtwG/Ie9+dzKB7gc3iPRRr0hdO2MiEkCQhFKMJY5lOpWRRkQXRGps8zzOIQQgVCiVFUR577DHbtqcq5bW1VUkWpirldqdVrU4FnqPpUrvdOHpkpd/v+76YYtVut5vP5wSB73Wap8+cdBzn1Mmj4/G4WMrSJNB1PYrdMAhkiQ98p1wpt9tt3VD8QJmdnRZFvlIpMQwSRb5SqcRJ2G4LkiTB+E82m0UkkCReEBmEEGYEhmEUVeJ53vP2WiNJkhiGY1m+WCwLgtBsdCnFo5GZTmeHw3GpVEkSks8X4zhOp7OQ7rrdPlzf5XIVPl4Yb6V0z7oSnDI2NjYWF5Z7vR7IhqrV6TiOR+ZYVtXx2NQ0/dTp09vb24IgbW1tzc7PHTl6iGGYbqtdKJeazfr995+nhJw5c/prX/saw+BcLscwjKIohw8fZhjGcbzp6dlOpyeKsqrqpmmXS9O1Wg3IGzhWgIGwLGs8HmuaVqvVZmdnT506BaPYkLFBZAs6FZiEgDxJCIFhImDhEELAuCaRDxew5wWZPEmn05lMptftA6tBKRWQhDFGiDAM4hisp9PAHwIpALOCwBTE+z6A7D79RgjJpFKUUhiCyefziGHa7fba2lpv0IcNNrBSBQTiPM+7vg98NScIcRyPTNPzPEGSEEIJpQQhijEHsMpE0gk9A7yIN8bM98VLDiRJdKDr20to+3cmPwhp8I2Pf7A0nXw/hONeikbfPQWYPbUHm1AaRRFFCGNGEAVBEHhOgEYIIZTJZGzTGo1GumrMzc1Vlme/9a3HZVm87577nn7mSde13v9D7+1229lsWpVFa9xPZ/TQ5yqVih/YLEdZOR1G7mjcO3H02Pb29r333n3l1Uuarlj2iGeYSqWMGSqJfCGf5TjOMLRyOR+GLsa0VCoUi3lVlSVJ9H0/k02xHHa9EM5soD0qlYrrdDmOYzBLKWVZDGq7hETFYjGKYkIIy/IIJXFEctkCxth1dwDq4nnBth2O4z3PSxLS7w8qlYphpDDGpmmBlnVqatrzbADPgMICkg0hDFMOMEoCLwk6GdcPJEVpdTqAkfh+yLJ8r9crlktHjhwBsqGYyzeN1Kg/uHLp4nz1raoqE0Isy+p223Dx9fv9fD7veb7juCzLBkEYBCEh1Bw77U57coGBJAtS1uLiIkKoWCwuLCyIothut0EDDM2FIAhQ+4FZ4OzsLOQrqF0RQmEYAlgyHI1Gls3zPGIZvG+8AMkKwsz3XSZkQt/HlAocN1kxAHwDNJzgAQNT/JNrG65hoAEhg8my3On1oP3TDH1nZ0fTtHQuKwgCkIRHjhyxBiPQS8DqX3hMMDqZpBAO7atV6D5bckDl/QbS77Xhhw6AMcxrNwpOMuf3S55wpOF9/hDt94fca2vXvRimrzkC9jId2ntJe6o6llUUFSAsBnOtVqtYKCiSCv4uiiRlMpmpqalv/P++8cADDxSL+e985zuzs7OVcmFjY2Pl0KI6Erq9JkJUlZWlhflWuxEEXhgGkhBpspLPZAWBO3r08LWrry4uLtTr9WIur6hSJpMZD0eGoR09egQUxmPTK+QyGONUKkWTiGdxEgUpXc0cO04pHfb6uqJOV6c4zLiuOzc9U28G8BHEEdk/nmLfT/K5IsaM4zj5fD4M4uFwGIbhcDhmMOe4Vqqc6XUHqqrWa02e54fDYafT1VQjiqJ8Pn/k8DHbtm/evNnvDcPIgcwAOw/hpIvjGGY7RqORoiitVmtxcVHTDN/3VUPN54sXLlyYm5sDQzRZlkF5t7a2Zprm/Pz8hVe+c/jwYUWVdnd3vvjFLwJvBnlDUZSdnZ0oinTduHLlSqVScWyPYZhSqXRndT2XKxQKBbiIofWChlDX9atXry4tLaXT6dXVVcuyNE0D2QogosVicWK+JkkSKJ+AexwOh81m0/M8QRCKxaIsy8Bk5rJ5gRfbrU4cJbIsFwoFsCYALCQMfUIIwyAoNwghE5UYnA6T9ZtJksDlBiprkFhDyMApwHFcOp12fU9RFIwxWGal0+lqtVoqlUgQEUJAb2AYRqFQGA6HMMQMyY9Syk2OyQlGAo3j5KKn+1bce4Xfa123mQMzfnsXUxwn390hwUymePEBZzS6b6t6MG73ytc4+u4/J3KcPWkfwhShfQAJMyzDMK7rKLqm63oUx1EU27ADIEyAWZZleWNjo9FovOdd7zq8cuTXfuVXT993zrIsjGmlUomjwLZtTdM827Ftu1QoDoZ9MP81x8NqpUII2W1bMJ7PMAxGKJVKpVIpEsWGYaiKxPN83bY1TYvCUJYkeKNqtVqlUikUChMVMshlgiDIZrOAts3MzDSbTdd1YcXsVHXGdd0giDiO6/UGmXQuiiKGYaMoMU2TJKjX67VanSRJ7r/vIXCaAhwCBuRv3759+vRpkDgPBoNcLifL8tTUFAwKViqVTCYzGo1qtcbhw4cFQbBtR5blbDbbbLRXV1dlWR4MBmEYp1IpNa15gZ8vFlieI2EwHo/7w0Gz3QIkc3F+IZPJnDpxkud5nuUW5uZfefn5EydOFAqFo0eP3rhxa2Fh4cUXX+Q4vt/vq4rOsQLLhjzPO443Gpm6nuJ4zvM8sCmo1Wqj0ejkyZOrq6scx2WzWfDC6Ha7hw4dAsxjt94AaX4URZZlgTdxv98HZrzf7zMMA6PS4M3F8lwqnQmCIEpi1/cUTQ2ikCAqy7JtWqqs8Cw3GAyWFheKxeJwONxY34aJwU6nw/N8pVIZDAYXL16emqpM+jIIEKgNAUEFNQtCSPI8mF3mRQFaTdt0QUQOczPrd+40m81isZjJZUHR1h8OEkpEWZqoPrmD2QwfgENfUw0eiJZJz8rsD0BABTXZWU8P+HbT15KKBzPkpFJ9Y2pFb7jBccWyLMdyUDZMXmShUPCj0HGcOEkkSVY0led5kiCMsSLLt27dknjh53/+523T/PVf//UHHnjAJYHneRy7t2zD9VyUxEkU6JpsWU6r2S7kM7MzM3R62rLGV69e5dVCFEW+68VhlMlkioXy3PRsxkj3e90wjEVRLuTyoW6AvVoURYIgwTaLCxculMtlx3EGg8GhQ4d0PdXpdFRVJwSZpk0ptW3XspyxOc5kcq7rc5yAMR+GYcrIQMWYzeYHg4FtueVymWX5MPSKxeLNmzeTJIFuCopA0IuEYWgYBvhwN5tNjuPAz+Lc4jmQXGezWUlSJjsFwPEJlD2gaEWIGY1GyRjZtr2wsLC5uanreqvVAvkYgzBYg3qeQ2mSzRZUVW42m7lcYXt7d3n5UBTFjuPs7tbS6Qyg9o7t9ft9WK6CELPfX6GJ0hI6uunp6UajEYbh1tYWyI9g4pFSCm9ppVLpdDrdbrdarYK4FOb34VxDCGUymYkquN3tybKKMTscDsHzBiHU6/V6ne7c/OxgMJBluVDMAz+0ubm5vLwMCieQlUHufeSRhzY3N+Eih1IWFqIwDAM6GFCTMwxjuy6UpnrKmHAhuVwO7PwAHYUxRXD6msxIgGo3n8/ncjluEn7Qp33PGDh4m5BOUEBCKsf7HOAkSU6+n933U5mgNXRftobeANVM7h98BLTvZcjsewTvFbqIYow9z4spYRiGZxiouVVV1VSD5/lbN28KnPhPf/pn4jD6+7//+2w6JwiCbbvmeBiFviQJlJLxYOj7rudr1ljgBZZnBUJw4Cc8z8YBiQOipSWRlyml3e4wDGOGop3txmDYy6YztmVxDKdpKUqJ53kkIiInekHY7/Y0RU2lUlEQirwgavLudm1+nuu2e7OzsyzmXNdlGCbyiTVyZSUl8MpwMK5WqzyHBv1xJpOJIrK9vc1xQqPewhjD0btnGToaRFEEck2QhmUyGZZldV1nGGaCrQPiBxUUDK1PGGRZlg0jxTBMr9cDYTpwGHFMNjc3WUXp9TqZfK7RbmU8z3MDeDrXdiilYehHURQFwXg4TKKIxFGpVL506VK/PyiVSo7jYsTxPI8RyzJ8KiVsbGyUSiWMcRQFkiRgTEejMcMwpmnW63Xo2ZrNJhR4pVJpdnaW4zjwdMrn88vLy5/7/Bfife8vKJ7B9wmsQ8DWudvtQo3tOI5p26qqIoZBDMdxgqqqQPQnUTwxblMlmedZz7ZK+Vyr0wfBAAwWg0g1k8koigLPC9tjwZ8OITTodSbkPqgOgNXA+6N/iGVABrSxsbG5uZlNpYH/oPuegFCntFotCCWWZV8ThAfz1esgTbRvYMFz3CTXTUpQdMAl8WAjCzf6P2DidjDe0GvzMEII0dc4ke6/Howxdl2XMhhOQUKoF/imafpeSAi59957Txw7+cILLziWffTo0bXV9U6no2ZVOCw4jsOEgg+ayEuj0bBULGSz+SiK+r2RpkgYceXSlEcYSVQ5jrFMW+JlhmGiKMGE4znZc/oMclVVRYgEbsiyQqlU3tzdIoTAmFy73YVf//btO/l8sVFv5bKFOI6HAzOTyWQy2Xy+kMqqg8HA9yPPCxmGCQMShbRYKK/d2RgNzSiKGIbrdHqwGqHRaFiWPR6Pg9Cr1Wrz8/OixJcrRYRQGIZh5AehJysiZqhuqJIkwUQvAOJhGEbRuFqtEkIYhh0Oh51ORxIVnucBAlFV3XVdFmNF0ba2dliW7w0HYG3GZtjl5eUkiSRJEnneskzf9xkGHzl0uFbvZDPFSxev/vAPL4uCyvPieDwEWoXn+f6gm82lM9lUq9USJd6yx45jr6ys3L59u9FonDx5stPpbGxsgGfk1tYWz/OKorTbbZD4yLJcKpVgTl9V1e985zuqqq6srHQ6HYQQqDTBkBKQSVEUp2bkKAhBM81xnOcFgFjKorRb20mn083arq7rqqpeuHDhrW95S73ZS6VSYDkHF61lWRsbG6COoJTClFyyv1MMIgda0Em5QSkdDofgUMzwHBAwMPWyubVlmibQZjzi/SicjHqGSdxot/qjITeJwEmCoq81R5uUlBBRYRRNIu3gOG+0//XJ33vTum+wEj4Ipb4xJv/vwt6zSY4syw5877lWoUVGSiQSKABV1VVd09NLLm2bazPkrPiD/BHD5bddcse4NNoOh5zp7mlZ1SUgMpEytHTt/sR+OBGOAFDDDSuDZWVGRkRG+H333nPPOXc/CN/9qwheIeabeGqqbemzSZ6hF7VtRzcNzrng6uDg4J//83/+y7//1fDhoVlvoM6ZzWbd4y5jxDZ02zAJVZZl6Yw26i3LsnRNJ0pKwT0/6HX7URTFUS5yahquZWiyJLwkjJE4TAnRNM1k1JCCFgUXRZkkBaNmLWgdH/NHjx5PJpOrq+t6vZ5lxXg8bjbaumbmeTGbzYVQk8lM181Wq31wcDhfzuKocJ36/d1E07RGo8W5VIoeHh4LIY6OTgzDAIXK85yrqzf1egtHMmMM6lIooeGf32w2MXCL4xiiHsNgtVpts9lYlvX27Q0E7JRS1Mm1QAZBkKYpAI96vb7Oi5OTY2wLFsJYhZtWvWEYhu/7eZ5mSbIpcimlKPl6uaJErpbp4/Onv/nNb8bj6fHx6dXVFdzzNE1LsxhOH4PBwdu3V4PBACYmEKGT3QJ2qIGOj4+vr6+TJAEzBjSM+XzeaLUfHh4wi68k7eAw4I8qyxLsE6T0lO/Sg2CEcGVZwDjCMJRSMrUt35I4ztLYMLQnT57AcBH5EP0nak62076ynQKBMea7NgJV7pw7gafEaYJfVIwiVtG04wiQUlbkUFgfDQYD8GTey4T7RebHubEKwn0Xtv3f+qCrpDsmhNpx1vaxzSrsP45D+v5MsvoXVx6yrr5zy0FYQnISxXEYRo7n9vv9Rr31+PHjf/Nv/s2nzz87PDz8w+9+H3ie67rddieNQkJIQmlZlrqhpXFSFMVisXIdC6cmEYQojSgtS0sldVNjZVbkiRyPZkRpusYUViApZtu+bdtE8LwsiNKklFlaHh+fYjEQISwMY8t0BgdHZcmjKLYsRwhVCxpxlMZR6tjlaDj7/uUPrVaj2+2+/OFS07TTk0ebzebm+r7d7sAKDRNexojjOPP5XAje63WbzaYQvCwLTWNhuJFS9vu9+Xzuug5jrNfr2rbtOPZ6vXry5DF2DD158kQIAYn98fGJ7/sAEjAGwKW5O+Otbrf33Xffdtrty8vX56dnjNFer2dZxu319fX12267c3xyuF6vb25u5jPe7w9OTx/94fff/OxnX/23//bfnj9/7jgWYyRO4DIqazXfsoz+QVupFqPmarWCdeJwOCyKAk0jIFPLsvI8H4/HWZY9e/as1Wq9vbl1HOfNmzeEkE6nszWeCYJmswl8FXJN1NW6rk+u7nr9jlJqOp1qlLXbbctyVqtVFG5++tOfLmfT09NTQ9cXi9mLFy/u7+/9em8+ny8WC0AvWP8MXQj6QDSEpmnuUhFFAoR0Tu2szDzPw7Fo2BbCcjQa3d7eVgbTkASCZ88YgzjT87xGo6Hvx8+7Cfs/XTSCZYfeD8cS6l1AumqP5oZgo7scu59OqzS7X9Zug20X+WrP/JeSvXHFDqqiCudlZjo23g4otaAF+du//dtPPvlkuVzOihJ/6tOnTzerdVFm5e7mB15ZltiedXgwKIqiWasnSZqlPE3KKIosU5eShWGqlCKEtdtd27TSLO73+2UhNWZoSouStOTCMKyiKDarMGjaV2/emqbZ7/am07ltWZ129/r6erMKPcdVgrSbHSXI7e1tHMab1WYynjYbrShMlWSlFLPZSimBq9DzHSF2K98MA7vchsNhq9XIssT3XSnlyckJpRQ80vv7W0qbZZl3u+2yLB3Hur6+evz4ERwA+v0+pm1gY+FqgAsBxuWaZkgpTdvCQR5F0dHh4cXFhW3blKnr6+ter7MlfNgO53yz2QzvH5LEXi7Ck5Ozv/u7v/v888891+ech2FRlnkYrYXgjJEwDJlGlFKHhwe2VXv9+jVUrW/evDFNs9PpYKgwn89RtmFXdq/XS9O01+udnJxcXl5Op9NWq4W1MHme53kOOAevBNt+arXa+cXjdrOVZnEcx1mSxnEsynKzWTm2VZblYrHgnJcajaKo2+1OxxNBLEoppgtkJ2rHEBLmVJjjQxcuhHDtLdcCjHZML9M0pRqD3jJo1G3bBpYLD3iYdHDOwVwDGIudGWDzvpcJ9/NVFRsffIFNRtVN29ud9EHNSd6HW/eDs+oVq8Rb/W7FTkD/VwUhbnSnvzQMQ5QcHMI0y8IwtB3ns88+O310dnd39+tf/eYv/uIvfv+7362Xm4tH55+9+DRcr7///vuzk1PHqYEuZJi67/tUESysB3WD5wUWGGqaFkcR8f3u4HATroqicBzP8wJR8ulkXhbi5PhQSpXnBerAVssjkuZ5OZnMfN+fTqd5noPIcnV1ZVmOZakgqK9WK6yCieOk2+3puu55dcb08XhaqzWKInvz5k2v15OSfPPNNxdPzsuylPKdA93x8bHrugcHB5hJ6rr+/PlzrEa5vr5eLBZPnz4FYDOdTrEUAcWP4zhJkgCTbDQaSIme5/V7A0rpdDrFBKLX6zlCWI4dh5HjOIZp/tVf/a9v37zWdDqdTk1TZ4w26w0gq2Ve9Hq9V6+Ws9ms1WoJIWaz2ePHj2/vbsJwbRiakKUQXNO08WQYx/H9/W273by/exNFEezlcRwsl0s4AhNC0MgBb4uiaLlcekGNMYadSrPZrN1uHx8fv3r1qpJrSilvbm5Wq5Vpmp7nbfL8YTSUXKAlBnmlXg/yLP3222+zOCry3HEs0zSzJD05OXl9PcRKXXhbVTJiLC+B3wKYt3BIytMY7DmkH3CPpJS+7zmOQymFpBs1s+d5wJ/Vzj20Cpkvv/wS7HnMxt91aHJPOaHtrF/IXn9ICNGZodSWUbYtO3dw5XbSKGHGxpQgQknNMqiUGmF0B5wKriSVmkbVbljJyFY6KKUUrrZT0BNFKN3iMqRRb6Bf94M604wwSiiljWYrz/M0589ffP7ll18+PDz8H3/971zb+d/+6n/57W9/65l297zlOPZ8MSWEtPpNachws4zjWCmla2o2Lcqy9AO71a7VW36UrheLhWmazKbrdGV7tuEbV5d/fPr0aRzzZt3UWS5IuViMGOOue7FaLe/v723bef78+cPDg1JqvV63eMM0vSgaep53eXl3dHS0WsetljUaTwGpfffDt57nFTwTqrRt+/io57mWFM7V1RUMLxazOS9Eu9m7fTvq9/tJnNu27Tudg+6jxWLRbvcuL6+fPn2KtHZ3N2SM9fuH63V0dmbf3Nw/fvx4s9nUavy3v/3D8fHxbLmazBdCiG+++/6TTz755rtvYZAzW85N01yGC8/zjh8dwVC03W7HcUgpdxpmr+WEy4fbt1YUhY7jXDx5dHV1tV5tbNt+9OSJ67qvLq/DMHZ9pWjy8vUfWm3vT9/+/he/+MXvfv+Pz58/H4/HR72Tu7u70XD5ySeftJvp/e3o4pyEm2Q6XazXa9j1Usvkko4m86wUpaLdwVFeluXdwzrNv7982263R6MJEO/1OrQsqyzFmzdXpmmHYbjZRJSOKaVK0SCoC6Fub+91x9hsNv1+P/Dt0WgUOL0g8K6vr03dKMuy1WrpjOGSzrIsSkulFMbu6OKAZAJHLcsSNpYIIaQBzlSeRkqn6yyOeU4ZpaZm6vYmDpM85ZwfHR2dnp6A+zadTmku83XChOJJbhOjzMrrHy4PDg5kUtrE0HSS57t12eTH5gQfNH67MpXu32H/nj/6nYqlUZEBKNm616gde1vt5UmAUVUyZFujDHX59u2Tx48ty7q5eksphb0vFOtffvmlZZr/9b/+1zAMP/nkE8eyv//++6IovJqDAaa7VwP4jgllDQabOC8x5WOMtVotVCB43xeLheOam3C5XC6Xy3m9Hriu22rVgsBbrqYlzylTSRqORsP7+zuUhZqp+v2+41pZniyWM9PSh6P7+WIahiFEVZZlFWXmerYiIssTSrTZbBLHcRiuKSW2bZZlrpRcLueu6/q+m+epEKVp6kkSTSajg6PPANyNx+N6vf6P//iPYRjCNRTGnoyxy8vLp0+f5nn+u9/97nmRYRlLvV5XShmGgcMb5RDZ8Y3QH87n8263PRqNgKm22+16vZmm+Xg8tiyr2+32ewfoIWezGWrgNMpRa8VxfH5+/vLly1qthkwSBEG1GhEjk8Vi4bouMg+KQMO2CCFQ/SA9sp2zPdwrw+Wq2+2apnl4eLhcLi8vL4+PjweDwc3NzXq9Bq4LA2w8Jnow6BVN00zTdDabZVlmBSZEEtbOVRATiG63ixeAtIlhia7rMDKEZAmFKH693qmz3VZpuqN5Sil/9rOfgVyOp4OXXKPRuPruDcIHbBsYGsHTiDEGexS9ipaqINwvSvf7wyqE9uOtusn3FUnVYwrYtwn5LpIJoZAj7TCb6oGUUlyW+79eVbjnZ2dXV1dCqfPjk1qtBu/08/PzTqcjpZxMJnB5qSp4WAzAuZkxhrUQx8fHpsYty1osFmBvwrgFvwVcDsYzSqlms9lqtYKamSQJ1STR+GI94arBVbEKZ6XIKaVZEa1W64KnsI0pimI0Kut13zBYUaRFkWZZnOdJHG9M06zVvDRNXdcSgjcagRAFjIxm8yEhxLRovV4/GLSn0ylT6vCou1qtuEgfX5wQQkajUZaHp2eD+/v7zWZDCAHxGrpYuBhiYZBt27e3t48ePcLU+9WrV5RSz/Ngb6OUSpKkXq8/e/YMC70wtQPgvlqt0jQGCbPRaOm6Ph6PHceBveejs3MU7cPhcD6fT6fTfr9fFJnnOUKo8/OzJ08e397etloNzstGo0cIcV3bsixCpGUZvu8mSeR5XczWtr5eGiOE5Hnu+h5MFl3fxyGoadpyubR1A+NQWGmt1+ujoyNYVKxWK1zxIOUBGsGkAaS8drvt2U69Xn/8+PF0PCl2y94RdSgvqWYAnYI/Nb7Wdf3s7Ay8cKxwYrBKSZKUp+CyAqGsxtevX7+GpxMGQnhti8XipHuEPITjHtQfpdRsNsMzGoah70fgx3G4D5kgzBhl++H3cQB/8MU2j4t3kw9V9Yq7e7L9lS/vr0yjuy0U0+m0Xq/DNwkd/OHh4cnJyWq1evPmTZ5lEKRtNpssSWG0yvMCHycMPxqNxsnJyXxyC9QLD4tGAkqTOI6hcENx32w2B4MB0xOp8v5BS5EiDOMoXpY8SRJeFInruiVP02xj2azSE+Z5GidrqQrT0jrdRrMVcNHGWOX45GCxWHielyRJr9fNsiyMlorkRRnV63XXa3a73VarESdLxhg4K5QK22GMMdthQqaO6y1WOWbBwN/wlq5Wq3q9jvXu0L+u12sp5enpKZeyLEvTsGzLWS5WvhfMpvPNOvz000/hHJMkSZpkRFGN6VEYF4WBOQekepPJ5Pnz50KI1XJNKR2Px+PxGKo/4HuiFEDCBoNBkiT9fh9eZsA20AXN53POOcAYvMIkScD1ofo2a5Vl2e/3cTICe4N70tnpWRRFeDQImpCmcGrgsgSVbLvvqeaCUApaQhJGSZKcnZ2B2qbrumUYSikEYZIkab71pEYFhLGBrus4sJCN8VCoqjRbp5QJIZUq1dagUIciCnvXwGjVdQPzWCiw8RTYeAFhJPI20ux7mbCqS6sESN8PCSkl0Bu2J3v/OIA/eMx9wKa6A2OM7IwS93kwTN8ybLZSQ7VNsMBgNE1Lk7Tdbl9cXFBKsfAM8HRF5VFKodw6PDhgjI3HY16Wp6eng8FgOp2uFgs8IBjAsFfFuGk0GsVxjIXGOLHSNFVsxUXWbDal7JRlzhhtNuuAxfzA0w1NN2i71XEcq1YL4jjudpumqXNeeJ5Tq/m2bSslkGSCwCuKzHFsTaOdTivLsjgOLVtvtmr1es0wjFrNMy2t3WlYluX59ld/9sVoNBqN7gkhjWZjuVz+/g+/se02qizTNL/++mus4xwOh0+fPp3NZm/fvr25ucHsC06HXhBg1TYGVo8ePfrDH/6wWCwAmuOUxMS52WzCQlfTtMVisVwu2+22bTmtZns8mjx79mwwGIBvBT2ulHKz2Xi2h9HC69ev8zw/Pz9XSnmeB94sGipgUfgaK4crv6Mt0E1pWZYgi8+XSxyOIMegigNfdDgcttvtbfRSCu5Yde1xzouiCFo1dMsYr2dpgimIbVqoM/chD1CoMUureJTQJSIU8VPbtuFsr5RahEs8EXKgZVnYWzidTkFbw0GD+lYpxbnwfb9izMPzW0p5fHxciX3fy4T7QfJB5FR9HaGyiqL9+3xwf/JBq/kRGQ5VKHl/aKGUEuTHVcWHh4fYtvX5i0+Pj483mw1WDiA9WqYJel4URUQqePvDgpYQ0mq1sCBtOp06pgKUXNHhweXnnNdqNSyvAniIQBVSosdwXAvFm67rQVCHSQznknNeMTmVUhcXF2AJ4ngmhOCJMMXCB2NZFhoSIQSeER85qql+v49xWb1ex3Cl+kRN0xRCgKHW6XTm8/njx4+BEyLMTNMcjUZYKA9e1WQyAf3ScRwEAwbfOPtxt16vB27XYDBAZwHk/dNPP6WUWpaFMx5ygVartVwukW9RvIVh+NOf/vS7777D4pdarYZJAKxx8GfiAgUiCoC92WzWajWqa8gGs9EckzcEuRACDhebzQbTlNlsBufSJEnw+VJKTdPMdzfM/W9ubur1OiEE4KSp6ajgHh4eoGNQQqCeRPAouiUMgDNAKcXRgLJos9lAYITGD68QoGhlgYEmCFQHQggeDW1qGIY1J5CUlFJkZZHzUhClG7pGaVrkcZqsozAMw3fAzH4m/CAg94NQEbE/fvg4YD6I4S13lLxTD6IF3C90q6dWO8UGqSaNO73SYrGAL60oym+++Qaek7AAQtmDiiWKIkPTYcuNhanHx8eB78NUE4d91TpiC5W2cx8PgoAQAhCGUroFaXSfc6mIrul2s91RSsVRKiRpd/pKqTRNNd0yLVdRRjVmux5jpmVpppmnaWrb1LIs163lec6YyTmxbR/igCQpGGN5LvK87Pf7i8XCNLdiv9PTWpJky+VS102laL8/WC6XWVYwpn/55VfTafTkyZPZbGYYxvn5Od5e7AbGZjz0eGhxDw8PuZTNZvPZs2fwAr++vp7P5+jKHMdZrVaoJEGb7Pf7cN02TVsIZRjWZrOZTGau67969QOuKgx4TNPs9jrT6TQJ46Dm+YH76WfPm83mN998o+n05vbedV2mkZLnuHaxRACnTFmWkC/5vh8mMfyUCCFXV1e+7wul0By+ffu21Wppiui6PhwOUbngOMOYju3tBcMoQtf1QpXgciRJslwuRVFCxGybFmaA6I/AKbVtO0qWbLeTE2HJGIPSBd0mwkzX9SrxIH/iCEBhqZQCI3S1WgF6QXWGF4w3DY8AHm+73R4Oh6iHi6L4kUz4cQTuJzelZJW4Pw6/DxAdpRRjKC/fEXEQhFJuTdLUBzf6jiVHCNG07QP+7Ks/Wy6Xb9++zeIExxgaCciu8yy7vb3dYdDaeDxGjY7VnCBegoaLfLXVzgqBK0DTNDjSwlqr1Wo9fvxY07TxeNzsOESVZSE1Znfa9bIsw/V9EpetphvHaRQWnHON2UlcBkHQbPjz2cZ1XaKMJN74HqnXAkN31+v1bLq2rcxxHKIM32smSeI6ruc2RsNpq9lN4tzQ7Tzj08miXmuNx+PZbHZ0eOrY2mw2u357V6vVNGZqzIThClySWq3WYrHA5Yj1w0VRnJ2dIc5x1pSlmM3GpmnHcex5wcXFU8fxxHYdHU2SjDHmOF6aput1WK83oyiJ49T3/STJlKKTyeyHH77DzobJZAJZY1mWrXbz4OBgs9lQSUDv/OKLL/Da8PIAuSGn9ft9IMO2bTOqAzuxLAv1Ht7/zz///PLy0nEc1/dxuk0mS8/zbN2AAzpQqLu7u0ajUZYlRAwY1kFXAeMMpW9Nn5BRDcdFCapRBpp7tNkgaHEZAHRBAsSyKogqdF3HX4qhPN+thxBcSUEo0XRdd2zPtlyNGVKoq5trrAPYNorMYFSnRENKL4oC+g+U8Shizd2KoXdBWMGbdNf1iT0DGBTulFKNvaOtbaNmj3Czn8QwlEzLnBBC1XtJdRvAO6iG7bFkdEOXUqJvVkrV/aDf77fbbRALkyTB0ILthFRSyvl8zssSLMqyLCUVkIRW6mTPdcFXZIyVZY6AhLESYO5vv/325OQEJGBs+cCFIoQQXCtynme5bdt5JotCdNoDwzDms8i27U570G53b29va0FnuVw2Gg3XcjRmM1qulvHhwImjQggRbjJe0rdX9+122zCMX//q951O5+TE63YOpzM5n22WiyiOijRNn1y8GA1Ho9GMEPLyh6sgCGazZZGrOCps2758c3t4eIhF8P1+v9FoYCvQxcXFs2fPvv/++1//+tfPnz/HdoSiKLAQt9/vYy53fX2NYtgwDCz6Mgzj5z//uRDij3/8IwyzdW24XC4p0TrtXriJ4zg+P7+Yz6etVgs7QDVNcz1HKQU8NlxtcBS+fv0aBx/nHP/2er3b21u0bZizTyaTPCsNw3j69Cnn/Pz8fPTLCVI3fDHKsuRSQkF/eNidTqd1z4/jmBDiOM5kMmm1Wu12+/7+/urqCppDuMWhiXh4eKi168irqGtEUTYajTiO18sVrnv0csvlEvW8ZVmz2ezRo0eYsoBJc3d3B12Y4zhIjABslFLAC+hOdYTeEjLI2Wzm+z5aCcTeer0mUjqOYzl2vdkwbYtqrNVux2mim0YYRxBkvQNm6Pvyv/3MRveat/3v/P/efjTB4sYYo3tVKNlR4fI4xRHVbrdrtZptmEIIbGvYWg9YdqVjlDunRrXTEFNKGWWU0na77ZgW6Py8LDGcNQwjixdgFcODAH0a8LrFYhEEASp7bE3Tdd20g+U64ZwbZlByuVqlrusdHB6G8ds0l0kSpbmcTNeEMM9vtdr9IkmTuIijnChdcKqkzLI8CjPBqeB0uQgPDw+LXNqWz0timZ6u2etVrDFrPlt1Oh3TcDvtAyW1JEke7ieWtXZd17EDJZXgVNdsYBJRFAFuAevi9evXp6eneZ5jbzF26y6Xy3q9LpTGGOt2u8PhsNFojUaTbrcbx/Mvv/zKtu3lcvny5et2u/3s2YskSf7tv/13n336xWq11jSdMe3+/l7XzE6nXREdTVOXUgrJlVKo08x2K4oiz3OlFPf3d6j0iiKXUobh5uCgH8dxrRas16s4juI4yjPOOUfSE0JUBvtAvwkh+KNAswQU1+l00GHGcRyGoRDC87zxeHx3dwfTGtiWojFDV4IMWRSFLDkOdMwe1+v14eGhUmoymaCLZroJ5o1t29fX1wBIa7Xa27dv8fJwVFW+UpbjGYal66XjuLVaw7bdOI5Xq81u/sEcZzvZLwqe52XgOWznsIoFYd1uF4U9qOdKKX2/qvwgwOieFp7u1vcQ9d6yiA+KUrWnCZb/XXUi0FHyUTmKjwRWJaZpllmOJVKmbpimqWmaZ291A8BFP0jgVbmLzAxqvLbL5HDIwk/BVkP3WA1kUaUAQEfyTC5vUNAzuuGch2FclnIymZWF4rwMN6kULE1z27Y5F0lSEM7RNYFyAXMNFCGcczwUcuxkMjFNM8/L9Xr9+eef397eP3nyyXodNptNIdTRkXN5eSmEME1b100URZgQ4l3FNhLMpkejUavVsm370aNHzWYTmm7UbOPpEo0QCj+M2nVdn81mlmV99tlnl5eX8/n89va21WodHx9DamxZDiFsPJ4MBgPX8RljlGq1Ws333SzLsjxVSrmuyxhxTAfjnzRNHx4eut2u3FlCZFmGRYX4mKAh7rT7Ukq0r1j/hFeFfSFFUYxGI4QN2vJ6vQ5vC4gScFifnJzc3d3NZrPNJnJd29/dgiC4G99jsy/+ZCK2rtbMMOH1cnhwgF1RWIxr2w6AFjDgwOFWSoFWRXb7nrePRohm2bVaDa8EPtG2bR8eHoL1ho9YKVXJaNnOJ0pKCZ+OWq0GgTI460mS6B8P2fcrzP0acssRVXsE638i46k90OWfCkIpJdkT4LMdmch2HcBTIA3FmxCzJkyBGGOWYeJNwbB1axb+EYaU57lkHNcuNYyqfA03S0xE8GahQcewFbDNdDqFcQiQxkLQZrPJmHZ9fS2EAFY5mUwM3cLHgJmkaZoYnTUDF0CRrmtRtAEYqJRwXUeI0rIMSlWjUQvDNT7mxXI6GAzCMHzx4sWf/vSnn/zkJ69evTo6OrJtu9fr4XLMsqzRaKDvD8MQi9rhHo8KEAEA6g98hNBH0d3Wx+FwqGkalntiDkEp/earxkMAADSASURBVPbbb3u9nq7r2P4nhDBN03ebgquyEIwxw7DKsry7u8uz0vMUUk1lDgRLVUIIIBagWbiIISMACo2cjKGfYRhxHPu+f3p6aprmw8MD/Lan06lQcr1e76jkGorGWq2GdYVYxgpd8u3tbZ7n/X5/OBw2GjUEKmBYlAl5nmPeyxjT6VaOFIahpmmY3MxmM6DNRVH4tQasa6o+DdaGMIACUKx2mnK8EsMw2u32zggrA0gDO2PEv9qtBDVNk0vpmqZNiGYYhDEu5WK1enN1BdnkaDJJ01QXe4a/+4BnlVcQb/LHVLn731E78LOKq+33K/bnu197LwgZY4puxU34Oa48/D1ESDi3F1m+fVW7lTI40fdfzPZJ5a64JXR7xey0+ZqmlVmEAMNprZRC3QJvvCAIqnkx7lOkWV5meZ5PZ1NKqW7qpSjjOGZMI4QkcUY1ommaYTUKnq43YZ7MMQSr1WrrzYwQQhn1fLPdbkiVe54XRotG07u9vcXBX5TJJly0O/XFcuG4xmw+0nSVpJtNuNhqi7I4juPFkjVUw3GNakaHTw2RVqvVxuNxJagROxfJPM8p0aQgUhDLNAO/XgsalmkrSV3PO+gf+l5tPJr6vn90eJKm6Tdff/uXf/G/C6Fub681TcPIYTi8DwK/KNws02zbrDyEgIjEcdjptHSdFUXW73dd1767u8MvYl5qmvr19egnP/lsNBo5jvVwPwU8OJ/PdV1fzFcnJyfdbrcUHFc/AIhqhIOFVpAgdzoddFzAxgkhGN8DyNlsNug+it3ePsaYVFtHIvwvIhkcNOhuAZkCjwGpqHLmrdAKXGk4u4VQaRqZpt1stlutDhakPTw81Ot1xnRCSkqZELwsha4rQhikmBiTwoe7LMuHhwesfMOcZhuEVbxV4cT2RL2oZHYX+4/vpt/vJ98LTvYu8PaDUO2USlW4boEfTQMAAxDJdk2EJQZxZLdRAHMFBKHaU/orpQACJUliMG07jTAMvttz2mvXtZ1BCIp+PHu73ea7ZWb4SPAeFWRSlLFlm59+9pQxbbPZxPFSERrUPF7KzWY5md4LIYQ8KnnGNJFkK8aYwRzN4KvN3Pd923FsWzNtZVhSM/h4PD48PCxF1Gg0TG74tQPGWJpt8iI6GPTRzcZxLJUgvBSCO65OqDmZ3sfJ6uTkZBMmhmFgLAZeVXVq4DQpiqLT6eBszrKMUl0IcX5+nqYpBsSGYQyHQ9u2sTsByvrZbOa6brPZvLx8m2XJ3d2DrrN6/VNoZ7vdTp7nnBeUKlyLZZmj1OecY/EtmjdcbUiMKOxRJ8NkLc9zKTSMT0H7xDr7R48eXV2/xbJUdGVoDWAJ43nexcUFsMRqhn57ezuZTNBcoDaGFBD7xfTdSm2MAaB+BrCEq//u7g7jB+zcBtiTpulkMoFfKJrSqnfA0YNhMnx6wG7FcwGIIoSgGMHpCdS60ajXajUQlbG1Al3Sy5cvqwnHNggRcvsZbD8mxc7QjlJKiUb+u64w1f9uUyi+uReE+Bq9m/roBmSJEILhtZQSosl2s7UFYNS7cWL1CuW+kzdlIHYJul23yPb+wHLnbwcYwNyt3QOYAbwL7y/OV2ZI29Db7eZgMBBCkHtRcsziqes6RennebFabUoe2w7z/EBTOt5PzzNn88KymWlRRYo4WVk2o4xruiS0bLZ837fxgp8/f/6f//N//pf/8l/+8pe//PLLL1erlWVrhDLPD8A7qTfcOFlF8ZKyAV75er0uyxKzKYyq0QOj5Ts+Pp7P5xhgWLYLClhV5KMoaLfbUsp//Md/rK57gGGj4Qy1XByn6OK63a7neUkaxXGm63qr1fA8LwwxjOAw8Ib+ixCS5zmWH4LFBgVdp9NBLcoYs+1cKfXdd9+9efOGc76Jt4UJnAKbzWaSZdh81mw25/O5pkiSJOijgAVEUfT27dvpdLrZhK1WixACpeVisRiNRjiesCt3vV6DtsY5bzWalNIKjcNvlWW5DuNarYYzGgw1jDH1nXB8H/ZTSimxbXaw4RSHHQgMEP6CIAr0wfM8QjggVvw6WpVyZ0OBzKxXhc3+nKBq0sjOkxsVCKbn5CPg9Edve0nuRzLhvlRK7Q1I5tMF6ml8ohkXwGmASaDKxxtEd/IrKWWl36/4N47jUKn274M/AQvu8OtorNEb1Ov1wWAALwkMADRNm0wmrmcFQWDb1mw+zrOCMdXtNoVQNzc3R0cn9UaglGKaajYb0MK4ZolBGQa8lqUxJrMsk9IAaH542HNdp1ZzkZkJYZwXf/EX//Pd3d2f/dlP/+Zv/uYv//IvX79+zTk/PT0VopxOJ91ut9GobTabNI2VssE+JYQ0m816vQ6EBmoA27b7/f7FxcUPP/zguu7bt29X6ywIgtVq5fs+xt/oyhBa8G5DtKB/xu7hZrN+d3cHpOH4+BDvHg5y0zQJ9bIswTdrtRr6Xsdx0EFgoHd2dvbNN9+AV+R53v39PYg+yJ/T6bRKC69evbJtu+Blu90+ODiYzudSykajAQu5Ikkx4J5Opxi1qZ2boOPYqGgw+gcFDNyAer0OQs9iOkN1KoTANP/t27fw9oVZVl5wQgj0n+v1utForFYrSikip6q6wcw2DCMtJQ7xwWDg+37VgsIAju/cij3POzg46PV6b9++3jKuHAf0LFBE0I1rMOemTN8mJUIxusN/QpIkTZBVFWFCEqUIZQwe2Eh2iKh35evu+5RSyii6JiJKQray3G1kEkIIKbiglFKNUko5kVwWSihCCAAYHNhVrGLMQLeuNu97CmNvzM4IhFIqiVJKKkqURhljSmNSZ8TUNSwJlaZhGGmqhMg0jRal0jTt+OTM8xw/cBljtmtacyPLEqF4t9+kVLeo7ZuB23Axk8RGsW6jX3frGByfDk5hoPTmzRu33YuiqEjWqzBpt09gsdVstDRNc23nzZs3FxcXUkqDabyM7h8eXvz0RZbzMkqTlM/mm6dPf6LrtfPzz/7jf/x/wg0pCm6a9eWCHB6e5+nEd08vr/84HN3863/9r//2//27NI0ajWCzWf7iF//Tcrn84x+/Nk0TC7fvbh9OT0//2f/wP/6f/+H/3oQrz/Nsx2x3mo5jnz8+e3h4MC19Mh09f/HJr3/9a8Mw6o3g/uHW853lctpoNOJ0ZNp5p+fEqa1I1u32nFj7Z//sZ5eXl9gV4Thev98PguCHb38Yj8effvrp7e3tfD4/PDy8vRk9efIkz6Tr1A/6x8PhkChD1xzfa87nc8t1er1eccUn81m73QZi2Wq1yrIM15vlfNFvd7KT0ySKV7P5oNu7ebh3a8EqCm3fe/32CkdzmqYHwcHJ+SPLcx3X1W3r8uZ6PB4vNuvBwdHt7W1ZiE6753u1rOCbKHn69GmSps1WW1BW7/byPJe6YXj+aac7HA7vRsOiKBaLxTrcSKJQPaJu1zSNKaJTZmq6bZimac7ml5Zech4nIfXsrpCyzLK6b1MpeMCKgjiObts+ISQJRw/5QuVespK6ro2miyjeaBrlqSBcdZsN13MYI0WRvdcTvldGSokyd39KQfesCj+oQquEtn9n+j49bf8L9f7aw+pHbO9lVJFeJerqR9XL0N7fpbGfVPefC4+gaRr2EKLWNQwLXU0cxwcHvSxPtN2ChIeHO6VUu90WYrso7u7ubj6fQ/NWlmWn0wEHEt2CZVmDweCzzz67vrtJkkxK4vs1TdOIkEopypjreLZtHwyODMNar9fKskzHffLs+Xq9Pjw8pjRnjCVxBl+J29vbp0+f+l4tiqKi4CjI0yxJ0hin+Hw+Pzo6Wq1Wd3d3jx8/uby8fPLkyaNHjzqdznfffXd/N9R1/c2bN2i3lssl1Iz4q0Evhgk0pq/41IBG9Pt9xhg8MxeLBdjtjx49+v3vf39/f48ar3o3Hj9+3Gg0xuPxaDQCmFmVu0hNQDLhVaHrOp4OboKMsYeHh88//xyvStd1rOZ9eHgYj8foxDabjb9bz0gprdoEYNHghQkh4FUD8ucf//jHR48eWZb1y1/+siiKVqslpfz6668/+eQTYDlgaGRZNhgMgA/h+9AlwmijXq+LkmNVliy3lA+UVM+ffwrIlBCi63oYhvi4PS+YTGY3N/dKkV6vDfg0TXNGnCjagO/JebFahWVZ1uv14XAY1Hxd17NsN6L4IAj3o6jCIZGIYLf6QfjtR291xbM9Pf5+MOzfuQqe/X/fv9uHs0dF2f5zsY/CuGoOqzgXuxvnnCtdiIKQjBBiGBqjmiACnI8sywxTw5wRHGsppRRMSbpZR1eX15PJxHXdTqfjed6/+Bf/YjgcrtdroqTGWBylD/ejPM9zWaRZTil1Pd82rSzLiqIwNM2wLKFUvd5I0nQ8mTqWbVnW+fl5PIlXyw1wAkpps9mO4zjLcl3XhVO6rmvbUtd1368pJQ4OequX96enp9PJHIzhly9fnp2dL5fLP/zhD0dHxy9fvuy0e+v1er1eP3nyZDwed/oH6NmwTQXgId5hXddh0wIUfgcJepvNptc7sCzn6uracZwwjO/uHrrd/tu3b4+OjuI4ffTocbO5hi0NlZxS+vDwMBgMANIWRfHw8IA+EMxJQJf9fr9er89Xy++++84wjPF4fHZ2tl6vj4+PUaweHR1JKV+/fg34VCkVRdFqter1erAtw+tHfdTr9Q4ODprNJoYEUkoQ2SmlQGtA3MMEC1u1q/oZzGFMRDabDcgxsLqRnKNotM2tHxyVquLicc4nb4bYn4GDAycyCs56vX50JBhj/X4fMtfNZjMdrymlYRhiS2xR5IoIz3POz88bjZrtWGka/4ioFze526akdtgjmlT2vgXbxwHw0Y8+TIDk/Zt6fyz5rmrdaymVUrB1opRKKqsuWe75cXxwHLCdpxuyN/AeTdOoCeZkXpalaeoVD/jhYRQEHqFyNBoLwXXdlFKORqNwU2Bv3nK5Ngzr5OTs/Pzcdd2XL19vNhuxE3THcRqG8WKx6J0c5aVSShoZL/LtkVkL7LuHyXqxDIIA1vS+KylNO92s0+nd3Nx8/fWfTk5OTNM0jEgIifYDJ3FRFIZhFEUShuvZbEIUOzk+u7y8nM1mL168mE7nd3d3R0dHWZaDqXz+6KLb7f72t79N0/T+/j5KM9u2W60WRqCAfB3HQT5J0xRYa61Wa7VarVaLUrVcLh3HGY/Hy+Xy8ePHaZoOh8Pj42NCCFw8oC2CfIxoOhzHB4MB8Eyc1MvlUt85iwF1NAyj2+3qun59fY3dwEdHR9DBgGwNdBpMSwCBuq4zZmiaBlkZ0hSG8tXyerClwQ2IoqjX6wFah8B/Pp8LIcDGVruJOXjVUBULIaDPIITYtq1RyjmPokhYHF9QqTRNA6mDEOK6frPZhABFCJEkWZrmUZTM50vf9weDI9xZKVoUvCi4rrNer6frdDIZapp2eHRAKc2yBFemrhmu6+v7AAluCDMUDFUaqbLKvmyPvF927sdnFRhVEqPve+xXd64eYYsg7U3w9x9N7qAXqlPMdvCeit0S0g8OBcQGngVBiKkDNzLUP4QQXTctyxSSa5pByNZHvCiKsixw6SyXy+kkAiul0Wg1m83T0zPLcjab6OXL14PB4PBwEMfxZDLhXPq+X683ozgNoyRN09lsgbQTBIEQ6ocfXt7d3MKpodtum7ZTFMU6jOxSZ9So1xu+HyRJslqtCSFgSyF5Z1mqaSxNk5JnRZli8fJgMBiPx0Ko4+Pjm5ubo6Njzvnf//3ff/GTn37//feDweD4+Pj+fnh0dPL9q5cHB/jgs/l8jkEiIWQ43J7osPfu9XrdbrfRaDw83KVpqpRChQnC12azubq6ajQat7e39XodyYoxNp/PfcdttVpwFgMh3nEcVBa46bp+cnICet319bXv+ycnJ9Pp9Oc///nbt2+/+uqrLMsODg4WiwVKfaVUURTT6RSzkEfnj2BAOhwOkyRtNhvNZrPb7YLqgLEH4BDkydlsBgtM1JbQvy8Wi263C6kkCn78yVBOr1YrcN+FEI1aDWgnL8qiKKIo0inDUhqcXJodBLWaECKPIiEkZRrT9KLkQeCbli0ViTbhYrkC9SrLMqr09WaRZrFuMMex2+22bZs4elB6UEp1bc/djOyVhXBWpJSCvVrFD/Lh/hVP90xE2UfOa1s54ftuolXcVk/HdoRvuWd8Wv06Ie/hsZj+KchYsIN893RVGYwivsrkqCiEELnU4NeANQCGoeV5zjSq6yzPM0II3prJZILTsdnuQPOCqaui7Pb+4f7+/umz52DrJlluWLbjeYZlx2k2WazBUUziGEms3SwFJ3kp80JEcdZ1g3qzk+e5UGw6X91ev/7iiy++/OJnQpR5NiSEuJ6NIieOI8uykiRqt5uaVnMcCz4rX3/99RdffGFZzuXlZa1Wq9Xqs9ns9vaWl9KyrCzL8rwkhJydndVqtQspCCGgYqH9gw/feDxGQQtaAiGk8sYsigLJQSn1m9/8BoM+KSWiC1wT4Jyz2Sx0Xdtz11F4dXONqToMrW3bprqW87Ioinqr6fieJR0cbZiqwTYC6Q5L2tBjdzod5BmkO13X4YVBCHFdB+a8GNxjBcV6vQagHQRBEATD4ZDueELAq1Eu4joHQwOnDxIsShV8Z71eY1hvGIbpGtvnpQRTRxBuJsNxHKdJkqxWK7iSel7gecHx8TGkLZpWSkmKgqdpmiSJZZh3dzemafZ6HU3ThCgtK8DT4TopiuKdoHj/WkfGQwrGT6uwqQZ0VetVpcHqd8lusKGUAk/74zjcj1X6/o3tbcWgO1gVH4xS22ExBoDV3aqCGeHH9gh71Y/gXxA49VazU6v7ux0MinNuMD3LCiEEY0RImec559uRzOnpaTWuhJsgLtYwDK+vr4uiqLT5YRje3t4mQuV5HkUJVvMFvgtvON+v9QaHtm03m23dsC6vrh3HSdJ8NlvEcRoE9eUyMQyrLPPAr0u5bDYbm81G11mWMdPSizIryiyMllGUcC6//fZ7vJM3N7dHR0cvX74scn5+fj6bzZ49e6Hr+tdff/306dOrq6s/+/mfAx2BIhmwBJrA2WzW7XZXqxVAdlTX9Xq93x8sl2vX9U3TnkwmcZzatlur1ZbLtW27nEuMCjabTavV0XWGQR/Ga47jAFdUSsF/5P7+Ho1co9EYDAb/8A//ALwEHSbAHlhoA2vB+I4xBifbyiQSgQ1b/jiOoYTG1QvcH4c4YwysQ9ScOHcajQZqXcijq9k69ltRStEWWpbFiwKUzlajicISlA8ACmma2pbrezVG9fUqLHJu6BI4X5HzshCM6rblCh4nWcZLaZnOQb/NeXF8fHx2djadTgkhvV5PKQU+g6YZWZa96wk/uGHgCDCtovBUmGTV++3DoWxvTZrYmawR8iNL7ck/AdLsp+L9tIkEhTtru2lEdf+9BnI7VK2IDghCsnNcZ4wFfuC6LlFss9kkSaKUYIz4vp9mSbfbNgxtPp/ned5sNoWorVZL23azLNtsInhDYQh7eHg8nc7AgW63u6ZpL5frh4eH+/uh3WpKJYnGYOkHVDCJYi4EnJdMx4aVxtHRkWmaF4+fZGk+HA7DMHRdO0kylCio5TzPUUToul6W+WazEqKMo/Lp06f/6T/9p6Ojo08+efbLX/5yMBgsF+snT55Mp1Pbds/OzoQQnU7v/n6ICQqiCxplAIkAaWDVA55KRUi6ubmBI3273ca2pru7O0JIlmWwloIrJFLrkydP1utlURR4h5GmhsMhSsTz83NMvdFcgVuLqMiy7MWLF8jb8N3BpY8PFIxcYCeW51awGWaA+m6zJ5b+Yh0qIgRJDyU3RrWYeEGSbxgGki02i0HQAONguLA6jsMcB50LWFmu61q6gak1MOosy+qtpuU6buCXZWk6NgwN0iKnlJqOLSnJ16tC8Have3p6moSLI3L4/MWzWq0Gyr5tm8PhOIqiNM23xAD2Tyh0q7lq9Zcg8KrSFLfq4q5iUu7WZf9oq7Zf+lYISjV8V7t9GBVBZ7++rQbxamc3UCVkRF1VGFdKQmNHWEPZY5omY3rFzzYMo1Zr+r5r26ams7u7u9lsYpo63EQsy3r06HyxXEKryjRNRzI1zZLz6WxWlmXJ+ZvLS1wBmqa1O52UKGAMvXYHezl1XYcIUEnZ7XYtyxoNR0dHRzjLeZE+PDycnB6jPoEUkGnEcWy4d2n6tlPFNFzXbaXUJ598outGkiTPnj0zdGswGPR6vWZzuw5+NJr4vo+MPR6PNU07Pj7mnB8eHqLRHY/HFxcXEA21Wq0gCO7u7hhjZ2dnYRh+++23X3311a9+9Stwu9FxgVr5i1/84r/8l/+CGq/dbj99+vQffvX3P7x6KYQ4OBzc3t4u16v+4GA4HD5+/Hi+XHz7/XcwtJdE3dzccCkqwAxLCBuNxtdffw1COUrlKIrg6YLrYbVaYWZQXWOO4ziO0+12R6MR0jigF+BMkF/gbrDrLori4OAABHfDMDDJABseFF/M8S3LWi6XkvPqaux0OrZtx5sQ8NXBwYHneS+vr4HxgIQAnhC2hYL2be6WTzw8PHiel4QL7DyE3x+OOayHkZLgk9Wrsm0fayG7RZxqbz1TFXXVpU/3tE4fcFCrUlDX31lC7d8qygvbGT0h0ioLtu2P6DYIcfjt/6LaOW1VQV7FIdmtcEPxjKkreuU0Te3dqleiWJIknBeGoUFU1u93fd9XSiyXy9l8slgsmOGnWbwFZhWXigpZckGYRhzDUkqlaQJALwgCz3eSTagRZdmWptGiyJSUghdECccGPT8URV4UmWVZjmMVRZGXSiohhLAsQ9cZITLPc6m4aRqEENe1a0EDiqeiKG3btq26aVr1egMDZSWpruvn5+dxnMKJdDKZIUv4vg/8EHDFeDx2XReNVr/fv7+/f/LkCdIFpdR1Xfg49XuDNMk77V6R80a9dXf7UBYCUn3H9r75+ttGvRVFUeDXKdHubh8ANn7//feAW3BSwEgbgQSSDQR7nHOdaVtfiSjK8/zy8tLzvDzPgyDYLzvhkorsJ3d0PHNnKC6lHA6HcGEDlQekeSklDPhQtgghcH/gLmzPnQxtBQorPEKe55vNRgkBRBqyj8ViUfeDFy9edDqd6XT6/fffp1JCuQJADj4jjuM8f/4cPCdMPtCkbDYbKqRju2EYhpvIHwRxHE8ms1arZZqmECUyjY46hO7tY8INLWyVFfczpNqDOvfjs4rJ/TaSvg+T7j8O28mXqiJWSkn2EiBjTGdbK0Q0gWoPVsXHY+jvrdPY/wJ1PNktkMHfqBlarVYLgiDP09lsRpk6PDzsdvtRtGk2m7quj8fD29trSPXare5wugR+QCkFXoLXg4sGY1/MiMCnSXgOL1NdI4LnmqbZlm5bupIlkazI4jTeaJT5Xt2xDcHzdrtZlnkUbRgjnHPTNKXihmE4jpskcRjGyOqr5cZxnIP+sWW5o9EI3iT1WhOoBucSAzHOpWVZ4/H45PjUMu0oipIixmu+uLiADljTtE8//fSHH36QUqLAhr5OKfXzn//8P/z7vwmC4Pr6+uTkZDabHRwcYFMSfnE4HGLzKex97+/vj04PwjBEhun3+5jZ4AM9ODjAe4L3DR+H57iAQFEn393dffHFF1jHjSUTuHzb7Taq0DLcgBzLGAPfAL9br9fR5UZRBLkTKjKgKZD8wamNcw6QtkIT0MVBSwEjRry8oigYIYwxz/NmsxmMZ0VRXl9fj0YjUAKn0ZrLMk4jRZVuaqUo1uGKUvry9Q+LxeLg4IBznpf505MnMKFsB+2rq2vP8zwvuLt7aLVaoAdjc6iUklLyDkKsqj7y0U3uqW/3I6qKq/1Wbf/R9mLwwyCs/pftyTXkznimyrraLgirmlnuiarU+zPG/adA74H7VHItTdMcxyWE5Hme5yWl1DQsnIWmaUqhcpEzxgaDI0pVHMej0aikWiFKYAA5L1Spcl6UZen7fpqlWZlLqryaDxXMcrM6OOxiKJfGCXzB6nXfcxwlcs/yAPB4ttPtNDRN40XCGNE0WpSJpmlZlqVZjEQNvB4qONM04zilVMuygjEjiiLH9uq1JhSx0+ncNNNut0uplqbpxcXFv/+//sPp6enDwwNyICEExAAU+dPpFPzm2WzW6XRev36dpulXX3318uXLn/3sZ9hDdnt7OxgMLi8vUaRh9LxYLD7//HNM4WazGei1myhah2G7210ul5999tnvfve7TqeTJEkpxJNaTQjBpVyu10VRCKUYY8vlErAKOrfDw0O4GIK9SfaafDB70Inx3RK4CqTAnQG9oItG1gUj6uTkBCxZpRREjNVcChAdzvTqmK44DLphQFjo2g6K4SLNMJxEVeV4VpIkcRzqus55IUSZprEQYrXa9Pvds7OT9XpNCGm3m2VZClESwqIo0TTDcTzDsGzb9TwvjmNwoTWN6jgP9svRqiLVdosH99OO2iN279/UbnXZfqzuEto7ff0HcYi7VUNIPAXbC6r9+Kx4HkwjVeZUO6Lcx48ssfltZyXEOQdIIIRYrdaaxjzPA2oSx/E334xqtdrZ2Umr3aSULhaz7Uei63GWCQGwDuXi9lxaLGa+73e77aIopORZlpimfnDQcx3DdUzDYJEs8jQqsrjI4jmlWZa16g1d1xkRTFN5FqORKHgphPB9H0pzsEySJLm9vYVSybIspYjjuFlaXl3eCCHKsgRhoCiKxWIVx3G3203THOif4BISHpRYEEys1+vf//73Uspnz55Np9M//elP8GI7PT29urqSUvZ6vT/84Q9//dd/Xa83wjAMgtpyuQqC2suXr0zTyrK80+k+PDw0Gs3FYkEpOzgYcC7a7c5o/sAYg6k7shN8lnADalJuV6b4EONiv5Jt22/evHn69Onr16/xV4MPTSlF8GCwCcEbgAnozvjOhx+5AT0hCmBkNnz6i8VivV6juEWWQ39eYaoIPLzCrRs3pYhMPN3Wlp9S2J/iTHS1gItSCJEX2Wq1WiwXSik/8A1T/+LLnxiGMRoPXdedziaz2SwIgvls9eyTT29urocP45/97GeT6ehv/uY/npwcpWlKqaKU6oam890u4v3g2W8FP7iy99PX/lhivw/c7y1xl4/jtoq6Kly3ma36YrcTuzoXEW8aofs6pqps3u8VKaUVQRQHKt2hpgCpHcfRNLbZRFJyTaemaaNdnE75cDicTEaaprVajU6ns7m/1TRq21ajUUdvqfa8DxDe1ahNCFHka1GmjHDLoLXAwTdFWWpUGCY1NGqZZuDbVAleprqmJGGe55imaVkGIbamab7vr9cb7EKIo1RoSgrSaraB7DHGCSFY/cMYm80WSZJMp/Plcn1/f1+vNW5v7uI4/u1vf1u5qkkpMXBP0xTerZBKdLvdy8tLmI7e3t4eHBy8evXq6PC0Xq/f3d1Bjlh13d1uN4qi4XAI1OHTTz/F2gnginCnHw6Hg8FAKeV5HkibWFmFwg+DuLrnNxqNXq8npYQrB8brKEaAl8IgGPQxw7ExpvM8D9TfsixBJ8IRg5as3W4TQvCeYFwppbQsCzxVDAP3QT6xkwgi9tATmaZJdzjCcrmEV28SRuCyttvto6OjSMZZlsAhBqsXESxxXGRZsl7nWZa0280sS1arxdHRIDC6lLI4zjgvZrM5ti9j3CpEKZXgvNCrUrOKjf2Scr/LYjsK2C66WDUZp7uWshoP0t0KKOxV2o9Dtev65O5W9Zn7ob6NaiU/yNJq74by9YOKVO3kF/tfsz32D3qDLEvX6zUhstvttlotXWdYcmJaxtOnTz3PWywW9/cPzVbdtHQwv+D9WhQFZQqe0GEY2natVqtRSqF/PRz0MBqp1zyn33EtG8+ra5rneWVZapShjISt7XSxwI4h09Q1TfM8Wq830zQ9OztjVC/ykWnaUsKOhwVBrdWqgRht23at1iCE4MxOkuTu7i5uJLBCiqKk0+mAbw0/MiBbr1696vf7x8fH4BALIU5PTzVN+/bbbx8/fnx6evr61dXJycloNOp2u5PJBLp1nDIQKDYajXq9Dihhs9ngcgfoP51Oge4sFgsArQB1N5sN+iVCyOvXr1ut1mq1siyr1Woxxo6Pjw3DuLq6QrQj9hD8mqYhSeD4wyxRSokDAkGodoRKCDJ0XUdaVkrBOA85E3g4alqMMQnZgpOgByL7Sc4p7BiZttls1ut1meUYGBZFMZ/P7Y5LGFWUaIberNWwgCQMwxeffSqEIIxePH3S6/UmkwnTtYKX3UH/V7/6VRyl9UZwfX3t+95PPv+y5HmjQUteFEUhRKmrvSZwP7lVViIfRKDck+3t94E4gcT71r2apknJP+4G9zPtByXlfqRJKSnZPqn2fhn8QVjul69qx06uCqFqyJnnucHMyWSilHJdeGyZnPO3b99KyZvNervdlkpMJhOUT48fPxaGBPcCcjgkZCjoGGNYU053JCld1y3LmM3WWKnVbjZZs2nbtqGzfr/HCF0ul5pGHceiSmlUBUEgKfU8z3EsZGNeSinJbDYbDA6VpIZhtVqtNM2jKJpN52XBV6sZDAsty1ou16vVqtlsUqoVRQEkxvO8yWTiuj6qvk6ng9IUVOl6vV6r1ZbL5U9+8pObm5uzs7PVaoXtOghXaO1fvHjBGPvzP//zt2/fws+bEALwSQhxcnIyHo/hcQZ+DMpLLFGB5ngymUDdZ9v2er0ej8fYREC46Ha7eK9OTk4g2AUbDgwkDKWra6bYbelCqKOVgk9hdZ6iV6w+esQ29O+YBoOOj6MEJylGi+ghAfYg0kRZSikNw1gvV2C9ebaDswAfcU3W6vX68fEh4C7Hsdrt5uHhwcXFxT/8wz/EcdhsNpUShEjXtYUor69vs6woioJR/fDwiBCZpqlusG63W5R5nqecFzoj78AVolS1kpIxRhQhUqndltzthIARxoihM01jlJKdA7LEGSaFYJQwDcinFFwwppSs0uzWk5BSvchzzrkopVKEMqoIQfwKUe7uLCmlbHcQGKYuJaRVOqWalIpSzbIMy9yipsjA1UvFdVNhsFXQztVCM5iu66lJJRdaqlFFiFKddotoWpJnBtN8r24aBo7M2+HIcWxDmb4VNBq1m5u3i8nE85xwOTdNkxIpi8IwDCaUo9u1Vi1M1GK5iSKv223rVmuxTDebKef8aCEoVWkad9ttL+Cz2SwMw4uL8/WsEJlp2y0iCSFEqnK1Xraa/X7v6OWb10meTRdzTdc55ylPf/PH3xz3HjebTV4aSZxYVhmGcRynus4opVmeUkrjWClF1+sw8Os31w/CzAkhPI5qtdomDLE4xTTN2XjyyflFFEUqLx3NiONsdvuA6mtYlFLKFy9eOKbVbjTLLBdCUKk0QmXJN8vV6P7h7Oxstlq/ffv24snjMisF5SUvDMls3aKCdJutgpc13725ueFKJkl8MOj+8PLber3++PTpfLECNfzw9Ozq6mqxCYlp6q4rDH2RxIs44qbBTDNK00WWbjZZu9VKBVmPp4zIZrfNebFYz4LA8wOPMb0sSyXKOAw5557j8FKu16HneZLQyWxONaZpmm7bm8WCJKlhWAYzsjxdTGdpnDBKbc2hnOlUI4LkccYY0zQ9S1JCiG2bcRzXW/X1ZmV5ZpSFTmAfN05N0zRNXZmiLAuRlkUeG6Y2vrqxOLOFxtf5fJktZ0uSsEW2SuwsTRbPPnlEKT0adF5fXWJJa5rxu7vher02bOs9G/wPUg39CDVVSmm69kHKIu/jqx/c5HvqPlkVjR/fk+4ATLKfgfe+L/eIcrhpO2M5+b4/AH1/Vdv+ayt4YRoGChK05kQqqsibN28Gg8HR4NC1bJD62Y45lWVZksRhtGaMRNFGKkEIwQ7XsizzvIiiaLMOy1JQSq/v5rA5gTGmKHPPc9vt9uvXr13X1nVGlZJSYCqdJFEey16v5/n+ZrOZzWZ5nhNGCSGz2YxSCvQlC0Nt56mz2Wy4KBaLhRClplEuSs9zOJeO43ieo+umFCrLCkIk0wjnfJ2sALe6rqvT7e4NSilXPNqEYRhmaco51y1TpyxNU6y2XywWIGSjkNN1/U9/+hMMPuDF5HkeiuowDFer1fPnz9EXzefz0WhUFNlP/+yrzWal6zolClgoHg0FC+d8Op0KotbrNYhEURTphYGGsBRC7LyYXMdJkiSM1qZptJt113XDsExTURQbnWmdTs+2bZ1qQqg4jrMsq9eabGe2r5Siu4uz1WrVg1qz3hBFORmP5/O5qRuNej3KC0IIVsphz4phGAAlgcNVHanc0ULyPF+vl3mRCsF1ndm2bVr67e1tkXPGGCGsLDlKMCHE/f09hkCe511eXo7Go/V6XavVNF0H0qPiSK8u2Y+jaL9Pq26apn8cXeTHVLy7R94vGj8Ecj74dUJI9fjboPqon9wHfiilSr3jzexHb1UY/+gNVSVThDFmaLppGKfHx41Gox7UiNgqXNHWu0wry5xSWpZFlke9Xq9/0PN9dz6fw1xos4miMM6yjHMZx3Gz2Tw9PY3j+Pr6Stf1i/OzIPDjOA58F4WoZRiEkHa73el0kiRq1dxGo0EYXa7KNIuLogBFcj6faqbhunaWJZtwBT69ptMo3mR5VJa5rjMuSl1nvu+GYShE6bq279eEQENueJ6nFA2oAyJ+u9EsyxJiBc/zDEODwQTwCeibZuMJDAthG0F3usosy+r1Okhb0CL88MMP6/X69PQ0Lyz4poEvJimZz+eWZaBN5ZxrpkHIdgkPBjloW2azWc7LrQu9xsqy5HIbeJQxfbevU0k9iqKSSwAn6HEcRzMMjRACyC0XinOJk/FwcFwIjh5V13XdNHCsO45T5sVisSBCSik9z9Moy7KMUJZlWavV8jwP86Fms1mWOfg0OGcJISAhQv+dJEkYrtMsJkRBDacbrCxLx/YsyypLAfQI12ezWU/TdDR66Pf76FaiaJNlie1aeZEWZSYJeaeikHvaPPpjZOvqKlfVhqb3udcfRCP5qHOrMuGPRu8O5Hx/pr/7AueKlFLf80fEUfcBZrOfqPcjEzfH8ZSQZSEIEZZu2Jbte55j2Y16Swo5ny+JkIwxx/ZM06REwwXturYQXEtou93EygF4eOMlwZU4z0shRK1Zm8/n0KQ/efLk5GiQpsnDw8PpydFms2o2641aLU0TsPLv729FTlar1SYKF4uFEMLzPKZpgM5BQFU7U0ac7ppmMEZqdQ8+vJZldLqtkueGodfrQbvV4Vyapm6atu8FRVFIs+k4DlOk3+8XRcHLMssypogeMEkUdPpJHNdqNThDj6fbZbpKKczicNmBsYCxPiZ7Sqmjo6PLqzcwaA2CoN1u2p776aefFkX26tUry9oaLidJjGbBdd3VbOuqzDnXiUKGzNJE13WhJCZD2m6Up2nachHZtu35jmkaZVnmeapptN/v27Ypyh28KYlSFNm+3W7PV0tQW7XdjVLqed48SZWQpqa7rlv3A0boerVq1htYCgScSQgB1CeOY6UEZBaKSJgjcs43m1jtRiZSCil5nudpxo+Ojjw3UErN50sEuev6pmkahgZyDzgPGlGw1apU/PhL33k9qT2zQ7mnJ9q/oNXeHELtaWc/ntTv56UPHqHSN+GLHccCheg/qf2tIrZ60h8Nsx99AdW/uq6XosAnVLXpQojb21vLsmpBUPcDx3E0uh0KB41GnuemaQjBhLQJYbPp/P7hDodFnpe6rteCOiFsPp9HUTKbzf70pz+ZpvlXf/WvPv/882izyrL0k08+MQ1ts1mBdbFabVf8LZfL5XRTlmWUxOBV27bNNA0Tv8V6ZVlWrVbbLj/NMkJIo1lHv+04FtOk73uNRhBFHmh3zVatKErOC00zdEPjgkZh6Fq2tUN316sVCDegpzRqddM0geADAe72DyGJwOZnOBTZtv369WssbPj+++8B8FiWNZ/PgyD46quv7u7uiqJYrVbpZMwYg3oY9bzjeXmelWUZx/FyudTY1qW3LEuLUThE5GWBTAjyJytLzJMQA4ZhGAw7DAspuWW5QRAQInWmGYbluq7BdIxJYQ8HcaOiW6ojHgrvj2PZiotRkuimddDrd9ptzXaw/oBSCm6NpmmrFbSgGSFktVoZpg7YJs/zohCQL2k6LcsiyxLOuSJC7NkimqZZrzVbrY5lWff3t6Zpnp+fg/h+d/0Af7rZbIZ0bQIWr/LeB6lp/ztV37VfuO5f3PvX/X4mrNo2pKvqV7YVKduOK8AoIoRIyX80m+E+lFJEDtkxCgR/xwncZ7Ttv7zq9VBK8zynioCJjbMt5lwJWfeDIAi63a5nO2VZlnmBzifw67oeoyINgsC2zSSJyrJUSt+y4DXD87yyFErRsiyHk9nTp08vLi5OTk5gvwXyVFnmURTd3ZVpHE8m4zzPYeFcUSsxSatgOtu26YZSSn3fN20Lf5RhGI3ANwxjE65KnmkaMQzNMLVa3WeMUaaUErpOLVsnhOg6sW29VA74N3e3t0EQVPF2dXWFckujjDEWJ8l8sciLoh7UFSHM0AVROS8Vo4Iow7YGx0eW60hKxEyto3ATR77vx1l6fDg4Pz9Hoo6izc3dLfDYVqcNnga86vI8x96IVs1HUk3TlGisMghkjIlSglEkd7Y3ZVlSaggh8lwqJT3Hct26bZu6ruV5qtFttlBU4SQFkTCKIs6547mWZUmiIO/gnM9mM40ynbIsy3rtTrvdXq9WScnBnmWMBUGA2nu3zaLcSrQMDZm/KAoiNSkl51wqUu78RXXD2mw2vJR0T91eOa+BhwBHvNF0QghZLBb4TMEl+JFNvep9+UKVx9ie8pB+dCM/dkPe2itK6Xs/2aKldL9sALxRvQz6PjIEEgww5eqVkL1JoNwZNLI9tu7+Lc9zyzAxscBRZJmmqRtYHhSGYbhaZ1lGFcGmWOygTrPYMLRefytTwN4VpEqN6egGcW1JKQ8PD1ut1u3tbRRFrm0qJVerlefaq9VyvSZ3NzeTydhxnMPDQzQJtm0pSgjBDpOMy7Isy3ojIBqBkGIdbhzHwqTLNs1ms0moKIrMD1yAPY5jWpYlpSKU27bjc1dKAtM+O/dbrdZ1koxGI2h2EBjQlUopDcuoNepCiIeHB875YhMppeB1DbMWMJIZY3d3d47jgJSH+rPZbN7d3ZmmuVgsIOFrNpuu615cXMyXC6UEpdSyLNu2MF7HS8LUIcuynJdZloVh6AU+AgaHI9/NnKWUGqOmaeIiIoRYlmUYuuCllNKyTEJIFEVMUV03cW0gfmzbBrM0ThMM2+bz+fX1tUZZs1a3TBO7D5bLZcpFxSVA9wiZhVKqLEs0GsCNQBAnAh6+GWVKKckYMQwDLw8jE8ZYWRZhGCpFXdetNwKpOBeFH7iWbfT73eVyKURpWb6UEhY2Otnr3HD8VHXmfrrbuyn60W0/sD6oJBnb92WqQrBafL/NYFVB+67Zw6Gye0AB26UdAKMq55vdC2B73HH5Y7todq+NUaoRwrIsL9JM13XP8VutNiEsjtMkSm3TdF3XsdADyDhPUBcZhmZbrlRcCGVbTpLGnHPBpdBFlmVRlOR5rjHj/Pz85ubm+vq63W7quj56uIvjSNd12zK63Xa3286SRNc1ZF2lxFTMCCEFL/Xd4vWCl2EYmqbZ7XZt28bmUwzWpJSM0UajLmUpZNnptCglJc8NUzs6HsRxbJmOZdlKqTTJGaO6rsWLzcHBQb1eL9LMcZwsTjCIOzo6wjkCSU4YhqPRKMuyVkeDDblSCqRwpRS0WoZhtFotCGHBWU/T9OjoCL5SUsp6PSiK4rvvvqvVfMKo41igXEdRiHIagcEYg+kLdPd5ntca9eoz0jQNkyV87kmcOo5j2UaSxGEYahr1PEdJURR5zQ9c18+yTJZC103GWDW+r1RskEowxsAp7Xd7zVo93GygQSnLkmk6ukHOueNaUso4jqXcajLozsZB7HbpaNVSRJ0ahg4DTugYqxCQUirJKaWe52V5hM2thBCsKpBSDgYD04YHQr5er/8/mJNilez1C2gAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1bEUwwzcVG8o" + }, + "source": [ + "You can also use the visualization API provided by MMClassification to show the inference result." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BcSNyvAWRx20", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "outputId": "0d68077f-2ec8-4f3d-8aaa-18d4021ca77b" + }, + "source": [ + "from mmcls.core.visualization import imshow_infos\n", + "\n", + "filepath = 'data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg'\n", + "\n", + "result = {\n", + " 'pred_class': results['pred_class'][0],\n", + " 'pred_label': results['pred_label'][0],\n", + " 'pred_score': results['pred_score'][0],\n", + "}\n", + "\n", + "img = imshow_infos(filepath, result)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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All configuration files are placed in the `configs` folder, which mainly contains the primitive configuration folder of `_base_` and many algorithm folders such as `resnet`, `swin_transformer`, `vision_transformer`, etc. + +If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config. + + + +- [Config File and Checkpoint Naming Convention](#config-file-and-checkpoint-naming-convention) +- [Config File Structure](#config-file-structure) +- [Inherit and Modify Config File](#inherit-and-modify-config-file) + - [Use intermediate variables in configs](#use-intermediate-variables-in-configs) + - [Ignore some fields in the base configs](#ignore-some-fields-in-the-base-configs) + - [Use some fields in the base configs](#use-some-fields-in-the-base-configs) +- [Modify config through script arguments](#modify-config-through-script-arguments) +- [Import user-defined modules](#import-user-defined-modules) +- [FAQ](#faq) + + + +## Config File and Checkpoint Naming Convention + +We follow the below convention to name config files. Contributors are advised to follow the same style. The config file names are divided into four parts: algorithm info, module information, training information and data information. Logically, different parts are concatenated by underscores `'_'`, and words in the same part are concatenated by dashes `'-'`. + +``` +{algorithm info}_{module info}_{training info}_{data info}.py +``` + +- `algorithm info`：algorithm information, model name and neural network architecture, such as resnet, etc.; +- `module info`： module information is used to represent some special neck, head and pretrain information; +- `training info`：Training information, some training schedule, including batch size, lr schedule, data augment and the like; +- `data info`：Data information, dataset name, input size and so on, such as imagenet, cifar, etc.; + +### Algorithm information + +The main algorithm name and the corresponding branch architecture information. E.g： + +- `resnet50` +- `mobilenet-v3-large` +- `vit-small-patch32` : `patch32` represents the size of the partition in `ViT` algorithm; +- `seresnext101-32x4d` : `SeResNet101` network structure, `32x4d` means that `groups` and `width_per_group` are 32 and 4 respectively in `Bottleneck`; + +### Module information + +Some special `neck`, `head` and `pretrain` information. In classification tasks, `pretrain` information is the most commonly used: + +- `in21k-pre` : pre-trained on ImageNet21k; +- `in21k-pre-3rd-party` : pre-trained on ImageNet21k and the checkpoint is converted from a third-party repository; + +### Training information + +Training schedule, including training type, `batch size`, `lr schedule`, data augment, special loss functions and so on: + +- format `{gpu x batch_per_gpu}`, such as `8xb32` + +Training type (mainly seen in the transformer network, such as the `ViT` algorithm, which is usually divided into two training type: pre-training and fine-tuning): + +- `ft` : configuration file for fine-tuning +- `pt` : configuration file for pretraining + +Training recipe. Usually, only the part that is different from the original paper will be marked. These methods will be arranged in the order `{pipeline aug}-{train aug}-{loss trick}-{scheduler}-{epochs}`. + +- `coslr-200e` : use cosine scheduler to train 200 epochs +- `autoaug-mixup-lbs-coslr-50e` : use `autoaug`, `mixup`, `label smooth`, `cosine scheduler` to train 50 epochs + +### Data information + +- `in1k` : `ImageNet1k` dataset, default to use the input image size of 224x224; +- `in21k` : `ImageNet21k` dataset, also called `ImageNet22k` dataset, default to use the input image size of 224x224; +- `in1k-384px` : Indicates that the input image size is 384x384; +- `cifar100` + +### Config File Name Example + +``` +repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py +``` + +- `repvgg-D2se`: Algorithm information + - `repvgg`: The main algorithm. + - `D2se`: The architecture. +- `deploy`: Module information, means the backbone is in the deploy state. +- `4xb64-autoaug-lbs-mixup-coslr-200e`: Training information. + - `4xb64`: Use 4 GPUs and the size of batches per GPU is 64. + - `autoaug`: Use `AutoAugment` in training pipeline. + - `lbs`: Use label smoothing loss. + - `mixup`: Use `mixup` training augment method. + - `coslr`: Use cosine learning rate scheduler. + - `200e`: Train the model for 200 epochs. +- `in1k`: Dataset information. The config is for `ImageNet1k` dataset and the input size is `224x224`. + +```{note} +Some configuration files currently do not follow this naming convention, and related files will be updated in the near future. +``` + +### Checkpoint Naming Convention + +The naming of the weight mainly includes the configuration file name, date and hash value. + +``` +{config_name}_{date}-{hash}.pth +``` + +## Config File Structure + +There are four kinds of basic component file in the `configs/_base_` folders, namely： + +- [models](https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/models) +- [datasets](https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/datasets) +- [schedules](https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/schedules) +- [runtime](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/default_runtime.py) + +You can easily build your own training config file by inherit some base config files. And the configs that are composed by components from `_base_` are called _primitive_. + +For easy understanding, we use [ResNet50 primitive config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) as a example and comment the meaning of each line. For more detaile, please refer to the API documentation. + +```python +_base_ = [ + '../_base_/models/resnet50.py', # model + '../_base_/datasets/imagenet_bs32.py', # data + '../_base_/schedules/imagenet_bs256.py', # training schedule + '../_base_/default_runtime.py' # runtime setting +] +``` + +The four parts are explained separately below, and the above-mentioned ResNet50 primitive config are also used as an example. + +### model + +The parameter `"model"` is a python dictionary in the configuration file, which mainly includes information such as network structure and loss function: + +- `type` ： Classifier name, MMCls supports `ImageClassifier`, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#classifier). +- `backbone` ： Backbone configs, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#backbones) for available options. +- `neck` ：Neck network name, MMCls supports `GlobalAveragePooling`, please refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#necks). +- `head`: Head network name, MMCls supports single-label and multi-label classification head networks, available options refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#heads). + - `loss`: Loss function type, supports `CrossEntropyLoss`, [`LabelSmoothLoss`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_label_smooth.py) etc., For available options, refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/models.html#losses). +- `train_cfg` ：Training augment config, MMCls supports [`mixup`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_mixup.py), [`cutmix`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_cutmix.py) and other augments. + +```{note} +The 'type' in the configuration file is not a constructed parameter, but a class name. +``` + +```python +model = dict( + type='ImageClassifier', # Classifier name + backbone=dict( + type='ResNet', # Backbones name + depth=50, # depth of backbone, ResNet has options of 18, 34, 50, 101, 152. + num_stages=4, # number of stages，The feature maps generated by these states are used as the input for the subsequent neck and head. + out_indices=(3, ), # The output index of the output feature maps. + frozen_stages=-1, # the stage to be frozen, '-1' means not be forzen + style='pytorch'), # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs. + neck=dict(type='GlobalAveragePooling'), # neck network name + head=dict( + type='LinearClsHead', # linear classification head， + num_classes=1000, # The number of output categories, consistent with the number of categories in the dataset + in_channels=2048, # The number of input channels, consistent with the output channel of the neck + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), # Loss function configuration information + topk=(1, 5), # Evaluation index, Top-k accuracy rate, here is the accuracy rate of top1 and top5 + )) +``` + +### data + +The parameter `"data"` is a python dictionary in the configuration file, which mainly includes information to construct dataloader: + +- `samples_per_gpu` : the BatchSize of each GPU when building the dataloader +- `workers_per_gpu` : the number of threads per GPU when building dataloader +- `train ｜ val ｜ test` : config to construct dataset + - `type`: Dataset name, MMCls supports `ImageNet`, `Cifar` etc., refer to [API documentation](https://mmclassification.readthedocs.io/en/latest/api/datasets.html) + - `data_prefix` : Dataset root directory + - `pipeline` : Data processing pipeline, refer to related tutorial [CUSTOM DATA PIPELINES](https://mmclassification.readthedocs.io/en/latest/tutorials/data_pipeline.html) + +The parameter `evaluation` is also a dictionary, which is the configuration information of `evaluation hook`, mainly including evaluation interval, evaluation index, etc.. + +```python +# dataset settings +dataset_type = 'ImageNet' # dataset name， +img_norm_cfg = dict( # Image normalization config to normalize the input images + mean=[123.675, 116.28, 103.53], # Mean values used to pre-training the pre-trained backbone models + std=[58.395, 57.12, 57.375], # Standard variance used to pre-training the pre-trained backbone models + to_rgb=True) # Whether to invert the color channel, rgb2bgr or bgr2rgb. +# train data pipeline +train_pipeline = [ + dict(type='LoadImageFromFile'), # First pipeline to load images from file path + dict(type='RandomResizedCrop', size=224), # RandomResizedCrop + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), # Randomly flip the picture horizontally with a probability of 0.5 + dict(type='Normalize', **img_norm_cfg), # normalization + dict(type='ImageToTensor', keys=['img']), # convert image from numpy into torch.Tensor + dict(type='ToTensor', keys=['gt_label']), # convert gt_label into torch.Tensor + dict(type='Collect', keys=['img', 'gt_label']) # Pipeline that decides which keys in the data should be passed to the detector +] +# test data pipeline +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) # do not pass gt_label while testing +] +data = dict( + samples_per_gpu=32, # Batch size of a single GPU + workers_per_gpu=2, # Worker to pre-fetch data for each single GPU + train=dict( # Train dataset config + train=dict( # train data config + type=dataset_type, # dataset name + data_prefix='data/imagenet/train', # Dataset root, when ann_file does not exist, the category information is automatically obtained from the root folder + pipeline=train_pipeline), # train data pipeline + val=dict( # val data config + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', # ann_file existes, the category information is obtained from file + pipeline=test_pipeline), + test=dict( # test data config + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict( # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details. + interval=1, # Evaluation interval + metric='accuracy') # Metrics used during evaluation +``` + +### training schedule + +Mainly include optimizer settings, `optimizer hook` settings, learning rate schedule and `runner` settings: + +- `optimizer`: optimizer setting , support all optimizers in `pytorch`, refer to related [mmcv](https://mmcv.readthedocs.io/en/latest/_modules/mmcv/runner/optimizer/default_constructor.html#DefaultOptimizerConstructor) documentation. +- `optimizer_config`: `optimizer hook` configuration file, such as setting gradient limit, refer to related [mmcv](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8) code. +- `lr_config`: Learning rate scheduler, supports "CosineAnnealing", "Step", "Cyclic", etc. refer to related [mmcv](https://mmcv.readthedocs.io/en/latest/_modules/mmcv/runner/hooks/lr_updater.html#LrUpdaterHook) documentation for more options. +- `runner`: For `runner`, please refer to `mmcv` for [`runner`](https://mmcv.readthedocs.io/en/latest/understand_mmcv/runner.html) introduction document. + +```python +# he configuration file used to build the optimizer, support all optimizers in PyTorch. +optimizer = dict(type='SGD', # Optimizer type + lr=0.1, # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch + momentum=0.9, # Momentum + weight_decay=0.0001) # Weight decay of SGD +# Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details. +optimizer_config = dict(grad_clip=None) # Most of the methods do not use gradient clip +# Learning rate scheduler config used to register LrUpdater hook +lr_config = dict(policy='step', # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9. + step=[30, 60, 90]) # Steps to decay the learning rate +runner = dict(type='EpochBasedRunner', # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner) + max_epochs=100) # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters` +``` + +### runtime setting + +This part mainly includes saving the checkpoint strategy, log configuration, training parameters, breakpoint weight path, working directory, etc.. + +```python +# Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation. +checkpoint_config = dict(interval=1) # The save interval is 1 +# config to register logger hook +log_config = dict( + interval=100, # Interval to print the log + hooks=[ + dict(type='TextLoggerHook'), # The Tensorboard logger is also supported + # dict(type='TensorboardLoggerHook') + ]) + +dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set. +log_level = 'INFO' # The output level of the log. +resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved. +workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. +work_dir = 'work_dir' # Directory to save the model checkpoints and logs for the current experiments. +``` + +## Inherit and Modify Config File + +For easy understanding, we recommend contributors to inherit from existing methods. + +For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3. + +For example, if your config file is based on ResNet with some other modification, you can first inherit the basic ResNet structure, dataset and other training setting by specifying `_base_ ='./resnet50_8xb32_in1k.py'` (The path relative to your config file), and then modify the necessary parameters in the config file. A more specific example, now we want to use almost all configs in `configs/resnet/resnet50_8xb32_in1k.py`, but change the number of training epochs from 100 to 300, modify when to decay the learning rate, and modify the dataset path, you can create a new config file `configs/resnet/resnet50_8xb32-300e_in1k.py` with content as below: + +```python +_base_ = './resnet50_8xb32_in1k.py' + +runner = dict(max_epochs=300) +lr_config = dict(step=[150, 200, 250]) + +data = dict( + train=dict(data_prefix='mydata/imagenet/train'), + val=dict(data_prefix='mydata/imagenet/train', ), + test=dict(data_prefix='mydata/imagenet/train', ) +) +``` + +### Use intermediate variables in configs + +Some intermediate variables are used in the configuration file. The intermediate variables make the configuration file clearer and easier to modify. + +For example, `train_pipeline` / `test_pipeline` is the intermediate variable of the data pipeline. We first need to define `train_pipeline` / `test_pipeline`, and then pass them to `data`. If you want to modify the size of the input image during training and testing, you need to modify the intermediate variables of `train_pipeline` / `test_pipeline`. + +```python +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=384, backend='pillow',), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=384, backend='pillow'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +``` + +### Ignore some fields in the base configs + +Sometimes, you need to set `_delete_=True` to ignore some domain content in the basic configuration file. You can refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) for more instructions. + +The following is an example. If you want to use cosine schedule in the above ResNet50 case, just using inheritance and directly modify it will report `get unexcepected keyword'step'` error, because the `'step'` field of the basic config in `lr_config` domain information is reserved, and you need to add `_delete_ =True` to ignore the content of `lr_config` related fields in the basic configuration file: + +```python +_base_ = '../../configs/resnet/resnet50_8xb32_in1k.py' + +lr_config = dict( + _delete_=True, + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + by_epoch=True, + warmup_iters=5, + warmup_ratio=0.1 +) +``` + +### Use some fields in the base configs + +Sometimes, you may refer to some fields in the `_base_` config, so as to avoid duplication of definitions. You can refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html#reference-variables-from-base) for some more instructions. + +The following is an example of using auto augment in the training data preprocessing pipeline， refer to [`configs/_base_/datasets/imagenet_bs64_autoaug.py`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/datasets/imagenet_bs64_autoaug.py). When defining `train_pipeline`, just add the definition file name of auto augment to `_base_`, and then use `{{_base_.auto_increasing_policies}}` to reference the variables: + +```python +_base_ = ['./pipelines/auto_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='AutoAugment', policies={{_base_.auto_increasing_policies}}), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [...] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict(..., pipeline=train_pipeline), + val=dict(..., pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') +``` + +## Modify config through script arguments + +When users use the script "tools/train.py" or "tools/test.py" to submit tasks or use some other tools, they can directly modify the content of the configuration file used by specifying the `--cfg-options` parameter. + +- Update config keys of dict chains. + + The config options can be specified following the order of the dict keys in the original config. + For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode. + +- Update keys inside a list of configs. + + Some config dicts are composed as a list in your config. For example, the training pipeline `data.train.pipeline` is normally a list + e.g. `[dict(type='LoadImageFromFile'), dict(type='TopDownRandomFlip', flip_prob=0.5), ...]`. If you want to change `'flip_prob=0.5'` to `'flip_prob=0.0'` in the pipeline, + you may specify `--cfg-options data.train.pipeline.1.flip_prob=0.0`. + +- Update values of list/tuples. + + If the value to be updated is a list or a tuple. For example, the config file normally sets `workflow=[('train', 1)]`. If you want to + change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark " is necessary to + support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value. + +## Import user-defined modules + +```{note} +This part may only be used when using MMClassification as a third party library to build your own project, and beginners can skip it. +``` + +After studying the follow-up tutorials [ADDING NEW DATASET](https://mmclassification.readthedocs.io/en/latest/tutorials/new_dataset.html), [CUSTOM DATA PIPELINES](https://mmclassification.readthedocs.io/en/latest/tutorials/data_pipeline.html), [ADDING NEW MODULES](https://mmclassification.readthedocs.io/en/latest/tutorials/new_modules.html). You may use MMClassification to complete your project and create new classes of datasets, models, data enhancements, etc. in the project. In order to streamline the code, you can use MMClassification as a third-party library, you just need to keep your own extra code and import your own custom module in the configuration files. For examples, you may refer to [OpenMMLab Algorithm Competition Project](https://github.com/zhangrui-wolf/openmmlab-competition-2021) . + +Add the following code to your own configuration files: + +```python +custom_imports = dict( + imports=['your_dataset_class', + 'your_transforme_class', + 'your_model_class', + 'your_module_class'], + allow_failed_imports=False) +``` + +## FAQ + +- None diff --git a/docs/en/tutorials/data_pipeline.md b/docs/en/tutorials/data_pipeline.md new file mode 100644 index 0000000..4b32280 --- /dev/null +++ b/docs/en/tutorials/data_pipeline.md @@ -0,0 +1,150 @@ +# Tutorial 4: Custom Data Pipelines + +## Design of Data pipelines + +Following typical conventions, we use `Dataset` and `DataLoader` for data loading +with multiple workers. Indexing `Dataset` returns a dict of data items corresponding to +the arguments of models forward method. + +The data preparation pipeline and the dataset is decomposed. Usually a dataset +defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict. +A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform. + +The operations are categorized into data loading, pre-processing and formatting. + +Here is an pipeline example for ResNet-50 training on ImageNet. + +```python +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=256), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +``` + +For each operation, we list the related dict fields that are added/updated/removed. +At the end of the pipeline, we use `Collect` to only retain the necessary items for forward computation. + +### Data loading + +`LoadImageFromFile` + +- add: img, img_shape, ori_shape + +By default, `LoadImageFromFile` loads images from disk but it may lead to IO bottleneck for efficient small models. +Various backends are supported by mmcv to accelerate this process. For example, if the training machines have setup +[memcached](https://memcached.org/), we can revise the config as follows. + +``` +memcached_root = '/mnt/xxx/memcached_client/' +train_pipeline = [ + dict( + type='LoadImageFromFile', + file_client_args=dict( + backend='memcached', + server_list_cfg=osp.join(memcached_root, 'server_list.conf'), + client_cfg=osp.join(memcached_root, 'client.conf'))), +] +``` + +More supported backends can be found in [mmcv.fileio.FileClient](https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py). + +### Pre-processing + +`Resize` + +- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio +- update: img, img_shape + +`RandomFlip` + +- add: flip, flip_direction +- update: img + +`RandomCrop` + +- update: img, pad_shape + +`Normalize` + +- add: img_norm_cfg +- update: img + +### Formatting + +`ToTensor` + +- update: specified by `keys`. + +`ImageToTensor` + +- update: specified by `keys`. + +`Collect` + +- remove: all other keys except for those specified by `keys` + +For more information about other data transformation classes, please refer to [Data Transformations](../api/transforms.rst) + +## Extend and use custom pipelines + +1. Write a new pipeline in any file, e.g., `my_pipeline.py`, and place it in + the folder `mmcls/datasets/pipelines/`. The pipeline class needs to override + the `__call__` method which takes a dict as input and returns a dict. + + ```python + from mmcls.datasets import PIPELINES + + @PIPELINES.register_module() + class MyTransform(object): + + def __call__(self, results): + # apply transforms on results['img'] + return results + ``` + +2. Import the new class in `mmcls/datasets/pipelines/__init__.py`. + + ```python + ... + from .my_pipeline import MyTransform + + __all__ = [ + ..., 'MyTransform' + ] + ``` + +3. Use it in config files. + + ```python + img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='MyTransform'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) + ] + ``` + +## Pipeline visualization + +After designing data pipelines, you can use the [visualization tools](../tools/visualization.md) to view the performance. diff --git a/docs/en/tutorials/finetune.md b/docs/en/tutorials/finetune.md new file mode 100644 index 0000000..98538fb --- /dev/null +++ b/docs/en/tutorials/finetune.md @@ -0,0 +1,236 @@ +# Tutorial 2: Fine-tune Models + +Classification models pre-trained on the ImageNet dataset have been demonstrated to be effective for other datasets and other downstream tasks. +This tutorial provides instructions for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance. + +There are two steps to fine-tune a model on a new dataset. + +- Add support for the new dataset following [Tutorial 3: Customize Dataset](new_dataset.md). +- Modify the configs as will be discussed in this tutorial. + +Assume we have a ResNet-50 model pre-trained on the ImageNet-2012 dataset and want +to take the fine-tuning on the CIFAR-10 dataset, we need to modify five parts in the +config. + +## Inherit base configs + +At first, create a new config file +`configs/tutorial/resnet50_finetune_cifar.py` to store our configs. Of course, +the path can be customized by yourself. + +To reuse the common parts among different configs, we support inheriting +configs from multiple existing configs. To fine-tune a ResNet-50 model, the new +config needs to inherit `configs/_base_/models/resnet50.py` to build the basic +structure of the model. To use the CIFAR-10 dataset, the new config can also +simply inherit `configs/_base_/datasets/cifar10_bs16.py`. For runtime settings such as +training schedules, the new config needs to inherit +`configs/_base_/default_runtime.py`. + +To inherit all above configs, put the following code at the config file. + +```python +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/cifar10_bs16.py', '../_base_/default_runtime.py' +] +``` + +Besides, you can also choose to write the whole contents rather than use inheritance, +like [`configs/lenet/lenet5_mnist.py`](https://github.com/open-mmlab/mmclassification/blob/master/configs/lenet/lenet5_mnist.py). + +## Modify model + +When fine-tuning a model, usually we want to load the pre-trained backbone +weights and train a new classification head. + +To load the pre-trained backbone, we need to change the initialization config +of the backbone and use `Pretrained` initialization function. Besides, in the +`init_cfg`, we use `prefix='backbone'` to tell the initialization +function to remove the prefix of keys in the checkpoint, for example, it will +change `backbone.conv1` to `conv1`. And here we use an online checkpoint, it +will be downloaded during training, you can also download the model manually +and use a local path. + +And then we need to modify the head according to the class numbers of the new +datasets by just changing `num_classes` in the head. + +```python +model = dict( + backbone=dict( + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', + prefix='backbone', + )), + head=dict(num_classes=10), +) +``` + +```{tip} +Here we only need to set the part of configs we want to modify, because the +inherited configs will be merged and get the entire configs. +``` + +Sometimes, we want to freeze the first several layers' parameters of the +backbone, that will help the network to keep ability to extract low-level +information learnt from pre-trained model. In MMClassification, you can simply +specify how many layers to freeze by `frozen_stages` argument. For example, to +freeze the first two layers' parameters, just use the following config: + +```python +model = dict( + backbone=dict( + frozen_stages=2, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', + prefix='backbone', + )), + head=dict(num_classes=10), +) +``` + +```{note} +Not all backbones support the `frozen_stages` argument by now. Please check +[the docs](https://mmclassification.readthedocs.io/en/latest/api/models.html#backbones) +to confirm if your backbone supports it. +``` + +## Modify dataset + +When fine-tuning on a new dataset, usually we need to modify some dataset +configs. Here, we need to modify the pipeline to resize the image from 32 to +224 to fit the input size of the model pre-trained on ImageNet, and some other +configs. + +```python +img_norm_cfg = dict( + mean=[125.307, 122.961, 113.8575], + std=[51.5865, 50.847, 51.255], + to_rgb=False, +) +train_pipeline = [ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']), +] +test_pipeline = [ + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) +``` + +## Modify training schedule + +The fine-tuning hyper parameters vary from the default schedule. It usually +requires smaller learning rate and less training epochs. + +```python +# lr is set for a batch size of 128 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[15]) +runner = dict(type='EpochBasedRunner', max_epochs=200) +log_config = dict(interval=100) +``` + +## Start Training + +Now, we have finished the fine-tuning config file as following: + +```python +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/cifar10_bs16.py', '../_base_/default_runtime.py' +] + +# Model config +model = dict( + backbone=dict( + frozen_stages=2, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', + prefix='backbone', + )), + head=dict(num_classes=10), +) + +# Dataset config +img_norm_cfg = dict( + mean=[125.307, 122.961, 113.8575], + std=[51.5865, 50.847, 51.255], + to_rgb=False, +) +train_pipeline = [ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']), +] +test_pipeline = [ + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) + +# Training schedule config +# lr is set for a batch size of 128 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict(policy='step', step=[15]) +runner = dict(type='EpochBasedRunner', max_epochs=200) +log_config = dict(interval=100) +``` + +Here we use 8 GPUs on your computer to train the model with the following +command: + +```shell +bash tools/dist_train.sh configs/tutorial/resnet50_finetune_cifar.py 8 +``` + +Also, you can use only one GPU to train the model with the following command: + +```shell +python tools/train.py configs/tutorial/resnet50_finetune_cifar.py +``` + +But wait, an important config need to be changed if using one GPU. We need to +change the dataset config as following: + +```python +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) +``` + +It's because our training schedule is for a batch size of 128. If using 8 GPUs, +just use `samples_per_gpu=16` config in the base config file, and the total batch +size will be 128. But if using one GPU, you need to change it to 128 manually to +match the training schedule. diff --git a/docs/en/tutorials/new_dataset.md b/docs/en/tutorials/new_dataset.md new file mode 100644 index 0000000..24e6fe9 --- /dev/null +++ b/docs/en/tutorials/new_dataset.md @@ -0,0 +1,239 @@ +# Tutorial 3: Customize Dataset + +We support many common public datasets for image classification task, you can find them in +[this page](https://mmclassification.readthedocs.io/en/latest/api/datasets.html). + +In this section, we demonstrate how to [use your own dataset](#use-your-own-dataset) +and [use dataset wrapper](#use-dataset-wrapper). + +## Use your own dataset + +### Reorganize dataset to existing format + +The simplest way to use your own dataset is to convert it to existing dataset formats. + +For multi-class classification task, we recommend to use the format of +[`CustomDataset`](https://mmclassification.readthedocs.io/en/latest/api/datasets.html#mmcls.datasets.CustomDataset). + +The `CustomDataset` supports two kinds of format: + +1. An annotation file is provided, and each line indicates a sample image. + + The sample images can be organized in any structure, like: + + ``` + train/ + ├── folder_1 + │ ├── xxx.png + │ ├── xxy.png + │ └── ... + ├── 123.png + ├── nsdf3.png + └── ... + ``` + + And an annotation file records all paths of samples and corresponding + category index. The first column is the image path relative to the folder + (in this example, `train`) and the second column is the index of category: + + ``` + folder_1/xxx.png 0 + folder_1/xxy.png 1 + 123.png 1 + nsdf3.png 2 + ... + ``` + + ```{note} + The value of the category indices should fall in range `[0, num_classes - 1]`. + ``` + +2. The sample images are arranged in the special structure: + + ``` + train/ + ├── cat + │ ├── xxx.png + │ ├── xxy.png + │ └── ... + │ └── xxz.png + ├── bird + │ ├── bird1.png + │ ├── bird2.png + │ └── ... + └── dog + ├── 123.png + ├── nsdf3.png + ├── ... + └── asd932_.png + ``` + + In this case, you don't need provide annotation file, and all images in the directory `cat` will be + recognized as samples of `cat`. + +Usually, we will split the whole dataset to three sub datasets: `train`, `val` +and `test` for training, validation and test. And **every** sub dataset should +be organized as one of the above structures. + +For example, the whole dataset is as below (using the first structure): + +``` +mmclassification +└── data + └── my_dataset + ├── meta + │ ├── train.txt + │ ├── val.txt + │ └── test.txt + ├── train + ├── val + └── test +``` + +And in your config file, you can modify the `data` field as below: + +```python +... +dataset_type = 'CustomDataset' +classes = ['cat', 'bird', 'dog'] # The category names of your dataset + +data = dict( + train=dict( + type=dataset_type, + data_prefix='data/my_dataset/train', + ann_file='data/my_dataset/meta/train.txt', + classes=classes, + pipeline=train_pipeline + ), + val=dict( + type=dataset_type, + data_prefix='data/my_dataset/val', + ann_file='data/my_dataset/meta/val.txt', + classes=classes, + pipeline=test_pipeline + ), + test=dict( + type=dataset_type, + data_prefix='data/my_dataset/test', + ann_file='data/my_dataset/meta/test.txt', + classes=classes, + pipeline=test_pipeline + ) +) +... +``` + +### Create a new dataset class + +You can write a new dataset class inherited from `BaseDataset`, and overwrite `load_annotations(self)`, +like [CIFAR10](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/datasets/cifar.py) and +[CustomDataset](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/datasets/custom.py). + +Typically, this function returns a list, where each sample is a dict, containing necessary data information, +e.g., `img` and `gt_label`. + +Assume we are going to implement a `Filelist` dataset, which takes filelists for both training and testing. +The format of annotation list is as follows: + +``` +000001.jpg 0 +000002.jpg 1 +``` + +We can create a new dataset in `mmcls/datasets/filelist.py` to load the data. + +```python +import mmcv +import numpy as np + +from .builder import DATASETS +from .base_dataset import BaseDataset + + +@DATASETS.register_module() +class Filelist(BaseDataset): + + def load_annotations(self): + assert isinstance(self.ann_file, str) + + data_infos = [] + with open(self.ann_file) as f: + samples = [x.strip().split(' ') for x in f.readlines()] + for filename, gt_label in samples: + info = {'img_prefix': self.data_prefix} + info['img_info'] = {'filename': filename} + info['gt_label'] = np.array(gt_label, dtype=np.int64) + data_infos.append(info) + return data_infos + +``` + +And add this dataset class in `mmcls/datasets/__init__.py` + +```python +from .base_dataset import BaseDataset +... +from .filelist import Filelist + +__all__ = [ + 'BaseDataset', ... ,'Filelist' +] +``` + +Then in the config, to use `Filelist` you can modify the config as the following + +```python +train = dict( + type='Filelist', + ann_file='image_list.txt', + pipeline=train_pipeline +) +``` + +## Use dataset wrapper + +The dataset wrapper is a kind of class to change the behavior of dataset class, such as repeat the dataset or +re-balance the samples of different categories. + +### Repeat dataset + +We use `RepeatDataset` as wrapper to repeat the dataset. For example, suppose the original dataset is +`Dataset_A`, to repeat it, the config looks like the following + +```python +data = dict( + train = dict( + type='RepeatDataset', + times=N, + dataset=dict( # This is the original config of Dataset_A + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) + ... +) +``` + +### Class balanced dataset + +We use `ClassBalancedDataset` as wrapper to repeat the dataset based on category frequency. The dataset to +repeat needs to implement method `get_cat_ids(idx)` to support `ClassBalancedDataset`. For example, to repeat +`Dataset_A` with `oversample_thr=1e-3`, the config looks like the following + +```python +data = dict( + train = dict( + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( # This is the original config of Dataset_A + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) + ... +) +``` + +You may refer to [API reference](https://mmclassification.readthedocs.io/en/latest/api/datasets.html#mmcls.datasets.ClassBalancedDataset) for details. diff --git a/docs/en/tutorials/new_modules.md b/docs/en/tutorials/new_modules.md new file mode 100644 index 0000000..5ac89de --- /dev/null +++ b/docs/en/tutorials/new_modules.md @@ -0,0 +1,272 @@ +# Tutorial 5: Adding New Modules + +## Develop new components + +We basically categorize model components into 3 types. + +- backbone: usually an feature extraction network, e.g., ResNet, MobileNet. +- neck: the component between backbones and heads, e.g., GlobalAveragePooling. +- head: the component for specific tasks, e.g., classification or regression. + +### Add new backbones + +Here we show how to develop new components with an example of ResNet_CIFAR. +As the input size of CIFAR is 32x32, this backbone replaces the `kernel_size=7, stride=2` to `kernel_size=3, stride=1` and remove the MaxPooling after stem, to avoid forwarding small feature maps to residual blocks. +It inherits from ResNet and only modifies the stem layers. + +1. Create a new file `mmcls/models/backbones/resnet_cifar.py`. + +```python +import torch.nn as nn + +from ..builder import BACKBONES +from .resnet import ResNet + + +@BACKBONES.register_module() +class ResNet_CIFAR(ResNet): + + """ResNet backbone for CIFAR. + + short description of the backbone + + Args: + depth(int): Network depth, from {18, 34, 50, 101, 152}. + ... + """ + + def __init__(self, depth, deep_stem, **kwargs): + # call ResNet init + super(ResNet_CIFAR, self).__init__(depth, deep_stem=deep_stem, **kwargs) + # other specific initialization + assert not self.deep_stem, 'ResNet_CIFAR do not support deep_stem' + + def _make_stem_layer(self, in_channels, base_channels): + # override ResNet method to modify the network structure + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + base_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, base_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): # should return a tuple + pass # implementation is ignored + + def init_weights(self, pretrained=None): + pass # override ResNet init_weights if necessary + + def train(self, mode=True): + pass # override ResNet train if necessary +``` + +2. Import the module in `mmcls/models/backbones/__init__.py`. + +```python +... +from .resnet_cifar import ResNet_CIFAR + +__all__ = [ + ..., 'ResNet_CIFAR' +] +``` + +3. Use it in your config file. + +```python +model = dict( + ... + backbone=dict( + type='ResNet_CIFAR', + depth=18, + other_arg=xxx), + ... +``` + +### Add new necks + +Here we take `GlobalAveragePooling` as an example. It is a very simple neck without any arguments. +To add a new neck, we mainly implement the `forward` function, which applies some operation on the output from backbone and forward the results to head. + +1. Create a new file in `mmcls/models/necks/gap.py`. + + ```python + import torch.nn as nn + + from ..builder import NECKS + + @NECKS.register_module() + class GlobalAveragePooling(nn.Module): + + def __init__(self): + self.gap = nn.AdaptiveAvgPool2d((1, 1)) + + def forward(self, inputs): + # we regard inputs as tensor for simplicity + outs = self.gap(inputs) + outs = outs.view(inputs.size(0), -1) + return outs + ``` + +2. Import the module in `mmcls/models/necks/__init__.py`. + + ```python + ... + from .gap import GlobalAveragePooling + + __all__ = [ + ..., 'GlobalAveragePooling' + ] + ``` + +3. Modify the config file. + + ```python + model = dict( + neck=dict(type='GlobalAveragePooling'), + ) + ``` + +### Add new heads + +Here we show how to develop a new head with the example of `LinearClsHead` as the following. +To implement a new head, basically we need to implement `forward_train`, which takes the feature maps from necks or backbones as input and compute loss based on ground-truth labels. + +1. Create a new file in `mmcls/models/heads/linear_head.py`. + + ```python + from ..builder import HEADS + from .cls_head import ClsHead + + + @HEADS.register_module() + class LinearClsHead(ClsHead): + + def __init__(self, + num_classes, + in_channels, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, )): + super(LinearClsHead, self).__init__(loss=loss, topk=topk) + self.in_channels = in_channels + self.num_classes = num_classes + + if self.num_classes <= 0: + raise ValueError( + f'num_classes={num_classes} must be a positive integer') + + self._init_layers() + + def _init_layers(self): + self.fc = nn.Linear(self.in_channels, self.num_classes) + + def init_weights(self): + normal_init(self.fc, mean=0, std=0.01, bias=0) + + def forward_train(self, x, gt_label): + cls_score = self.fc(x) + losses = self.loss(cls_score, gt_label) + return losses + + ``` + +2. Import the module in `mmcls/models/heads/__init__.py`. + + ```python + ... + from .linear_head import LinearClsHead + + __all__ = [ + ..., 'LinearClsHead' + ] + ``` + +3. Modify the config file. + +Together with the added GlobalAveragePooling neck, an entire config for a model is as follows. + +```python +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) + +``` + +### Add new loss + +To add a new loss function, we mainly implement the `forward` function in the loss module. +In addition, it is helpful to leverage the decorator `weighted_loss` to weight the loss for each element. +Assuming that we want to mimic a probabilistic distribution generated from another classification model, we implement a L1Loss to fulfil the purpose as below. + +1. Create a new file in `mmcls/models/losses/l1_loss.py`. + + ```python + import torch + import torch.nn as nn + + from ..builder import LOSSES + from .utils import weighted_loss + + @weighted_loss + def l1_loss(pred, target): + assert pred.size() == target.size() and target.numel() > 0 + loss = torch.abs(pred - target) + return loss + + @LOSSES.register_module() + class L1Loss(nn.Module): + + def __init__(self, reduction='mean', loss_weight=1.0): + super(L1Loss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss = self.loss_weight * l1_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss + ``` + +2. Import the module in `mmcls/models/losses/__init__.py`. + + ```python + ... + from .l1_loss import L1Loss, l1_loss + + __all__ = [ + ..., 'L1Loss', 'l1_loss' + ] + ``` + +3. Modify loss field in the config. + + ```python + loss=dict(type='L1Loss', loss_weight=1.0)) + ``` diff --git a/docs/en/tutorials/runtime.md b/docs/en/tutorials/runtime.md new file mode 100644 index 0000000..b212744 --- /dev/null +++ b/docs/en/tutorials/runtime.md @@ -0,0 +1,257 @@ +# Tutorial 7: Customize Runtime Settings + +In this tutorial, we will introduce some methods about how to customize workflow and hooks when running your own settings for the project. + + + +- [Customize Workflow](#customize-workflow) +- [Hooks](#hooks) + - [Default training hooks](#default-training-hooks) + - [Use other implemented hooks](#use-other-implemented-hooks) + - [Customize self-implemented hooks](#customize-self-implemented-hooks) +- [FAQ](#faq) + + + +## Customize Workflow + +Workflow is a list of (phase, duration) to specify the running order and duration. The meaning of "duration" depends on the runner's type. + +For example, we use epoch-based runner by default, and the "duration" means how many epochs the phase to be executed in a cycle. Usually, +we only want to execute training phase, just use the following config. + +```python +workflow = [('train', 1)] +``` + +Sometimes we may want to check some metrics (e.g. loss, accuracy) about the model on the validate set. +In such case, we can set the workflow as + +```python +[('train', 1), ('val', 1)] +``` + +so that 1 epoch for training and 1 epoch for validation will be run iteratively. + +By default, we recommend using **`EvalHook`** to do evaluation after the training epoch, but you can still use `val` workflow as an alternative. + +```{note} +1. The parameters of model will not be updated during the val epoch. +2. Keyword `max_epochs` in the config only controls the number of training epochs and will not affect the validation workflow. +3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EvalHook` because `EvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`. + Therefore, the only difference between `[('train', 1), ('val', 1)]` and ``[('train', 1)]`` is that the runner will calculate losses on the validation set after each training epoch. +``` + +## Hooks + +The hook mechanism is widely used in the OpenMMLab open-source algorithm library. Combined with the `Runner`, the entire life cycle of the training process can be managed easily. You can learn more about the hook through [related article](https://www.calltutors.com/blog/what-is-hook/). + +Hooks only work after being registered into the runner. At present, hooks are mainly divided into two categories: + +- default training hooks + +The default training hooks are registered by the runner by default. Generally, they are hooks for some basic functions, and have a certain priority, you don't need to modify the priority. + +- custom hooks + +The custom hooks are registered through `custom_hooks`. Generally, they are hooks with enhanced functions. The priority needs to be specified in the configuration file. If you do not specify the priority of the hook, it will be set to 'NORMAL' by default. + +**Priority list** + +| Level | Value | +| :-------------: | :---: | +| HIGHEST | 0 | +| VERY_HIGH | 10 | +| HIGH | 30 | +| ABOVE_NORMAL | 40 | +| NORMAL(default) | 50 | +| BELOW_NORMAL | 60 | +| LOW | 70 | +| VERY_LOW | 90 | +| LOWEST | 100 | + +The priority determines the execution order of the hooks. Before training, the log will print out the execution order of the hooks at each stage to facilitate debugging. + +### default training hooks + +Some common hooks are not registered through `custom_hooks`, they are + +| Hooks | Priority | +| :-------------------: | :---------------: | +| `LrUpdaterHook` | VERY_HIGH (10) | +| `MomentumUpdaterHook` | HIGH (30) | +| `OptimizerHook` | ABOVE_NORMAL (40) | +| `CheckpointHook` | NORMAL (50) | +| `IterTimerHook` | LOW (70) | +| `EvalHook` | LOW (70) | +| `LoggerHook(s)` | VERY_LOW (90) | + +`OptimizerHook`, `MomentumUpdaterHook` and `LrUpdaterHook` have been introduced in [sehedule strategy](./schedule.md). +`IterTimerHook` is used to record elapsed time and does not support modification. + +Here we reveal how to customize `CheckpointHook`, `LoggerHooks`, and `EvalHook`. + +#### CheckpointHook + +The MMCV runner will use `checkpoint_config` to initialize [`CheckpointHook`](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/hooks/checkpoint.py). + +```python +checkpoint_config = dict(interval=1) +``` + +We could set `max_keep_ckpts` to save only a small number of checkpoints or decide whether to store state dict of optimizer by `save_optimizer`. +More details of the arguments are [here](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.CheckpointHook) + +#### LoggerHooks + +The `log_config` wraps multiple logger hooks and enables to set intervals. Now MMCV supports `TextLoggerHook`, `WandbLoggerHook`, `MlflowLoggerHook`, `NeptuneLoggerHook`, `DvcliveLoggerHook` and `TensorboardLoggerHook`. +The detailed usages can be found in the [doc](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook). + +```python +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook') + ]) +``` + +#### EvalHook + +The config of `evaluation` will be used to initialize the [`EvalHook`](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/core/evaluation/eval_hooks.py). + +The `EvalHook` has some reserved keys, such as `interval`, `save_best` and `start`, and the other arguments such as `metrics` will be passed to the `dataset.evaluate()` + +```python +evaluation = dict(interval=1, metric='accuracy', metric_options={'topk': (1, )}) +``` + +You can save the model weight when the best verification result is obtained by modifying the parameter `save_best`: + +```python +# "auto" means automatically select the metrics to compare. +# You can also use a specific key like "accuracy_top-1". +evaluation = dict(interval=1, save_best="auto", metric='accuracy', metric_options={'topk': (1, )}) +``` + +When running some large experiments, you can skip the validation step at the beginning of training by modifying the parameter `start` as below: + +```python +evaluation = dict(interval=1, start=200, metric='accuracy', metric_options={'topk': (1, )}) +``` + +This indicates that, before the 200th epoch, evaluations would not be executed. Since the 200th epoch, evaluations would be executed after the training process. + +```{note} +In the default configuration files of MMClassification, the evaluation field is generally placed in the datasets configs. +``` + +### Use other implemented hooks + +Some hooks have been already implemented in MMCV and MMClassification, they are: + +- [EMAHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/ema.py) +- [SyncBuffersHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/sync_buffer.py) +- [EmptyCacheHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/memory.py) +- [ProfilerHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/profiler.py) +- ...... + +If the hook is already implemented in MMCV, you can directly modify the config to use the hook as below + +```python +mmcv_hooks = [ + dict(type='MMCVHook', a=a_value, b=b_value, priority='NORMAL') +] +``` + +such as using `EMAHook`, interval is 100 iters: + +```python +custom_hooks = [ + dict(type='EMAHook', interval=100, priority='HIGH') +] +``` + +## Customize self-implemented hooks + +### 1. Implement a new hook + +Here we give an example of creating a new hook in MMClassification and using it in training. + +```python +from mmcv.runner import HOOKS, Hook + + +@HOOKS.register_module() +class MyHook(Hook): + + def __init__(self, a, b): + pass + + def before_run(self, runner): + pass + + def after_run(self, runner): + pass + + def before_epoch(self, runner): + pass + + def after_epoch(self, runner): + pass + + def before_iter(self, runner): + pass + + def after_iter(self, runner): + pass +``` + +Depending on the functionality of the hook, the users need to specify what the hook will do at each stage of the training in `before_run`, `after_run`, `before_epoch`, `after_epoch`, `before_iter`, and `after_iter`. + +### 2. Register the new hook + +Then we need to make `MyHook` imported. Assuming the file is in `mmcls/core/utils/my_hook.py` there are two ways to do that: + +- Modify `mmcls/core/utils/__init__.py` to import it. + + The newly defined module should be imported in `mmcls/core/utils/__init__.py` so that the registry will + find the new module and add it: + +```python +from .my_hook import MyHook +``` + +- Use `custom_imports` in the config to manually import it + +```python +custom_imports = dict(imports=['mmcls.core.utils.my_hook'], allow_failed_imports=False) +``` + +### 3. Modify the config + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value) +] +``` + +You can also set the priority of the hook as below: + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value, priority='ABOVE_NORMAL') +] +``` + +By default, the hook's priority is set as `NORMAL` during registration. + +## FAQ + +### 1. `resume_from` and `load_from` and `init_cfg.Pretrained` + +- `load_from` : only imports model weights, which is mainly used to load pre-trained or trained models; + +- `resume_from` : not only import model weights, but also optimizer information, current epoch information, mainly used to continue training from the checkpoint. + +- `init_cfg.Pretrained` : Load weights during weight initialization, and you can specify which module to load. This is usually used when fine-tuning a model, refer to [Tutorial 2: Fine-tune Models](./finetune.md). diff --git a/docs/en/tutorials/schedule.md b/docs/en/tutorials/schedule.md new file mode 100644 index 0000000..1afc4b7 --- /dev/null +++ b/docs/en/tutorials/schedule.md @@ -0,0 +1,341 @@ +# Tutorial 6: Customize Schedule + +In this tutorial, we will introduce some methods about how to construct optimizers, customize learning rate and momentum schedules, parameter-wise finely configuration, gradient clipping, gradient accumulation, and customize self-implemented methods for the project. + + + +- [Customize optimizer supported by PyTorch](#customize-optimizer-supported-by-pytorch) +- [Customize learning rate schedules](#customize-learning-rate-schedules) + - [Learning rate decay](#learning-rate-decay) + - [Warmup strategy](#warmup-strategy) +- [Customize momentum schedules](#customize-momentum-schedules) +- [Parameter-wise finely configuration](#parameter-wise-finely-configuration) +- [Gradient clipping and gradient accumulation](#gradient-clipping-and-gradient-accumulation) + - [Gradient clipping](#gradient-clipping) + - [Gradient accumulation](#gradient-accumulation) +- [Customize self-implemented methods](#customize-self-implemented-methods) + - [Customize self-implemented optimizer](#customize-self-implemented-optimizer) + - [Customize optimizer constructor](#customize-optimizer-constructor) + + + +## Customize optimizer supported by PyTorch + +We already support to use all the optimizers implemented by PyTorch, and to use and modify them, please change the `optimizer` field of config files. + +For example, if you want to use `SGD`, the modification could be as the following. + +```python +optimizer = dict(type='SGD', lr=0.0003, weight_decay=0.0001) +``` + +To modify the learning rate of the model, just modify the `lr` in the config of optimizer. +You can also directly set other arguments according to the [API doc](https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim) of PyTorch. + +For example, if you want to use `Adam` with the setting like `torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)` in PyTorch, +the config should looks like. + +```python +optimizer = dict(type='Adam', lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) +``` + +## Customize learning rate schedules + +### Learning rate decay + +Learning rate decay is widely used to improve performance. And to use learning rate decay, please set the `lr_confg` field in config files. + +For example, we use step policy as the default learning rate decay policy of ResNet, and the config is: + +```python +lr_config = dict(policy='step', step=[100, 150]) +``` + +Then during training, the program will call [`StepLRHook`](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L153) periodically to update the learning rate. + +We also support many other learning rate schedules [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py), such as `CosineAnnealing` and `Poly` schedule. Here are some examples + +- ConsineAnnealing schedule: + + ```python + lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 10, + min_lr_ratio=1e-5) + ``` + +- Poly schedule: + + ```python + lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) + ``` + +### Warmup strategy + +In the early stage, training is easy to be volatile, and warmup is a technique +to reduce volatility. With warmup, the learning rate will increase gradually +from a minor value to the expected value. + +In MMClassification, we use `lr_config` to configure the warmup strategy, the main parameters are as follows： + +- `warmup`: The warmup curve type. Please choose one from 'constant', 'linear', 'exp' and `None`, and `None` means disable warmup. +- `warmup_by_epoch` : if warmup by epoch or not, default to be True, if set to be False, warmup by iter. +- `warmup_iters` : the number of warm-up iterations, when `warmup_by_epoch=True`, the unit is epoch; when `warmup_by_epoch=False`, the unit is the number of iterations (iter). +- `warmup_ratio` : warm-up initial learning rate will calculate as `lr = lr * warmup_ratio`。 + +Here are some examples + +1. linear & warmup by iter + + ```python + lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=20 * 1252, + warmup_by_epoch=False) + ``` + +2. exp & warmup by epoch + + ```python + lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='exp', + warmup_iters=5, + warmup_ratio=0.1, + warmup_by_epoch=True) + ``` + +```{tip} +After completing your configuration file，you could use [learning rate visualization tool](https://mmclassification.readthedocs.io/en/latest/tools/visualization.html#learning-rate-schedule-visualization) to draw the corresponding learning rate adjustment curve. +``` + +## Customize momentum schedules + +We support the momentum scheduler to modify the model's momentum according to learning rate, which could make the model converge in a faster way. + +Momentum scheduler is usually used with LR scheduler, for example, the following config is used to accelerate convergence. +For more details, please refer to the implementation of [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327) +and [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130). + +Here is an example + +```python +lr_config = dict( + policy='cyclic', + target_ratio=(10, 1e-4), + cyclic_times=1, + step_ratio_up=0.4, +) +momentum_config = dict( + policy='cyclic', + target_ratio=(0.85 / 0.95, 1), + cyclic_times=1, + step_ratio_up=0.4, +) +``` + +## Parameter-wise finely configuration + +Some models may have some parameter-specific settings for optimization, for example, no weight decay to the BatchNorm layer or using different learning rates for different network layers. +To finely configuration them, we can use the `paramwise_cfg` option in `optimizer`. + +We provide some examples here and more usages refer to [DefaultOptimizerConstructor](https://mmcv.readthedocs.io/en/latest/_modules/mmcv/runner/optimizer/default_constructor.html#DefaultOptimizerConstructor). + +- Using specified options + + The `DefaultOptimizerConstructor` provides options including `bias_lr_mult`, `bias_decay_mult`, `norm_decay_mult`, `dwconv_decay_mult`, `dcn_offset_lr_mult` and `bypass_duplicate` to configure special optimizer behaviors of bias, normalization, depth-wise convolution, deformable convolution and duplicated parameter. E.g: + + 1. No weight decay to the BatchNorm layer + + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=dict(norm_decay_mult=0.)) + ``` + +- Using `custom_keys` dict + + MMClassification can use `custom_keys` to specify different parameters to use different learning rates or weight decays, for example: + + 1. No weight decay for specific parameters + + ```python + paramwise_cfg = dict( + custom_keys={ + 'backbone.cls_token': dict(decay_mult=0.0), + 'backbone.pos_embed': dict(decay_mult=0.0) + }) + + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=paramwise_cfg) + ``` + + 2. Using a smaller learning rate and a weight decay for the backbone layers + + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + # 'lr' for backbone and 'weight_decay' are 0.1 * lr and 0.9 * weight_decay + paramwise_cfg=dict( + custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=0.9)})) + ``` + +## Gradient clipping and gradient accumulation + +Besides the basic function of PyTorch optimizers, we also provide some enhancement functions, such as gradient clipping, gradient accumulation, etc., refer to [MMCV](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py). + +### Gradient clipping + +During the training process, the loss function may get close to a cliffy region and cause gradient explosion. And gradient clipping is helpful to stabilize the training process. More introduction can be found in [this page](https://paperswithcode.com/method/gradient-clipping). + +Currently we support `grad_clip` option in `optimizer_config`, and the arguments refer to [PyTorch Documentation](https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html). + +Here is an example: + +```python +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# norm_type: type of the used p-norm, here norm_type is 2. +``` + +When inheriting from base and modifying configs, if `grad_clip=None` in base, `_delete_=True` is needed. For more details about `_delete_` you can refer to [TUTORIAL 1: LEARN ABOUT CONFIGS](https://mmclassification.readthedocs.io/en/latest/tutorials/config.html#ignore-some-fields-in-the-base-configs). For example, + +```python +_base_ = [./_base_/schedules/imagenet_bs256_coslr.py] + +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2), _delete_=True, type='OptimizerHook') +# you can ignore type if type is 'OptimizerHook', otherwise you must add "type='xxxxxOptimizerHook'" here +``` + +### Gradient accumulation + +When computing resources are lacking, the batch size can only be set to a small value, which may affect the performance of models. Gradient accumulation can be used to solve this problem. + +Here is an example: + +```python +data = dict(samples_per_gpu=64) +optimizer_config = dict(type="GradientCumulativeOptimizerHook", cumulative_iters=4) +``` + +Indicates that during training, back-propagation is performed every 4 iters. And the above is equivalent to: + +```python +data = dict(samples_per_gpu=256) +optimizer_config = dict(type="OptimizerHook") +``` + +```{note} +When the optimizer hook type is not specified in `optimizer_config`, `OptimizerHook` is used by default. +``` + +## Customize self-implemented methods + +In academic research and industrial practice, it may be necessary to use optimization methods not implemented by MMClassification, and you can add them through the following methods. + +```{note} +This part will modify the MMClassification source code or add code to the MMClassification framework, beginners can skip it. +``` + +### Customize self-implemented optimizer + +#### 1. Define a new optimizer + +A customized optimizer could be defined as below. + +Assume you want to add an optimizer named `MyOptimizer`, which has arguments `a`, `b`, and `c`. +You need to create a new directory named `mmcls/core/optimizer`. +And then implement the new optimizer in a file, e.g., in `mmcls/core/optimizer/my_optimizer.py`: + +```python +from mmcv.runner import OPTIMIZERS +from torch.optim import Optimizer + + +@OPTIMIZERS.register_module() +class MyOptimizer(Optimizer): + + def __init__(self, a, b, c): + +``` + +#### 2. Add the optimizer to registry + +To find the above module defined above, this module should be imported into the main namespace at first. There are two ways to achieve it. + +- Modify `mmcls/core/optimizer/__init__.py` to import it into `optimizer` package, and then modify `mmcls/core/__init__.py` to import the new `optimizer` package. + + Create the `mmcls/core/optimizer` folder and the `mmcls/core/optimizer/__init__.py` file if they don't exist. The newly defined module should be imported in `mmcls/core/optimizer/__init__.py` and `mmcls/core/__init__.py` so that the registry will find the new module and add it: + +```python +# In mmcls/core/optimizer/__init__.py +from .my_optimizer import MyOptimizer # MyOptimizer maybe other class name + +__all__ = ['MyOptimizer'] +``` + +```python +# In mmcls/core/__init__.py +... +from .optimizer import * # noqa: F401, F403 +``` + +- Use `custom_imports` in the config to manually import it + +```python +custom_imports = dict(imports=['mmcls.core.optimizer.my_optimizer'], allow_failed_imports=False) +``` + +The module `mmcls.core.optimizer.my_optimizer` will be imported at the beginning of the program and the class `MyOptimizer` is then automatically registered. +Note that only the package containing the class `MyOptimizer` should be imported. `mmcls.core.optimizer.my_optimizer.MyOptimizer` **cannot** be imported directly. + +#### 3. Specify the optimizer in the config file + +Then you can use `MyOptimizer` in `optimizer` field of config files. +In the configs, the optimizers are defined by the field `optimizer` like the following: + +```python +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +``` + +To use your own optimizer, the field can be changed to + +```python +optimizer = dict(type='MyOptimizer', a=a_value, b=b_value, c=c_value) +``` + +### Customize optimizer constructor + +Some models may have some parameter-specific settings for optimization, e.g. weight decay for BatchNorm layers. + +Although our `DefaultOptimizerConstructor` is powerful, it may still not cover your need. If that, you can do those fine-grained parameter tuning through customizing optimizer constructor. + +```python +from mmcv.runner.optimizer import OPTIMIZER_BUILDERS + + +@OPTIMIZER_BUILDERS.register_module() +class MyOptimizerConstructor: + + def __init__(self, optimizer_cfg, paramwise_cfg=None): + pass + + def __call__(self, model): + ... # Construct your optimzier here. + return my_optimizer +``` + +The default optimizer constructor is implemented [here](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/optimizer/default_constructor.py#L11), which could also serve as a template for new optimizer constructor. diff --git a/docs/zh_CN/Makefile b/docs/zh_CN/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/zh_CN/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/zh_CN/_static/css/readthedocs.css b/docs/zh_CN/_static/css/readthedocs.css new file mode 100644 index 0000000..577a67a --- /dev/null +++ b/docs/zh_CN/_static/css/readthedocs.css @@ -0,0 +1,27 @@ +.header-logo { + background-image: url("../image/mmcls-logo.png"); + background-size: 204px 40px; + height: 40px; + width: 204px; +} + +pre { + white-space: pre; +} + +article.pytorch-article section code { + padding: .2em .4em; + background-color: #f3f4f7; + border-radius: 5px; +} + +/* Disable the change in tables 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JE;56U{Rxs2h5P^j literal 0 HcmV?d00001 diff --git a/docs/zh_CN/_static/js/custom.js b/docs/zh_CN/_static/js/custom.js new file mode 100644 index 0000000..44a4057 --- /dev/null +++ b/docs/zh_CN/_static/js/custom.js @@ -0,0 +1 @@ +var collapsedSections = ['Model zoo']; diff --git a/docs/zh_CN/api b/docs/zh_CN/api new file mode 120000 index 0000000..0ef434a --- /dev/null +++ b/docs/zh_CN/api @@ -0,0 +1 @@ +../en/api \ No newline at end of file diff --git a/docs/zh_CN/changelog.md b/docs/zh_CN/changelog.md new file mode 120000 index 0000000..6b731cd --- /dev/null +++ b/docs/zh_CN/changelog.md @@ -0,0 +1 @@ +../en/changelog.md \ No newline at end of file diff --git a/docs/zh_CN/community/CONTRIBUTING.md b/docs/zh_CN/community/CONTRIBUTING.md new file mode 100644 index 0000000..5554800 --- /dev/null +++ b/docs/zh_CN/community/CONTRIBUTING.md @@ -0,0 +1,62 @@ +# 参与贡献 OpenMMLab + +欢迎任何类型的贡献，包括但不限于 + +- 修改拼写错误或代码错误 +- 添加文档或将文档翻译成其他语言 +- 添加新功能和新组件 + +## 工作流程 + +1. fork 并 pull 最新的 OpenMMLab 仓库 (MMClassification) +2. 签出到一个新分支（不要使用 master 分支提交 PR） +3. 进行修改并提交至 fork 出的自己的远程仓库 +4. 在我们的仓库中创建一个 PR + +```{note} +如果你计划添加一些新的功能，并引入大量改动，请尽量首先创建一个 issue 来进行讨论。 +``` + +## 代码风格 + +### Python + +我们采用 [PEP8](https://www.python.org/dev/peps/pep-0008/) 作为统一的代码风格。 + +我们使用下列工具来进行代码风格检查与格式化： + +- [flake8](https://github.com/PyCQA/flake8): Python 官方发布的代码规范检查工具，是多个检查工具的封装 +- [isort](https://github.com/timothycrosley/isort): 自动调整模块导入顺序的工具 +- [yapf](https://github.com/google/yapf): 一个 Python 文件的格式化工具。 +- [codespell](https://github.com/codespell-project/codespell): 检查单词拼写是否有误 +- [mdformat](https://github.com/executablebooks/mdformat): 检查 markdown 文件的工具 +- [docformatter](https://github.com/myint/docformatter): 一个 docstring 格式化工具。 + +yapf 和 isort 的格式设置位于 [setup.cfg](https://github.com/open-mmlab/mmclassification/blob/master/setup.cfg) + +我们使用 [pre-commit hook](https://pre-commit.com/) 来保证每次提交时自动进行代 +码检查和格式化，启用的功能包括 `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`, 修复 `end-of-files`, `double-quoted-strings`, +`python-encoding-pragma`, `mixed-line-ending`, 对 `requirments.txt`的排序等。 +pre-commit hook 的配置文件位于 [.pre-commit-config](https://github.com/open-mmlab/mmclassification/blob/master/.pre-commit-config.yaml) + +在你克隆仓库后，你需要按照如下步骤安装并初始化 pre-commit hook。 + +```shell +pip install -U pre-commit +``` + +在仓库文件夹中执行 + +```shell +pre-commit install +``` + +在此之后，每次提交，代码规范检查和格式化工具都将被强制执行。 + +```{important} +在创建 PR 之前，请确保你的代码完成了代码规范检查，并经过了 yapf 的格式化。 +``` + +### C++ 和 CUDA + +C++ 和 CUDA 的代码规范遵从 [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html) diff --git a/docs/zh_CN/compatibility.md b/docs/zh_CN/compatibility.md new file mode 100644 index 0000000..178e555 --- /dev/null +++ b/docs/zh_CN/compatibility.md @@ -0,0 +1,7 @@ +# 0.x 相关兼容性问题 + +## MMClassification 0.20.1 + +### MMCV 兼容性 + +在 Twins 骨干网络中，我们使用了 MMCV 提供的 `PatchEmbed` 模块，该模块是在 MMCV 1.4.2 版本加入的，因此我们需要将 MMCV 依赖版本升至 1.4.2。 diff --git a/docs/zh_CN/conf.py b/docs/zh_CN/conf.py new file mode 100644 index 0000000..31d94e5 --- /dev/null +++ b/docs/zh_CN/conf.py @@ -0,0 +1,241 @@ +# flake8: noqa +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import subprocess +import sys + +import pytorch_sphinx_theme +from sphinx.builders.html import StandaloneHTMLBuilder + +sys.path.insert(0, os.path.abspath('../..')) + +# -- Project information ----------------------------------------------------- + +project = 'MMClassification' +copyright = '2020, OpenMMLab' +author = 'MMClassification Authors' + +# The full version, including alpha/beta/rc tags +version_file = '../../mmcls/version.py' + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +release = get_version() + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', + 'sphinx.ext.intersphinx', + 'sphinx.ext.napoleon', + 'sphinx.ext.viewcode', + 'myst_parser', + 'sphinx_copybutton', +] + +autodoc_mock_imports = ['mmcv._ext', 'matplotlib'] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = { + '.rst': 'restructuredtext', + '.md': 'markdown', +} + +language = 'zh_CN' + +# The master toctree document. +master_doc = 'index' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'pytorch_sphinx_theme' +html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()] + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# yapf: disable +html_theme_options = { + 'logo_url': 'https://mmclassification.readthedocs.io/zh_CN/latest/', + 'menu': [ + { + 'name': 'GitHub', + 'url': 'https://github.com/open-mmlab/mmclassification' + }, + { + 'name': 'Colab 教程', + 'children': [ + { + 'name': '用命令行工具训练和推理', + 'url': 'https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb', + }, + { + 'name': '用 Python API 训练和推理', + 'url': 'https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb', + }, + ] + }, + { + 'name': '版本', + 'children': [ + { + 'name': 'MMClassification 0.x', + 'url': 'https://mmclassification.readthedocs.io/zh_CN/latest/', + 'description': 'master 分支' + }, + { + 'name': 'MMClassification 1.x', + 'url': 'https://mmclassification.readthedocs.io/zh_CN/dev-1.x/', + 'description': '1.x 分支' + }, + ], + } + ], + # Specify the language of shared menu + 'menu_lang': 'cn', + 'header_note': { + 'content': + '您正在阅读 MMClassification 0.x 版本的文档。MMClassification 0.x 会在 2022 年末' + '被切换为次要分支。建议您升级到 MMClassification 1.0 版本，体验更多新特性和新功能。' + '请查阅 MMClassification 1.0 的' + '安装教程、' + '迁移教程' + '以及更新日志。', + } +} +# yapf: enable + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] +html_css_files = ['css/readthedocs.css'] +html_js_files = ['js/custom.js'] + +# -- Options for HTMLHelp output --------------------------------------------- + +# Output file base name for HTML help builder. +htmlhelp_basename = 'mmclsdoc' + +# -- Options for LaTeX output ------------------------------------------------ + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'mmcls.tex', 'MMClassification Documentation', author, + 'manual'), +] + +# -- Options for manual page output ------------------------------------------ + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [(master_doc, 'mmcls', 'MMClassification Documentation', [author], + 1)] + +# -- Options for Texinfo output ---------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'mmcls', 'MMClassification Documentation', author, 'mmcls', + 'OpenMMLab image classification toolbox and benchmark.', 'Miscellaneous'), +] + +# -- Options for Epub output ------------------------------------------------- + +# Bibliographic Dublin Core info. +epub_title = project + +# The unique identifier of the text. This can be a ISBN number +# or the project homepage. +# +# epub_identifier = '' + +# A unique identification for the text. +# +# epub_uid = '' + +# A list of files that should not be packed into the epub file. +epub_exclude_files = ['search.html'] + +# set priority when building html +StandaloneHTMLBuilder.supported_image_types = [ + 'image/svg+xml', 'image/gif', 'image/png', 'image/jpeg' +] + +# -- Extension configuration ------------------------------------------------- +# Ignore >>> when copying code +copybutton_prompt_text = r'>>> |\.\.\. ' +copybutton_prompt_is_regexp = True +# Auto-generated header anchors +myst_heading_anchors = 3 +# Configuration for intersphinx +intersphinx_mapping = { + 'python': ('https://docs.python.org/3', None), + 'numpy': ('https://numpy.org/doc/stable', None), + 'torch': ('https://pytorch.org/docs/stable/', None), + 'mmcv': ('https://mmcv.readthedocs.io/zh_CN/latest/', None), +} + + +def builder_inited_handler(app): + subprocess.run(['./stat.py']) + + +def setup(app): + app.add_config_value('no_underscore_emphasis', False, 'env') + app.connect('builder-inited', builder_inited_handler) diff --git a/docs/zh_CN/device/npu.md b/docs/zh_CN/device/npu.md new file mode 100644 index 0000000..7adcf0e --- /dev/null +++ b/docs/zh_CN/device/npu.md @@ -0,0 +1,34 @@ +# NPU (华为昇腾) + +## 使用方法 + +首先，请参考 {external+mmcv:doc}`教程 ` 安装带有 NPU 支持的 MMCV。 + +使用如下命令，可以利用 8 个 NPU 在机器上训练模型（以 ResNet 为例）： + +```shell +bash tools/dist_train.sh configs/cspnet/resnet50_8xb32_in1k.py 8 --device npu +``` + +或者，使用如下命令，在一个 NPU 上训练模型（以 ResNet 为例）： + +```shell +python tools/train.py configs/cspnet/resnet50_8xb32_in1k.py --device npu +``` + +## 经过验证的模型 + +| 模型 | Top-1 (%) | Top-5 (%) | 配置文件 | 相关下载 | +| :--------------------------------------------------------: | :-------: | :-------: | :------------------------------------------------------------: | :------------------------------------------------------------: | +| [CSPResNeXt50](../papers/cspnet.md) | 77.10 | 93.55 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/cspnet/cspresnext50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/cspresnext50_8xb32_in1k.log.json) | +| [DenseNet121](../papers/densenet.md) | 72.62 | 91.04 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/densenet/densenet121_4xb256_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/densenet121_4xb256_in1k.log.json) | +| [EfficientNet-B4(AA + AdvProp)](../papers/efficientnet.md) | 75.55 | 92.86 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/efficientnet/efficientnet-b4_8xb32-01norm_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/efficientnet-b4_8xb32-01norm_in1k.log.json) | +| [HRNet-W18](../papers/hrnet.md) | 77.01 | 93.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/hrnet/hrnet-w18_4xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/hrnet-w18_4xb32_in1k.log.json) | +| [ResNetV1D-152](../papers/resnet.md) | 77.11 | 94.54 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_8xb32_in1k.py) | [model](<>) \| [log](<>) | +| [ResNet-50](../papers/resnet.md) | 76.40 | - | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) | [model](<>) \| [log](<>) | +| [ResNetXt-32x4d-50](../papers/resnext.md) | 77.55 | 93.75 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50-32x4d_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/resnext50-32x4d_8xb32_in1k.log.json) | +| [SE-ResNet-50](../papers/seresnet.md) | 77.64 | 93.76 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/seresnet50_8xb32_in1k.log.json) | +| [VGG-11](../papers/vgg.md) | 68.92 | 88.83 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_8xb32_in1k.py) | [model](<>) \| [log](https://download.openmmlab.com/mmclassification/v0/device/npu/vgg11_8xb32_in1k.log.json) | +| [ShuffleNetV2 1.0x](../papers/shufflenet_v2.md) | 69.53 | 88.82 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py) | [model](<>) \| [log](<>) | + +**以上所有模型权重及训练日志均由华为昇腾团队提供** diff --git a/docs/zh_CN/docutils.conf b/docs/zh_CN/docutils.conf new file mode 100644 index 0000000..0c00c84 --- /dev/null +++ b/docs/zh_CN/docutils.conf @@ -0,0 +1,2 @@ +[html writers] +table_style: colwidths-auto diff --git a/docs/zh_CN/faq.md b/docs/zh_CN/faq.md new file mode 100644 index 0000000..03243a2 --- /dev/null +++ b/docs/zh_CN/faq.md @@ -0,0 +1,74 @@ +# 常见问题 + +我们在这里列出了一些常见问题及其相应的解决方案。如果您发现任何常见问题并有方法 +帮助解决，欢迎随时丰富列表。如果这里的内容没有涵盖您的问题，请按照 +[提问模板](https://github.com/open-mmlab/mmclassification/issues/new/choose) +在 GitHub 上提出问题，并补充模板中需要的信息。 + +## 安装 + +- MMCV 与 MMClassification 的兼容问题。如遇到 + "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx." + + 这里我们列举了各版本 MMClassification 对 MMCV 版本的依赖，请选择合适的 MMCV + 版本来避免安装和使用中的问题。 + + | MMClassification version | MMCV version | + | :----------------------: | :--------------------: | + | dev | mmcv>=1.7.0, \<1.9.0 | + | 0.25.0 (master) | mmcv>=1.4.2, \<1.9.0 | + | 0.24.1 | mmcv>=1.4.2, \<1.9.0 | + | 0.23.2 | mmcv>=1.4.2, \<1.7.0 | + | 0.22.1 | mmcv>=1.4.2, \<1.6.0 | + | 0.21.0 | mmcv>=1.4.2, \<=1.5.0 | + | 0.20.1 | mmcv>=1.4.2, \<=1.5.0 | + | 0.19.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.18.0 | mmcv>=1.3.16, \<=1.5.0 | + | 0.17.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.16.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.15.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.14.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.13.0 | mmcv>=1.3.8, \<=1.5.0 | + | 0.12.0 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.1 | mmcv>=1.3.1, \<=1.5.0 | + | 0.11.0 | mmcv>=1.3.0 | + | 0.10.0 | mmcv>=1.3.0 | + | 0.9.0 | mmcv>=1.1.4 | + | 0.8.0 | mmcv>=1.1.4 | + | 0.7.0 | mmcv>=1.1.4 | + | 0.6.0 | mmcv>=1.1.4 | + + ```{note} + 由于 `dev` 分支处于频繁开发中，MMCV 版本依赖可能不准确。如果您在使用 + `dev` 分支时遇到问题，请尝试更新 MMCV 到最新版。 + ``` + +- 使用 Albumentations + + 如果你希望使用 `albumentations` 相关的功能，我们建议使用 `pip install -r requirements/optional.txt` 或者 + `pip install -U albumentations>=0.3.2 --no-binary qudida,albumentations` 命令进行安装。 + + 如果你直接使用 `pip install albumentations>=0.3.2` 来安装，它会同时安装 `opencv-python-headless` + （即使你已经安装了 `opencv-python`）。具体细节可参阅 + [官方文档](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies)。 + +## 开发 + +- 如果我对源码进行了改动，需要重新安装以使改动生效吗？ + + 如果你遵照[最佳实践](install.md)的指引，从源码安装 mmcls，那么任何本地修改都不需要重新安装即可生效。 + +- 如何在多个 MMClassification 版本下进行开发？ + + 通常来说，我们推荐通过不同虚拟环境来管理多个开发目录下的 MMClassification。 + 但如果你希望在不同目录（如 mmcls-0.21, mmcls-0.23 等）使用同一个环境进行开发， + 我们提供的训练和测试 shell 脚本会自动使用当前目录的 mmcls，其他 Python 脚本 + 则可以在命令前添加 `` PYTHONPATH=`pwd` `` 来使用当前目录的代码。 + + 反过来，如果你希望 shell 脚本使用环境中安装的 MMClassification，而不是当前目录的， + 则可以去掉 shell 脚本中如下一行代码： + + ```shell + PYTHONPATH="$(dirname $0)/..":$PYTHONPATH + ``` diff --git a/docs/zh_CN/getting_started.md b/docs/zh_CN/getting_started.md new file mode 100644 index 0000000..d3e9899 --- /dev/null +++ b/docs/zh_CN/getting_started.md @@ -0,0 +1,266 @@ +# 基础教程 + +本文档提供 MMClassification 相关用法的基本教程。 + +## 准备数据集 + +MMClassification 建议用户将数据集根目录链接到 `$MMCLASSIFICATION/data` 下。 +如果用户的文件夹结构与默认结构不同，则需要在配置文件中进行对应路径的修改。 + +``` +mmclassification +├── mmcls +├── tools +├── configs +├── docs +├── data +│ ├── imagenet +│ │ ├── meta +│ │ ├── train +│ │ ├── val +│ ├── cifar +│ │ ├── cifar-10-batches-py +│ ├── mnist +│ │ ├── train-images-idx3-ubyte +│ │ ├── train-labels-idx1-ubyte +│ │ ├── t10k-images-idx3-ubyte +│ │ ├── t10k-labels-idx1-ubyte + +``` + +对于 ImageNet，其存在多个版本，但最为常用的一个是 [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/)，可以通过以下步骤获取该数据集。 + +1. 注册账号并登录 [下载页面](http://www.image-net.org/download-images) +2. 获取 ILSVRC2012 下载链接并下载以下文件 + - ILSVRC2012_img_train.tar (~138GB) + - ILSVRC2012_img_val.tar (~6.3GB) +3. 解压下载的文件 +4. 使用 [该脚本](https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh) 获取元数据 + +对于 MNIST，CIFAR10 和 CIFAR100，程序将会在需要的时候自动下载数据集。 + +对于用户自定义数据集的准备，请参阅 [教程 3：如何自定义数据集 +](tutorials/new_dataset.md) + +## 使用预训练模型进行推理 + +MMClassification 提供了一些脚本用于进行单张图像的推理、数据集的推理和数据集的测试（如 ImageNet 等） + +### 单张图像的推理 + +```shell +python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} + +# Example +python demo/image_demo.py demo/demo.JPEG configs/resnet/resnet50_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth +``` + +### 数据集的推理与测试 + +- 支持单 GPU +- 支持 CPU +- 支持单节点多 GPU +- 支持多节点 + +用户可使用以下命令进行数据集的推理： + +```shell +# 单 GPU +python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] + +# CPU: 禁用 GPU 并运行单 GPU 测试脚本 +export CUDA_VISIBLE_DEVICES=-1 +python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] + +# 多 GPU +./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--metrics ${METRICS}] [--out ${RESULT_FILE}] + +# 基于 slurm 分布式环境的多节点 +python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [--out ${RESULT_FILE}] --launcher slurm +``` + +可选参数： + +- `RESULT_FILE`：输出结果的文件名。如果未指定，结果将不会保存到文件中。支持 json, yaml, pickle 格式。 +- `METRICS`：数据集测试指标，如准确率 (accuracy), 精确率 (precision), 召回率 (recall) 等 + +例子： + +在 ImageNet 验证集上，使用 ResNet-50 进行推理并获得预测标签及其对应的预测得分。 + +```shell +python tools/test.py configs/resnet/resnet50_8xb16_cifar10.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20210528-f54bfad9.pth \ + --out result.pkl +``` + +## 模型训练 + +MMClassification 使用 `MMDistributedDataParallel` 进行分布式训练，使用 `MMDataParallel` 进行非分布式训练。 + +所有的输出（日志文件和模型权重文件）会被将保存到工作目录下。工作目录通过配置文件中的参数 `work_dir` 指定。 + +默认情况下，MMClassification 在每个周期后会在验证集上评估模型，可以通过在训练配置中修改 `interval` 参数来更改评估间隔 + +```python +evaluation = dict(interval=12) # 每进行 12 轮训练后评估一次模型 +``` + +### 使用单个 GPU 进行训练 + +```shell +python tools/train.py ${CONFIG_FILE} [optional arguments] +``` + +如果用户想在命令中指定工作目录，则需要增加参数 `--work-dir ${YOUR_WORK_DIR}` + +### 使用 CPU 训练 + +使用 CPU 训练的流程和使用单 GPU 训练的流程一致，我们仅需要在训练流程开始前禁用 GPU。 + +```shell +export CUDA_VISIBLE_DEVICES=-1 +``` + +之后运行单 GPU 训练脚本即可。 + +```{warning} +我们不推荐用户使用 CPU 进行训练，这太过缓慢。我们支持这个功能是为了方便用户在没有 GPU 的机器上进行调试。 +``` + +### 使用单台机器多个 GPU 进行训练 + +```shell +./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments] +``` + +可选参数为： + +- `--no-validate` (**不建议**): 默认情况下，程序将会在训练期间的每 k （默认为 1) 个周期进行一次验证。要禁用这一功能，使用 `--no-validate` +- `--work-dir ${WORK_DIR}`：覆盖配置文件中指定的工作目录。 +- `--resume-from ${CHECKPOINT_FILE}`：从以前的模型权重文件恢复训练。 + +`resume-from` 和 `load-from` 的不同点： +`resume-from` 加载模型参数和优化器状态，并且保留检查点所在的周期数，常被用于恢复意外被中断的训练。 +`load-from` 只加载模型参数，但周期数从 0 开始计数，常被用于微调模型。 + +### 使用多台机器进行训练 + +如果您想使用由 ethernet 连接起来的多台机器， 您可以使用以下命令: + +在第一台机器上: + +```shell +NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS +``` + +在第二台机器上: + +```shell +NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS +``` + +但是，如果您不使用高速网路连接这几台机器的话，训练将会非常慢。 + +如果用户在 [slurm](https://slurm.schedmd.com/) 集群上运行 MMClassification，可使用 `slurm_train.sh` 脚本。（该脚本也支持单台机器上进行训练） + +```shell +[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} +``` + +用户可以在 [slurm_train.sh](https://github.com/open-mmlab/mmclassification/blob/master/tools/slurm_train.sh) 中检查所有的参数和环境变量 + +如果用户的多台机器通过 Ethernet 连接，则可以参考 pytorch [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility)。如果用户没有高速网络，如 InfiniBand，速度将会非常慢。 + +### 使用单台机器启动多个任务 + +如果用使用单台机器启动多个任务，如在有 8 块 GPU 的单台机器上启动 2 个需要 4 块 GPU 的训练任务，则需要为每个任务指定不同端口，以避免通信冲突。 + +如果用户使用 `dist_train.sh` 脚本启动训练任务，则可以通过以下命令指定端口 + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4 +CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4 +``` + +如果用户在 slurm 集群下启动多个训练任务，则需要修改配置文件中的 `dist_params` 变量，以设置不同的通信端口。 + +在 `config1.py` 中， + +```python +dist_params = dict(backend='nccl', port=29500) +``` + +在 `config2.py` 中， + +```python +dist_params = dict(backend='nccl', port=29501) +``` + +之后便可启动两个任务，分别对应 `config1.py` 和 `config2.py`。 + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} +CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} +``` + +## 实用工具 + +我们在 `tools/` 目录下提供的一些对训练和测试十分有用的工具 + +### 计算 FLOPs 和参数量（试验性的） + +我们根据 [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) 提供了一个脚本用于计算给定模型的 FLOPs 和参数量 + +```shell +python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] +``` + +用户将获得如下结果： + +``` +============================== +Input shape: (3, 224, 224) +Flops: 4.12 GFLOPs +Params: 25.56 M +============================== +``` + +```{warning} +此工具仍处于试验阶段，我们不保证该数字正确无误。您最好将结果用于简单比较，但在技术报告或论文中采用该结果之前，请仔细检查。 +- FLOPs 与输入的尺寸有关，而参数量与输入尺寸无关。默认输入尺寸为 (1, 3, 224, 224) +- 一些运算不会被计入 FLOPs 的统计中，例如 GN 和自定义运算。详细信息请参考 [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) +``` + +### 模型发布 + +在发布模型之前，你也许会需要 + +1. 转换模型权重至 CPU 张量 +2. 删除优化器状态 +3. 计算模型权重文件的哈希值，并添加至文件名之后 + +```shell +python tools/convert_models/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME} +``` + +例如： + +```shell +python tools/convert_models/publish_model.py work_dirs/resnet50/latest.pth imagenet_resnet50.pth +``` + +最终输出的文件名将会是 `imagenet_resnet50_{date}-{hash id}.pth` + +## 详细教程 + +目前，MMClassification 提供以下几种更详细的教程： + +- [如何编写配置文件](tutorials/config.md) +- [如何微调模型](tutorials/finetune.md) +- [如何增加新数据集](tutorials/new_dataset.md) +- [如何设计数据处理流程](tutorials/data_pipeline.md) +- [如何增加新模块](tutorials/new_modules.md) +- [如何自定义优化策略](tutorials/schedule.md) +- [如何自定义运行参数](tutorials/runtime.md)。 diff --git 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documentation in the lower-left corner of the layout. + +您可以在页面左下角切换中英文文档。 + +.. toctree:: + :maxdepth: 1 + :caption: 开始你的第一步 + + install.md + getting_started.md + + +.. toctree:: + :maxdepth: 1 + :caption: 教程 + + tutorials/config.md + tutorials/finetune.md + tutorials/new_dataset.md + tutorials/data_pipeline.md + tutorials/new_modules.md + tutorials/schedule.md + tutorials/runtime.md + + +.. toctree:: + :maxdepth: 1 + :caption: 模型库 + :glob: + + modelzoo_statistics.md + model_zoo.md + papers/* + + +.. toctree:: + :maxdepth: 1 + :caption: 实用工具 + + tools/pytorch2onnx.md + tools/onnx2tensorrt.md + tools/pytorch2torchscript.md + tools/model_serving.md + tools/visualization.md + tools/analysis.md + tools/miscellaneous.md + + +.. toctree:: + :maxdepth: 1 + :caption: 社区 + + community/CONTRIBUTING.md + + +.. toctree:: + :caption: API 参考文档 + + mmcls.apis + mmcls.core + mmcls.models + mmcls.models.utils + mmcls.datasets + 数据转换 + 批数据增强 + mmcls.utils + + +.. toctree:: + :maxdepth: 1 + :caption: 其他说明 + + changelog.md + compatibility.md + faq.md + + +.. toctree:: + :maxdepth: 1 + :caption: 设备支持 + + device/npu.md + + +.. toctree:: + :caption: 语言切换 + + English + 简体中文 + + +索引与表格 +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/docs/zh_CN/install.md b/docs/zh_CN/install.md new file mode 100644 index 0000000..e881586 --- /dev/null +++ b/docs/zh_CN/install.md @@ -0,0 +1,210 @@ +# 依赖环境 + +在本节中，我们将演示如何准备 PyTorch 相关的依赖环境。 + +MMClassification 适用于 Linux、Windows 和 macOS。它需要 Python 3.6+、CUDA 9.2+ 和 PyTorch 1.5+。 + +```{note} +如果你对配置 PyTorch 环境已经很熟悉，并且已经完成了配置，可以直接进入[下一节](#安装)。 +否则的话，请依照以下步骤完成配置。 +``` + +**第 1 步** 从[官网](https://docs.conda.io/en/latest/miniconda.html)下载并安装 Miniconda。 + +**第 2 步** 创建一个 conda 虚拟环境并激活它。 + +```shell +conda create --name openmmlab python=3.8 -y +conda activate openmmlab +``` + +**第 3 步** 按照[官方指南](https://pytorch.org/get-started/locally/)安装 PyTorch。例如： + +在 GPU 平台： + +```shell +conda install pytorch torchvision -c pytorch +``` + +```{warning} +以上命令会自动安装最新版的 PyTorch 与对应的 cudatoolkit，请检查它们是否与你的环境匹配。 +``` + +在 CPU 平台： + +```shell +conda install pytorch torchvision cpuonly -c pytorch +``` + +# 安装 + +我们推荐用户按照我们的最佳实践来安装 MMClassification。但除此之外，如果你想根据 +你的习惯完成安装流程，也可以参见[自定义安装](#自定义安装)一节来获取更多信息。 + +## 最佳实践 + +**第 1 步** 使用 [MIM](https://github.com/open-mmlab/mim) 安装 [MMCV](https://github.com/open-mmlab/mmcv) + +```shell +pip install -U openmim +mim install mmcv-full +``` + +**第 2 步** 安装 MMClassification + +根据具体需求，我们支持两种安装模式： + +- [从源码安装（推荐）](#从源码安装)：希望基于 MMClassification 框架开发自己的图像分类任务，需要添加新的功能，比如新的模型或是数据集，或者使用我们提供的各种工具。 +- [作为 Python 包安装](#作为-python-包安装)：只是希望调用 MMClassification 的 API 接口，或者在自己的项目中导入 MMClassification 中的模块。 + +### 从源码安装 + +这种情况下，从源码按如下方式安装 mmcls： + +```shell +git clone https://github.com/open-mmlab/mmclassification.git +cd mmclassification +pip install -v -e . +# "-v" 表示输出更多安装相关的信息 +# "-e" 表示以可编辑形式安装，这样可以在不重新安装的情况下，让本地修改直接生效 +``` + +另外，如果你希望向 MMClassification 贡献代码，或者使用试验中的功能，请签出到 `dev` 分支。 + +```shell +git checkout dev +``` + +### 作为 Python 包安装 + +直接使用 pip 安装即可。 + +```shell +pip install mmcls +``` + +## 验证安装 + +为了验证 MMClassification 的安装是否正确，我们提供了一些示例代码来执行模型推理。 + +**第 1 步** 我们需要下载配置文件和模型权重文件 + +```shell +mim download mmcls --config resnet50_8xb32_in1k --dest . +``` + +**第 2 步** 验证示例的推理流程 + +如果你是**从源码安装**的 mmcls，那么直接运行以下命令进行验证： + +```shell +python demo/image_demo.py demo/demo.JPEG resnet50_8xb32_in1k.py resnet50_8xb32_in1k_20210831-ea4938fc.pth --device cpu +``` + +你可以看到命令行中输出了结果字典，包括 `pred_label`，`pred_score` 和 `pred_class` 三个字段。另外如果你拥有图形 +界面（而不是使用远程终端），那么可以启用 `--show` 选项，将示例图像和对应的预测结果在窗口中进行显示。 + +如果你是**作为 PyThon 包安装**，那么可以打开你的 Python 解释器，并粘贴如下代码： + +```python +from mmcls.apis import init_model, inference_model + +config_file = 'resnet50_8xb32_in1k.py' +checkpoint_file = 'resnet50_8xb32_in1k_20210831-ea4938fc.pth' +model = init_model(config_file, checkpoint_file, device='cpu') # 或者 device='cuda:0' +inference_model(model, 'demo/demo.JPEG') +``` + +你会看到输出一个字典，包含预测的标签、得分及类别名。 + +## 自定义安装 + +### CUDA 版本 + +安装 PyTorch 时，需要指定 CUDA 版本。如果您不清楚选择哪个，请遵循我们的建议： + +- 对于 Ampere 架构的 NVIDIA GPU，例如 GeForce 30 series 以及 NVIDIA A100，CUDA 11 是必需的。 +- 对于更早的 NVIDIA GPU，CUDA 11 是向前兼容的，但 CUDA 10.2 能够提供更好的兼容性，也更加轻量。 + +请确保你的 GPU 驱动版本满足最低的版本需求，参阅[这张表](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions)。 + +```{note} +如果按照我们的最佳实践进行安装，CUDA 运行时库就足够了，因为我们提供相关 CUDA 代码的预编译，你不需要进行本地编译。 +但如果你希望从源码进行 MMCV 的编译，或是进行其他 CUDA 算子的开发，那么就必须安装完整的 CUDA 工具链，参见 +[NVIDIA 官网](https://developer.nvidia.com/cuda-downloads)，另外还需要确保该 CUDA 工具链的版本与 PyTorch 安装时 +的配置相匹配（如用 `conda install` 安装 PyTorch 时指定的 cudatoolkit 版本）。 +``` + +### 不使用 MIM 安装 MMCV + +MMCV 包含 C++ 和 CUDA 扩展，因此其对 PyTorch 的依赖比较复杂。MIM 会自动解析这些 +依赖，选择合适的 MMCV 预编译包，使安装更简单，但它并不是必需的。 + +要使用 pip 而不是 MIM 来安装 MMCV，请遵照 [MMCV 安装指南](https://mmcv.readthedocs.io/zh_CN/latest/get_started/installation.html)。 +它需要你用指定 url 的形式手动指定对应的 PyTorch 和 CUDA 版本。 + +举个例子，如下命令将会安装基于 PyTorch 1.10.x 和 CUDA 11.3 编译的 mmcv-full。 + +```shell +pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html +``` + +### 在 CPU 环境中安装 + +MMClassification 可以仅在 CPU 环境中安装，在 CPU 模式下，你可以完成训练（需要 MMCV 版本 >= 1.4.4）、测试和模型推理等所有操作。 + +在 CPU 模式下，MMCV 的部分功能将不可用，通常是一些 GPU 编译的算子。不过不用担心， +MMClassification 中几乎所有的模型都不会依赖这些算子。 + +### 在 Google Colab 中安装 + +[Google Colab](https://research.google.com/) 通常已经包含了 PyTorch 环境，因此我们只需要安装 MMCV 和 MMClassification 即可，命令如下： + +**第 1 步** 使用 [MIM](https://github.com/open-mmlab/mim) 安装 [MMCV](https://github.com/open-mmlab/mmcv) + +```shell +!pip3 install openmim +!mim install mmcv-full +``` + +**第 2 步** 从源码安装 MMClassification + +```shell +!git clone https://github.com/open-mmlab/mmclassification.git +%cd mmclassification +!pip install -e . +``` + +**第 3 步** 验证 + +```python +import mmcls +print(mmcls.__version__) +# 预期输出： 0.23.0 或更新的版本号 +``` + +```{note} +在 Jupyter 中，感叹号 `!` 用于执行外部命令，而 `%cd` 是一个[魔术命令](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd)，用于切换 Python 的工作路径。 +``` + +### 通过 Docker 使用 MMClassification + +MMClassification 提供 [Dockerfile](https://github.com/open-mmlab/mmclassification/blob/master/docker/Dockerfile) +用于构建镜像。请确保你的 [Docker 版本](https://docs.docker.com/engine/install/) >=19.03。 + +```shell +# 构建默认的 PyTorch 1.8.1，CUDA 10.2 版本镜像 +# 如果你希望使用其他版本，请修改 Dockerfile +docker build -t mmclassification docker/ +``` + +用以下命令运行 Docker 镜像： + +```shell +docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmclassification/data mmclassification +``` + +## 故障解决 + +如果你在安装过程中遇到了什么问题，请先查阅[常见问题](faq.md)。如果没有找到解决方法，可以在 GitHub +上[提出 issue](https://github.com/open-mmlab/mmclassification/issues/new/choose)。 diff --git a/docs/zh_CN/model_zoo.md b/docs/zh_CN/model_zoo.md new file mode 120000 index 0000000..013a9ac --- /dev/null +++ b/docs/zh_CN/model_zoo.md @@ -0,0 +1 @@ +../en/model_zoo.md \ No newline at end of file diff --git a/docs/zh_CN/stat.py b/docs/zh_CN/stat.py new file mode 100755 index 0000000..f6d5b3a --- /dev/null +++ b/docs/zh_CN/stat.py @@ -0,0 +1,99 @@ +#!/usr/bin/env python +import functools as func +import glob +import os +import re +from pathlib import Path + +import numpy as np + +MMCLS_ROOT = Path(__file__).absolute().parents[1] +url_prefix = 'https://github.com/open-mmlab/mmclassification/blob/master/' + +papers_root = Path('papers') +papers_root.mkdir(exist_ok=True) +files = [Path(f) for f in sorted(glob.glob('../../configs/*/README.md'))] + +stats = [] +titles = [] +num_ckpts = 0 +num_configs = 0 + +for f in files: + with open(f, 'r') as content_file: + content = content_file.read() + + # Extract checkpoints + ckpts = set(x.lower().strip() + for x in re.findall(r'\[model\]\((https?.*)\)', content)) + if len(ckpts) == 0: + continue + num_ckpts += len(ckpts) + + # Extract paper title + match_res = list(re.finditer(r'> \[(.*)\]\((.*)\)', content)) + if len(match_res) > 0: + title, paperlink = match_res[0].groups() + else: + title = content.split('\n')[0].replace('# ', '').strip() + paperlink = None + titles.append(title) + + # Replace paper link to a button + if paperlink is not None: + start = match_res[0].start() + end = match_res[0].end() + link_button = f'[{title}]({paperlink})' + content = content[:start] + link_button + content[end:] + + # Extract paper type + _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)] + assert len(_papertype) > 0 + papertype = _papertype[0] + paper = set([(papertype, title)]) + + # Write a copy of README + copy = papers_root / (f.parent.name + '.md') + if copy.exists(): + os.remove(copy) + + def replace_link(matchobj): + # Replace relative link to GitHub link. + name = matchobj.group(1) + link = matchobj.group(2) + if not link.startswith('http') and (f.parent / link).exists(): + rel_link = (f.parent / link).absolute().relative_to(MMCLS_ROOT) + link = url_prefix + str(rel_link) + return f'[{name}]({link})' + + content = re.sub(r'\[([^\]]+)\]\(([^)]+)\)', replace_link, content) + + with open(copy, 'w') as copy_file: + copy_file.write(content) + + statsmsg = f""" +\t* [{papertype}] [{title}]({copy}) ({len(ckpts)} ckpts) +""" + stats.append(dict(paper=paper, ckpts=ckpts, statsmsg=statsmsg, copy=copy)) + +allpapers = func.reduce(lambda a, b: a.union(b), + [stat['paper'] for stat in stats]) +msglist = '\n'.join(stat['statsmsg'] for stat in stats) + +papertypes, papercounts = np.unique([t for t, _ in allpapers], + return_counts=True) +countstr = '\n'.join( + [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)]) + +modelzoo = f""" +# 模型库统计 + +* 论文数量： {len(set(titles))} +{countstr} + +* 模型权重文件数量： {num_ckpts} +{msglist} +""" + +with open('modelzoo_statistics.md', 'w') as f: + f.write(modelzoo) diff --git a/docs/zh_CN/tools/analysis.md b/docs/zh_CN/tools/analysis.md new file mode 100644 index 0000000..840ff39 --- /dev/null +++ b/docs/zh_CN/tools/analysis.md @@ -0,0 +1,211 @@ +# 分析 + + + +- [日志分析](#日志分析) + - [绘制曲线图](#绘制曲线图) + - [统计训练时间](#统计训练时间) +- [结果分析](#结果分析) + - [评估结果](#查看典型结果) + - [查看典型结果](#查看典型结果) +- [模型复杂度分析](#模型复杂度分析) +- [常见问题](#常见问题) + + + +## 日志分析 + +### 绘制曲线图 + +指定一个训练日志文件，可通过 `tools/analysis_tools/analyze_logs.py` 脚本绘制指定键值的变化曲线 + +

+ +```shell +python tools/analysis_tools/analyze_logs.py plot_curve \ + ${JSON_LOGS} \ + [--keys ${KEYS}] \ + [--title ${TITLE}] \ + [--legend ${LEGEND}] \ + [--backend ${BACKEND}] \ + [--style ${STYLE}] \ + [--out ${OUT_FILE}] \ + [--window-size ${WINDOW_SIZE}] +``` + +所有参数的说明 + +- `json_logs` ：模型配置文件的路径（可同时传入多个，使用空格分开）。 +- `--keys` ：分析日志的关键字段，数量为 `len(${JSON_LOGS}) * len(${KEYS})` 默认为 'loss'。 +- `--title` ：分析日志的图片名称，默认使用配置文件名， 默认为空。 +- `--legend` ：图例名（可同时传入多个，使用空格分开，数目与 `${JSON_LOGS} * ${KEYS}` 数目一致）。默认使用 `"${JSON_LOG}-${KEYS}"`。 +- `--backend` ：matplotlib 的绘图后端，默认由 matplotlib 自动选择。 +- `--style` ：绘图配色风格，默认为 `whitegrid`。 +- `--out` ：保存分析图片的路径，如不指定则不保存。 +- `--window-size`: 可视化窗口大小，如果没有指定，默认为 `12*7`。如果需要指定，需按照格式 `'W*H'`。 + +```{note} +`--style` 选项依赖于第三方库 `seaborn`，需要设置绘图风格请现安装该库。 +``` + +例如: + +- 绘制某日志文件对应的损失曲线图。 + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys loss --legend loss + ``` + +- 绘制某日志文件对应的 top-1 和 top-5 准确率曲线图，并将曲线图导出为 results.jpg 文件。 + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve your_log_json --keys accuracy_top-1 accuracy_top-5 --legend top1 top5 --out results.jpg + ``` + +- 在同一图像内绘制两份日志文件对应的 top-1 准确率曲线图。 + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys accuracy_top-1 --legend run1 run2 + ``` + +```{note} +本工具会自动根据关键字段选择从日志的训练部分还是验证部分读取，因此如果你添加了 +自定义的验证指标，请把相对应的关键字段加入到本工具的 `TEST_METRICS` 变量中。 +``` + +### 统计训练时间 + +`tools/analysis_tools/analyze_logs.py` 也可以根据日志文件统计训练耗时。 + +```shell +python tools/analysis_tools/analyze_logs.py cal_train_time \ + ${JSON_LOGS} + [--include-outliers] +``` + +**所有参数的说明**： + +- `json_logs` ：模型配置文件的路径（可同时传入多个，使用空格分开）。 +- `--include-outliers` ：如果指定，将不会排除每个轮次中第一轮迭代的记录（有时第一轮迭代会耗时较长） + +**示例**: + +```shell +python tools/analysis_tools/analyze_logs.py cal_train_time work_dirs/some_exp/20200422_153324.log.json +``` + +预计输出结果如下所示： + +```text +-----Analyze train time of work_dirs/some_exp/20200422_153324.log.json----- +slowest epoch 68, average time is 0.3818 +fastest epoch 1, average time is 0.3694 +time std over epochs is 0.0020 +average iter time: 0.3777 s/iter +``` + +## 结果分析 + +利用 `tools/test.py` 的 `--out` 参数，我们可以将所有的样本的推理结果保存到输出 +文件中。利用这一文件，我们可以进行进一步的分析。 + +### 评估结果 + +`tools/analysis_tools/eval_metric.py` 可以用来再次计算评估结果。 + +```shell +python tools/analysis_tools/eval_metric.py \ + ${CONFIG} \ + ${RESULT} \ + [--metrics ${METRICS}] \ + [--cfg-options ${CFG_OPTIONS}] \ + [--metric-options ${METRIC_OPTIONS}] +``` + +**所有参数说明**： + +- `config` ：配置文件的路径。 +- `result` ： `tools/test.py` 的输出结果文件。 +- `metrics` ： 评估的衡量指标，可接受的值取决于数据集类。 +- `--cfg-options`: 额外的配置选项，会被合入配置文件，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 +- `--metric-options`: 如果指定了，这些选项将被传递给数据集 `evaluate` 函数的 `metric_options` 参数。 + +```{note} +在 `tools/test.py` 中，我们支持使用 `--out-items` 选项来选择保存哪些结果。为了使用本工具，请确保结果文件中包含 "class_scores"。 +``` + +**示例**： + +```shell +python tools/analysis_tools/eval_metric.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py ./result.pkl --metrics accuracy --metric-options "topk=(1,5)" +``` + +### 查看典型结果 + +`tools/analysis_tools/analyze_results.py` 可以保存预测成功/失败，同时得分最高的 k 个图像。 + +```shell +python tools/analysis_tools/analyze_results.py \ + ${CONFIG} \ + ${RESULT} \ + [--out-dir ${OUT_DIR}] \ + [--topk ${TOPK}] \ + [--cfg-options ${CFG_OPTIONS}] +``` + +**所有参数说明**： + +- `config` ：配置文件的路径。 +- `result` ： `tools/test.py` 的输出结果文件。 +- `--out-dir` ：保存结果分析的文件夹路径。 +- `--topk` ：分别保存多少张预测成功/失败的图像。如果不指定，默认为 `20`。 +- `--cfg-options`: 额外的配置选项，会被合入配置文件，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 + +```{note} +在 `tools/test.py` 中，我们支持使用 `--out-items` 选项来选择保存哪些结果。为了使用本工具，请确保结果文件中包含 "pred_score"、"pred_label" 和 "pred_class"。 +``` + +**示例**： + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/resnet/resnet50_xxxx.py \ + result.pkl \ + --out-dir results \ + --topk 50 +``` + +## 模型复杂度分析 + +### 计算 FLOPs 和参数量（试验性的） + +我们根据 [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) 提供了一个脚本用于计算给定模型的 FLOPs 和参数量。 + +```shell +python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] +``` + +**所有参数说明**： + +- `config` ：配置文件的路径。 +- `--shape`: 输入尺寸，支持单值或者双值， 如： `--shape 256`、`--shape 224 256`。默认为`224 224`。 + +用户将获得如下结果： + +```text +============================== +Input shape: (3, 224, 224) +Flops: 4.12 GFLOPs +Params: 25.56 M +============================== +``` + +```{warning} +此工具仍处于试验阶段，我们不保证该数字正确无误。您最好将结果用于简单比较，但在技术报告或论文中采用该结果之前，请仔细检查。 +- FLOPs 与输入的尺寸有关，而参数量与输入尺寸无关。默认输入尺寸为 (1, 3, 224, 224) +- 一些运算不会被计入 FLOPs 的统计中，例如 GN 和自定义运算。详细信息请参考 [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) +``` + +## 常见问题 + +- 无 diff --git a/docs/zh_CN/tools/miscellaneous.md b/docs/zh_CN/tools/miscellaneous.md new file mode 100644 index 0000000..a2cb625 --- /dev/null +++ b/docs/zh_CN/tools/miscellaneous.md @@ -0,0 +1,59 @@ +# 其他工具 + + + +- [打印完整配置](#打印完整配置) +- [检查数据集](#检查数据集) +- [常见问题](#常见问题) + + + +## 打印完整配置 + +`tools/misc/print_config.py` 脚本会解析所有输入变量，并打印完整配置信息。 + +```shell +python tools/misc/print_config.py ${CONFIG} [--cfg-options ${CFG_OPTIONS}] +``` + +**所有参数说明**： + +- `config` ：配置文件的路径。 +- `--cfg-options`: 额外的配置选项，会被合入配置文件，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 + +**示例**： + +```shell +python tools/misc/print_config.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py +``` + +## 检查数据集 + +`tools/misc/verify_dataset.py` 脚本会检查数据集的所有图片，查看是否有已经损坏的图片。 + +```shell +python tools/print_config.py \ + ${CONFIG} \ + [--out-path ${OUT-PATH}] \ + [--phase ${PHASE}] \ + [--num-process ${NUM-PROCESS}] + [--cfg-options ${CFG_OPTIONS}] +``` + +**所有参数说明**: + +- `config` ： 配置文件的路径。 +- `--out-path` ： 输出结果路径，默认为 'brokenfiles.log'。 +- `--phase` ： 检查哪个阶段的数据集，可用值为 "train" 、"test" 或者 "val"， 默认为 "train"。 +- `--num-process` ： 指定的进程数，默认为1。 +- `--cfg-options`: 额外的配置选项，会被合入配置文件，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 + +**示例**: + +```shell +python tools/misc/verify_dataset.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py --out-path broken_imgs.log --phase val --num-process 8 +``` + +## 常见问题 + +- 无 diff --git a/docs/zh_CN/tools/model_serving.md b/docs/zh_CN/tools/model_serving.md new file mode 100644 index 0000000..4eed488 --- /dev/null +++ b/docs/zh_CN/tools/model_serving.md @@ -0,0 +1,87 @@ +# 模型部署至 TorchServe + +为了使用 [`TorchServe`](https://pytorch.org/serve/) 部署一个 `MMClassification` 模型，需要进行以下几步： + +## 1. 转换 MMClassification 模型至 TorchServe + +```shell +python tools/deployment/mmcls2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \ +--output-folder ${MODEL_STORE} \ +--model-name ${MODEL_NAME} +``` + +```{note} +${MODEL_STORE} 需要是一个文件夹的绝对路径。 +``` + +示例： + +```shell +python tools/deployment/mmcls2torchserve.py \ + configs/resnet/resnet18_8xb32_in1k.py \ + checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \ + --output-folder ./checkpoints \ + --model-name resnet18_in1k +``` + +## 2. 构建 `mmcls-serve` docker 镜像 + +```shell +docker build -t mmcls-serve:latest docker/serve/ +``` + +## 3. 运行 `mmcls-serve` 镜像 + +请参考官方文档 [基于 docker 运行 TorchServe](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment). + +为了使镜像能够使用 GPU 资源，需要安装 [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)。之后可以传递 `--gpus` 参数以在 GPU 上运。 + +示例： + +```shell +docker run --rm \ +--cpus 8 \ +--gpus device=0 \ +-p8080:8080 -p8081:8081 -p8082:8082 \ +--mount type=bind,source=`realpath ./checkpoints`,target=/home/model-server/model-store \ +mmcls-serve:latest +``` + +```{note} +`realpath ./checkpoints` 是 "./checkpoints" 的绝对路径，你可以将其替换为你保存 TorchServe 模型的目录的绝对路径。 +``` + +参考 [该文档](https://github.com/pytorch/serve/blob/master/docs/rest_api.md) 了解关于推理 (8080)，管理 (8081) 和指标 (8082) 等 API 的信息。 + +## 4. 测试部署 + +```shell +curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T demo/demo.JPEG +``` + +您应该获得类似于以下内容的响应： + +```json +{ + "pred_label": 58, + "pred_score": 0.38102269172668457, + "pred_class": "water snake" +} +``` + +另外，你也可以使用 `test_torchserver.py` 来比较 TorchServe 和 PyTorch 的结果，并进行可视化。 + +```shell +python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME} +[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}] +``` + +示例： + +```shell +python tools/deployment/test_torchserver.py \ + demo/demo.JPEG \ + configs/resnet/resnet18_8xb32_in1k.py \ + checkpoints/resnet18_8xb32_in1k_20210831-fbbb1da6.pth \ + resnet18_in1k +``` diff --git a/docs/zh_CN/tools/onnx2tensorrt.md b/docs/zh_CN/tools/onnx2tensorrt.md new file mode 100644 index 0000000..f6a25fa --- /dev/null +++ b/docs/zh_CN/tools/onnx2tensorrt.md @@ -0,0 +1,75 @@ +# ONNX 转 TensorRT（试验性的） + + + +- [如何将模型从 ONNX 转换到 TensorRT](#如何将模型从-onnx-转换到-tensorrt) + - [准备工作](#准备工作) + - [使用方法](#使用方法) +- [支持转换至 TensorRT 的模型列表](#支持转换至-tensorrt-的模型列表) +- [提示](#提示) +- [常见问题](#常见问题) + + + +## 如何将模型从 ONNX 转换到 TensorRT + +### 准备工作 + +1. 请参照 [安装指南](https://mmclassification.readthedocs.io/zh_CN/latest/install.html#mmclassification) 从源码安装 MMClassification。 +2. 使用我们的工具 [pytorch2onnx.md](./pytorch2onnx.md) 将 PyTorch 模型转换至 ONNX。 + +### 使用方法 + +```bash +python tools/deployment/onnx2tensorrt.py \ + ${MODEL} \ + --trt-file ${TRT_FILE} \ + --shape ${IMAGE_SHAPE} \ + --workspace-size {WORKSPACE_SIZE} \ + --show \ + --verify \ +``` + +所有参数的说明： + +- `model` : ONNX 模型的路径。 +- `--trt-file`: TensorRT 引擎文件的输出路径。如果没有指定，默认为当前脚本执行路径下的 `tmp.trt`。 +- `--shape`: 模型输入的高度和宽度。如果没有指定，默认为 `224 224`。 +- `--workspace-size` : 构建 TensorRT 引擎所需要的 GPU 空间大小，单位为 GiB。如果没有指定，默认为 `1` GiB。 +- `--show`: 是否展示模型的输出。如果没有指定，默认为 `False`。 +- `--verify`: 是否使用 ONNXRuntime 和 TensorRT 验证模型转换的正确性。如果没有指定，默认为`False`。 + +示例： + +```bash +python tools/deployment/onnx2tensorrt.py \ + checkpoints/resnet/resnet18_b16x8_cifar10.onnx \ + --trt-file checkpoints/resnet/resnet18_b16x8_cifar10.trt \ + --shape 224 224 \ + --show \ + --verify \ +``` + +## 支持转换至 TensorRT 的模型列表 + +下表列出了保证可转换为 TensorRT 的模型。 + +| 模型 | 配置文件 | 状态 | +| :----------: | :-----------------------------------------------------: | :--: | +| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | +| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | +| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | +| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | +| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | + +注： + +- *以上所有模型转换测试基于 Pytorch==1.6.0 和 TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0 进行* + +## 提示 + +- 如果你在上述模型的转换中遇到问题，请在 GitHub 中创建一个 issue，我们会尽快处理。未在上表中列出的模型，由于资源限制，我们可能无法提供很多帮助，如果遇到问题，请尝试自行解决。 + +## 常见问题 + +- 无 diff --git a/docs/zh_CN/tools/pytorch2onnx.md b/docs/zh_CN/tools/pytorch2onnx.md new file mode 100644 index 0000000..c66991a --- /dev/null +++ b/docs/zh_CN/tools/pytorch2onnx.md @@ -0,0 +1,88 @@ +# Pytorch 转 ONNX （试验性的） + + + +- [如何将模型从 PyTorch 转换到 ONNX](#如何将模型从-pytorch-转换到-onnx) + - [准备工作](#准备工作) + - [使用方法](#使用方法) +- [支持导出至 ONNX 的模型列表](#支持导出至-onnx-的模型列表) +- [提示](#提示) +- [常见问题](#常见问题) + + + +## 如何将模型从 PyTorch 转换到 ONNX + +### 准备工作 + +1. 请参照 [安装指南](https://mmclassification.readthedocs.io/zh_CN/latest/install.html#mmclassification) 从源码安装 MMClassification。 +2. 安装 onnx 和 onnxruntime。 + +```shell +pip install onnx onnxruntime==1.5.1 +``` + +### 使用方法 + +```bash +python tools/deployment/pytorch2onnx.py \ + ${CONFIG_FILE} \ + --checkpoint ${CHECKPOINT_FILE} \ + --output-file ${OUTPUT_FILE} \ + --shape ${IMAGE_SHAPE} \ + --opset-version ${OPSET_VERSION} \ + --dynamic-shape \ + --show \ + --simplify \ + --verify \ +``` + +所有参数的说明： + +- `config` : 模型配置文件的路径。 +- `--checkpoint` : 模型权重文件的路径。 +- `--output-file`: ONNX 模型的输出路径。如果没有指定，默认为当前脚本执行路径下的 `tmp.onnx`。 +- `--shape`: 模型输入的高度和宽度。如果没有指定，默认为 `224 224`。 +- `--opset-version` : ONNX 的 opset 版本。如果没有指定，默认为 `11`。 +- `--dynamic-shape` : 是否以动态输入尺寸导出 ONNX。 如果没有指定，默认为 `False`。 +- `--show`: 是否打印导出模型的架构。如果没有指定，默认为 `False`。 +- `--simplify`: 是否精简导出的 ONNX 模型。如果没有指定，默认为 `False`。 +- `--verify`: 是否验证导出模型的正确性。如果没有指定，默认为`False`。 + +示例： + +```bash +python tools/deployment/pytorch2onnx.py \ + configs/resnet/resnet18_8xb16_cifar10.py \ + --checkpoint checkpoints/resnet/resnet18_b16x8_cifar10.pth \ + --output-file checkpoints/resnet/resnet18_b16x8_cifar10.onnx \ + --dynamic-shape \ + --show \ + --simplify \ + --verify \ +``` + +## 支持导出至 ONNX 的模型列表 + +下表列出了保证可导出至 ONNX，并在 ONNX Runtime 中运行的模型。 + +| 模型 | 配置文件 | 批推理 | 动态输入尺寸 | 备注 | +| :----------: | :-----------------------------------------------------: | :----: | :----------: | ---- | +| MobileNetV2 | `configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py` | Y | Y | | +| ResNet | `configs/resnet/resnet18_8xb16_cifar10.py` | Y | Y | | +| ResNeXt | `configs/resnext/resnext50-32x4d_8xb32_in1k.py` | Y | Y | | +| SE-ResNet | `configs/seresnet/seresnet50_8xb32_in1k.py` | Y | Y | | +| ShuffleNetV1 | `configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py` | Y | Y | | +| ShuffleNetV2 | `configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py` | Y | Y | | + +注： + +- *以上所有模型转换测试基于 Pytorch==1.6.0 进行* + +## 提示 + +- 如果你在上述模型的转换中遇到问题，请在 GitHub 中创建一个 issue，我们会尽快处理。未在上表中列出的模型，由于资源限制，我们可能无法提供很多帮助，如果遇到问题，请尝试自行解决。 + +## 常见问题 + +- 无 diff --git a/docs/zh_CN/tools/pytorch2torchscript.md b/docs/zh_CN/tools/pytorch2torchscript.md new file mode 100644 index 0000000..9ef846f --- /dev/null +++ b/docs/zh_CN/tools/pytorch2torchscript.md @@ -0,0 +1,54 @@ +# Pytorch 转 TorchScript （试验性的） + + + +- [如何将 PyTorch 模型转换至 TorchScript](#如何将-pytorch-模型转换至-torchscript) + - [使用方法](#使用方法) +- [提示](#提示) +- [常见问题](#常见问题) + + + +## 如何将 PyTorch 模型转换至 TorchScript + +### 使用方法 + +```bash +python tools/deployment/pytorch2torchscript.py \ + ${CONFIG_FILE} \ + --checkpoint ${CHECKPOINT_FILE} \ + --output-file ${OUTPUT_FILE} \ + --shape ${IMAGE_SHAPE} \ + --verify \ +``` + +所有参数的说明： + +- `config` : 模型配置文件的路径。 +- `--checkpoint` : 模型权重文件的路径。 +- `--output-file`: TorchScript 模型的输出路径。如果没有指定，默认为当前脚本执行路径下的 `tmp.pt`。 +- `--shape`: 模型输入的高度和宽度。如果没有指定，默认为 `224 224`。 +- `--verify`: 是否验证导出模型的正确性。如果没有指定，默认为`False`。 + +示例： + +```bash +python tools/deployment/pytorch2torchscript.py \ + configs/resnet/resnet18_8xb16_cifar10.py \ + --checkpoint checkpoints/resnet/resnet18_b16x8_cifar10.pth \ + --output-file checkpoints/resnet/resnet18_b16x8_cifar10.pt \ + --verify \ +``` + +注： + +- *所有模型基于 Pytorch==1.8.1 通过了转换测试* + +## 提示 + +- 由于 `torch.jit.is_tracing()` 只在 PyTorch 1.6 之后的版本中得到支持，对于 PyTorch 1.3-1.5 的用户，我们建议手动提前返回结果。 +- 如果你在本仓库的模型转换中遇到问题，请在 GitHub 中创建一个 issue，我们会尽快处理。 + +## 常见问题 + +- 无 diff --git a/docs/zh_CN/tools/visualization.md b/docs/zh_CN/tools/visualization.md new file mode 100644 index 0000000..75d8142 --- /dev/null +++ b/docs/zh_CN/tools/visualization.md @@ -0,0 +1,302 @@ +# 可视化 + + + +- [数据流水线可视化](#数据流水线可视化) +- [学习率策略可视化](#学习率策略可视化) +- [类别激活图可视化](#类别激活图可视化) +- [常见问题](#常见问题) + + + +## 数据流水线可视化 + +```bash +python tools/visualizations/vis_pipeline.py \ + ${CONFIG_FILE} \ + [--output-dir ${OUTPUT_DIR}] \ + [--phase ${DATASET_PHASE}] \ + [--number ${BUNBER_IMAGES_DISPLAY}] \ + [--skip-type ${SKIP_TRANSFORM_TYPE}] \ + [--mode ${DISPLAY_MODE}] \ + [--show] \ + [--adaptive] \ + [--min-edge-length ${MIN_EDGE_LENGTH}] \ + [--max-edge-length ${MAX_EDGE_LENGTH}] \ + [--bgr2rgb] \ + [--window-size ${WINDOW_SIZE}] \ + [--cfg-options ${CFG_OPTIONS}] +``` + +**所有参数的说明**： + +- `config` : 模型配置文件的路径。 +- `--output-dir`: 保存图片文件夹，如果没有指定，默认为 `''`,表示不保存。 +- `--phase`: 可视化数据集的阶段，只能为 `[train, val, test]` 之一，默认为 `train`。 +- `--number`: 可视化样本数量。如果没有指定，默认展示数据集的所有图片。 +- `--skip-type`: 预设跳过的数据流水线过程。如果没有指定，默认为 `['ToTensor', 'Normalize', 'ImageToTensor', 'Collect']`。 +- `--mode`: 可视化的模式，只能为 `[original, transformed, concat, pipeline]` 之一，如果没有指定，默认为 `concat`。 +- `--show`: 将可视化图片以弹窗形式展示。 +- `--adaptive`: 自动调节可视化图片的大小。 +- `--min-edge-length`: 最短边长度，当使用了 `--adaptive` 时有效。 当图片任意边小于 `${MIN_EDGE_LENGTH}` 时，会保持长宽比不变放大图片，短边对齐至 `${MIN_EDGE_LENGTH}`，默认为200。 +- `--max-edge-length`: 最长边长度，当使用了 `--adaptive` 时有效。 当图片任意边大于 `${MAX_EDGE_LENGTH}` 时，会保持长宽比不变缩小图片，短边对齐至 `${MAX_EDGE_LENGTH}`，默认为1000。 +- `--bgr2rgb`: 将图片的颜色通道翻转。 +- `--window-size`: 可视化窗口大小，如果没有指定，默认为 `12*7`。如果需要指定，按照格式 `'W*H'`。 +- `--cfg-options` : 对配置文件的修改，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 + +```{note} + +1. 如果不指定 `--mode`，默认设置为 `concat`，获取原始图片和预处理后图片拼接的图片；如果 `--mode` 设置为 `original`，则获取原始图片；如果 `--mode` 设置为 `transformed`，则获取预处理后的图片；如果 `--mode` 设置为 `pipeline`，则获得数据流水线所有中间过程图片。 + +2. 当指定了 `--adaptive` 选项时，会自动的调整尺寸过大和过小的图片，你可以通过设定 `--min-edge-length` 与 `--max-edge-length` 来指定自动调整的图片尺寸。 +``` + +**示例**： + +1. **'original'** 模式，可视化 `CIFAR100` 验证集中的100张原始图片，显示并保存在 `./tmp` 文件夹下： + +```shell +python ./tools/visualizations/vis_pipeline.py configs/resnet/resnet50_8xb16_cifar100.py --phase val --output-dir tmp --mode original --number 100 --show --adaptive --bgr2rgb +``` + +
+ +2. **'transformed'** 模式，可视化 `ImageNet` 训练集的所有经过预处理的图片，并以弹窗形式显示： + +```shell +python ./tools/visualizations/vis_pipeline.py ./configs/resnet/resnet50_8xb32_in1k.py --show --mode transformed +``` + +
+ +3. **'concat'** 模式，可视化 `ImageNet` 训练集的10张原始图片与预处理后图片对比图，保存在 `./tmp` 文件夹下： + +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --output-dir tmp --number 10 --adaptive +``` + +
+ +4. **'pipeline'** 模式，可视化 `ImageNet` 训练集经过数据流水线的过程图像： + +```shell +python ./tools/visualizations/vis_pipeline.py configs/swin_transformer/swin_base_224_b16x64_300e_imagenet.py --phase train --adaptive --mode pipeline --show +``` + +
+ +## 学习率策略可视化 + +```bash +python tools/visualizations/vis_lr.py \ + ${CONFIG_FILE} \ + [--dataset-size ${Dataset_Size}] \ + [--ngpus ${NUM_GPUs}] \ + [--save-path ${SAVE_PATH}] \ + [--title ${TITLE}] \ + [--style ${STYLE}] \ + [--window-size ${WINDOW_SIZE}] \ + [--cfg-options ${CFG_OPTIONS}] \ +``` + +**所有参数的说明**： + +- `config` : 模型配置文件的路径。 +- `--dataset-size` : 数据集的大小。如果指定，`build_dataset` 将被跳过并使用这个大小作为数据集大小，默认使用 `build_dataset` 所得数据集的大小。 +- `--ngpus` : 使用 GPU 的数量。 +- `--save-path` : 保存的可视化图片的路径，默认不保存。 +- `--title` : 可视化图片的标题，默认为配置文件名。 +- `--style` : 可视化图片的风格，默认为 `whitegrid`。 +- `--window-size`: 可视化窗口大小，如果没有指定，默认为 `12*7`。如果需要指定，按照格式 `'W*H'`。 +- `--cfg-options` : 对配置文件的修改，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 + +```{note} + +部分数据集在解析标注阶段比较耗时，可直接将 `dataset-size` 指定数据集的大小，以节约时间。 + +``` + +**示例**： + +```bash +python tools/visualizations/vis_lr.py configs/resnet/resnet50_b16x8_cifar100.py +``` + +
+ +当数据集为 ImageNet 时，通过直接指定数据集大小来节约时间，并保存图片： + +```bash +python tools/visualizations/vis_lr.py configs/repvgg/repvgg-B3g4_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py --dataset-size 1281167 --ngpus 4 --save-path ./repvgg-B3g4_4xb64-lr.jpg +``` + +
+ +## 类别激活图可视化 + +MMClassification 提供 `tools\visualizations\vis_cam.py` 工具来可视化类别激活图。请使用 `pip install "grad-cam>=1.3.6"` 安装依赖的 [pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam)。 + +目前支持的方法有： + +| Method | What it does | +| :----------: | :-----------------------------------------------------------------------------------------------: | +| GradCAM | 使用平均梯度对 2D 激活进行加权 | +| GradCAM++ | 类似 GradCAM，但使用了二阶梯度 | +| XGradCAM | 类似 GradCAM，但通过归一化的激活对梯度进行了加权 | +| EigenCAM | 使用 2D 激活的第一主成分（无法区分类别，但效果似乎不错） | +| EigenGradCAM | 类似 EigenCAM，但支持类别区分，使用了激活 * 梯度的第一主成分，看起来和 GradCAM 差不多，但是更干净 | +| LayerCAM | 使用正梯度对激活进行空间加权，对于浅层有更好的效果 | + +**命令行**： + +```bash +python tools/visualizations/vis_cam.py \ + ${IMG} \ + ${CONFIG_FILE} \ + ${CHECKPOINT} \ + [--target-layers ${TARGET-LAYERS}] \ + [--preview-model] \ + [--method ${METHOD}] \ + [--target-category ${TARGET-CATEGORY}] \ + [--save-path ${SAVE_PATH}] \ + [--vit-like] \ + [--num-extra-tokens ${NUM-EXTRA-TOKENS}] + [--aug_smooth] \ + [--eigen_smooth] \ + [--device ${DEVICE}] \ + [--cfg-options ${CFG-OPTIONS}] +``` + +**所有参数的说明**： + +- `img`：目标图片路径。 +- `config`：模型配置文件的路径。 +- `checkpoint`：权重路径。 +- `--target-layers`：所查看的网络层名称，可输入一个或者多个网络层, 如果不设置，将使用最后一个`block`中的`norm`层。 +- `--preview-model`：是否查看模型所有网络层。 +- `--method`：类别激活图图可视化的方法，目前支持 `GradCAM`, `GradCAM++`, `XGradCAM`, `EigenCAM`, `EigenGradCAM`, `LayerCAM`，不区分大小写。如果不设置，默认为 `GradCAM`。 +- `--target-category`：查看的目标类别，如果不设置，使用模型检测出来的类别做为目标类别。 +- `--save-path`：保存的可视化图片的路径，默认不保存。 +- `--eigen-smooth`：是否使用主成分降低噪音，默认不开启。 +- `--vit-like`: 是否为 `ViT` 类似的 Transformer-based 网络 +- `--num-extra-tokens`: `ViT` 类网络的额外的 tokens 通道数，默认使用主干网络的 `num_extra_tokens`。 +- `--aug-smooth`：是否使用测试时增强 +- `--device`：使用的计算设备，如果不设置，默认为'cpu'。 +- `--cfg-options`：对配置文件的修改，参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html)。 + +```{note} +在指定 `--target-layers` 时，如果不知道模型有哪些网络层，可使用命令行添加 `--preview-model` 查看所有网络层名称； +``` + +**示例（CNN）**： + +`--target-layers` 在 `Resnet-50` 中的一些示例如下: + +- `'backbone.layer4'`，表示第四个 `ResLayer` 层的输出。 +- `'backbone.layer4.2'` 表示第四个 `ResLayer` 层中第三个 `BottleNeck` 块的输出。 +- `'backbone.layer4.2.conv1'` 表示上述 `BottleNeck` 块中 `conv1` 层的输出。 + +```{note} +对于 `ModuleList` 或者 `Sequential` 类型的网络层，可以直接使用索引的方式指定子模块。比如 `backbone.layer4[-1]` 和 `backbone.layer4.2` 是相同的，因为 `layer4` 是一个拥有三个子模块的 `Sequential`。 +``` + +1. 使用不同方法可视化 `ResNet50`，默认 `target-category` 为模型检测的结果，使用默认推导的 `target-layers`。 + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/resnet/resnet50_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \ + --method GradCAM + # GradCAM++, XGradCAM, EigenCAM, EigenGradCAM, LayerCAM + ``` + + | Image | GradCAM | GradCAM++ | EigenGradCAM | LayerCAM | + | ------------------------------------ | --------------------------------------- | ----------------------------------------- | -------------------------------------------- | ---------------------------------------- | + |
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| + +2. 同一张图不同类别的激活图效果图，在 `ImageNet` 数据集中，类别238为 'Greater Swiss Mountain dog'，类别281为 'tabby, tabby cat'。 + + ```shell + python tools/visualizations/vis_cam.py \ + demo/cat-dog.png configs/resnet/resnet50_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth \ + --target-layers 'backbone.layer4.2' \ + --method GradCAM \ + --target-category 238 + # --target-category 281 + ``` + + | Category | Image | GradCAM | XGradCAM | LayerCAM | + | -------- | ---------------------------------------------- | ------------------------------------------------ | ------------------------------------------------- | ------------------------------------------------- | + | Dog |
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| + +3. 使用 `--eigen-smooth` 以及 `--aug-smooth` 获取更好的可视化效果。 + + ```shell + python tools/visualizations/vis_cam.py \ + demo/dog.jpg \ + configs/mobilenet_v3/mobilenet-v3-large_8xb32_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_large-3ea3c186.pth \ + --target-layers 'backbone.layer16' \ + --method LayerCAM \ + --eigen-smooth --aug-smooth + ``` + + | Image | LayerCAM | eigen-smooth | aug-smooth | eigen&aug | + | ------------------------------------ | --------------------------------------- | ------------------------------------------- | ----------------------------------------- | ----------------------------------------- | + |
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| + +**示例（Transformer）**： + +`--target-layers` 在 Transformer-based 网络中的一些示例如下: + +- Swin-Transformer 中：`'backbone.norm3'` +- ViT 中：`'backbone.layers[-1].ln1'` + +对于 Transformer-based 的网络，比如 ViT、T2T-ViT 和 Swin-Transformer，特征是被展平的。为了绘制 CAM 图，我们需要指定 `--vit-like` 选项，从而让被展平的特征恢复方形的特征图。 + +除了特征被展平之外，一些类 ViT 的网络还会添加额外的 tokens。比如 ViT 和 T2T-ViT 中添加了分类 token，DeiT 中还添加了蒸馏 token。在这些网络中，分类计算在最后一个注意力模块之后就已经完成了，分类得分也只和这些额外的 tokens 有关，与特征图无关，也就是说，分类得分对这些特征图的导数为 0。因此，我们不能使用最后一个注意力模块的输出作为 CAM 绘制的目标层。 + +另外，为了去除这些额外的 toekns 以获得特征图，我们需要知道这些额外 tokens 的数量。MMClassification 中几乎所有 Transformer-based 的网络都拥有 `num_extra_tokens` 属性。而如果你希望将此工具应用于新的，或者第三方的网络，而且该网络没有指定 `num_extra_tokens` 属性，那么可以使用 `--num-extra-tokens` 参数手动指定其数量。 + +1. 对 `Swin Transformer` 使用默认 `target-layers` 进行 CAM 可视化： + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/swin_transformer/swin-tiny_16xb64_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth \ + --vit-like + ``` + +2. 对 `Vision Transformer(ViT)` 进行 CAM 可视化： + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/vision_transformer/vit-base-p16_ft-64xb64_in1k-384.py \ + https://download.openmmlab.com/mmclassification/v0/vit/finetune/vit-base-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384_20210928-98e8652b.pth \ + --vit-like \ + --target-layers 'backbone.layers[-1].ln1' + ``` + +3. 对 `T2T-ViT` 进行 CAM 可视化： + + ```shell + python tools/visualizations/vis_cam.py \ + demo/bird.JPEG \ + configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py \ + https://download.openmmlab.com/mmclassification/v0/t2t-vit/t2t-vit-t-14_3rdparty_8xb64_in1k_20210928-b7c09b62.pth \ + --vit-like \ + --target-layers 'backbone.encoder[-1].ln1' + ``` + +| Image | ResNet50 | ViT | Swin | T2T-ViT | +| --------------------------------------- | ------------------------------------------ | -------------------------------------- | --------------------------------------- | ------------------------------------------ | +|
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| + +## 常见问题 + +- 无 diff --git a/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb b/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb new file mode 100755 index 0000000..b81bf87 --- /dev/null +++ b/docs/zh_CN/tutorials/MMClassification_python_cn.ipynb @@ -0,0 +1,2041 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XjQxmm04iTx4" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UdMfIsMpiODD" + }, + "source": [ + "# MMClassification Python API 教程\n", + "\n", + "在本教程中会介绍如下内容：\n", + "\n", + "* 如何安装 MMClassification\n", + "* 使用 Python API 进行模型推理\n", + "* 使用 Python API 进行模型微调" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iOl0X9UEiRvE" + }, + "source": [ + "## 安装 MMClassification\n", + "\n", + "在使用 MMClassification 之前，我们需要配置环境，步骤如下：\n", + "\n", + "- 安装 Python, CUDA, C/C++ compiler 和 git\n", + "- 安装 PyTorch (CUDA 版)\n", + "- 安装 mmcv\n", + "- 克隆 mmcls github 代码库然后安装\n", + "\n", + "因为我们在 Google Colab 进行实验，Colab 已经帮我们完成了基本的配置，我们可以直接跳过前面两个步骤 。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_i7cjqS_LtoP" + }, + "source": [ + "### 检查环境" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c6MbAw10iUJI", + "outputId": "dd37cdf5-7bcf-4a03-f5b5-4b17c3ca16de" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content\n" + ] + } + ], + "source": [ + "%cd /content" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4IyFL3MaiYRu", + "outputId": "5008efdf-0356-4d93-ba9d-e51787036213" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content\n" + ] + } + ], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DMw7QwvpiiUO", + "outputId": "33fa5eb8-d083-4a1f-d094-ab0f59e2818e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2020 NVIDIA Corporation\n", + "Built on Mon_Oct_12_20:09:46_PDT_2020\n", + "Cuda compilation tools, release 11.1, V11.1.105\n", + "Build cuda_11.1.TC455_06.29190527_0\n" + ] + } + ], + "source": [ + "# 检查 nvcc 版本\n", + "!nvcc -V" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4VIBU7Fain4D", + "outputId": "ec20652d-ca24-4b82-b407-e90354d728f8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ], + "source": [ + "# 检查 GCC 版本\n", + "!gcc --version" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24lDLCqFisZ9", + "outputId": "30ec9a1c-cdb3-436c-cdc8-f2a22afe254f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.9.0+cu111\n", + "True\n" + ] + } + ], + "source": [ + "# 检查 PyTorch 的安装情况\n", + "import torch, torchvision\n", + "print(torch.__version__)\n", + "print(torch.cuda.is_available())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R2aZNLUwizBs" + }, + "source": [ + "### 安装 MMCV\n", + "\n", + "MMCV 是 OpenMMLab 代码库的基础库。Linux 环境的安装 whl 包已经提前打包好，大家可以直接下载安装。\n", + "\n", + "需要注意 PyTorch 和 CUDA 版本，确保能够正常安装。\n", + "\n", + "在前面的步骤中，我们输出了环境中 CUDA 和 PyTorch 的版本，分别是 11.1 和 1.9.0，我们需要选择相应的 MMCV 版本。\n", + "\n", + "另外，也可以安装完整版的 MMCV-full，它包含所有的特性以及丰富的开箱即用的 CUDA 算子。需要注意的是完整版本可能需要更长时间来编译。" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nla40LrLi7oo", + "outputId": "162bf14d-0d3e-4540-e85e-a46084a786b1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "Collecting mmcv\n", + " Downloading mmcv-1.3.15.tar.gz (352 kB)\n", + "\u001b[K |████████████████████████████████| 352 kB 5.2 MB/s \n", + "\u001b[?25hCollecting addict\n", + " Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv) (1.19.5)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv) (21.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv) (7.1.2)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv) (3.13)\n", + "Collecting yapf\n", + " Downloading yapf-0.31.0-py2.py3-none-any.whl (185 kB)\n", + "\u001b[K |████████████████████████████████| 185 kB 49.9 MB/s \n", + "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv) (2.4.7)\n", + "Building wheels for collected packages: mmcv\n", + " Building wheel for mmcv (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for mmcv: filename=mmcv-1.3.15-py2.py3-none-any.whl size=509835 sha256=793fe3796421336ca7a7740a1397a54016ba71ce95fd80cb80a116644adb4070\n", + " Stored in directory: /root/.cache/pip/wheels/b2/f4/4e/8f6d2dd2bef6b7eb8c89aa0e5d61acd7bff60aaf3d4d4b29b0\n", + "Successfully built mmcv\n", + "Installing collected packages: yapf, addict, mmcv\n", + "Successfully installed addict-2.4.0 mmcv-1.3.15 yapf-0.31.0\n" + ] + } + ], + "source": [ + "# 安装 mmcv\n", + "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.9.0/index.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GDTUrYvXjlRb" + }, + "source": [ + "### 克隆并安装 MMClassification\n", + "\n", + "接着，我们从 github 上克隆下 mmcls 最新代码库并进行安装。" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Bwme6tWHjl5s", + "outputId": "eae20624-4695-4cd9-c3e5-9c59596d150a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cloning into 'mmclassification'...\n", + "remote: Enumerating objects: 4152, done.\u001b[K\n", + "remote: Counting objects: 100% (994/994), done.\u001b[K\n", + "remote: Compressing objects: 100% (576/576), done.\u001b[K\n", + "remote: Total 4152 (delta 476), reused 765 (delta 401), pack-reused 3158\u001b[K\n", + "Receiving objects: 100% (4152/4152), 8.20 MiB | 21.00 MiB/s, done.\n", + "Resolving deltas: 100% (2524/2524), done.\n" + ] + } + ], + "source": [ + "# 下载 mmcls 代码库\n", + "!git clone https://github.com/open-mmlab/mmclassification.git\n", + "%cd mmclassification/\n", + "\n", + "# 从源码安装 MMClassification\n", + "!pip install -e . " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hFg_oSG4j3zB", + "outputId": "05a91f9b-d41c-4ae7-d4fe-c30a30d3f639" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.16.0\n" + ] + } + ], + "source": [ + "# 检查 MMClassification 的安装情况\n", + "import mmcls\n", + "print(mmcls.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Mi3g6yzj96L" + }, + "source": [ + "## 使用 Python API 进行模型推理\n", + "\n", + "MMClassification 提供很多预训练好的模型，可以访问链接查看[模型库](https://mmclassification.readthedocs.io/zh_CN/latest/model_zoo.html)。\n", + "绝大部分模型都能够复现原始论文的精度，或者达到更高的精度。\n", + "我们能够直接使用这些模型进行推理计算。\n", + "\n", + "在使用预训练模型之前，我们需要进行如下操作:\n", + "\n", + "- 准备模型\n", + " - 准备 config 配置文件 \n", + " - 准备模型权重参数文件\n", + "- 构建模型\n", + "- 进行推理计算" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nDQchz8CkJaT", + "outputId": "9805bd7d-cc2a-4269-b43d-257412f1df93" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-10-21 03:52:36-- https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.3.18, 2620:100:601b:18::a27d:812\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.3.18|:443... connected.\n", + "HTTP request sent, awaiting response... 301 Moved Permanently\n", + "Location: /s/raw/k5fsqi6qha09l1v/banana.png [following]\n", + "--2021-10-21 03:52:36-- https://www.dropbox.com/s/raw/k5fsqi6qha09l1v/banana.png\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com/cd/0/inline/BYYklQk6LNPXNm7o5xE_fxE2GA9reePyNajQgoe9roPlSrtsJd4WN6RVww7zrtNZWFq8iZv349MNQJlm7vVaqRBxTcd0ufxkqbcJYJvOrORpxOPV7mHmhMjKYUncez8YNqELGwDd-aeZqLGKBC8spSnx/file# [following]\n", + "--2021-10-21 03:52:36-- https://uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com/cd/0/inline/BYYklQk6LNPXNm7o5xE_fxE2GA9reePyNajQgoe9roPlSrtsJd4WN6RVww7zrtNZWFq8iZv349MNQJlm7vVaqRBxTcd0ufxkqbcJYJvOrORpxOPV7mHmhMjKYUncez8YNqELGwDd-aeZqLGKBC8spSnx/file\n", + "Resolving uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com (uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com)... 162.125.3.15, 2620:100:601b:15::a27d:80f\n", + "Connecting to uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com (uc10f85c3c33c4b5233bac4d074e.dl.dropboxusercontent.com)|162.125.3.15|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 297299 (290K) [image/png]\n", + "Saving to: ‘demo/banana.png’\n", + "\n", + "demo/banana.png 100%[===================>] 290.33K --.-KB/s in 0.08s \n", + "\n", + "2021-10-21 03:52:36 (3.47 MB/s) - ‘demo/banana.png’ saved [297299/297299]\n", + "\n" + ] + } + ], + "source": [ + "# 获取示例图片\n", + "!wget https://www.dropbox.com/s/k5fsqi6qha09l1v/banana.png?dl=0 -O demo/banana.png" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 420 + }, + "id": "o2eiitWnkQq_", + "outputId": "192b3ebb-202b-4d6e-e178-561223024318" + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from PIL import Image\n", + "Image.open('demo/banana.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sRfAui8EkTDX" + }, + "source": [ + "### 准备配置文件和模型权重文件\n", + "\n", + "预训练模型通过配置文件和模型权重文件来定义。配置文件定义了模型结构，模型权重文件保存了训练好的模型参数。\n", + "\n", + "在 GitHub 上 MMClassification 通过不同的页面来提供预训练模型。\n", + "比如, MobileNetV2 的配置文件和模型权重文件就在这个[链接](https://github.com/open-mmlab/mmclassification/tree/master/configs/mobilenet_v2)下。\n", + "\n", + "我们在 MMClassification 库中已经内置了大量模型训练所需要的配置文件，可以直接读取。而模型权重文件则需要下载，方便的是，我们的 API 提供了读取模型权重 url 的功能，因此可以直接以 url 的方式指定模型权重文件。" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VvRoZpBGkgpC", + "outputId": "68282782-015e-4f5c-cef2-79be3bf6a9b7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n" + ] + } + ], + "source": [ + "# 检查确保配置文件存在\n", + "!ls configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py\n", + "\n", + "# 指明配置文件和权重参数文件的路径\n", + "# 其中，权重参数文件的路径可以是一个 url，会在加载权重时自动下载。\n", + "config_file = 'configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py'\n", + "checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eiYdsHoIkpD1" + }, + "source": [ + "### 进行模型推理\n", + "\n", + "MMClassification 提供了 high level 的 Python API 用来进行推理计算. \n", + "\n", + "首先，我们构建模型。" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323, + "referenced_widgets": [ + "badf240bbb7d442fbd214e837edbffe2", + "520112917e0f4844995d418c5041d23a", + "9f3f6b72b4d14e2a96b9185331c8081b", + "a275bef3584b49ab9b680b528420d461", + "c4b2c6914a05497b8d2b691bd6dda6da", + "863d2a8cc4074f2e890ba6aea7c54384", + "be55ab36267d4dcab1d83dfaa8540270", + "31475aa888da4c8d844ba99a0b3397f5", + "e310c50e610248dd897fbbf5dd09dd7a", + "8a8ab7c27e404459951cffe7a32b8faa", + "e1a3dce90c1a4804a9ef0c687a9c0703" + ] + }, + "id": "KwJWlR2QkpiV", + "outputId": "982b365e-d3be-4e3d-dee7-c507a8020292" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Use load_from_http loader\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "badf240bbb7d442fbd214e837edbffe2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0.00/13.5M [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "# 可视化分类结果\n", + "show_result_pyplot(model, img, result)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oDMr3Bx_lESy" + }, + "source": [ + "## 使用 Python API 进行模型微调\n", + "\n", + "模型微调是将预训练好的模型在特定的数据集上对模型参数进行非常精细调整的过程，最终让预训练的模型能够适配新的数据集及对应的任务。相比于模型的训练过程，模型微调大大降低了训练的时间，并减少了数据量很小的数据集在训练过程中会出现的过拟合问题。\n", + "\n", + "模型微调的基本步骤如下：\n", + "\n", + "1. 准备新数据集并满足 MMClassification 的要求\n", + "2. 根据数据集修改训练配置 \n", + "3. 进行训练和验证\n", + "\n", + "更多细节可以查看 [文档](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/finetune.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TJtKKwAvlHX_" + }, + "source": [ + "### 准备数据集并满足 MMClassification 的要求\n", + "\n", + "这里我们下载猫狗分类数据集，关于数据集格式的详细介绍参考 [tools 教程](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs_zh-CN/tutorials/MMClassification_tools_cn.ipynb)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3vBfU8GGlFPS", + "outputId": "b12dadb4-ccbc-45b4-bb08-3d24977ed93c" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-10-21 03:57:58-- https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.80.18, 2620:100:6018:18::a27d:312\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.80.18|:443... connected.\n", + "HTTP request sent, awaiting response... 301 Moved Permanently\n", + "Location: /s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip [following]\n", + "--2021-10-21 03:57:58-- https://www.dropbox.com/s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://ucfd8157272a6270e100392293da.dl.dropboxusercontent.com/cd/0/inline/BYbFG6Zo1S3l2kJtqLrJIne9lTLgQn-uoJxmUjhLSkp36V7AoiwlyR2gP0XVoUQt9WzF2ZsmeERagMy7rpsNoIYG4MjsYA90i_JsarFDs9PHhXHw9qwHpHqBvgd4YU_mwDQHuouJ_oCU1kft04QgCVRg/file# [following]\n", + "--2021-10-21 03:57:59-- https://ucfd8157272a6270e100392293da.dl.dropboxusercontent.com/cd/0/inline/BYbFG6Zo1S3l2kJtqLrJIne9lTLgQn-uoJxmUjhLSkp36V7AoiwlyR2gP0XVoUQt9WzF2ZsmeERagMy7rpsNoIYG4MjsYA90i_JsarFDs9PHhXHw9qwHpHqBvgd4YU_mwDQHuouJ_oCU1kft04QgCVRg/file\n", + "Resolving ucfd8157272a6270e100392293da.dl.dropboxusercontent.com (ucfd8157272a6270e100392293da.dl.dropboxusercontent.com)... 162.125.3.15, 2620:100:6018:15::a27d:30f\n", + "Connecting to ucfd8157272a6270e100392293da.dl.dropboxusercontent.com (ucfd8157272a6270e100392293da.dl.dropboxusercontent.com)|162.125.3.15|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: /cd/0/inline2/BYYSXb-0kWS7Lpk-cdrgBGzcOBfsvy7KjhqWEgjI5L9xfcaXohKlVeFMNFVyqvCwZLym2kWCD0nwURRpQ2mnHICrNsrvTvavbn24hk1Bd3_lXX08LBBe3C6YvD2U_iP8UMXROqm-B3JtnBjeMpk1R4YZ0O6aVLgKu0eET9RXsRaNCczD2lTK_i72zmbYhGmBvlRWmf_yQnnS5WKpGhSAobznIqKzw78yPzo5FsgGiEj5VXb91AElrKVAW8HFC9EhdUs7RrL3q9f0mQ9TbQpauoAp32TL3YQcuAp891Rv-EmDVxzfMwKVTGU8hxR2SiIWkse4u2QGhliqhdha7qBu7sIPcIoeI5-DdSoc6XG77vTYTRhrs_cf7rQuTPH2gTIUwTY/file [following]\n", + "--2021-10-21 03:57:59-- https://ucfd8157272a6270e100392293da.dl.dropboxusercontent.com/cd/0/inline2/BYYSXb-0kWS7Lpk-cdrgBGzcOBfsvy7KjhqWEgjI5L9xfcaXohKlVeFMNFVyqvCwZLym2kWCD0nwURRpQ2mnHICrNsrvTvavbn24hk1Bd3_lXX08LBBe3C6YvD2U_iP8UMXROqm-B3JtnBjeMpk1R4YZ0O6aVLgKu0eET9RXsRaNCczD2lTK_i72zmbYhGmBvlRWmf_yQnnS5WKpGhSAobznIqKzw78yPzo5FsgGiEj5VXb91AElrKVAW8HFC9EhdUs7RrL3q9f0mQ9TbQpauoAp32TL3YQcuAp891Rv-EmDVxzfMwKVTGU8hxR2SiIWkse4u2QGhliqhdha7qBu7sIPcIoeI5-DdSoc6XG77vTYTRhrs_cf7rQuTPH2gTIUwTY/file\n", + "Reusing existing connection to ucfd8157272a6270e100392293da.dl.dropboxusercontent.com:443.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 228802825 (218M) [application/zip]\n", + "Saving to: ‘cats_dogs_dataset.zip’\n", + "\n", + "cats_dogs_dataset.z 100%[===================>] 218.20M 86.3MB/s in 2.5s \n", + "\n", + "2021-10-21 03:58:02 (86.3 MB/s) - ‘cats_dogs_dataset.zip’ saved [228802825/228802825]\n", + "\n" + ] + } + ], + "source": [ + "# 下载分类数据集文件\n", + "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", + "!mkdir -p data\n", + "!unzip -qo cats_dogs_dataset.zip -d ./data/" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15iKNG0SlV9y" + }, + "source": [ + "### 读取配置文件并进行修改\n", + "\n", + "在 [tools 教程](https://colab.research.google.com/github/open-mmlab/mmclassification/blob/master/docs_zh-CN/tutorials/MMClassification_tools_cn.ipynb) 中，我们详细介绍了模型微调所需要修改的各部分配置文件，这里我们可以以 Python 代码的方式修改基础配置文件如下：" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "WCfnDavFlWrK" + }, + "outputs": [], + "source": [ + "# 载入已经存在的配置文件\n", + "from mmcv import Config\n", + "from mmcls.utils import auto_select_device\n", + "\n", + "cfg = Config.fromfile('configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py')\n", + "cfg.device = auto_select_device()\n", + "\n", + "# 修改模型分类头中的类别数目\n", + "cfg.model.head.num_classes = 2\n", + "cfg.model.head.topk = (1, )\n", + "\n", + "# 加载预训练权重\n", + "cfg.model.backbone.init_cfg = dict(type='Pretrained', checkpoint=checkpoint_file, prefix='backbone')\n", + "\n", + "# 根据你的电脑情况设置 sample size 和 workers \n", + "cfg.data.samples_per_gpu = 32\n", + "cfg.data.workers_per_gpu = 2\n", + "\n", + "# 指定训练集路径\n", + "cfg.data.train.data_prefix = 'data/cats_dogs_dataset/training_set/training_set'\n", + "cfg.data.train.classes = 'data/cats_dogs_dataset/classes.txt'\n", + "\n", + "# 指定验证集路径\n", + "cfg.data.val.data_prefix = 'data/cats_dogs_dataset/val_set/val_set'\n", + "cfg.data.val.ann_file = 'data/cats_dogs_dataset/val.txt'\n", + "cfg.data.val.classes = 'data/cats_dogs_dataset/classes.txt'\n", + "\n", + "# 指定测试集路径\n", + "cfg.data.test.data_prefix = 'data/cats_dogs_dataset/test_set/test_set'\n", + "cfg.data.test.ann_file = 'data/cats_dogs_dataset/test.txt'\n", + "cfg.data.test.classes = 'data/cats_dogs_dataset/classes.txt'\n", + "\n", + "# 设定数据集归一化参数\n", + "normalize_cfg = dict(type='Normalize', mean=[124.508, 116.050, 106.438], std=[58.577, 57.310, 57.437], to_rgb=True)\n", + "cfg.data.train.pipeline[3] = normalize_cfg\n", + "cfg.data.val.pipeline[3] = normalize_cfg\n", + "cfg.data.test.pipeline[3] = normalize_cfg\n", + "\n", + "# 修改评价指标选项\n", + "cfg.evaluation['metric_options']={'topk': (1, )}\n", + "\n", + "# 设置优化器\n", + "cfg.optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n", + "cfg.optimizer_config = dict(grad_clip=None)\n", + "\n", + "# 设置学习率策略\n", + "cfg.lr_config = dict(policy='step', step=1, gamma=0.1)\n", + "cfg.runner = dict(type='EpochBasedRunner', max_epochs=2)\n", + "\n", + "# 设置工作目录以保存模型和日志\n", + "cfg.work_dir = './work_dirs/cats_dogs_dataset'\n", + "\n", + "# 设置每 10 个训练批次输出一次日志\n", + "cfg.log_config.interval = 10\n", + "\n", + "# 设置随机种子，并启用 cudnn 确定性选项以保证结果的可重复性\n", + "from mmcls.apis import set_random_seed\n", + "cfg.seed = 0\n", + "set_random_seed(0, deterministic=True)\n", + "\n", + "cfg.gpu_ids = range(1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HDerVUPFmNR0" + }, + "source": [ + "### 模型微调\n", + "\n", + "基于我们修改的训练配置，开始对我们的数据集进行模型微调计算。 我们调用 `train_model` API 进行计算. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "P7unq5cNmN8G", + "outputId": "bf32711b-7bdf-45ee-8db5-e8699d3eff91" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:04:12,758 - mmcv - INFO - initialize MobileNetV2 with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth', 'prefix': 'backbone'}\n", + "2021-10-21 04:04:12,759 - mmcv - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n", + "2021-10-21 04:04:12,815 - mmcv - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n", + "2021-10-21 04:04:12,818 - mmcv - INFO - \n", + "backbone.conv1.conv.weight - torch.Size([32, 3, 3, 3]): 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"backbone.conv2.bn.weight - torch.Size([1280]): \n", + "PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth \n", + " \n", + "2021-10-21 04:04:13,054 - mmcv - INFO - \n", + "backbone.conv2.bn.bias - torch.Size([1280]): \n", + "PretrainedInit: load from https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth \n", + " \n", + "2021-10-21 04:04:13,055 - mmcv - INFO - \n", + "head.fc.weight - torch.Size([2, 1280]): \n", + "NormalInit: mean=0, std=0.01, bias=0 \n", + " \n", + "2021-10-21 04:04:13,057 - mmcv - INFO - \n", + "head.fc.bias - torch.Size([2]): \n", + "NormalInit: mean=0, std=0.01, bias=0 \n", + " \n", + "2021-10-21 04:04:13,408 - mmcls - INFO - Start running, host: root@cc5b42005207, work_dir: /content/mmclassification/work_dirs/cats_dogs_dataset\n", + "2021-10-21 04:04:13,412 - mmcls - INFO - Hooks will be executed in the following order:\n", + "before_run:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_epoch:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_iter:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + " -------------------- \n", + "after_train_iter:\n", + "(ABOVE_NORMAL) OptimizerHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "after_train_epoch:\n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_epoch:\n", + "(LOW ) IterTimerHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_epoch:\n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "2021-10-21 04:04:13,417 - mmcls - INFO - workflow: [('train', 1)], max: 2 epochs\n", + "2021-10-21 04:04:18,924 - mmcls - INFO - Epoch [1][10/201]\tlr: 5.000e-03, eta: 0:03:29, time: 0.535, data_time: 0.259, memory: 1709, loss: 0.3917\n", + "2021-10-21 04:04:21,743 - mmcls - INFO - Epoch [1][20/201]\tlr: 5.000e-03, eta: 0:02:35, time: 0.281, data_time: 0.019, memory: 1709, loss: 0.3508\n", + "2021-10-21 04:04:24,552 - mmcls - INFO - Epoch [1][30/201]\tlr: 5.000e-03, eta: 0:02:15, time: 0.280, data_time: 0.020, memory: 1709, loss: 0.3955\n", + "2021-10-21 04:04:27,371 - mmcls - INFO - Epoch [1][40/201]\tlr: 5.000e-03, eta: 0:02:04, time: 0.282, data_time: 0.021, memory: 1709, loss: 0.2485\n", + "2021-10-21 04:04:30,202 - mmcls - INFO - Epoch [1][50/201]\tlr: 5.000e-03, eta: 0:01:56, time: 0.283, data_time: 0.021, memory: 1709, loss: 0.4196\n", + "2021-10-21 04:04:33,021 - mmcls - INFO - Epoch [1][60/201]\tlr: 5.000e-03, eta: 0:01:50, time: 0.282, data_time: 0.023, memory: 1709, loss: 0.4994\n", + "2021-10-21 04:04:35,800 - mmcls - INFO - Epoch [1][70/201]\tlr: 5.000e-03, eta: 0:01:45, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.4372\n", + "2021-10-21 04:04:38,595 - mmcls - INFO - Epoch [1][80/201]\tlr: 5.000e-03, eta: 0:01:40, time: 0.280, data_time: 0.019, memory: 1709, loss: 0.3179\n", + "2021-10-21 04:04:41,351 - mmcls - INFO - Epoch [1][90/201]\tlr: 5.000e-03, eta: 0:01:36, time: 0.276, data_time: 0.018, memory: 1709, loss: 0.3175\n", + "2021-10-21 04:04:44,157 - mmcls - INFO - Epoch [1][100/201]\tlr: 5.000e-03, eta: 0:01:32, time: 0.280, data_time: 0.021, memory: 1709, loss: 0.3412\n", + "2021-10-21 04:04:46,974 - mmcls - INFO - Epoch [1][110/201]\tlr: 5.000e-03, eta: 0:01:28, time: 0.282, data_time: 0.019, memory: 1709, loss: 0.2985\n", + "2021-10-21 04:04:49,767 - mmcls - INFO - Epoch [1][120/201]\tlr: 5.000e-03, eta: 0:01:25, time: 0.280, data_time: 0.021, memory: 1709, loss: 0.2778\n", + "2021-10-21 04:04:52,553 - mmcls - INFO - Epoch [1][130/201]\tlr: 5.000e-03, eta: 0:01:21, time: 0.278, data_time: 0.021, memory: 1709, loss: 0.2229\n", + "2021-10-21 04:04:55,356 - mmcls - INFO - Epoch [1][140/201]\tlr: 5.000e-03, eta: 0:01:18, time: 0.280, data_time: 0.021, memory: 1709, loss: 0.2318\n", + "2021-10-21 04:04:58,177 - mmcls - INFO - Epoch [1][150/201]\tlr: 5.000e-03, eta: 0:01:14, time: 0.282, data_time: 0.022, memory: 1709, loss: 0.2333\n", + "2021-10-21 04:05:01,025 - mmcls - INFO - Epoch [1][160/201]\tlr: 5.000e-03, eta: 0:01:11, time: 0.285, data_time: 0.020, memory: 1709, loss: 0.2783\n", + "2021-10-21 04:05:03,833 - mmcls - INFO - Epoch [1][170/201]\tlr: 5.000e-03, eta: 0:01:08, time: 0.281, data_time: 0.022, memory: 1709, loss: 0.2132\n", + "2021-10-21 04:05:06,648 - mmcls - INFO - Epoch [1][180/201]\tlr: 5.000e-03, eta: 0:01:05, time: 0.281, data_time: 0.019, memory: 1709, loss: 0.2096\n", + "2021-10-21 04:05:09,472 - mmcls - INFO - Epoch [1][190/201]\tlr: 5.000e-03, eta: 0:01:02, time: 0.282, data_time: 0.020, memory: 1709, loss: 0.1729\n", + "2021-10-21 04:05:12,229 - mmcls - INFO - Epoch [1][200/201]\tlr: 5.000e-03, eta: 0:00:59, time: 0.275, data_time: 0.018, memory: 1709, loss: 0.1969\n", + "2021-10-21 04:05:12,275 - mmcls - INFO - Saving checkpoint at 1 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 104.1 task/s, elapsed: 15s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:05:27,767 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", + "2021-10-21 04:05:32,987 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:57, time: 0.505, data_time: 0.238, memory: 1709, loss: 0.1764\n", + "2021-10-21 04:05:35,779 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:54, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1514\n", + "2021-10-21 04:05:38,537 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:51, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1395\n", + "2021-10-21 04:05:41,283 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:48, time: 0.275, data_time: 0.020, memory: 1709, loss: 0.1508\n", + "2021-10-21 04:05:44,017 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:44, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.1771\n", + "2021-10-21 04:05:46,800 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:41, time: 0.278, data_time: 0.020, memory: 1709, loss: 0.1438\n", + "2021-10-21 04:05:49,570 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:38, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1321\n", + "2021-10-21 04:05:52,314 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:35, time: 0.275, data_time: 0.021, memory: 1709, loss: 0.1629\n", + "2021-10-21 04:05:55,052 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:32, time: 0.273, data_time: 0.021, memory: 1709, loss: 0.1574\n", + "2021-10-21 04:05:57,791 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:29, time: 0.274, data_time: 0.019, memory: 1709, loss: 0.1220\n", + "2021-10-21 04:06:00,534 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:26, time: 0.274, data_time: 0.021, memory: 1709, loss: 0.2550\n", + "2021-10-21 04:06:03,295 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:23, time: 0.276, data_time: 0.019, memory: 1709, loss: 0.1528\n", + "2021-10-21 04:06:06,048 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:20, time: 0.275, data_time: 0.022, memory: 1709, loss: 0.1223\n", + "2021-10-21 04:06:08,811 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:17, time: 0.276, data_time: 0.021, memory: 1709, loss: 0.1734\n", + "2021-10-21 04:06:11,576 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:14, time: 0.277, data_time: 0.020, memory: 1709, loss: 0.1527\n", + "2021-10-21 04:06:14,330 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:11, time: 0.276, data_time: 0.020, memory: 1709, loss: 0.1910\n", + "2021-10-21 04:06:17,106 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:09, time: 0.277, data_time: 0.019, memory: 1709, loss: 0.1922\n", + "2021-10-21 04:06:19,855 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:06, time: 0.274, data_time: 0.023, memory: 1709, loss: 0.1760\n", + "2021-10-21 04:06:22,638 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.278, data_time: 0.019, memory: 1709, loss: 0.1739\n", + "2021-10-21 04:06:25,367 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.272, data_time: 0.020, memory: 1709, loss: 0.1654\n", + "2021-10-21 04:06:25,410 - mmcls - INFO - Saving checkpoint at 2 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>] 1601/1601, 105.5 task/s, elapsed: 15s, ETA: 0s" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-10-21 04:06:40,694 - mmcls - INFO - Epoch(val) [2][51]\taccuracy_top-1: 97.5016\n" + ] + } + ], + "source": [ + "import time\n", + "import mmcv\n", + "import os.path as osp\n", + "\n", + "from mmcls.datasets import build_dataset\n", + "from mmcls.models import build_classifier\n", + "from mmcls.apis import train_model\n", + "\n", + "# 创建工作目录\n", + "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", + "# 创建分类器\n", + "model = build_classifier(cfg.model)\n", + "model.init_weights()\n", + "# 创建数据集\n", + "datasets = [build_dataset(cfg.data.train)]\n", + "# 添加类别属性以方便可视化\n", + "model.CLASSES = datasets[0].CLASSES\n", + "# 开始微调\n", + "train_model(\n", + " model,\n", + " datasets,\n", + " cfg,\n", + " distributed=False,\n", + " validate=True,\n", + " timestamp=time.strftime('%Y%m%d_%H%M%S', time.localtime()),\n", + " meta=dict())" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "HsoGBZA3miui", + "outputId": "eb2e09f5-55ce-4165-b754-3b75dbc829ab" + }, + "outputs": [ + { + "data": { + "image/png": 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", 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null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + } + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb b/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb new file mode 100755 index 0000000..1914956 --- /dev/null +++ b/docs/zh_CN/tutorials/MMClassification_tools_cn.ipynb @@ -0,0 +1,1247 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "MMClassification_tools_cn.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "XjQxmm04iTx4", + "tags": [] + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4z0JDgisPRr-" + }, + "source": [ + "# MMClassification 命令行工具教程\n", + "\n", + "在本教程中会介绍如下内容：\n", + "\n", + "* 如何安装 MMClassification\n", + "* 准备数据\n", + "* 准备配置文件\n", + "* 使用 shell 命令进行模型训练和测试" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "inm7Ciy5PXrU" + }, + "source": [ + "## 安装 MMClassification\n", + "\n", + "在使用 MMClassification 之前，我们需要配置环境，步骤如下：\n", + "\n", + "- 安装 Python, CUDA, C/C++ compiler 和 git\n", + "- 安装 PyTorch (CUDA 版)\n", + "- 安装 mmcv\n", + "- 克隆 mmcls github 代码库然后安装\n", + "\n", + "因为我们在 Google Colab 进行实验，Colab 已经帮我们完成了基本的配置，我们可以直接跳过前面两个步骤 。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TDOxbcDvPbNk" + }, + "source": [ + "### 检查环境" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "c6MbAw10iUJI", + "outputId": "5f95ad09-7b96-4d27-dfa8-17f31caba50d" + }, + "source": [ + "%cd /content" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4IyFL3MaiYRu", + "outputId": "b0ab6848-12ea-49a1-98ec-691e2c9814e1" + }, + "source": [ + "!pwd" + ], + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/content\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DMw7QwvpiiUO", + "outputId": "d699b9d2-22e5-431c-83d8-9317a694cb0e" + }, + "source": [ + "# 检查 nvcc 版本\n", + "!nvcc -V" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2020 NVIDIA Corporation\n", + "Built on Mon_Oct_12_20:09:46_PDT_2020\n", + "Cuda compilation tools, release 11.1, V11.1.105\n", + "Build cuda_11.1.TC455_06.29190527_0\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4VIBU7Fain4D", + "outputId": "7eb1d91f-86c7-43cf-d335-3d37ae014060" + }, + "source": [ + "# 检查 GCC 版本\n", + "!gcc --version" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "24lDLCqFisZ9", + "outputId": "3c553c42-e7ac-4c6a-863e-13ad158bac22" + }, + "source": [ + "# 检查 PyTorch 的安装情况\n", + "import torch, torchvision\n", + "print(torch.__version__)\n", + "print(torch.cuda.is_available())" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1.9.0+cu111\n", + "True\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R2aZNLUwizBs" + }, + "source": [ + "### 安装 MMCV\n", + "\n", + "MMCV 是 OpenMMLab 代码库的基础库。Linux 环境的安装 whl 包已经提前打包好，大家可以直接下载安装。\n", + "\n", + "需要注意 PyTorch 和 CUDA 版本，确保能够正常安装。\n", + "\n", + "在前面的步骤中，我们输出了环境中 CUDA 和 PyTorch 的版本，分别是 11.1 和 1.9.0，我们需要选择相应的 MMCV 版本。\n", + "\n", + "另外，也可以安装完整版的 MMCV-full，它包含所有的特性以及丰富的开箱即用的 CUDA 算子。需要注意的是完整版本可能需要更长时间来编译。" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nla40LrLi7oo", + "outputId": "475dcd11-0b58-45d3-ad81-a3b7772d3132" + }, + "source": [ + "# 安装 mmcv\n", + "!pip install mmcv -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "# !pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n", + "Collecting mmcv\n", + " Downloading mmcv-1.3.15.tar.gz (352 kB)\n", + "\u001b[K |████████████████████████████████| 352 kB 5.2 MB/s \n", + "\u001b[?25hCollecting addict\n", + " Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv) (1.19.5)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv) (21.0)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv) (7.1.2)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv) (3.13)\n", + "Collecting yapf\n", + " Downloading yapf-0.31.0-py2.py3-none-any.whl (185 kB)\n", + "\u001b[K |████████████████████████████████| 185 kB 45.4 MB/s \n", + "\u001b[?25hRequirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv) (2.4.7)\n", + "Building wheels for collected packages: mmcv\n", + " Building wheel for mmcv (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for mmcv: filename=mmcv-1.3.15-py2.py3-none-any.whl size=509835 sha256=0296cfd1e3e858ece30623050be2953941a442daf0575389030aa25603e5c205\n", + " Stored in directory: /root/.cache/pip/wheels/b2/f4/4e/8f6d2dd2bef6b7eb8c89aa0e5d61acd7bff60aaf3d4d4b29b0\n", + "Successfully built mmcv\n", + "Installing collected packages: yapf, addict, mmcv\n", + "Successfully installed addict-2.4.0 mmcv-1.3.15 yapf-0.31.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GDTUrYvXjlRb" + }, + "source": [ + "### 克隆并安装 MMClassification\n", + "\n", + "接着，我们从 github 上克隆下 mmcls 最新代码库并进行安装。" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Bwme6tWHjl5s", + "outputId": "07c0ca6f-8a10-4ac3-a6bc-afabff6aba51" + }, + "source": [ + "# 下载 mmcls 代码库\n", + "!git clone https://github.com/open-mmlab/mmclassification.git\n", + "%cd mmclassification/\n", + "\n", + "# 从源码安装 MMClassification\n", + "!pip install -e . " + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'mmclassification'...\n", + "remote: Enumerating objects: 4152, done.\u001b[K\n", + "remote: Counting objects: 100% (994/994), done.\u001b[K\n", + "remote: Compressing objects: 100% (574/574), done.\u001b[K\n", + "remote: Total 4152 (delta 476), reused 764 (delta 403), pack-reused 3158\u001b[K\n", + "Receiving objects: 100% (4152/4152), 8.20 MiB | 20.90 MiB/s, done.\n", + "Resolving deltas: 100% (2525/2525), done.\n", + "/content/mmclassification\n", + "Obtaining file:///content/mmclassification\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmcls==0.16.0) (3.2.2)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcls==0.16.0) (1.19.5)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcls==0.16.0) (21.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (1.3.2)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (0.10.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmcls==0.16.0) (2.4.7)\n", + "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->mmcls==0.16.0) (1.15.0)\n", + "Installing collected packages: mmcls\n", + " Running setup.py develop for mmcls\n", + "Successfully installed mmcls-0.16.0\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hFg_oSG4j3zB", + "outputId": "521a6a75-2dbb-4ff2-ab9f-4fbe785b4400" + }, + "source": [ + "# 检查 MMClassification 的安装情况\n", + "import mmcls\n", + "print(mmcls.__version__)" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.16.0\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "arpM46CZOPtR" + }, + "source": [ + "## 准备数据" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XHCHnKb_Qd3P", + "outputId": "4f6eaa3f-7b96-46e4-e75b-aae28c8ec42d" + }, + "source": [ + "# 下载分类数据集文件 （猫狗数据集）\n", + "!wget https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0 -O cats_dogs_dataset.zip\n", + "!mkdir data\n", + "!unzip -q cats_dogs_dataset.zip -d ./data/" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2021-10-21 02:53:27-- https://www.dropbox.com/s/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip?dl=0\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.3.18, 2620:100:6018:18::a27d:312\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.3.18|:443... connected.\n", + "HTTP request sent, awaiting response... 301 Moved Permanently\n", + "Location: /s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip [following]\n", + "--2021-10-21 02:53:27-- https://www.dropbox.com/s/raw/wml49yrtdo53mie/cats_dogs_dataset_reorg.zip\n", + "Reusing existing connection to www.dropbox.com:443.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc2e142222b11f678e96f89b0223.dl.dropboxusercontent.com/cd/0/inline/BYaBa5-WWfPf_jhSt9A5JMet_BB55MzZhB2D3RXLo53VGHSIYbVMnFTdccihcsD-kwc9FxBG8qOwqA50z7XD6-3yUXWK9iA0x4L8IV5wegYKilKuDauDKWiNAsbgZoEBg4nC1UWR5pLSiH3j0Dn68b2V/file# [following]\n", + "--2021-10-21 02:53:27-- 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"│ │ │ ├── cat.5.jpg\n", + "│ │ │ ├── ...\n", + "│ │ ├── dogs\n", + "│ │ │ ├── dog.1.jpg\n", + "│ │ │ ├── dog.6.jpg\n", + "│ │ │ ├── ...\n", + "├── test_set\n", + "│ ├── test_set\n", + "│ │ ├── cats\n", + "│ │ │ ├── cat.4001.jpg\n", + "│ │ │ ├── cat.4002.jpg\n", + "│ │ │ ├── ...\n", + "│ │ ├── dogs\n", + "│ │ │ ├── dog.4001.jpg\n", + "│ │ │ ├── dog.4002.jpg\n", + "│ │ │ ├── ...\n", + "```\n", + "\n", + "可以通过 shell 命令 `tree data/cats_dogs_dataset` 查看文件结构。" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "id": "46tyHTdtQy_Z", + "outputId": "a6e89ddb-431e-4ba0-f1f5-3581a702fd2a" + }, + "source": [ + "# 获取一张图像可视化\n", + "from PIL import Image\n", + "Image.open('data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg')" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "My5Z6p7pQ3UC" + }, + "source": [ + "### 支持新的数据集\n", + "\n", + "MMClassification 要求数据集必须将图像和标签放在同级目录下。有两种方式可以支持自定义数据集。\n", + "\n", + "最简单的方式就是将数据集转换成现有的数据集格式（比如 ImageNet）。另一种方式就是新建一个新的数据集类。细节可以查看 [文档](https://github.com/open-mmlab/mmclassification/blob/master/docs_zh-CN/tutorials/new_dataset.md).\n", + "\n", + "在这个教程中，为了方便学习，我们已经将 “猫狗分类数据集” 按照 ImageNet 的数据集格式进行了整理。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P335gKt9Q5U-" + }, + "source": [ + "除了图片文件外，数据集还包括以下文件：\n", + "\n", + "1. 类别列表。每行代表一个类别。\n", + " ```\n", + " cats\n", + " dogs\n", + " ```\n", + "2. 训练/验证/测试标签。\n", + "每行包括一个文件名和其相对应的标签。\n", + " ```\n", + " ...\n", + " cats/cat.3769.jpg 0\n", + " cats/cat.882.jpg 0\n", + " ...\n", + " dogs/dog.3881.jpg 1\n", + " dogs/dog.3377.jpg 1\n", + " ...\n", + " ```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BafQ7ijBQ8N_" + }, + "source": [ + "## 使用 shell 命令进行模型训练和测试\n", + "\n", + "MMCls 同样提供了命令行工具，提供如下功能:\n", + "\n", + "1. 模型训练\n", + "2. 模型微调\n", + "3. 模型测试\n", + "4. 推理计算\n", + "\n", + "模型训练的过程与模型微调的过程一致，我们已经看到 Python API 的推理和模型微调过程。接下来我们将会看到如何使用命令行工具完成这些任务。更过细节可以参考 [文档](https://github.com/open-mmlab/mmclassification/blob/master/docs_zh-CN/getting_started.md)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Aj5cGMihURrZ" + }, + "source": [ + "### 模型微调\n", + "\n", + "通过命令行进行模型微调步骤如下：\n", + "\n", + "1. 准备自定义数据集\n", + "2. 在 py 脚本中修改配置文件\n", + "3. 使用命令行工具进行模型微调\n", + "\n", + "第 1 步与之前的介绍一致，我们将会介绍后面两个步骤的内容。" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wl-FNFP8O0dh" + }, + "source": [ + "#### 创建一个新的配置文件\n", + "\n", + "为了能够复用不同配置文件中常用的部分，我们支持多配置文件继承。比如模型微调 MobileNetV2 ，新的配置文件可以通过继承 `configs/_base_/models/mobilenet_v2_1x.py` 来创建模型的基本结构。\n", + "\n", + "根据以往的实践，我们通常把完整的配置拆分成四个部分：模型、数据集、优化器、运行设置。每个部分的配置单独保存到一个文件，并放在 `config/_base_` 的对应目录下。\n", + "\n", + "这样一来，在创建新的配置文件时，我们就可以选择继承若干个需要的配置文件，然后覆盖其中需要修改的部分内容。\n", + "\n", + "我们的新配置文件开头的继承部分为：\n", + "\n", + "```python\n", + "_base_ = [\n", + " '../_base_/models/mobilenet_v2_1x.py',\n", + " '../_base_/schedules/imagenet_bs256_epochstep.py',\n", + " '../_base_/default_runtime.py'\n", + "]\n", + "```\n", + "\n", + "这里，因为我们使用了一个新的数据集，所以没有继承任何数据集相关的配置。\n", + "\n", + "此外，也可以不使用这种继承的方式，而直接构建完整的配置文件，比如 `configs/mnist/lenet5.py`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_UV3oBhLRG8B" + }, + "source": [ + "之后，我们只需要设定配置文件中我们希望修改的部分，其他部分的设置会自动从继承的配置文件中读取。" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8QfM4qBeWIQh", + "outputId": "0e658dca-722e-4bed-dd0b-601731b00457" + }, + "source": [ + "%%writefile configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py\n", + "_base_ = [\n", + " '../_base_/models/mobilenet_v2_1x.py',\n", + " '../_base_/schedules/imagenet_bs256_epochstep.py',\n", + " '../_base_/default_runtime.py'\n", + "]\n", + "\n", + "# ---- 模型配置 ----\n", + "# 这里使用 init_cfg 来加载预训练模型，通过这种方式，只有主干网络的权重会被加载。\n", + "# 另外还修改了分类头部的 num_classes 来匹配我们的数据集。\n", + "\n", + "model = dict(\n", + " backbone=dict(\n", + " init_cfg = dict(\n", + " type='Pretrained', \n", + " checkpoint='https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth', \n", + " prefix='backbone')\n", + " ),\n", + " head=dict(\n", + " num_classes=2,\n", + " topk = (1, )\n", + " ))\n", + "\n", + "# ---- 数据集配置 ----\n", + "# 我们已经将数据集重新组织为 ImageNet 格式\n", + "dataset_type = 'ImageNet'\n", + "img_norm_cfg = dict(\n", + " mean=[124.508, 116.050, 106.438],\n", + " std=[58.577, 57.310, 57.437],\n", + " to_rgb=True)\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='RandomResizedCrop', size=224, backend='pillow'),\n", + " dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n", + " dict(type='Normalize', **img_norm_cfg),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='ToTensor', keys=['gt_label']),\n", + " dict(type='Collect', keys=['img', 'gt_label'])\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(type='Normalize', **img_norm_cfg),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + "]\n", + "data = dict(\n", + " # 设置每个 GPU 上的 batch size 和 workers 数, 根据你的硬件来修改这些选项。\n", + " samples_per_gpu=32,\n", + " workers_per_gpu=2,\n", + " # 指定训练集类型和路径\n", + " train=dict(\n", + " type=dataset_type,\n", + " data_prefix='data/cats_dogs_dataset/training_set/training_set',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=train_pipeline),\n", + " # 指定验证集类型和路径\n", + " val=dict(\n", + " type=dataset_type,\n", + " data_prefix='data/cats_dogs_dataset/val_set/val_set',\n", + " ann_file='data/cats_dogs_dataset/val.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=test_pipeline),\n", + " # 指定测试集类型和路径\n", + " test=dict(\n", + " type=dataset_type,\n", + " data_prefix='data/cats_dogs_dataset/test_set/test_set',\n", + " ann_file='data/cats_dogs_dataset/test.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=test_pipeline))\n", + "\n", + "# 设置验证指标\n", + "evaluation = dict(metric='accuracy', metric_options={'topk': (1, )})\n", + "\n", + "# ---- 优化器设置 ----\n", + "# 通常在微调任务中，我们需要一个较小的学习率，训练轮次可以较短。\n", + "# 设置学习率\n", + "optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n", + "optimizer_config = dict(grad_clip=None)\n", + "# 设置学习率调度器\n", + "lr_config = dict(policy='step', step=1, gamma=0.1)\n", + "runner = dict(type='EpochBasedRunner', max_epochs=2)\n", + "\n", + "# ---- 运行设置 ----\n", + "# 每 10 个训练批次输出一次日志\n", + "log_config = dict(interval=10)" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Writing configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "chLX7bL3RP2F" + }, + "source": [ + "#### 使用命令行进行模型微调\n", + "\n", + "我们使用 `tools/train.py` 进行模型微调:\n", + "\n", + "```\n", + "python tools/train.py ${CONFIG_FILE} [optional arguments]\n", + "```\n", + "\n", + "如果你希望指定训练过程中相关文件的保存位置，可以增加一个参数 `--work_dir ${YOUR_WORK_DIR}`.\n", + "\n", + "通过增加参数 `--seed ${SEED}`，设置随机种子以保证结果的可重复性，而参数 `--deterministic`则会启用 cudnn 的确定性选项，进一步保证可重复性，但可能降低些许效率。\n", + "\n", + "这里我们使用 `MobileNetV2` 和数据集 `CatsDogsDataset` 作为示例" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gbFGR4SBRUYN", + "outputId": "66019f0f-2ded-4fae-9a5f-ece9729a7c2d" + }, + "source": [ + "!python tools/train.py \\\n", + " configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py \\\n", + " --work-dir work_dirs/mobilenet_v2_1x_cats_dogs \\\n", + " --seed 0 \\\n", + " --deterministic" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n", + "2021-10-21 02:53:42,465 - mmcls - INFO - Environment info:\n", + "------------------------------------------------------------\n", + "sys.platform: linux\n", + "Python: 3.7.12 (default, Sep 10 2021, 00:21:48) [GCC 7.5.0]\n", + "CUDA available: True\n", + "GPU 0: Tesla K80\n", + "CUDA_HOME: /usr/local/cuda\n", + "NVCC: Build cuda_11.1.TC455_06.29190527_0\n", + "GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "PyTorch: 1.9.0+cu111\n", + "PyTorch compiling details: PyTorch built with:\n", + " - GCC 7.3\n", + " - C++ Version: 201402\n", + " - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\n", + " - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\n", + " - OpenMP 201511 (a.k.a. OpenMP 4.5)\n", + " - NNPACK is enabled\n", + " - CPU capability usage: AVX2\n", + " - CUDA Runtime 11.1\n", + " - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\n", + " - CuDNN 8.0.5\n", + " - Magma 2.5.2\n", + " - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \n", + "\n", + "TorchVision: 0.10.0+cu111\n", + "OpenCV: 4.1.2\n", + "MMCV: 1.3.15\n", + "MMCV Compiler: n/a\n", + "MMCV CUDA Compiler: n/a\n", + "MMClassification: 0.16.0+77a3834\n", + "------------------------------------------------------------\n", + "\n", + "2021-10-21 02:53:42,465 - mmcls - INFO - Distributed training: False\n", + "2021-10-21 02:53:43,086 - mmcls - INFO - Config:\n", + "model = dict(\n", + " type='ImageClassifier',\n", + " backbone=dict(\n", + " type='MobileNetV2',\n", + " widen_factor=1.0,\n", + " init_cfg=dict(\n", + " type='Pretrained',\n", + " checkpoint=\n", + " 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth',\n", + " prefix='backbone')),\n", + " neck=dict(type='GlobalAveragePooling'),\n", + " head=dict(\n", + " type='LinearClsHead',\n", + " num_classes=2,\n", + " in_channels=1280,\n", + " loss=dict(type='CrossEntropyLoss', loss_weight=1.0),\n", + " topk=(1, )))\n", + "optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)\n", + "optimizer_config = dict(grad_clip=None)\n", + "lr_config = dict(policy='step', gamma=0.1, step=1)\n", + "runner = dict(type='EpochBasedRunner', max_epochs=2)\n", + "checkpoint_config = dict(interval=1)\n", + "log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])\n", + "dist_params = dict(backend='nccl')\n", + "log_level = 'INFO'\n", + "load_from = None\n", + "resume_from = None\n", + "workflow = [('train', 1)]\n", + "dataset_type = 'ImageNet'\n", + "img_norm_cfg = dict(\n", + " mean=[124.508, 116.05, 106.438], std=[58.577, 57.31, 57.437], to_rgb=True)\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='RandomResizedCrop', size=224, backend='pillow'),\n", + " dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='ToTensor', keys=['gt_label']),\n", + " dict(type='Collect', keys=['img', 'gt_label'])\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + "]\n", + "data = dict(\n", + " samples_per_gpu=32,\n", + " workers_per_gpu=2,\n", + " train=dict(\n", + " type='ImageNet',\n", + " data_prefix='data/cats_dogs_dataset/training_set/training_set',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='RandomResizedCrop', size=224, backend='pillow'),\n", + " dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='ToTensor', keys=['gt_label']),\n", + " dict(type='Collect', keys=['img', 'gt_label'])\n", + " ]),\n", + " val=dict(\n", + " type='ImageNet',\n", + " data_prefix='data/cats_dogs_dataset/val_set/val_set',\n", + " ann_file='data/cats_dogs_dataset/val.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ]),\n", + " test=dict(\n", + " type='ImageNet',\n", + " data_prefix='data/cats_dogs_dataset/test_set/test_set',\n", + " ann_file='data/cats_dogs_dataset/test.txt',\n", + " classes='data/cats_dogs_dataset/classes.txt',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='Resize', size=(256, -1), backend='pillow'),\n", + " dict(type='CenterCrop', crop_size=224),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[124.508, 116.05, 106.438],\n", + " std=[58.577, 57.31, 57.437],\n", + " to_rgb=True),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ]))\n", + "evaluation = dict(metric='accuracy', metric_options=dict(topk=(1, )))\n", + "work_dir = 'work_dirs/mobilenet_v2_1x_cats_dogs'\n", + "gpu_ids = range(0, 1)\n", + "\n", + "2021-10-21 02:53:43,086 - mmcls - INFO - Set random seed to 0, deterministic: True\n", + "2021-10-21 02:53:43,251 - mmcls - INFO - initialize MobileNetV2 with init_cfg {'type': 'Pretrained', 'checkpoint': 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth', 'prefix': 'backbone'}\n", + "2021-10-21 02:53:43,252 - mmcv - INFO - load backbone in model from: https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n", + "Use load_from_http loader\n", + "Downloading: \"https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\" to /root/.cache/torch/hub/checkpoints/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth\n", + "100% 13.5M/13.5M [00:01<00:00, 9.62MB/s]\n", + "2021-10-21 02:53:46,164 - mmcls - INFO - initialize LinearClsHead with init_cfg {'type': 'Normal', 'layer': 'Linear', 'std': 0.01}\n", + "2021-10-21 02:54:01,365 - mmcls - INFO - Start running, host: root@3a8df14fab46, work_dir: /content/mmclassification/work_dirs/mobilenet_v2_1x_cats_dogs\n", + "2021-10-21 02:54:01,365 - mmcls - INFO - Hooks will be executed in the following order:\n", + "before_run:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_epoch:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_train_iter:\n", + "(VERY_HIGH ) StepLrUpdaterHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + " -------------------- \n", + "after_train_iter:\n", + "(ABOVE_NORMAL) OptimizerHook \n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) IterTimerHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "after_train_epoch:\n", + "(NORMAL ) CheckpointHook \n", + "(LOW ) EvalHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_epoch:\n", + "(LOW ) IterTimerHook \n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "before_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_iter:\n", + "(LOW ) IterTimerHook \n", + " -------------------- \n", + "after_val_epoch:\n", + "(VERY_LOW ) TextLoggerHook \n", + " -------------------- \n", + "2021-10-21 02:54:01,365 - mmcls - INFO - workflow: [('train', 1)], max: 2 epochs\n", + "2021-10-21 02:54:07,010 - mmcls - INFO - Epoch [1][10/201]\tlr: 5.000e-03, eta: 0:03:34, time: 0.548, data_time: 0.260, memory: 1709, loss: 0.3917\n", + "2021-10-21 02:54:09,888 - mmcls - INFO - Epoch [1][20/201]\tlr: 5.000e-03, eta: 0:02:39, time: 0.288, data_time: 0.021, memory: 1709, loss: 0.3508\n", + "2021-10-21 02:54:12,795 - mmcls - INFO - Epoch [1][30/201]\tlr: 5.000e-03, eta: 0:02:19, time: 0.291, data_time: 0.020, memory: 1709, loss: 0.3955\n", + "2021-10-21 02:54:15,744 - mmcls - INFO - Epoch [1][40/201]\tlr: 5.000e-03, eta: 0:02:08, time: 0.295, data_time: 0.019, memory: 1709, loss: 0.2485\n", + "2021-10-21 02:54:18,667 - mmcls - INFO - Epoch [1][50/201]\tlr: 5.000e-03, eta: 0:02:00, time: 0.292, data_time: 0.021, memory: 1709, loss: 0.4196\n", + "2021-10-21 02:54:21,590 - mmcls - INFO - Epoch [1][60/201]\tlr: 5.000e-03, eta: 0:01:54, time: 0.293, data_time: 0.022, memory: 1709, loss: 0.4994\n", + "2021-10-21 02:54:24,496 - mmcls - INFO - Epoch [1][70/201]\tlr: 5.000e-03, eta: 0:01:48, time: 0.291, data_time: 0.021, memory: 1709, loss: 0.4372\n", + "2021-10-21 02:54:27,400 - mmcls - INFO - Epoch [1][80/201]\tlr: 5.000e-03, eta: 0:01:44, time: 0.290, data_time: 0.020, memory: 1709, loss: 0.3179\n", + "2021-10-21 02:54:30,313 - mmcls - INFO - Epoch [1][90/201]\tlr: 5.000e-03, eta: 0:01:39, time: 0.292, data_time: 0.020, memory: 1709, loss: 0.3175\n", + "2021-10-21 02:54:33,208 - mmcls - INFO - Epoch [1][100/201]\tlr: 5.000e-03, eta: 0:01:35, time: 0.289, data_time: 0.020, memory: 1709, loss: 0.3412\n", + "2021-10-21 02:54:36,129 - mmcls - INFO - Epoch [1][110/201]\tlr: 5.000e-03, eta: 0:01:31, time: 0.292, data_time: 0.021, memory: 1709, loss: 0.2985\n", + "2021-10-21 02:54:39,067 - mmcls - INFO - Epoch [1][120/201]\tlr: 5.000e-03, eta: 0:01:28, time: 0.294, data_time: 0.021, memory: 1709, loss: 0.2778\n", + "2021-10-21 02:54:41,963 - mmcls - INFO - Epoch [1][130/201]\tlr: 5.000e-03, eta: 0:01:24, time: 0.289, data_time: 0.020, memory: 1709, loss: 0.2229\n", + "2021-10-21 02:54:44,861 - mmcls - INFO - Epoch [1][140/201]\tlr: 5.000e-03, eta: 0:01:21, time: 0.290, data_time: 0.021, memory: 1709, loss: 0.2318\n", + "2021-10-21 02:54:47,782 - mmcls - INFO - Epoch [1][150/201]\tlr: 5.000e-03, eta: 0:01:17, time: 0.293, data_time: 0.020, memory: 1709, loss: 0.2333\n", + "2021-10-21 02:54:50,682 - mmcls - INFO - Epoch [1][160/201]\tlr: 5.000e-03, eta: 0:01:14, time: 0.290, data_time: 0.020, memory: 1709, loss: 0.2783\n", + "2021-10-21 02:54:53,595 - mmcls - INFO - Epoch [1][170/201]\tlr: 5.000e-03, eta: 0:01:11, time: 0.291, data_time: 0.019, memory: 1709, loss: 0.2132\n", + "2021-10-21 02:54:56,499 - mmcls - INFO - Epoch [1][180/201]\tlr: 5.000e-03, eta: 0:01:07, time: 0.290, data_time: 0.021, memory: 1709, loss: 0.2096\n", + "2021-10-21 02:54:59,381 - mmcls - INFO - Epoch [1][190/201]\tlr: 5.000e-03, eta: 0:01:04, time: 0.288, data_time: 0.023, memory: 1709, loss: 0.1729\n", + "2021-10-21 02:55:02,270 - mmcls - INFO - Epoch [1][200/201]\tlr: 5.000e-03, eta: 0:01:01, time: 0.288, data_time: 0.020, memory: 1709, loss: 0.1969\n", + "2021-10-21 02:55:02,313 - mmcls - INFO - Saving checkpoint at 1 epochs\n", + "[ ] 0/1601, elapsed: 0s, ETA:[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n", + "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n", + "[>>] 1601/1601, 171.8 task/s, elapsed: 9s, ETA: 0s2021-10-21 02:55:11,743 - mmcls - INFO - Epoch(val) [1][51]\taccuracy_top-1: 95.6277\n", + "2021-10-21 02:55:16,920 - mmcls - INFO - Epoch [2][10/201]\tlr: 5.000e-04, eta: 0:00:59, time: 0.501, data_time: 0.237, memory: 1709, loss: 0.1764\n", + "2021-10-21 02:55:19,776 - mmcls - INFO - Epoch [2][20/201]\tlr: 5.000e-04, eta: 0:00:56, time: 0.286, data_time: 0.021, memory: 1709, loss: 0.1514\n", + "2021-10-21 02:55:22,637 - mmcls - INFO - Epoch [2][30/201]\tlr: 5.000e-04, eta: 0:00:52, time: 0.286, data_time: 0.019, memory: 1709, loss: 0.1395\n", + "2021-10-21 02:55:25,497 - mmcls - INFO - Epoch [2][40/201]\tlr: 5.000e-04, eta: 0:00:49, time: 0.286, data_time: 0.020, memory: 1709, loss: 0.1508\n", + "2021-10-21 02:55:28,338 - mmcls - INFO - Epoch [2][50/201]\tlr: 5.000e-04, eta: 0:00:46, time: 0.284, data_time: 0.018, memory: 1709, loss: 0.1771\n", + "2021-10-21 02:55:31,214 - mmcls - INFO - Epoch [2][60/201]\tlr: 5.000e-04, eta: 0:00:43, time: 0.287, data_time: 0.019, memory: 1709, loss: 0.1438\n", + "2021-10-21 02:55:34,075 - mmcls - INFO - Epoch [2][70/201]\tlr: 5.000e-04, eta: 0:00:40, time: 0.286, data_time: 0.020, memory: 1709, loss: 0.1321\n", + "2021-10-21 02:55:36,921 - mmcls - INFO - Epoch [2][80/201]\tlr: 5.000e-04, eta: 0:00:36, time: 0.285, data_time: 0.023, memory: 1709, loss: 0.1629\n", + "2021-10-21 02:55:39,770 - mmcls - INFO - Epoch [2][90/201]\tlr: 5.000e-04, eta: 0:00:33, time: 0.285, data_time: 0.018, memory: 1709, loss: 0.1574\n", + "2021-10-21 02:55:42,606 - mmcls - INFO - Epoch [2][100/201]\tlr: 5.000e-04, eta: 0:00:30, time: 0.284, data_time: 0.019, memory: 1709, loss: 0.1220\n", + "2021-10-21 02:55:45,430 - mmcls - INFO - Epoch [2][110/201]\tlr: 5.000e-04, eta: 0:00:27, time: 0.282, data_time: 0.021, memory: 1709, loss: 0.2550\n", + "2021-10-21 02:55:48,280 - mmcls - INFO - Epoch [2][120/201]\tlr: 5.000e-04, eta: 0:00:24, time: 0.285, data_time: 0.021, memory: 1709, loss: 0.1528\n", + "2021-10-21 02:55:51,131 - mmcls - INFO - Epoch [2][130/201]\tlr: 5.000e-04, eta: 0:00:21, time: 0.285, data_time: 0.020, memory: 1709, loss: 0.1223\n", + "2021-10-21 02:55:53,983 - mmcls - INFO - Epoch [2][140/201]\tlr: 5.000e-04, eta: 0:00:18, time: 0.285, data_time: 0.019, memory: 1709, loss: 0.1734\n", + "2021-10-21 02:55:56,823 - mmcls - INFO - Epoch [2][150/201]\tlr: 5.000e-04, eta: 0:00:15, time: 0.284, data_time: 0.022, memory: 1709, loss: 0.1527\n", + "2021-10-21 02:55:59,645 - mmcls - INFO - Epoch [2][160/201]\tlr: 5.000e-04, eta: 0:00:12, time: 0.283, data_time: 0.021, memory: 1709, loss: 0.1910\n", + "2021-10-21 02:56:02,514 - mmcls - INFO - Epoch [2][170/201]\tlr: 5.000e-04, eta: 0:00:09, time: 0.287, data_time: 0.019, memory: 1709, loss: 0.1922\n", + "2021-10-21 02:56:05,375 - mmcls - INFO - Epoch [2][180/201]\tlr: 5.000e-04, eta: 0:00:06, time: 0.286, data_time: 0.018, memory: 1709, loss: 0.1760\n", + "2021-10-21 02:56:08,241 - mmcls - INFO - Epoch [2][190/201]\tlr: 5.000e-04, eta: 0:00:03, time: 0.287, data_time: 0.019, memory: 1709, loss: 0.1739\n", + "2021-10-21 02:56:11,081 - mmcls - INFO - Epoch [2][200/201]\tlr: 5.000e-04, eta: 0:00:00, time: 0.282, data_time: 0.019, memory: 1709, loss: 0.1654\n", + "2021-10-21 02:56:11,125 - mmcls - INFO - Saving checkpoint at 2 epochs\n", + "[>>] 1601/1601, 170.9 task/s, elapsed: 9s, ETA: 0s2021-10-21 02:56:20,592 - mmcls - INFO - Epoch(val) [2][51]\taccuracy_top-1: 97.5016\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m_ZSkwB5Rflb" + }, + "source": [ + "### 测试模型\n", + "\n", + "使用 `tools/test.py` 对模型进行测试:\n", + "\n", + "```\n", + "python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]\n", + "```\n", + "\n", + "这里有一些可选参数可以进行配置:\n", + "\n", + "- `--metrics`: 评价指标。可以在数据集类中找到所有可用的选择，一般对单标签分类任务，我们都可以使用 \"accuracy\" 进行评价。\n", + "- `--metric-options`: 传递给评价指标的自定义参数。比如指定了 \"topk=1\"，那么就会计算 \"top-1 accuracy\"。\n", + "\n", + "更多细节请参看 `tools/test.py` 的帮助文档。\n", + "\n", + "这里使用我们微调好的 `MobileNetV2` 模型进行测试" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zd4EM00QRtyc", + "outputId": "e0be9ba6-47f5-45d9-cca2-d2c5a38b1407" + }, + "source": [ + "!python tools/test.py configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py work_dirs/mobilenet_v2_1x_cats_dogs/latest.pth --metrics accuracy --metric-options topk=1" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n", + "Use load_from_local loader\n", + "[>>] 2023/2023, 169.7 task/s, elapsed: 12s, ETA: 0s\n", + "accuracy : 97.38\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IwThQkjaRwF7" + }, + "source": [ + "### 推理计算\n", + "\n", + "有时我们会希望保存模型在数据集上的推理结果，可以使用如下命令：\n", + "\n", + "```shell\n", + "python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}]\n", + "```\n", + "\n", + "参数：\n", + "\n", + "- `--out`: 输出结果的文件名。如果不指定，计算结果不会被保存。支持的格式包括json, pkl 和 yml\n", + "- `--out-items`: 哪些推理结果需要被保存，可以从 \"class_scores\", \"pred_score\", \"pred_label\" 和 \"pred_class\" 中选择若干个，或者使用 \"all\" 来保存所有推理结果。\n", + "\n", + "这些项的具体含义：\n", + "- `class_scores`: 各个样本在每个类上的分类得分。\n", + "- `pred_score`: 各个样本在预测类上的分类得分。\n", + "- `pred_label`: 各个样本预测类的标签。标签文本将会从模型权重文件中读取，如果模型权重文件中没有标签文本，则会使用 ImageNet 的标签文本。\n", + "- `pred_class`: 各个样本预测类的 id，为一组整数。\n", + "- `all`: 保存以上所有项。\n", + "- `none`: 不保存以上任何项。因为输出文件除了推理结果，还会保存评价指标，如果你只希望保存总体评价指标，可以设置不保存任何项，可以大幅减小输出文件大小。" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6GVKloPHR0Fn", + "outputId": "1efde0e4-97cd-4e62-ce98-1cbc79da3a6c" + }, + "source": [ + "!python tools/test.py configs/mobilenet_v2/mobilenet_v2_1x_cats_dogs.py work_dirs/mobilenet_v2_1x_cats_dogs/latest.pth --out results.json --out-items all" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "/usr/local/lib/python3.7/dist-packages/mmcv/cnn/bricks/transformer.py:28: UserWarning: Fail to import ``MultiScaleDeformableAttention`` from ``mmcv.ops.multi_scale_deform_attn``, You should install ``mmcv-full`` if you need this module. \n", + " warnings.warn('Fail to import ``MultiScaleDeformableAttention`` from '\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/lib/python3.7/importlib/_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n", + " return f(*args, **kwds)\n", + "/usr/local/lib/python3.7/dist-packages/yaml/constructor.py:126: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n", + " if not isinstance(key, collections.Hashable):\n", + "Use load_from_local loader\n", + "[>>] 2023/2023, 170.3 task/s, elapsed: 12s, ETA: 0s\n", + "dumping results to results.json\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G0NJI1s6e3FD" + }, + "source": [ + "导出的json 文件中保存了所有样本的推理结果、分类结果和分类得分" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 370 + }, + "id": "HJdJeLUafFhX", + "outputId": "486c0652-2124-419a-ec7d-fd3583baedb1" + }, + "source": [ + "import json\n", + "\n", + "with open(\"./results.json\", 'r') as f:\n", + " results = json.load(f)\n", + "\n", + "# 展示第一张图片的结果信息\n", + "print('class_scores:', results['class_scores'][0])\n", + "print('pred_class:', results['pred_class'][0])\n", + "print('pred_label:', results['pred_label'][0])\n", + "print('pred_score:', results['pred_score'][0])\n", + "Image.open('data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg')" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "class_scores: [1.0, 5.184615757547473e-13]\n", + "pred_class: cats\n", + "pred_label: 0\n", + "pred_score: 1.0\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1bEUwwzcVG8o" + }, + "source": [ + "也可以使用 MMClassification 提供的可视化函数 imshow_infos 更好地展示预测结果。" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BcSNyvAWRx20", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "outputId": "1db811f7-9637-44c4-8aec-330f5765e20c" + }, + "source": [ + "from mmcls.core.visualization import imshow_infos\n", + "\n", + "filepath = 'data/cats_dogs_dataset/training_set/training_set/cats/cat.1.jpg'\n", + "\n", + "result = {\n", + " 'pred_class': results['pred_class'][0],\n", + " 'pred_label': results['pred_label'][0],\n", + " 'pred_score': results['pred_score'][0],\n", + "}\n", + "\n", + "img = imshow_infos(filepath, result)" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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Z1OIkMD4YoaQk0XFDeOcQUpLoyLY3NjawxjEvCoSUSAnzyZirlyHNEqSKi9+amrWVITeuXeVgf5edI0fI8wznLIe3tyjLkquXLzFcGVLMpjhbs7WxTl2WSCFQQmBMhU4UiRYURcG+taysDNBKkiUpIsS0tSrn4ANaJwgtOtABcMHhvEdYSwgSi2VWO2YmMCkdU2MpLNQIPPG8ZjqJG1qB9OCMx3uHF6bZGxnG1HjrqU1N8KZjNt571tbW6KUZeZLinO3OfxnmHcsTWmLrQJ6kyAAhODKt8LWgKuadpuZqgyR0QRog0RrvHKYqMVUZ0880JTiLd44bN24wnU5J05TNzU1WVlbi+zfgqrXu9LTJZBI/T/N+URYKKC2QSnfpZ9STBWU1ZzgcUlYBHyy1KcEsCn69Xo/aWZQW2NJS1yXGSJRKSJK4/5VUKAES0YjWnwXMOk1KtsDRCuS3Vu/ae2QnjZ6lFEVRddXD48ePM5/PWVlZIUkSjhw5wpkzZ4BYSMjzHK01RVFQVbGicurYcbRSXZolhGioaBQzTVPtDIGOKbQMSYh/3GkSdZ1Igdt7kiRxGwaF9wJJACWRXi4ihpRIpQgiamSyZWwhIIPAe/A+VhDTvE9ZVWynKcYYjp88yd7BAVnWYzqdopRi7/w+6+sbEODgYIROU575/Odz5pEzBK3YPzjgCXfdiSRw/pGznDhxgkwpHn7gAQqiVjboD9AiYK0h+MClS5cwxrC7f8CJU6dY31iL4FZVOGcp5xVVVXL27FnG43G3IJXSpGnGYDBgNBoTgDzPKcuSoii6iNwKtUIIUp0QnI/s3Hmcd+RZjneukwza62rrmsFwSN7rk2c9Hve4xzErCh548CFC8KysrEY2W06pKkFdFmgh2NxcJ4TA1atXUVIwn06YzeccPnyYwaBPqiVjZ5kc7JPphHxtlel4SjGfMxwMEYkG4ZtqasJsPmEymSKER0qFTiSIBGsc1lYN+wQVJL4R429dO40MITVJmqC9RSmPsIJAC1YC56C0Dq0aHawBeecsUY6N18yaeJdSoETSyTbOuQ5IsiwlbTRamr9VSlJVxeJapAnOGcrSUhQF4/GYfkMAQghIJZDoJV3KsLqxxc2b15lNpvR6PQbDAdbV2KomSRImkwlSSjY2Njh27Bhaa6bTKVVVdQShTR+jzpZ1v3fOgAhkut+lxC3BWZaDWo2sZfRtpqZ042JIFd6U1JXFueZ3GoRwTVoa0EqwjGafEcxiriubPDZGylYTa0FBa81gMOxKsVUV6PX7ZFlKUiXs7+93Amm/3+fKlSudINxqNi393Nra4oEHHmBjY6PLodscva2qKRUvtBSq0dNEFyVaHc4502k+QkCapl3uvmB1souCWjmSJB6zszElWN9Y48aNa2xsrKPThCzrMToYkaY5MtF4H1lVALRKGI3GrK2vY51nY3ON8WzE5vYWh4/scP78ee697162trbZ2z2gKAqGwyFZnuNDiCXqPDJXlWiO3XEHx6RkMplw8eIlTh4/xu7uLufOnWM2OmiAOxZizp49y53H7yBNU/b29uj3cvb29lhdW2djY4MTp07gg2M0OugKBYnW3LhxgyzLuHHjBr1enyc8YYeiKNnc3GR3d4+D8T7aa9JME3DMiylJqmL6gAPhcT4yjKqMOkuapZi6gACDfg8RoK4qaKrN1hgKP2MyHpPlCUeP3cGhQ1vcvLmLkpCkGVeu7FEWc/q9nCAkBwcVPgS0jtW52cySJCnT6YiymHbVOWct3lmUko22klIUU/A5gkBdFARn6GcZeaKQeLxt2DwSqRMEMorJIgAe721kF0ogkHjrqYynrAtUmrG2vo0TBXWoIUkZqIw6SOaVwdQ1WshG54Uk1SgBZRk3tZRxg+tEoZSIwVFKtFZdlT5LojUjOEeWZThbNyl0Aj4yEgnkaUIxm0WhXWmqsmAw6EUQ8RZvGptTkiAl1FXNfD7nalVzcLBHv99j+9AmMsDly5dxzrG+tcXW6gazecl0OsVay3gcq8v9fj/uG625cOECOzs7HDt2jIODgw4TnPMMhjkhOLwPCBGrqcYsSE6SKNI0BruimHWkxtqazc0j7I8mVNMCLeiKRiE4rHUc3t5iOMxIlAPvMLb+7GDmvaeuasajCcF7vKdJ3WTH2qaThDRNgVgmtTZSy/HBGCEUzntuTKa85y/+oruoo4MRe9evU5ZlB5ptSmuNZSQFu9eusXv5CuPxhKoqm+JDQEqFlKqJbqqpYAp8WKSYAhHL4SL+TSvwtwG2jWSx+ODI0hznAv3+gLqu0SpS+mvXNLPZlL3dAb75+yjGiiZy+phqu4aNhMDu9fMoJblyMWdre4PZ3g0+uXeVoii5FgLTnR1GozHOeS7WdWdD0IlGq4SqrnjzH/4mW1tbbGxscu36NfZu7nLmU0Nu3rzJ5XOfZtDLGR0c4Oqa8ewmF66cZWtjo2Fd885akqQpW1tbpFlGVZVcv34tWipkm1LTeKssvY/9A9vbh2JZX0jGkwlFWZClKU84dRfHjxx7VKDrmLv3BOcJUhJ8tGzIBmjbVD/RCaH5YNYanPPs7e6RpinFfIoxJf3+IbIs48iRbZxzzCYTyqJY2BmIQi8EvDM4E8Cr+N7W4uoaAgQd10AiBWkvVtOiTzCJgUjquMEb4ZnQasIQiKw8BAlBEJzDEyAIRHP900QhlcALSTGbE2zU5xAaIxO89ZHVI1qtIm7oxn6hlULoFrSiTIOUOCFQSnYkot3wkdVYrFucz1YCEc0a727NW7bMpyxLRAN47ecQIhYasixhOp4wGAzY2t6IroTaNDrWjLIq6VnL2tpaLFDt7XH27Fm2trYYjUZsbW1hjOH69etddrO/v8/6+jqHDx/mzJmH6Q9ydKMht9rpsm7W+k2XU8s2TZ02wTPUMfgKoh3FeYsXgURLynKOEx7na+q6/OxgVpUl169ex7so/C+L7ELIJtVTVDpWXyLgOXzzHO8DSiuqqubKJUvw0TxrTY2ZxZI3gGo0thCinQABtjaUsznOxvIsLZgphRQS62yX7tJt36XKYwhorZoSvu82w7L8J5uoaY3B+0DwtgGWBELAzSwueAIW2WhkSiuc882CixfEWoc1ZazolhVCSsbjQPAlxWxGbQxSCOZFgSZEwdNH0DDWQIC6AGNj9L16cYTwNeV8zM0bN5lMxlTzPs5ZZuM9ZnlOVZYE7wDHwSgwmo7Is4yyKHDWRrYa4MruDfI8Cvmz2bQpoauO4nvvm20nuLq727FWax3OWRBw9tJ5Xvi8F3Di6DFCI1IrJfE26kzeB7ypQcpoZ1EKqVqztQcFvX4P3Yj+ZVWhdaAs55w7d5bV1VWyRHPl8iWe8tSnMlwZcPnyZZJEY40kOB/F8ZZmhxCrbs4i0hQpJNFQEyJLlgKtk7g+bavRWMrSIqRHa0maxs2slMC5QF3V1LXFmJoKiapKZKJQqeyATwmJUikKFQ2+eJypEUEigyQ4hwmB2jq8dQjvm7Qz/r0jIIIHCaoJytaaWFgLMQh0tgRvCERrigjx9YKSCB9QhE7ekM2+CCG+tmqupQwgvMPVMUNp0cw7R1BRZ8qSlLmU5L2oGe7duElVzqN1QmuUjEBaFAWHDh3i3e9+N0eOHKEsyy61VEqxvr7eec3aaiQQNa+6BHSTyXmUilJUS2y0bolFQp7nrK6u4lys3NZ1TZpq6trH4JQolJY4ZzHeEoIjkTHlFsGDX1QAHgVmswZMola2/MitVUHv/cKuJgSiAY42/3XOYmoT2U2DJn7p4kW9yUdACYHWttqidvc8v3isDXwhxKL5Z2qRb53ciLAAPkFTrQwdm4smUdkVENro5xtDqRCycf/X8blaNouwKdM30TRNUqyxKKUpyoLZbB4FaBUfK4qCqq5iWm0tOpXooPFNpTDM5gTvSbOMujYkSd0BtDWGJNGQpk3UXlRwBXFxyF6PNEupfKwoS6Is0O/3Oo9SC2aRyi8uvvMeY6IJ2tqm4tcEJec9py+c48TRyM5EI+q3UTY0lah2swhAaI2UAmvjayilyNIMgYxA3jjEx5Nxt7aquuZgtIdzjv29XXpZSp4lTfq40EO88xhjIXhE8OhEQ/BIoj1BS8hTTVXVlGVBmkW7g7EV4/E+3ruuCNWyAB8WoAMyFrKCbIAzgglSgpdIEfABhEhIU4210Ss3Kw2T2lJ5COjItpzvtCIIDWMDF8B728ksDeHEs3DsCwQssRdoU62mmuyj+TSaphfaXmTMkYBorUnTBCFilbRLeZUEQpQQQqCYz+P1bNhNmqYoHSWYsorXua5rDh8+zM2bN/HeUxQFeZ5z8uRJHnnkEcqy5MSJE0wmE27cuMGdd97JxUuPUBSmy75a3XthK1nIPq0Nq11ztoqv74Olth4tHFIqCI7aFhTFnMHGAB3pBt4udPJHKebeuc8IEu1NNDuqLavGMvOiS2Dhto8+LOd8g7gG75sDic8m+sRiRRAikB3s7fFFL/iiThsKEFONpurY2jqEXJhf259tdAghlr7/6E1v5MqlS4z29/nmb/7m7sRqrfHBo7XCOotUy2zNkyRp3HxSoqTCeRdBpUlt4+9l5xLXSULeiyVsU9Vd2hvNogJT1fR6PRKlMVUsfIgQo2QblbIkZTIeM58XHX0vinnnzVtOK5xr2WJMpaJOGK+LarxMsTgSU+KuBN5olnHhi4aluq6QEmKU6FLz2tSd565NMVOdRPuFVLFqKRVaSLSI56o9x945bBXTgKouMKbC2gqpYHV1wP7eTcaTA+44doRrVy7xyOmHkDJgTBnbfFyMwgTXVOMCSgLeYesKV1c4W2NNhakrTFVQFXOsiWk8PpAoxcpgwPrqGnmaYY1hMh6xt3uT8WifqmgCSZIw6PVZHQ4Z9ntkSpFpTao0ChHZlnMEEwHOOxvTXOdxxmDreHfGNOm0a9aqaER/0Wxk08gyFmPjT+ct1tXUpmyCQ1hyw0fdrvNxubpra/LeNdfW4hvGEkJojkeRaIVWKpICZ/FN61R0jAq8sUgR2FhfZXt7mzzPCdZSVwWTyYRer8dHP/pRnvnMZzIajUiShOl0ysZGTE2VUl2hYDgcNvp1bNXr9wcIITpzfOtuyLLoT53P5xRFwWw2YzqddvdiPu8YIMHjnMFhI6vOo+5X1TNqU2BsFYsN4R8pANyKXDSbcqkVqPWcNawJ0fAmEdmClKJL96QQCK2wDe3XWscTGmKa2ZZ0rXNdVa5Fxbb7QHT/Dg3otZ6325ljzEYGg363yV/y0pcC8MlPfKJ7QggepXTHIEOI1Tsp4iJ0wTXisum6G4wx5Hmv0fhCTKXaPs2yIklT0jS2B7WVJu8dZdlWXkOjV6S0Rt0kSaJPKElIk4SimIMQzGYzsjxe9ACNqTWNbVbCdJpVmqQRMKwl0QrRCMvehkYXqsjzjE+feZj/5Ru/ifvuvbcD65ahSinxblGVFkvXsv3c7WNtKhTFat1SRBIV7QhCCHTDVAWSsio74G3Xj6kNgYo0S1nfWKOqa/YP9vDesbG5gU4009EIlgKkagKHFBKtJVJo8LFAFbzHq9Yi5KiqAqUT+r2MoiwQMrCxusXq6grj8Yi9/UV3SbTSRHtNDKgxbAogaVIyr5LYUeECPgiUjLKLKUscCiUkeZphgiZYi7GR0aEkSsUkuM1G4vmM1qG26B5CaHTGBeDpxrUfWYvqWn1aJrlg6Mu2pIWvrWXX7Xm31kaDbqOXChmaVLBaOO6bx7XWqDRlWpYMhq4r2BljmM/nnU0D4MaNGwghOHToEOPxmBACd9xxB/v7e03PZehAfVkfa5n9sgdtPp93tpQsz3DBIZPoMCAYpIJeL6NymhAcdV0iFA2QLbDpM4BZXNCtHSO0iNJ2Asj2wrRI09zax0W8gEppgvcorQnBdz1l1rpoGBQxVVMyahHBe2xw3UX2TdoUN9ICrAKLDbbcuwkxddRNe8Oy0XX5NeO/Gye1dUjVgqqiriuEg6qugNCUomOSYK2h9dotElxBbQxJkwbmeS8yEGO63sPgY4tUWZSAIE3jRsuzPEbo2qCU7tzQpq47prowQJqO8YglJipFZKF1bZBC4kLUGpWUVFVJvx8NxkmSgBBU9aLyE3xYaCreI9tjCwut0dkH8t0AACAASURBVPlGQ5OSgMDZuDEFAbxFeIeQsR8xhIBREp2kaKWilynEnshemqGEYF7GzgBjTWyMLgsuXb7E0WNH2dpcZ3d3twl2C5HbeU/wUeuMoBnwuEZqiFqZULphg77buM458EuG52bdRKaTolRklxCtNXVdU1cGqWRkNt3ajOmbCICMYOqNR/gIXKF9T09MvTuNVtM2lCtBF7jbu/cxAwpNUIgBL1b5sGHpeb7zdgoZumLBckvdcmse0LVSOWdwNhZIlIrDCELT05ymCd45RgcHXdU0y3NQCtVUMO+66y5Onz7d2TOOHj3K3t4eWdPX2/5s25jSNOH8+bPsHNnq0sd237UA3Xoe27QzVkAX5tn+oMe8mKK1JNEZ1XzeVEJjZThaWaKBVgqBVkum+UdBmZBdNQYheOc73s7//YY38Nd/9V4uX7rAH/z+77G1tbWojiH4xMc/zqu++1W86Y1v4sqlSzz8wAM89SlPxjrH4+66iz/8/T/g4Qce4CMf+jDf+YpXNPpALLV+8zd9Ex/+4Ac5d/Ysr/m+71vCRdGAxxJgNQtYyEVaefToUX7ll3+J+z/9Sc6dP8t//e3fIs3SRgNqOwFaPS6yu/b1n/e8e/izd72Dj330w3zqkx/h13/9l9k5crhL3VaGK/zGr/8KZ08/wAOf/jh//mdv4/GPv6tryj516gR/9Mbf5VMfv48P/v37+O3f+hX6vR7zouiY5M7OET7w/r/mm77xZRjbOKSbNq7V1VWyPKOuq07jklIiQtSIhsMhP/Haf8v73vuXfPzjH+Gtf/pmjp84gQ+BQ4cO8Su/8Wv8zb1/z/s/8kF+542/x+d83jOjPynPeMdf/hn//W/+CoD//Gu/ykc+8XHe+Zd/0bHeJNH82E+8lr+7717u+9hHeMu73sbn3f2sLnDQpCzBefAhblRjsHWNqU2nxVhru66NqiopihnzYkpVxTYia2uUEk2aHlAy6qF7u7tYYzi0vU1VlFy9fAVbG9JE08szenlGppPGo+WadMpTG8NsNmM+n1NWFda7WDEVAk8MRPP5lBCVKKbTKVeuXOHa1WsURUHUnhadLUJE466UAkTAuZrZfEppaoy3uGBx3uBCnPCRJAl5vwdKUtc1s6ansayqrg2nrirqck5VzCjnM6qywJoaH1xkvU3a2jGupvJOs7aRMcWM2bKNqWujlbXeRynjZlYysletRMdiF658mqJOaLINS1mW7O7ukiQJ6+vr9Pp9kiSl1+shpWQ2mzXFBo+WgroqmE3H3PPcZ6MknD/3CNevXeHY0R20lhwc7LG6OqTfz9nb22U2mzGbzLC1Q5JAEDjjsbVDBEmiUkxlwQuU1Igg4+PGgBckQjMdTxBArhOC99RlgXcu9rA2AUwrRaJ0p7fF8HE7mEnVRQzRrOyv/7qv40Vf/hU8cvYsv/Wb/4Wfef3r+ecv/47GRxL/7pWvfCUv/xev4KXf+A088QlPZDQ6YHV1hbe99a387n/7b/zP3/ItnDp1ine87W08+OBD/Pd3v5snPfGJ/PTrXsfXfv3X8/d//wFe91M/Fc2ZoukmWKJk3ocmuYyprQ+W7UOH+L3f+10efPAh7nneFzKbzXnRl70QrTOs84tuhkYHiq8dtbwQAjpN+cl//zo+8IEPkmU5v/Wbv8Zrf/zH+P7X/BBFUfCq7/4ujh09yrPufi7T6ZS7n/UsyqLCmIo87/Ej//qHKIuS5z7vS5hOJnzJP31B1+TunEcqRZZlPOlJT+Tw4UNYG9Mn5yHv5VRN2re2vsbo4ACpFN55vPQIIfml//QL9Ps9vvzFX8d0MubZn383WZaT6BStE9719nfymld/L1VZ8H/8wPfzc7/4C3zJ876QNE158Qu/HCHg02ce5nu/+1X8zV/9NUBkzCHw9d/wMp73hV/I13zlV7K3u8cTnvREnG0YUZPiCRcw8wKaaQ1KSBIVz6FTugOx1hTpvWc2n6LTlP6g39kgahN1kEQJ8I1Hrym3t8ZogWz+a/a7awT4ZuKFaCwfUkryfr/Taj0B6x1CeIIIBOFxDbOLrTtlF6S1ip6y2LfYMv4Fe5cyEERMaQ0WbxxCKIRSCBSWwLyYsj8rMUJTB4Ej4CVNoUCA86RaxOKEjF0NQoim8harr847BL51ES1plk3ze5qS9aNJvLI1iGit8dbEeQ+pRuCRIjQs2cWJEr7G+4DzDegJTS/vE2zAGUNNTHE317fAC1wd289Qjtkk+tUCsLo25Mrla6yurvLUxz+ejY0Nrl+8yMWzj7C1tkplas6eOR3bV/EoCRvrq1y6cpk8TZiOS7a3t3HWc/nKJQBOnDjOYNBnPB4zUXPqMjaNJ0nrAwUvAuP9MWv9NZytKcZzlIHVjRWUh3JWsNFfRQpi5bjxBX5WMHNthST6GwjAW976Vj716U+BEPzcG36et7/1T3E+9q0JFZncH77xjfzN3/4thMBDDz0IwEte8hKSJOHf/cRrEULy8OnT/PGf/Akve9lLefd73sPXfs3X8IEPfIB77/0gzjle/zM/w794xcsXwNoAWZvSRk1OdprYU57yZJ7+tKfxrf/bt1GWFWVZ8advewdKaZSKc6xoFkyMVCFOh3AOIQPvec/7YpQTknlR8qY/+mO++5XfSZLEWWStsfXOO0/xwAMP8cH7PtS0AulGa5Gsr69x/I5jfPRjH+O9731fo4lIhIrVmXPnL7B56DhpmqGUxgbbpOIeKQS94QDvHEmWxvMZoni+tb3FV734y7nnC/8Zl69eJUsSPvjBDxGsI5GCi+fP86bz5+OmEPCm3/99vv3l38Ghw9vs7e7F/s8mrbTG0Hru2uATnCfLc07deSejgxEPP/ggyxJDe/6jAB6afscIFixNeFiukLoQA0es2JW03SHeR+d5K1yHEPAi2mdwPl5j4fCiqchK1fnA4FbtrmUmLQC45mc3AUVEW8ftrWxCKEKQtGOPlm0q0GpWEpSIn4OW0YuYagqJFIp+r89w+zAH05LcCtZ0hpER2FAJ3hmunH8Ege9eP06uWFQV0zRtq2W3VPZaBloUccKEVC3DasYlqTYta7RPH2WFaOMIDeMEobLu3HnvOxkAQPh20oyMtU+h8ATKuuqsKFVR4p0hTTXbm+vkecb+aMR8OkGnCXVZIDZW2d7eIABVVTArptR1Se0sobTMpvNGQ83QndYYe697aUbtarwNuKYC7xubhRBpDF4ygrFQGi0kwhOtJ00l1waLx4NftC9+1gLAsj/rytWrHSu6du0aWZaxubHJzd3dLro9+OCD7arpXuPkiZNsb29z+cLF7ndKKf7u/e8HYGdnh2vXrzcLWHL16tVm8XZL8PaXpG1i1Vqxc/gwN2/udoPiWg2t7dOs67gB42ssRge1F/Zzn/k5/Ni//T95xjOeHofi6YSbN29EEAjwhjf8AmmS8ou/8POcPHmCd7/7vXz/a/4lk8mEoiz5v37ydbzyu17BL/3nN7C5scE73vkufvjf/CjOu0Uri2grta5p4m41BIfTjizNqOqSNE2jlcVGu8qxo0cBOHfhPDRCbp4mXRV3fX2Df/Uj/5LnfsEXMBwOu83bGnm9qRcLWEqSJG0mF8QF8ZY/+RM2t7b4oX/9wzzhCU/gwx/+MD/+Iz/KpQsXm8roQpRuN0ULKO3mb0XczurgA0LStam0LVDgu428DCDL7Wjta9IZoG/VPOO1C7eAz7Ims3hM3KIn3bKmGytA65dqPwMsGYI7rbgFGY8i0NjMCN4zHo24eOUG+9Man2RUQTKpDOiEtdUhhw5tL+wYTeBt+yvb87BcdHFNISeCv8LWFUkSAzIh4LrAEZl+NMFG7SgWYhp9NjRaomh+F6L73jbjkJSIlnpPoLYGZMOyg6Cso6bVzxMCkuHKGkqn7I8muP0DamNQSUqS5RyMpxRlzdr6JkHQFQB6vQFSanZv3GRejellOfkgMvrKzCmrOJoqG2iqqaW2rsm0oq/P+EBKCiL6CLXQ4BNQEusdxsVWNKXiuCoceBZg9ijNTGt9q68rBI7s7DTm2MCRnSNUVcX+wX77hGgANYuStGhY1IWLF7j//vu548Rx7jhxguMnT3L0+HFe+g3fQCBw5cqVSEcbentkZ6d52za9XGyobmM1izjNMs488gjb21s4F9ue2kpqK5IuKiY1Uslmcfkm2Er+y2/8Gp/45Kd4zj3P54lPfgY/9uORQc7nM6SSzOcFr/2Jn+TLXvRi7rnni3jyk5/Eq1/1XV017Pz5C7zmB3+YF7zghbzwy76SL3vhl/INL3tJt7mUUt04oKIoO6CVSuKsjWyyioWBRW9bNBBfunwFgFMnT3WtW3VVx9RbCF7zr36AnSNHeNlXfx3P+dzP5Vte9rLFuQsgpepGrVRl1X2ediM55/nVX/plvvklL+NLX/DFaKX5ntd834KPLDnKlxlGO4G3BYO2vzWmoXGDtQ3Hi9E1t4LYMgDd/ng7hHO5CtqCVzvcoJ0gDHHKR6/X6xhiO6Cz/ZtlQG5vy16nZaBrQSUWPqKmG++W0PjOlFIcPXKUEydOcPTYEVZWVqJ4nqUkiaKqCs6cPsOZM2c4e/YsFy9e5MqVK9y8eZP9/f1mncp/9K617go9qmkJbNv8bq8Oqi5gtAzWdftSiDaQLwpmUkrKqmJelnFKDR4XrXQIJRFJyqwuGayvolLN3sE+V65eZV6WpHmOJ7Bz9AhIwayYM51OGY1G1M6S9nJUmpDlCQGDF4YsV6gUinLCaLJPUU5RiUDKAMqiVEClIFXAC4PzVRxp1MhDsqlW18ZQmRpjo0zjPNTOUZl/pADQgkjr8QL4uq/9Wp72tKfR6+W8+lWv4m1vfzvOLVqFIIqR7Ult6fmf/fmfs76+zitf+croMdGau591N89+9rMhwJvf8la+4J57eM6zPx+tFD/4gz+4tLhYrrp2FzFLo7M9+MB9932IT37yU7z+p1/H6uoqWmte9KIv66ZhysbA+On77+eLnv/8zsSolSbRmpWVIbs3d5nN5tx56hTf9m3fSlseV0rxFV/xIp785CcRWzLixhyPx6ytrSGF4Gu++qt4ypOfRFVXcbJC8zg+zoNz1nH82FH+4UPv5ztf8e2Ng7/VSiLITsZN25hz+AYgpBDs7u3yjnf+Ga/7yR/n2LFjCAF33/153HnqJCF4hsMB8/mc6TQOUvyuV78agF6v3zGWqqq4dvUaT3rKk7tNbF2MwM+557k845nPoB11LARMJ9PP6jFcBrRW/O8MznLRlN8JQSFWJYN3sWsheKQArSSqcYu2/9aqMZASGUVwFryL7nYRzZ7Ld0nA1hWmKnGm7p4TJylEzS3KKSHem9UfbHTH26qOPqsQp58ootbljMEZi/AggkA2nqxlMCznBRcuXqAsCuq64uBgj4ODPUwzVcTWNavDAcN+j0EvZ9DLWR0OWF9bZXtzg/XVFapiTl0WmKrE1lV3b48nSRIIt05lbm1NVVV29ob2WizfnXNIHEp4lAhoCUoLdCIRWoAC66OuaFzAugBCoZIMdIIlUFQVZV1jQ2BtY4PV9fVuqKgncOquO8n6PSbzGePZFNvIUfOiYHc3jnNCggsm5obS40IEN5kEHDUqbaqTWiBUQCXRNlKZktpV0ZtoLbbRmGtjKcqaojbsj6bsH0zZH83YPxh/djBzto2IodOs/uQtb+bXfvVXOXv6DEpJvv81r7kVaRr8atdj+9h0MuGrvvp/4ku++It54NOf5tyZM/z0636KPMtAwP3338/3fO/38au//MucfvghLlw4f4tDvTFi3PI21tnuogkh+KZv+RZCgI986IOcOf0Qr/yu76SuTWPglOhE8+P/7rU84+lP4/rVy/z93/1tF5Vf/T3fx7d+6//KI2ce4Bd/8ed5x9vfRSDQ68Wx1KdOnuR3fuc3eeihT/F3f/c+7r33Pv7TL/1K3Lha8Tmf83T++I/+gAvnHuKdb38L//W3f5e3vOVPu43tnCNJU+666052dg7Hi2NjNVNKiQDKMo5HjsKtb8YoBYILvPJV38/Dp8/wrre/mXOPPMCP/ui/iZ49BD/3Mz/L1tYW7//IfbzxrW/hvns/ANC4v9vR4p6f+anX8e0v/w7+8q/fx5ve+mYSHacwbGxs8u9f/3o+8A8f5i/e+x729/f5+f/wH+G2M347M172nLWG3O65TZuJlIsJwMsbcvneBr/Wy9SeD+AW1tS+Z5s6tuNqgG6wZ1mWtF6+JIlucVrLg19oVy0jvJ35LY6LplIdf6dVO11lMeHYhxh40iwh72VxnLcW5L04w19K0a3RsozN2pPJhMl4HH9OJrcazsOt63v5M3ajlxotra5riiKaa4O/Pb2mO7fWxqmzrelW66Xz3mQGsd1JxHHwAtI8Q6oEYxyb21uUdcXNvV2yXs7K2iq1NUit2N7epqij9tXOJOs3M/3rusY4i9KS1mfmfE3AobTovucgShCKJI1FoGjoFkjd9vA2qbk1CxnDgbWe2vgIalVNWTuqxfJD3H4yDx+/I0wODgghMoR3vfOdvOc97+F1P/3TTVXRo5v0zYfQjANu2pCg80jdvijbVCoaNT1CxsUSzYAL4GzFykWqAK2/S4hYam5fczERw3dVuhBoxqdkXdtIVRUdi2yHQ7bjdkOIPXM6iVM3y6JkuDKgKqvORS+kYH19nbIs48x3Y1hf34ijcZRma2uLvf19XMNAWmY3L+bkWc7a6hqz+az7HoT2s0RQiNM5kzShrmoIorN1GGuRSjAYDsF55vMZK70eqVaI0LbJRJ9TEG1FTpGlaWSSUnQRPH7xzJDg45x5U8eUtWMdYWFjEY1Z+vjhHb702c/tgOT2VpRlVtbpZs2gzhbM26DT9u+1G7Wdiddew5bpqabnUiwxotvTxNvF+2WNrO0+oTFHLn/eZfBYTuuiGNa8ZqvbNTYHJWMl0wdBV4PUGXVQ7M9KruyPmBQGrxOC1HhX403ZecduvwONf42u06M1j7Zg7V2jpzVfjKIaDdGauCbTVC8sPN4TnGnALLYrzebTKMWkadxvdNSEQMDUseAEsZiW6AyVJlRlyWQ6ZefQFvP5lL29Pba2tuJYoPmMzc1NlFJcv34dWPgfW822ts3gUme6IJYk7ZzB5twL30yP6QPELzAJgZWVAUJIiqKg1xviTE2WKnqZ4tDmkLs/9xkc7F7m1PEdenmK8KY5HsdXvvRVAj7LCCDR0KzWIBv/iNh/6ZtJCQuXbKOxyCWhvl1Qj761kV2wmA5AYy5td3n7nOYvaE2u0E6ucLdoI/FCKubzovmuAUevF1348YsaZt3ftTpaCB4ldSztW4uQMk7DbQ2uIl545xwixBK/1vHLMZJ+Gv04KrZDlVXJ1tYme3v7lGWBEPG1tIrfyFQ07KvdzG26297mxZyNbCOmEs4jpUYqRWUqgoujjFOt8S6CUqJkk4r5CH5N90WUOH2nX/o6urizNMV5H2eN9WMaaowB77vzGho2cntq37GmJVbWppvL4NAabj2Q6AVItXPe21SpjbrL79H9P3G8jXoUOC0CYwuovV6v+yKNtkG5tYgopVt8uuV1bgfH9lja49NJgguBytrGjtGwtjZbdWCBsqw5mBbszyqmtaG0gboUCJ2ipKAuythO1OiJy7pg21C9fFtmVwDO+k6HFEIQGqP5orBVd58r7pu2iKK7hu5YRY7rqNVsWstVXdeoJDaBW+tIs5TBsIcWkvlswoUL5zh27Ah5njObTVhdXefIzmHyLOdT938KHGxsrJFIhTEVpqxic7nSHD26w3Q0RjZAGVxonD6C4EIc/V57lIjQU5dxrQ77A5JUI0JsA2vwuTHfpiRJipQaSFAyjeZeRKcLwmfymREbqVttf7HoFv82zZdraK1x1pKkKf08p5jPonAXbk0Pu7I3iw/YVqxg8aUgSkf/jHOOH/qBH+AHf+A1t36ApRT2n7/8FbztbW/vHjJNU3brum43jGootRCim8XUHqkPHoEkaQbgzWezWzZsO71DCklV1dS1iRM5lzZx3JwWIZqvMXMOY+N00I2NDfb395shh9zSs2dtOwpYI5yjbvo3oxE1LnglI0Ooq5o8zVhZWWE2HiGyjDTRDSMOXXuab76xxjd2h1Ywb2+2SV1Cw4S7boYWMGilghjM2laf7otiGg0y/vs2dgN4H/sYvTF4QvyGpDQGEVuV4CLACe+QIWpYxlmkUmgRbVo+WMJS6rAMZu35DiF0k0qVUiSpQqq0syG0wa89/ltTShomr5ZAzTeZSGRNSbNetFRoGSdJOATSQSIk/SSldoFxUSGJAxeUlHE4Z5KwMTiEFLcyxu4aWNvYXJpbYyNJ5NLnkTJOPRHxuJM0je1H1jXfu1HG8UPEwQiEsAC+4Dt3fVch1RpBLH4571hZHXRrcW1lSK/Xa6bMGrJEQ0iZTSdoFbsjnK2pq4L5ZEKWaObVHGcNvV4fQWwjS5I43zAYy8HuQccMhZA447o1lMiMpJ+BV/GrEZMcEiAobO0JLjA+GKEThagduRYooaiLmqc+5RkM+yl1UWBdSXDuFs70aGamWpIf4egrXvzibvO37D1N0kXlMvbUxO+JLOa3v9zyquzSUR88shmSGBqrQiAucogN0P/hZ3+W//izP0dsz1G3tPaE28CyKfB14ND2UwIURUk7EG8ZqCLSq+aixxJx/IIE2eksbUN53KgtTY4L3hpDr9/D/T+MvXm8pVlZ3/tda73D3vsMdWqeuqu7qpueaJV7jQEaJTESo2AjComE5BrE3GsTBBnUi/lo7jVEBQFphsSIE7nXGLEVBEG4N04YmlY0CUhDT/RU3V3dp6ZT55x99n6HNdw/nrXe992nCm9eOF1VZ+/97vdd71rPep7f83t+j/Nsb29JW724u8/mM7KsVwuYzWZx8dCFFGVZkBRDsjzHedkhpV60iTw+wRC2p9uSkYp1mgDVvEKr0H0PiGaW2PuhRx1SwrmjeKSjM2bDxwTdsy+j57PbO+rHfTF8EqPgCEF3yYEhrjOkWwxD1fQAe4rH4ncOv29ICRnWiopOncX7dLNc9vndx2IEIHfvvWxOIUCDeJW5zoXQjMYinvt0OhXYobFizgygPD5YZvNWQuVdnmD6M2WZOyMbYYWEM7auZSFMJnRzJdFLtNYg7ITuPvsMsYSbhOgw+IECBwrbtN19N8xRqc1dVeFcQ6ZFJURpSWTZtqKppONUsC15ptAqoL2l0ApdSEItx0PTUpqcTOfkKpOSO63xmMglk2uxc9lUS11IdLMl0dOkHLG8dxkQ3tmkyBmXY8bjJVaX9rCyPGajOR/v2eHD3yABNF5eZmd7ShuVQuUpyH+Sd5YKRtOO5qIEjB+8X6GG4NDQqeofrJJrjgyQgfqGkEdRxHq73gvoMR5ARYmgDpyW8K5tRTctdHymlKruJ2/CcSCWfMSdQ2giyZCJ0KS1FqNFu11kenKWYgu9ZCST7EvHFWotm5c2WVld6TyiLLKdTQrlE1Md6QnYNq0oizpHVUfNMSW413R7SnCOPSsr5Fkm3KHYTSokN1oRxy2N/cA7lpvG2rbTCFMMFn6XTRRbOClKbj11ik6NIUAIyTj5gccX2/QRDX7crBx+wTgppXAk+SXBFtPcEGOmUUSt+mG0O3hmATow3lobdcVEFDGEgG2aGKan0EqRmPKCD6apE1AkIYNUWSJjp5SmMDnOA96jg+6eqdbS87OyjYDrWpEZMXAO8NZKPaoL2LBILRl6sQKFiBfpUkeOEDO6OhGCpRxPzhHVT2zbeZzdOogRy8JGo6UeOiBryjgvWmpBRsT5WHwewAdHbUXmyrsW5URlV4UM5T3at/jG4pSXBtka6uAI1YzWNtHb0hgPrrK0LpATv1M5sqgK7WhpGklkiKcpmOkoz6m9Z1pNMd5RTla5eHadPDcsjcboosC3nrPPnGN26RIqePne0MZxavm275bbviJmtv/wIWbTnV7JYhBS2NaytLwEm9J4IwGX+w8fgixJYcf9fWEXJi7kLBapjihLwbemOzsSrua5KBR437nNbdNgsozlpSW0EZVOZyWUc1GDy6SsWJZzYP9BNi9tkhcFGxsXIjh/gcl4LPVzbUtmDKPxEkePHKOqakAx3Z7hXGQnW5GRyfOceVUxne6wvLzC8ePH2YmNdK8+cYKnnjrDmTNPc+zYcaqmom1abGPJszzW7U05eepm5vM5lzY3Os+sKAq2t6aieBtbsYFiNBpx1bFj7Mx3OH/xIirT0lS4rcmUot6ZcvTwEa676jilVtSzKbat8AhG0lobS392J6kjqK9E4TZlG5OhSbhYIr4WecbVhw+zPB4vhKm7PbH0uyEJNsQQNK213V5WMnBEA9LhVvQb3JAblj6T/hwu5oSdJrA5Gc0+udNvXFov/ju+obsOpXpstih6Y9bhhkaDNhhtWC0KWg9tMPg6UjKc9Ct1zpMpc7mBid+R8MaUHFnQl3PScxOvumglEYGd6xsJgScMMD0G4+UIKCUM/+BTzWdABxdFTuX8QqyNNbfWieFWilJB1dQo46VxcnCoANobirygzEdoHHiLq1uM0hRlKRuKEwzOWC1F8UpkzwtjhOnfQhUsmdegITcBrQPagM002JZqOkUpi84z8kwzLkdoFNsb25yfb1PtTFlZXcIYjdFSZZOOK7aa08awtLqykHpPKfQsy9jZ2WHfwYOAYACTyQSVZVxz6mTstWcR7zbparluA84zIf+lPpltayk2N/uGoCrrCr21FuOYMnHpgdd1TVVVl4UZShny0RIrazkHDx1gaXUP+/fvA2NE7dZaNjc3WV5eZnt7zvETJ9m4uMnXfd3X88jDj3Pu/FluueFGnnnmKc6dOyfFuFq0yo3JmSzt4+T1z+ahhx5isnKAA4cN5XgvR44cZWPzEqOiBA/bmxJ2OtcynW6ztvcgN9/89Vy4cJYLFy6wb98BZrOKrc3tGO7mZKagbWucz9i3f429B6/COnkMhgAAIABJREFUG83mjjSDXRmNyIOndI7D+w9zbN9eQjvH1js0tiIrRMFWB4UKSSppYM4GYVzCuFLIXhRFJ/UyZOQn43B5OHa5Ydi9cIcGbvi73RhS+mz3WgjiXQ2+Y+G8IXQd01X00BI2JHM06119ks8onEk/vOb4ns5wRuPjg0jE+0BSQpJF6gPWW1SWYUajuMELVy6pwOrQ1Q5ICOSH3pkYIfEq+36tWvc4oPMWpQxlOcZElRnnnFC1BkmYrns5ffjaPd+gkCsCsBJqOim7NzokH1pgFZxQOLztvkNrjcp1DDUVJhjRhgPGRpRLJqur1HNJuhhtKHRf4F5ohQ0BZRRloSjHhnFR4JXH5lC1Wrh0ZUFuDI1r0XnO8mQPO1vbXNy8wIHjR3BBVEls02Br4afpVKheeSiU9FEYYPFXNGbOWqabW52USLcrxnrEupJOSm3bgoJ2Puf8M8+wurLCLAre+RTD+zTJ08DrLsSTjBexv6HrXk8LTmgc8uA28nwQNg6ln+ndgADPPPk4o9GYi+eeZDQecfbM4/jguXjuGYpCGkXsbF5kezrjC15A/bba4tKlTba2Ntk8/2QkJooCK8mTCPDMGcNDD0nZ0WOPfhmtBKN76vT9rKyuSpfnVmRhilIA0AsXzmMyxZOn97G6Z5WzZ9d5+qlH2LdP+olubW1H6SBLlokAXVEWWOdobEtjW6xruRATBZM8Z+PCUxxeW+O5t34dS6OS1lU0dS29O5WGoLs4bTdeldL1va58b7iuZDyGxmeY+buSgevfv/j53ecdnnORsiAL1FzB4A2vIUTAe0gLSQKASmnqqpc6SnNj6O3tToz01yI8MsG+Bhr6KRwPkglsqpqt7R2mOzPm1mMTupK8OAUos3DvQ48y/X33fSVV5aIoIgQiyScVArY1sQN63xtWoIJFY6aCGDRZFzlSESBF3ShEB8wHNCLxrUmRFxgNOlNMsiVUbILsnCE46fJUxLA6yzKMs+RBcOaMAEEUYUdZDgWxmVHBaFQO5lzRedOTpVHnrOhIQdmYZPhQc/H8eTzSinJSlIRxbNzdepwLzLa3hQicieJJOi4zZrZtubh+dgG47YUSI9CcZTR1TdtGekQ0WBvtxe79goElLan4WaU6XKxOgGfMyHnnIt9MvqNv6uC6CSgTTgDQTlfNxyLVQIddeddSzTXVPO8aCocQaIsc21rqucdZy4Xz64Di0Uck+9nUNRdjprbLhHm/MPnsJReVaofAN7SNkDfb1pKbDL8lWmmEQDW3zHa22D/fx2w2p20bQrC0rWU222K2s4X3oau9m88VVV1ho5YYIVCFqHwwKmnqHc5fWKdtK77jtud34VnwPgonq4E+WX+dQ24W0FUKJA8ted9DImxaiLv/vRvUHr6W2Pwh4VQqGS2iR9DrwgUfKx9UVM4womA73ADDwDD38EUqnQvoSNZNWdb+vYvXuvt+0ns6wxIkC2y0BieYkgJUDMsznaGdZ2e6TdvWNE1N6zxtENwsaEPQCp1U+ZXu7l0egJwwi1n3EEReSYXYcclolM4WjH8IocMh02c6w0jCR/v3Og9BaYjge5Qg6fC34AO5zoSnqCUkTgRzI9ktslEZax41kEVP35OpjMwogg2MMkOppL+oazzWNRg0ZSGCl3muGY0y8lxhjHjOuQGlDGppEkv9NEpJQqttW7wtQB/iq0+uY5EQc3VpmZXlZXSAuZVKglFZ4r3FtZbhTL28oclsB+8dGoVXCdeQ/4SInU2WlqJGewK8FZnJpWu11uLE+j5PNoztO7g6DDg+JEOZmu72mR1x1ftJ2YUUERexwRJXsGQCI+UjyzOaiOe1bUOR5yhk93fOibx0lAmazWaEgHQYj9fpfCr+TSJzdCTcqq4YjcZoJSq5TVPHXoc5o1I8NwFwdQ/QB9jc3IpGWlNVFXlekA0UZ513tLbt7l0PDLpC5FLaRgiaeZ7xyJOn2dq6FZNLh/CmaWirFq0cucnRmRiGxAtNC955Mbh5UeCdp25Ebz/PY5G3ChErTQsyUTnEOHVrPxqnFNFqhWS5dC+ouXgkA9NNhf4V7wkdnhbY7c2lvw/7Sw4pF/Lv2E9S5x0R2wMmCmyiUqpEEZLXpBAlEID4pw89Q18pRaEKTK4xGRR5xiSUzNqasta0vsW5FufAK0NwhloJ0XZ43cNMZEryJPysy9JqDVrF5EZLSBCN99ioIdd5f9GLTVFD73wQSb+ylsS9jFUbIWAAU+RoJ7OzNIrCmFhSFqKShmRJFZBlOZkWTlqmMkymqOfSzSzTsYXdvGE2c+S5YVzkNO0MowrJ/Qb61n6xAkEBTTUnuJzxeIRznvl0E9fUrC5NuPbqq9iaVjI3K5HxLozB48nKgjwfU9dz6vnOwsZ7eZjppGlESLjCQupeiridlXKhzBgBPq2wkBPHyUcN/jDY2VIGLZiEYgyykginSZGxceE83/ni7+Lzf/l5MRhaUdfNwi4qLH7NL7z73bzi5d/LeDzm3/zMz/IL77lTClOdZ14JJUMjlQxp4o/KMiYRPL/4ix/AWcfrXv/GSLQUzMZGgmtZlN1E053RFgNz5NAh7rnnzzBRqfbIkWvEwEeeXSp2dzF80LHXptaGprV434DSsdGIxnuLib0JXNtnMsXjJJY6ClYUYtZQnGIvXcXrSp5PptA+GpkuYyg1jsroTlhSK0RL3zlMfK7BWbTJyI0RqkHKkgaZ+CbP4j+9EKgHrfzE6xPJ6J5TuDh3uues+nPr2Nkd0oaVlCuiIUuepFKdcWpSBlDRQwzpW6IEtvcJM0vevviE1lmatomUIo/3LcZH/CWXzcc6C0aRl0n8z7M52+DCxYtsbm/TtI6gNLOqZjqruLCxRZaXHD12HEdJ7cW7UdB59snjbduW6UyMLtpg8r6ZTIjVD/hArg3leEJVV+zsbGO0Ymk8QRrGtCKaGWsunTNdPwFBwxqCk6jJKAHKic5JC2AthkDuA3NrqcIc7T0iJxUi2degdUYdHI2SXpdNUHhnWZosM5vP2WmnFEVGWY5ZWVvGWk9tawieebVD01aRa9ZjtXkuyTHpiG7Z2HgaY3KWlyeMx0tM5w37Vib41lPNmwg5BLbmUgrWtjWZMSyvLoGCjWlfm3kFCaAQ/z/APdK/AthW2r1pI6S3pGsWOj6W7T6lY4gBA+8u/Ur17r6kiEO3MMSbsiyvRJJl9GQER4vQpXP86I/9OG/4kTfyB5/4/Qh+BrK8794cv7RbSh2W5xzOd182wDakq5InXo8TADWEGGpowWW895w7d46TJ2/g+c9/Hh/5yIcFXHZR3FAPGq2g8D65IvKnXJ/HRo2vFIp476IgpgKteOG3fAs/+zM/zdVXXcV9993HG97wZr56//2XPS75Q+7XkIr+6cI9rUT4TgNSKpzMAtHTUt3fh2J3C/Mh4jHyLHvoof9sIkaHy/C4HidazFJ282vhfXSvJ1zoax5XeM15H9sZKpSSJjQhXqfRRvT6vGJpsoTSgdlsSxIvoQU81nm0LilGY2bziqcefpIzZ86AChw+coTVPatsbUtx/2RyhMwUnD+/wbnzF9mZXuD8VsPqkZMRC+r5dkDnje3Gy9L889EB0AjcY7TuBCoh6ZoNPdZFGIHkfceOVSjxshEzhQlS/1s3DWUW56S3+GYGto3qrRrb1ISyJM/TMw7Y4CN/s2G6vd33UohVDommhPLgpG1eMl4pukgYZ9u2rKysdJp7qcQQoGosa3sPMd2a4ggxc6koyxxrS0wuJVB6HjmgfxNmFjpvTPVgxUJIIBctzOOBh0Xsqxj91C5j1D2x5CH5XZNwSICNDy3IjVfzitXVVba3t8nyLPbX1F1XIWPixFBgUjFwzMqEaLSSSoVgNLG2kwHo7aWWEKXxbcuoHBMyF4u/xTiJJ6IpyqIPGWABN0uehNKq0xxLmEkquRCRwsi58oG2abuWdUOsUSnFwQMH+Q+//kF+6v98G3fd9bu8+Y2v5zd+40Pc9twXLIxdmtjKp3FcZJ1HZ3PRiAQJJzqD22FUixnHrwXY9+TMRTC7L3PqjVl67UrvHS7EYSXBMES90jX8jUfEmIzOYjIEgpNm1loracAS+WJtI/0aMmOYTEoCHh/A5AVPP32Gc+fPgVbcdNMpVtdWuiL+w4dvpXWe+bzBB8XScsnRYwdoLTxyZoMzl2zvUQ7uY5hRXgDtB8YsyfVIFYsYsG4+p6JsY0B5QkibsJCV0zJVKq0o1anxSiwauvFRSmSGRNba4W0rMIyCeQzfksZ/usZU/J4kl1K42xky6MsFB/Nw99xK7y8KketO7ARrhYB8/vx55pUlzw2ulR4KJvbZyKwYPZNJhUZRrHZjfEUJoJAmBfDpT32K97/vA9z92c+y/swz/M5dv8O+/fskXInA6Jfv/RKv/+Ef5rc//GGePvM0D97/ADfdKLIzJ0+e5K677uKxRx7hr7/4Re6444e6G/Te809e9Sr++otf5PTpx3nzm98cJ3a00lENQWsd1V1110txAcwOgrPpiDf4IIz+W599C3/wid/nsUcf4cknnuBjv/dRTp68FhPVELz3LK+s8JHf+S0ef/g+/uCTv8d1p052qg6rKyu8613v4Itf/Cu+/OUv8Pa3/wwoOiWDMJiw4jHGxEfoAjQOHz7MPfd8hh98zaul1CmJJga6cySwvjf+ipe8+Ds5e/Yc//E3f4umqXnPe97H4cOHeN7zn0d/dhZXPr3iQxrjKwHhu38/NCTDZzPEenb/dFc6+LxM7l1h3+AzQyP4tY8rv3alzOkVP61EwinLirjbC+VBMuYNILym6c42Ozs75FnOZCJFz9PplDNnnuD++/+are0LrK0tcfKao1x7zVGOHtnPgf2rjCc5lzYvUM+3MNqyslJw4MAqBw+tsWdtwmicLWTYhmTvRCNK/LJhofyQCCub26IaRlHkfRUJ7srPQ/V+guqSLrHEK5Yyee9iCNmrlGitBb6IXLaUTR1ed3q+iZ843JiSQkhKJKXnNcQ1d8+BWeydsLsyBx8EZomNYoJ1BOfIFBS5oSwyDuzfSxmzvUXZ+2OXGbPOze/+A9/7Pd/DD91xBydPncIHz7t+/p0LqXyAO+64gw/8u3/HiWuu4bu/52Vsbm4xWZrwyU98gi984QvccOONfO/LX8Gb3/QmXvSibyOEwLNvuYX3vOcX+KEf+iFuuOFGTpw40e1+TduQZRmbm5td+UcKz9KC6KRUUkchL9kxccsVRVny73/pg9x869dx87OfzcalS7z//e/rEegAL/7Of8A7330nN9zyHB544EHe+95f6GpFP/CB93Li6qt54Qv/Hn/7b9/Gtddcw4//6Ju6EK7Hf5CBD6ELl2NUTZ5n3PCs60VjLQTa1i7szAuTcRBm33jDs/jKffdz6tS1fOLjH+XYsWM89thj3HjTDbseWNTwGjy7dOw2POnc6bUr0RPSa1c6z/+YQbncmCWu4deSvVm81isbzStd15W/XWFiLwEdotSyj92vg0cFR7AWnGNU5CyNR7RVxenHHuehBx7gzJmnCN6yd22Zo0cOsLI6pqqmbFxYZ3vrIjvTi5xdf4LNrQvU9Q7e1uhgMVp0xFRsWnKlsqzdWeWhMUsKGkJhsLioPOFS/1DiJrCwITlC6JsNp/vXEjbE+w1RICJ6ZCGQaS3d4RvRT9NaR9jI0lRz9u/fz969e9mzZw8rKyssLy+zsrLC2toa+/bt68LHJBg5NHrGSEa+yHJyk0mf1bxgXI4YlyMmozHjcgQhMN+ZUc3mEKRloYk0lFwbCi0ORWqniIryVllOZjR1NePSxjm2Lm10z/7yQvMFaybHxz72Me677z5CCLz//R/g9z/+ccKrf6BrowZw11138ad/8ieEEHjwgQdQSvM9L3sZeZ7ztre9DYD77r+Pj3zko7z8e1/Of/7DP+L2l97Of/nsZ7n7c/eQZRl33nkn//wHXyPupBHSoPdJRiY2CdE9ryjEjujDyTJcpP/tv/1XPm9F26ooSj7823fx67/6K50cidKauz93D3fffQ9KG3711/8vPvuZP+TgwQO0TcN3fddLuO3538KlSxsoFL/yK7/C29720/zMz76dDjdKk5XYrb3DCQVpfOKJp9h38HjK+kcXPMQ2Z7Jz9uG1sK4Jgclkia3tLdZW93DjDc9iz+oy21tTlpeWFsyFUopOLifGlB0vij68Se8dUlw6Jv7/Twi3m96wmzuWzpPGf3dYOvwzhSNXMlZ9iHv554ff+zddbwiizKB17G/KkN+VMrGKImppXbx4kQsXz7O1dYlROeLksRNcde0xLm1usLOzhZ4rxpMxRSlKI4rA1VcfxznPdDrnXFWhdcbKyh5W9yyzb1/NI+vrOFw3LkOu5tALS2OQ8KL++lusVQNPNnThm1KLmd7dXvYQEkqjJNMxxHKtSHtC1GAyY8jzDOUNVimsk8x/yqQPvbAQJDmQJH+Sl5aMWJZlLE9G5JmhqsQDFY25XjkkGe6kQ6e1pixKlpeWqeua+bymqQNkkvXHu5hcHJNpKURLdBxUwPmeU3iF7kx6OB4oFOvr63FDUF0PgP3797Fx6VL3rgcffDCWFkj2SOO5+uqrOXDgAE89eXkPAKXgyOEjnF0XbaTgPWeeFqno1rZRBtlRjkp2ZjtkJiNElQTvfScGJ89vkZyYJs/Ja6/hbf/6X/O3vvEbWVpaxmSm6w6edNfW18/KQ3OWs+vrABw4sL+bYJ/69CcWFpPoTdlIcByA1/GcbjjBVEqkSIZNwuHItVOptAdAspTdrktgZ2fKsWPH+Pxf/hWnnnULKnhWVpeZ7cy65IlSIXqI4oF0mlUdftZfd1o8ib83LAHare4wNFYLc2MXvgOLIamoVrgFvKgfn3j4IUeMzkPuxAa1dOb6Wp7Z/4jhlfyGJyBdrmQDjKFPpPY417C5eYmNjQs4Zzl04CCHDx+gmBRcOH8Ra0WlYt7WzOYzxuMRy6sikT3fmRNULLzGELxnPpuztT3j3NkL0ojERb4Q/rJxLops4BX7bjwk+TIsyHeRTC5jkeWGTKW6aCnoT95sGuO0tWoYLmKSGo4BnLPSsi3TFEZTRqJ1qTXOF2xvbXZh7RDT9F7k0JPog41zwTYVrq3BWyoNoRB+57Ad4fD5Jekm733HA034G0CuDTpqtmUR98S2tMrTNBXFOEoW5Sp6rXJcQQIo3X0/kQ4fOdIN1aFDh6QHwMYGw/GysR9kwil8CDz+xGnuv/9+nvf858eHQ0wEiCVfP7vOc5/7XECyUIf27yed1MZaTdvKonO4zpBZa4WrEyVvUuYo4Wvimnvee+edrK+f5Ru/6blsbm3xXS95CXd9+D/FGjUJWQ8dOiKJC+dYXl4C4Oz6M52E9XO+4Tlsb213WIfoQ0liQLhqUec+diYKMb5MuNhwssXVJg8yZfYGO7ZMepl4Dz74EC960beRsoCjYsy1117LAw88IBMzPqdkUPtFvuiZLTzbAQjf8dcGO+bu9+7+3PB7hp5FZ8i8xy900B5e3+Uk3tTsJdUgduH35Zf+P3wIvmq6JswyED4mXyxNU0f9OpjNdrDWsW/fXo4fP4bWinPnznPu4jrj8YjJZMzS0h6yTDCrPCsgpEa7gaYR/C3PM7Qq8K7FWYkq+gL13jCnzTD1K9jtpaYf8XTSeEeepZY6R6U0bVtDUp0Ji1CBUqprERmXW2fMtOwaTKc7jPNMGgFrcN6igyMzkJmMrek23pedQks62ralaZquq/mVcFeBXvrJNwy30/vn83kHHSWdPxiKVWpyWWCYTKECUXm2YVZvk9uSLFexNrzun/2VJkSXC4kT97tf+lJuvOlGJpMJb3jD6/n9T3yi3yXTDSWvYID/fPpTn2ZtbY3Xve51jMZjsjzjm/7WN/G85z0PYwyf/OQf8ILbbuMFt91Gnmf86FtEv2xUivKqi7VtqRlJwl6Sbn0WuWFfue8+nv/853XurI6dqpeXl9na2qJuao4dO8YP/4vXyqUNFs43v+A2nv/cb6LIDXf8bz/In9/z5zz6yMM89dQTfPITn+Ad73g7e9f20DYVV119nG9/0bdJMXWcGI88/FWcc3zzN9+GIpAZ1XUaD95x5PAh/urzn+WH/tfXRHA8jVu/UwlYauOYy+B98g8+xeFDB3nVP/4+8izjjW98A2fPnuPzf/4XA6PgpZYwuKir1e/+Q+OQDNWQMpEm39AQDY3N0OglMDftsincGDbaGNbuZpHFrzvIIvVEEEOnVeQd7vq9iVFBwta6xbmwWPqmH0mMIC2W9FrTWnZmc1zw5EWB9ZbpbBtlFEsrS1zYOM9X7v8yG5sXOXbVUfbt38eTZ87wxS/dyzPPnGNUrKL1hBBKNCPybILJJihGKFVSjlYZlSuMRisEn+OtYTaznL+wTRt16ooioygyskxTlkIOzXODc20sl6uR9nvS/1HCRxczwX1DnHKUYzLZsHd2Zsznc0LwEVeTOaPTohewDKMj8ddbrLekhJGQcC1FljMqcrRStFVNNZ/FSEKe9bgcoRVU8xnT7S3apiYzWhozF7noFto2RkWBzGiKPHZ6inptuTbdj0F1vRY0MMoLyiynMBkG1fVmSK9lpteim8/nNHXdYXHGGKbTKRubl9jYvMTOQHbs8h4AzsWdvzdUH/v4x/iVD/4yX33oITKT8ZYffcvCRO+8AYgLTBbEdDrlu26/nb/zwhdy/1e+wunHHuMdb397bJcV+MpXvsKb3/wWPvjBX+KRh7/K6dOPx11Tvj/4SAPppLQXU/hpor/nzvditOHxRx/mqdOPk9ySt/7ET/CC227jyccf4/c/9lH+6I//pN8tnIUAn/rUp3nTG9/Aw1+9n1tuuYUfecPr2bd3D3vX9vAv3/q/U9cVf/pnn+GxJ57gtz78W5y45oRkgxDJlLPrT/Mv3/pWPvjLH+TRxx7hTT/yejFYQdLsZZFz6uRJ9q6tScVEDAbFcxsUOtNjRSEEzp87zz979T/n9a97LQ8/dB/f+nf/Dv/LP/n+aJDiBhIg9TYdRBQLx+4SpiEovRsL+1pYzJWOKwH6CtFUS9mwZNiuhBMNw9NFb03FqgV92bUNjeqwgUfnFUYKRlaU7Nm7htaanWpGOSpYXVtl/ewz/Pnn7+H8xfMcOXaM6667jqXlZbKiYG3fPvbu24fJSpQuyLIxeT4mL5bIixXyYomiWCbLJrhWY63B+wxUgdYlkBN8hnOKqm46uoF0epfSp6Tpnza0IbWhx8fic9EKbVh4j3Nt1wu1HxcJU9PzSPXUuxMtEmaCNtHopU0iev4qSOOZEHzs5XC5x74bJ1UkDcHUZV11cyx9dvccS1l8YEFMYj6fxyoUcUjaVpptG6NwWC5tbrCxeYnGeukipY38afrg8vIeAEcPhtnOXL7XBz79qU/zp5/5U975znd15RTSu1JUUBOjXGnV1WIONuTuNeG8JDZ8XyC8MKkRN7fDjobAJkPtMlCDECXVe0IsFE6jhsTnRWTyt00Tw4/IiVG6I/ZmxpBnhqXJiMzoDlMLQYiGzor0kPS+FM6YdZY2lpkIXqhQWRbNlerxGhQupsg7o6PihtE957SoF567TDSthEfmHLnRjIuSPNPoEPgn3/4ixuOcNrQxnDDSXHXQK2GoqtqXiS0akt3h4zCs3B0CDb2h4WILzovGV+znOAwxfKrXHRgteV7x+5HyLWJTac/l4dfw2H1dsngMZTHGZDnb0y0IPso47XD27DoXL54nBMfBgwc5fPgwJsvY2pS62LIc4V2gqluK0RLjyYSlyZhiVEgYmcXw1Qem0x0aZ2ka4SwWWYF1cPbcBk9e2GQ7GBr0ZZtvGothy7hu/g9wzuCgLEqKsgBCZwhTFy1h1RPnTtoYepUOpbNOgVYpRZ4J5070Ix3Btoxzw0gp8C0mWAoFChFPFKzMDXT8c/LYuLhpGpqm6fpsmJioS167D4E8wkHpkDJDIlnd0zYNq6urGGPY2tpiPp8zGo1YW1tjVE6Y11Ie6IGltVWysmBWz6m9JS8zsiLDq95z/+Cv/ZaCK2YzB85aZ5RkMqZSIonTRd1gAYNRqTFJgJBi5xCrahYnZG/lB18eoIouZQpblIqF6D7s+kwfMqkEDJAoCv1uZ61DqVZwuKg+m5m4+LzFmIzlyRJFkUMQSZYQnAjt+RDDxdBNDmsbRKhPU5qc3GjaTMq6AtDaSCgW3zLaVBW7NEfNsLSoNV0mMo1DUiz3HdY2wJBCMpNSnBySAYwgstz95TvpggqJvwLGsgvL2h1u7gbdhzWFKeRMwHsIARdcJz7QnTdc7jUOpli3wXnAOj8Uy+2uafffhyEyEAnJGb5tuXhxgyw3aAOPn36M9fWnuer4UW686dkopdja2o6djmSDnVUVZTlhNJlgcqkAKMZLlKPYT0ArnFI4HG3wWNdj/K0P1HXLznwuWmCjFSnaHtxX+vsQKknjvNC2Twku5kOvRLsY9kePJ2JTyVtNvLQQAlotev2yWQy88dhfNigwcW6GIHwu7x1ZpntHJG06u8QH0rjnear8oMNME6M/bXpDbDbVmw4b2qREQ+ptoLXB5LIeXWzCnBU5de2YzWu0dd38GM7my4xZlvVdqoeS0QQ6fKRt245P1bnFEbgNpP+kqbpoxHaHD90RP5jHFlg/+pY38eY3veny98Uj9QDoPYg0yQf0g3jtyYj5EHWwiDyxLGNcloxKUcO0rUilBGJtKdEoIwXUaDpi4e4jN1LiJSVJSBKAgLexfEj1D727XY9MrG6sOqdUuEEk70R151t4E8S+lLp/PXgIizI9uzeR3UZqt+fVPfNdz+tKXlIyKELCVF3J2PAJD3G7K21ouzE8vsZnh58bYnh9yBvY3Jpivaexlu2Lm2xuXsR5y/Grr+Lqa45hCsPFjQ02Lm2QZQV71/YRgmI2mwngXIrXFEyQ1WGUhDMqhmZaYQojheoafNA4L4KGVTOnsTXelrTrA1SYAAAgAElEQVShT0gteK+D8R8a427BG8i00I6apuqehVJKKBTxuRkji14+b0k4m6zDMJhHCGFWBYIKaHRMfklJX6ZBBYm28L7XcBt468BlhnUYUSVjPfx9z5lzC+VK6ZwpytNaR4ZBTDC0LcV4RF7muLZhVs0g00z2rGC1YuPcWXydFFcWs+6XGbOOz4I8xBe/5MUxs1hIRicvmM/n1FGiuJvcSvWLLwVau3dXvSgauDBx+7wyBHj3L7yHO9/7/oUBSg92cUGJUU0ZPLkWFiRwXIrvY7jqrCU3huXJmPF4hAoB1zSCP0VvUsFCl6DkGSolTT2Sh+oHEkRSOC5lIqLeEaQjtnMQknKDGvhaYWHhq/R6kMnY39eA6pA+rUQ2Jo/F6d57sdDeQ1gE76+EfV3JaKQ/d2NnV/rc7hIo4vheyYjtPvfwWS5cS3xuqffXbmwtfSbhZamuL/201lI1lllVUzU129ub5Lnm2pPXcOLEMXxwnH7ySabTqeB5GrZnW3gHBE1WKjxSrB+Ux2NRJicvMtnICAQXKMYlJvcYK7Wc86qh9jUuWNkUh943V/Z+d9/7cMMQj0YSAkOowBgZS+sadEjJrh4O6I1+MkRxE4zXkmkFOiZ6rDAEvNJkEKGayJXctbmlzWMIM0CPm/ZNglhIrimlFtR0kwc/ZCUkFZT079ZaglYUEykFrOYVKs8YswJGY12gqhs88dwD1+wyYzaajCiKvOtPGQidVXLOY0yMy6FbcPEpDDwyCY+GnlnnuYUQFTl2c4YkTEnSwUopsqy/8d2Tejcwma41BMTI6t5jE3kZugyaDGwsEVEx/PRWDAO+x7/iZAk+dAZE8A3JQAkNQEVMzcXMYhSu1jpm9DRai6icDx3rrBsl4u+IYZrq1nW07mEwmrHWMx3feMONQrL1vp+0XsIMwev6cHs4TsNxGx67F13yuIbHkCQpc6IHm3XMLkuS48qJheF3pPN1u70PhBh6JwmmYffuNOETbnOZMWsttXVM64qqadi3b41Tp67h4MF9VG3DdGebxrXkpWBA1jm2N3cAzfLyHvKioByVlJOReFOxMa3JFWhiXwmHMrEbu9bSu8HVNK7CYlEmztnomQ2B/mFmeGjEhmVCMj8XZZBUxMbSc0whmjGLnkmq6+yUaLtH128mWisypaJAhHQ3Cjqtx1iWN5gz6RkN8bN0rclQJWMmcFQ0gjF8ddZhG5HXVrGeMpgsbgwStSilurpRkRCTRiUiy6ZonGVruo0FysmY+ZbFBbBeYd3fIJsNcPjoYap5RdO2QtoLgTwvu7bxmfMUeRkpE1oAUq2pmhofohRvBL/TTqqVFv0sL52ZVCqB6DwfRK8oEx0ySGFtEb0rKaSdVxWFySKpL+twMectzntMlkUJl0w6pwP1vALvKLMM7SzLo5KDa3tYXRqDbbBNRZFrlpeX0Lmmjm29TCbCdalcSkVvq6orbOS2mcg/q2PvxllVo7OCyWRCUY7RuqBuHTvzmnllRZVUZwSV4UJ8hmiUkS7p0qADXPBdU2GjBbw13rE6GrF/zyrH9+/l+NoeQnAor9DR8xOlVOH7JIxGdmZZEEoNPJ7Og47+nu4TElfyzhKKh1KdrLNPtX9BhAbElAVivN55zCn1YUyG1oYQRK8+ygZKnasXQcDUREYY4XOqqur6N1gnu3fTtNRN24WZ0kVcepj64Dl69Ai33HILx68+yqyasn52A+8dq6t7hL7RSBf5pckqRTGiLCcsLy0zWV4hK3J8cEIxMbnIIyEZ8Lapca2McdLOa11L1dZY7wjKdGM2DMmSMZExMAuezpD3F1wSQhVjGVQU3A6hA9C9d3jjCE5eV9EQqCDJk2BbfDbw9iK+jUmqM4IFu0i8TZ6/Hhiz3WHmMCLSWnfJvmG/W0K47F47TbbQzzUzSBD0mJ9Ihue5xkeDpoxhMhlRu8DOzg4qz0XyfjoDD14r9CD5d7kxi2nZyeqEcQjyIe9RXhEi6qaVQWNkFyuiHnpQBAM7do7Xi55YekghiGxzpjJS/8U8zxmNRgQHs/mcEAIjRAUjDejq6iqZ0ly8eJHVPWsCWu7yzHzEamrbonIDHQcpkE/G5EExQpO3lptOneLA8oTlUYFr52xtXWLPniX27NtD0OIpaaWisY6WIKatvbNsb29TVbP4YDTWWZFqiYv13PnzzGYV4yUpcxlPlrBBs7G9Q20VW7OG7arF64JgChwGZXLK0ajDLqR/pAdn0d5jvMU4x8E9qxxcW2HvZCyeYvQqlQKlITNK5I+Vw9kmNksJKGM6Y5SMs+6wHIcPkg10XjGrKuq5dJMqS40L0tjGZBkuOCEy4zGZSLDoTBout64maOETOe8iD0p1O3fTNvimkk0oBOqmwbYWkwmdQ3lNcKImMq9rrPVUdcW8qrDBUbcCss/ruNF6AbF9CFR1zWy+Qx48p646wjd83Q1cffVRpvMZ6888zdZ0m6IoUdrFzuCygS5NxuTZBBUM9dxick8+LsmUJFq8A9dKhydlA9oGsgB100ImDXh2ZhXz2uJ1Bsbgo+4ckTdHEGjDRS9c5MHj5hAjEaPFELVBekdqDRoThSJlY0tJH+UDrrW0HnRmUEFjVNYJpk5GS/i0UQTZcJwPNLbGBM/yeITODKG1VM0clWvGhUE5T1NV5GVJlhWdMQouSNNeBXhFcGIDxBkRWe0QAtorWdvBYz3kRhoH+aBwHtrYxBonOnYJ2U7lSWIrLGQGFxItRUn/TgJOeYJrmIxzmsbKeQYG94qeWdM07GzvRIUKFa0v0SrnkeRIlDg2XUYOo2h8GzWU0tMaZrR6t9ukwUipfudlsg8wgvT6fHuTzBiqquqavKZzd0fE7BrbiteXmU5yGg86yCQ0Xtzxx7MsTmqHNopyVOKDJyske6UHAHkyaAHhj0lY05LqRqXiQELfPMs6+Wytt1HqGUzsHOWDompaGgetC7QerBfPDNV7q52yaxDXLXgn7bS945nlZZYnI0Z5LkmJJHMUr88HB6bvLem9X/C2lNJ9FlQRvc0kBqlxVkLmQ6trHD2wXzBFTfTOcxLjvC0LSdao0PGbQhSBTAXCziY+UQ5a0cSQUSuNNqJlVdc1SgmbuyxH2NYx3Z5SVw3Oe6qmZl7XuCCgfu0sXmnqEDC5dOyebk+ZzudMxmNuuOY4X3/DSZZGJU8//QSXtrfRJmPf3n1sbm5zbusCK6t78A68CzSNxblAWRSsLK+ytLqKE0xCtOGisKjRnlyJPHYTdeeCE4WHunY0rcf61FCkp5skiECmqGzwKUwcekA+hfcp8YQBI01SJFnUpX/kPAmTS1huiGFk7FkaOzX2OG38bo2KcId0bHJIhQFBRYgjXdPCEu5xz2jgIObNrcd6Ucvw1qF8IMtyvA80oZVs5KD2NNVZd+fqvkt8eg9xfLX05ywKtPe0Toxq27aElEr2DlyfjLvMmLVNy4WzFwfcLcl0JB6M0ZHPEiWAVJSGThpeIv6XQhR6TGhgeHRauPTUBB8fpEutseJ7tFb4qMtvncOmJsADqkaiBSilaK2Vc2s14IqJYdABCm04t3ERo1K6WZo1mCzDeYdJSrhpgPvIqw+HuvvrM1GEVErlF6R8U2cqYzKUNoSIKDkfaJzDWk+n/aR0Rxju7Wj0Cr1kWK2r2Z5JZxsd8cAUR4bke2kxTMNwJ8RJqKLRT9SP/j09QO+s5/Qz69y4cxUH1/bgfUqlK4qipCxzyrIgL3KUokuIeBe1pxR467EudU3KpW41Nnk22mCd1PklHAbV0lo5z6yqaOq2M2ZVUxMUNE6aYTgrbpULLbYWJv3a3j2cOH6cm6+7lv37Vrh47izbO1NAPMBmNsfojAMHDpAXI0IA24h8elKA7UMfj1ICuGcmNs5QOjYUSbW5MlZtY6mqlqbx+GA63HgYng8rF/o5sYhd9kkQNZCjvxyvhMjHo49IunU1AOsFMhCjnDZmrTRGifej4wYo8xecYSGTmRyPBXqHUh1+nqCJhKeBfF5phckEmklNikym4hQVGkpuhomKYUjrYwJCDFeeFZSTCXVjwTpcaLDzupcWcp5BaeblxqyaV3Ewe42tbo8JdDtzj+/G3GXcgvpuynGxRFB7mKzvjEJgYcCUFiJoiAKP3QMLHtelYVU6Q/y3fL8W0pZMgMFnE5SeHpTJMqFPoDo3NpENzcLkSTtRb8pU3NUgeW46AqjJuA4B7USPSAbPCum1KDuDknlNUAHrHWAw3VqQsU6IR0jDodKk8NEDWLhUBm/EhygGGTeejo+lZPxcl0zpJaZTBkwazHgee+YZXNtibUtrBcccj0aUo5Isk8WuVb+oQvCxZEV1pTMQO3HlOZ7A0tISRaHYnm7TNtIVflwWeKWYTqfYVigWjW0jHmWxXtoHJ/BfKfGAd2YzZnXF3r17OXXdKY4fPUJm4OmnzlBVM7KYfW93RGxwbe8+llfW2NrapiwKcikjxnvPzs4OBAW5ZryyRKalFCnPNJmRQn434IYBWBeY16m5LdGzlrBvaKyGPL/k4Qw5Z8Nsr1aSUBoasiGNZve/h0mV9NrQwHWZTiVdyFUsZFdpE/NesEsnUFF3vWqRSL1wjfFeDL2og6wfkdIP3drrjyTdZTq14Q7I7f6tlO48SokESiblhLbZQQGFKZiGGURCcqeeEY/LlWb90DEeHvFGB2qoaeEA7F5bIV1kSHZukMlLm1AkAarkoynYvLDBS26/nc/efXd3PeL5sPCQ4tXKOeMlhwhCC7jd7zDpqnpnJ4aMSrhh0h4nXnV0xxcmkCJKRg9qKb3v2s0PvZwsyxdqIFEZSvnuMyBhowqQ57EwN2mcBQkEBiat2xRSOcowZPTd6yyE8yEaqF4cb0AhSZ/1uzwziPyjnoYyU4qt6U73PqUUblaxM69IGTcVvcCO0JnuwPfd3lMNp2TgsuiZCs9P6yx6ZA3T2Zw2erl1XeO9p2lb6qYmQJe1zPOC4C2ZURzYu8Y1J67mmmPHGJUFWxfPM9/eZHlpCecD9axidXUPR1f2UDct586do25airLEIMmITGcCBxEz1XhMFshy8coyRdcbI01f56GJIWbbeAIZSueCK4cuwLviMVSIuKzcLPRGpJvlg0V/pZ90dB5U8sqGAH6cRzr4y7LpMlfApOJ170FZtJYOT14n2AMUIlOlUhldcAQvmU5RXOq9LhPx97YVQmymlZSL0Rs3WV6hu16lpFmRw1MUI4p8hHNbkQKSo8gQCNaDVxidd/d4hULzKxmy+Gu1+IiSQesMmQTU4qJ2H+mNyPDMwv6VLsmpmcgwazb870LlPWkA4s2j4nX1ix6l4kPq60QTvuCdhJLaSEjoIxs761LjcpYFOxgYeIL97iU9EASEzExGkUvplMn6kqzkAZrIdPbW4mwLQQp0R0XOqBDOU/CpYFzA0be//We5+57Psn5unR9/649LCBwkTGmtlYXeNhKK1YItzauK+bxmPq949Q/+c/7rvV/modNP8sEPfYjJZJnWOtrIffupn/7XfPXxJ3jszDPc+f5/izIZzvcCk84HqtZSWUftPJV1VNYyaxq2ZhWXpjMuTedsz2t26oZ5Y6lqx7zxVNZTu0DjwaIJOkflJZXzTKsaneeMJksEY9iezbi0vU1tLS545k3NvG2o2pbatjS7yLGz2Q47Ozusra5y6003cd211zLKc7y1lEXB0tIKWV5gdBbFAzNaa5nN57TWUpQlCk3qelWWJUvLyywvLzEal6KEYTRFZsiMhBbC5RMwniCUg7puqKqG1oEiQ6ucxCfc7ckwmMfD+ZxC3HR/YeDFia3pDVmicHwtgzakuQw9N/nx3XrQWnfJH7WwviRt4JNxWjCivW101mIjzy/Vx3b3EqGDPmru6SgdXhZcvz6TMU/fpySJoFTGqJxQFCOaxtLWLQq590znmNg4e8hxu8yYaaU7Tyl5Vb09Gwye6oI9+V8qZQrJgtN1Rhpk/DsXkhCzolp+lOpLerpv61zO3V7Worvb7U5BcCGfVBe8qIt2DzP4LjxURD5NSGDtgP8UH0byYDqWdkjh2dAbSViX1Kr6EBJlpgv1fPyFNDcR8FjhJZMVPHlmKHIjKqU+dskBHnzgAX7ix9/KX37+L0nekbUyieomGa6K+XzOrJozr+bMKzFq3/Kt38qP/cRP8MpXvIIbT51idXUPP/eud0ZjJRUU//D7XsnffeEL+Z+/4ev52899Lj/+1rd2gDFakhIuKFrraVpH3Via1tFaT2strXNYJxw66wKtC9Te0/qADQqnDEEZlM4wRUExGqOzAheU0FLQoAwh9VBSksWqbEvjLFZ8JZTRmMxIeZKWnXxlacKxI4c5eugghTHMptvMpzsYpclMTltb8rxkZXkPs1nF448/wdb2lOXVVVZWVhmNxxSjEcvLy52q6mhUSm2pVhgDWS5cLhQdbmhjmVvTSmlNVbVYB6iMQCYlTqgrzs8h2383+Tu9P216Q74f9GVRiXi6WOC92IOh04YbeuvRM0vn2m0w5dr6eU9wIpYQn46sZfl78BZnG5xtpCQQaealgvSsLXJD8BbbtOCFrBuHUXAuHzlmUVmGKOutBmKnWhvG4wmj0RhnPU2TyueSiyT2Zdgw+gqy2aYPZYA/+MQned+d7+XP/vQznHnqKe768G9z4MCBzkqH4LnvS/fy+te9jrt++8Oceeop7r//fm6+4Ua01lx/3Sl+567f4fTjj/OVe+/ltXfcEQdemr/+41e+kv/+Xz/P448+zJve9COdwVMdJjXE2tJ/FAnwv+OOO7j3S1/izFNP8aUv/TWvec1rIlgug3Lq5Ck+/J9+k0cffpjHTz/Or/6HXydEDtdkMuZfve1t/PHdn+OPP3cP73zPe1lb2xvdXVnUH/qN3+SNb/kxfu6dv8Bf/Le/5nN/+d95/gu+mSzLOHLkKO95/y/y2c//d/747r/gtT/8RqwTvhtKSl8OHTnCf/6zu/mnr34NMTBER5KiNNRoMAryzMTwzEaA2fKhX/01Pvtnn6Gu5kg1QduxpJu26X/i71onP9Zavu+Vr+R377qLv/qrv2Jza4t3/NzP8b0vfwVFWRIIvPJVr+JXf/mXefTRR1lfP8t733snr/qn/5RhHawHHApMhi4KTFHglKLxnjbIa15JzaIN0FgXm+IGWhQOpJ5Ra9AZOsspxxOKoqSqG3bmM5RWUZp5BaU1TSsZzrptaKxkw3yI2KsW/O3gwQM861nXcezoEay1nD+3zoVzZ7m0cYFLGxfZmc2oraVqWmZVhfM+6tF5dqY7zOfSiFlpxWgyZmV1hclk0uFYWvULUOS3Y52vc9GbD9jWU9ctdd3iXJB1E6ShT/LMdpeCJUM29J6GRqooCsrY/Xv4uW6xDgxQMoC7PbMFz2+XwRwazSsrmfg+g96FgPFz9ET5YWirlZRZFUXeVcyk/hqSVLG9EQbpwxC/K1ULyHeLt6ZCiB2pFGWU3FZB4a0TCkjMaorBFTHLr2nMQpCUtXe9QXjZy17GHa99Ldc963q8c7zrnT+PVn1GBeB1r/0XvO997+Oq48f4npe+lO2tTZbGEz72sY/zxS98getPneKlt9/Om9/8Jr79778IrTTPfvYtvOud7+C1r30d199wEyeuujrWdPUPpxu8QcyXsqsnrz3Jz//8O/hnr341R48e4+9927dx7733RsATliYTPvXJT/LgQw9x6623csvNN/PHf/RHhCCh4eve+Ea+/hu+gZff/l285EV/j7379vEv/9X/0bnJ6d7+4fe9ks/8yZ/wd297Lv/oe1/GuXNnyYuCf/vLv8a8mvMtz/tGvucl/4C//x3fySu+71VdFYMxGUtLE65/1g0cPHhQmv4mBYu0U8XwJTOGleWlztCJUqh4mSngFgqFp7VNp6LggnhJtW2xLrbSC4Ebb76Ze+/9Mq/4h/+Ij3784zz44IOMRiOuu+46tDbcdPPN3PvlL/O617+eX/3Qh/jyvV/mxIkTjJcm3Tl8iF5RXpDlBVlZipxy9CC0yTBZQTEaMV6aMJqMMUWBynKUyciLMatr+4QbWOTCdVNgg0dnGTZ4Lm1tcf7iBTa3N6maOVVd49Esr65hTM50Oouh5awTBD1+/BjPec5zGI1KnnrqCcBz/OhhVPCsr69TVVXErQxNY6nqBq0z9u8/wPLyCufOn2Ne1xw8eJBn3fAsjh0/ysrqMsWokMyl1pI5j1k6oRIJLNHWQs5tW8uljU2qqmY8XsJ6mNcNo9GEodDBEBMbqsV0VRO6FwAYhtLDZiDpHKk7UqqGSK8NdeaATmtueXmZtbU19uzZw3g8wfsg+mAxrE1cQzEy4nEmg5SoH0qpuMn2dZghRlYSegxCSGJVRFMRnO39kODBSy+DPtmn6Fsl94YzOTKz2YytrS32799PU4vKxs5sJ8JYHh1ipcsgdrwcMwsLfwDwsY9/nPvuv4+6rnn/v/0A3/3S74ZoqdMJfvuuu/jsf/kv5EZz+vFH2bx0iZe85MUUec7P/cy/wTvHY488ykd+9yO84uWvAAK3v+Ql3H3357jnz/+ctml457vfvWhVd1/PIBZPh/ee66+/nuXlZZ55+mnuueeejrbxnd/xHWit+cmf/ElmMxG2u+u3PizXrTUvvv12/u9f/zU2Ll5gujPlV/79L/Lt3/Ed4hEOwux7Pnc3//n//X9wzrNx8SKnHz/NDTfcxE0338LPve2nMdpw6dIlPvyb/5HbX/rdC+KCpx8/zfVXH+ED730PwXuhNGRZ10TX2UQLcORZxngkEkRt2xC8oyyLuHv6TsolQqZ9SkUPvQD5WV5aZnt7i+NXHeemm2+WBQ5MlpbwIbC8vMx0e5trT57kpptuYmtbmqmurK721RnaUJZjRuMReVFEIrGhHI2YLIlMTha7vQfAZDlKS6g1Go2ZLC0zniyR5QVKG/KikFmjoBwVjEYjVGpqGzyjsmRUjmjbls3NTepG9OhTgmJtbQ+33HIzt9xyC9tbm5w58ySz+ZTp9habm5fYt28vN910E4cPH+PIsas4cPAgk+UV8mIESjHd2cF6x7Hjx1maTNjY2ODBhx7i9OnTtG3D6uqKELSznEwZ0cwPxLAtTsIgTWmm0ynTnR0a6/BKcMy6FXzvbzqG+NHwJ5VttYPP91niRcN4OR62iKuBXI80EBZ8cT6fC6dSqQ6jIxpTE3XnskEYmw6169oTvSIpSy98t44VM76PQIaOSHJMAulzsY8GKekRGQOtpcwLmrpmMhoDUnTfVBVNXQGxnNBIyWM6LifNdimznqS3vr6OQtK758+dpyxL9q2tSQ+AOHgPPfQgRksqu8wkS3TNiRPsP3CAx04/0Z1eG8M9n7sHBRw6fIj19fWOK7X+zDNx1BhkQONNdr+jG5hHH32UH/iBH+D7v//7efe73sWjjz7Kv/qpn+IP/+gPCcCJq09w+vHHY82f71PmSozZwUOHOHfuvPBzFJw9d5ayLNmzdy+bFze6+3/k4UfEJVaa1lZopbjqxAmMMfzp3X/RXZRWmqefPsPy0nJX/G0yI2CnE8ltAdY9IXpuaaI2TUsIsXDce+q6wrlU2JvGThIXKKAVLfeObqFY0HWb7kxZWVnhzve8hzvvvJP9+/YB0kDVe890OmVldZUfe8tb8CHwnP/pOQDMdna67xPOUJTm8Q4bJ12WSRMM61qqao5tPYGcIi8xeUZQvivPKkclRSFEaxt5fVmWUe2IWmmeZ0zGI7x38Vw1RovAoYQvBbPZDqsry9zwrOs5euQwGxsXuXjhgsAY119PZjRbW5s0TkLu8+c3KMoRo/GE5T0rXHPiWiYryyijqSPemGV5bOaRMx6NQWs2Ll3Ce8uRw4dACYZkW4drLU1V4xor/U7blq3tbWazOapYlgXehaEObS7PZCYvp0tyDXblBfwsJoauxENLv0sGa/fvOy8vZq9d42i960D/PJOuSj4KRGqt0cpgQobxoeNlZhpAOlmB7jtbIbiYip5RcmggZkjTvXnT4eSaHi8OMWIKVtSRQwKXdYzNg3BlrHVMVlaYTrc7HLOOyrx1PR9IikcoKh6XGTOhKgzDOun9mBbs8WPHqOuaSxuXOna0Arx1nTaSANyBM08+wYMPPMC3vuA2OvhRaXwQd3R9fZ3nPfd5ImMDHDly9LJJ0D3Qhb/11vj3fu9jfPSjv4fWip/8yZ/k3//SL3H9dadQKE6fPs2Ja65Z+JisexmIc2fPcvjw4Rj2BQ4dOkxd12xuXBLMK36pjQx/IfXJRHvyiSeYbm/zjV9/i4SOWnodBqTUJSkczGZztFIURU7btB1twxiDNpIwCDZJ2FTkRUmWZYzGI6lJrGZdWOydxWTS7UachJ6YqLqssniU9993H7feeispn3zLs5/N/0fZm8VIlmb3fb/v++4aa0ZmVmZV9Trdw+lZKBs0BVuiIAkyCAugDBC2JcCwYcggKEgkIArScLFhwA+yRMsgKIMPhmXyyQ+ibZmUJcgjboIlPlHmYpJDDmd6lp7u6q69MjP2u32LH853b0RV14zhGBRqujIyM+LGveee8z//pa5r3nvvPUIIfOUrX+Fz3/md/NIv/RLKOT772c9x7949drvt4RCHMHSNcveUEIw0NeJY6p/HbQLSXZlE5EVaCS8vTRMInt12gyKQJpr9fouzHePxiDw1eNex226ompbEpNKRedE9KgV37tzm0++8g+1aPvzwA6xtxNq5qRiPS0bjkq7ryNKSt98+Z7vbU9U1z66u+ejhQ1prUUYzPznh/PxcCLveU5Ylt87OOT87Y3p5KcuXiNvYIBKkrqnZ7/ZUdYPtZKSTcTiQp31YikcnUsB788+PncMvgP3H4+Zxx3VMVD02V3jxZx3rG49/pu3ckUkpfKx7iwYJRkQGKC8YlXNCGRqCfMPRRvPoZ/U33OOvCR4oCxujerOFWEo4GDTK+4pgfk9JOfpdKnhQcsNbLZcoBWVZ4qstIUtIUp/8efUAACAASURBVAlI1gaMSlH622BmesB0+pEFvv/7v5/PfPozlEXJD//wD/NL//yfy0FRGhMr4zByOkdwFm87/q9f+VVOTk74qz/0Q5RFTpok/PHv/m7+xL/zb5OmCb/0hS/wPX/qe/iTf/JPkCUpP/ajkgEwAPAc/f38RwnAW2+9xfd+7/fKm40f+nq1GjCpX/21X4EQ+Lt/5+8wGo0oy5L/6C/9peFD+bVf/iX+07/8lzk9O2UynfKDf/Wv8Wu/+iuHE0oLnjCE9IKs+o3hD774Re7d+4D/4r/6r5nO52il+cRbb/Fd3/3HCcgo0bYtd195hV//jd/iP/nP/vNBJWHMYfPpXIjLAtnodV0HBEajkul0Ql9ZksGtQKTZ0rXk/PW/8Tf4wz/6Mq++JnijiaqJ//Xnf57/8C/+Rf6t7/5uTk7m/PhP/AT/+Bd/cUjD+V9+/uf5gR/4AT7x5ptcXF7yIz/yI/z8P/yH8fyMnXDw2Laha2sIjiLPmIzH5Hk2uCIUWcZ0MmEyGpFnGb2fvXMxSSseB6Vks7vf7dis1+RZynw2ITGaar+l3m2lUzImhtYGmn1FsI47F5e8/dZbZGnGerNGG8NoMkYZw/Xqhqubayk83vHs6hkf3b/Pzc0Kaz3ewWa9Y7PZMZnMWJyesVytWK3X+BCYzWbMZjPSNKPIC8bjKVobCAbvwMZNWlW1tE1HZ2WcdM6RpClJmkiHHFzE2z5uu/TiWHhcZJ47q+PXj+2mX/bnuCN72dayV8lofci37KMZu64bNtaHRduBeiFFsd/u+dhohNgECEBvtMJoFc0M1MBQ6JcIErbso0tNPJ9UbwQpzyUEGTdxA/YmnDUvQSvBUtd7mqbiZDFjPCkZj0tOZlOyXPIVstSQpwep1EsDTYjdS19B/uk/+Sf8T//gH/Duu+9itOG//ImfGF6YigdG951M3FIEL3fi//g/+H7+9J/5s/zOH3yJL3/jPf72T/4kZVmSGsPXvvpVfuzzn+dnf+7n+Pp7X+fevQ+PjA/VC69JDWWsx4uyLOXHf/zH+NrXvsq9e/f4nu/5Hn7wB39w2Lrstju+7y98H++88w5/9OUv80df+Qr/3p//88MQ/T/8zM/wR1/6Q/7RP/1n/J+//C9Y3tzwd/+bv/3cpqn/lGWzqumsjZ1Wxl//ob/GxeUl/+yX/wW/8Tu/x0/99z8zjHN5njOdypbszU98gouLC8RBMyHJ0kHiI7Iroaf079E50e79b7/4f/BkueHP/Nk/y9/8/I/y4OkVf/PzPypFNZGQjFu3bvH222+TZ1l/jgDwq7/yq/y3P/mT/MI//kXe/frX2W63/GhMjFdK8XM/+7P84i/8Av/q13+d3/v93+d3fue3+e/+3t87LFziyW0S2U6VZcl4PKIoshhcIcoAIcWm5IWEs3ZNg+060jRlNpsOKUjBO4oio+zpD4m4reSpIU8TsjwlzxKSxAgf0Ium79atW3zuc5/jzu07XF094/79j9jttnS2YzqbcHq6wCQyPrZOOqmmaViulqy3G5I05fzWLW5dXFCWJbPZnHfeeYfLy0uyPMc6R1VXWNsxHo85XZySmgyjEoxKBMz3vdWRXKxt1+K9w2g9LGQAtFE4bz9WtI4L1YuA/cswsY9TKp5/vNitHW9KrbWCzfecsyMKh3Dj5POx0epdpFv9VvUQRKx4kdLRU6+ex8l6qyvB2+T39Mnm/ejZd4ny336gfx1Bc4f3iyPLErq6Ic9z2qbibHHCaDRiVOacnp4ynY6ZTEaMxyXlqDiUiBcP1t233g6rZ8/kqlDwy1/4Av/yX/4rfuqnfgoF5FkWtxP9Gzxs5jQBgxfdoOkj5kNUa8rV4QPxIo5bHRj0jKK275lrxzXsaOzt+Rn92EjvOiknmlbI+lYxdD/Be7n485xxLKQm3oGSNMGkInESy2t5J/qFFTT97K+FoT9IlOLLck68t3QkyColkh7p7Bier9Qx7ygc/gTxUTNRXpVlGVme0dStxNhHHpCKd9teFmWdpW1aup50GQ6iY/ldDCf3cFKpniuonrtphRB6TyIAiizjlYtLRmVBUeTiAdZ1VPWe/X5P27WkiWE0HpMYI0Jy6+mcdLCv3LmLVoGuayjznLauSaKzR1NXBGfJUkOiFU1Ts1lvqVtPa6Ujmk4nvPX2J/jkd7yNd5YPPnifpq3IspS63jOejDBasa925HnGYnFKqgpWVxVPny3ZNxVFWVBORngFTdugEsNiMSfLMk4Xp9y6dYvpeEyaJBRZRp7k2NbSVTXeCbVgu15zc3ND07Z0PvD0ZslHD5/ShASVlVQWvEnBCJTQayGPCbPHxam3zT7OYzgmvObmecLsyzq6/vtfRvlI05TOyfmgEkOSCku+ayts25BpTZFqxgZS5UicRXtLoY04Jms9jOF9cRPnnAjUH1E3tD5MZ957rHdsqoq8LCiyDNTB885EnM4ojQ+H4BUVoouG1pAkpLMFy+2eW7cv+FN/+nu4urnm3W98g7quGE0n4q6Dh+gK/T/+z/+7gpdgZj7KX5BL+rmLWQHO9R8WUckfIm6m4sVObEGluvvgBq2lUjpW9lj8dIwki3Ovd3HDcYyQBTWsX6Xa93UtiN2vOmhFh3aXEL8vPo5OrH4bKLU13rV0723qhqIVjk6moZghrXIIMeTEeXQU1hqTRcsghXXCoRNaRSBL0xiGoaOXVSyEyH2h10maozti13WkaUZZloQQ2Gy3cRuF2MHELIJhc6QUyiQDDqHi6DpIXBSDndPwbuIH0Y/Rrje6U5HoqyQ8YzSJGQk+4I0U0yxLAR/VEwIuKxLyLGVXtbhOgivGo4JUFxACm/WSNDVcXpxjDDTVnn21p6klTqzrHF4ldF1Aa8PJyZzZbEa13/Pw4QO22y23bp3hbIvViciQFAQHrgu4LpDmhrPLC84ub7NcrblaXuGCZzqdcZqfEpSiLAvGoxGjssR3wi7PkhTimB+clQ7rqFuy1or1kTIYncRzWQ6kiRte6xV1t8ekqWzbMMjtur/lglP9BlG2f0oFvJebfPDyIXXOHsfWDuf+y/77RY6ZUor5fMG+rrCuwjtxzjVGkSYpeEeqNakCpUKkVniUC3jtcEFh8gNh4vi39pLpPM+e7w6V6HmlGZGmxHG4hnpTUh+PQmK0eK/10qoe/0eOQ6Kg8R1n52cily0KOm+pu5ZpnpCGFO/FdusYnnyJn5kaHC0Ob0U0eL3tdN8WaRO/1s/AIdCHffRblcPoo1AIqTBJZczqq02eCf/KVXtSI1Yxn/9bn+dH/9bnP/by+scP/pW/whe+8IVY3PpfItQNpXVfkwghdn460OvGgg+EvjsJhyIpzhwv6j+f24WgtabrZMTAGOqmJvEmZiPIdrFvvU3k8Gh9SGdabbcQx9RhMRCTn5yz4tGOIfhA27SUZSkXnXdsdzvh1gRJi/LKHYDTWMy9VtjOxsLdYyFq0PwpJRwqozVZnskqXO42h63ocPLBerdlMp2AUuzq/SDFGo9GJFreW5GnTEYjkiSjqhyp6mSz13RU3uFcR/AdRZExn08pRgXV3qE6jVfQBo9Vii6IMmI0LplOZ8zmEwiWZ0+fUu8rjNJs11vapmY6mWJrT9dZ5uNTJuMx2+2Oq6ePmJwsSIscqzz5qKQsS87OzhhPxlw9u+LrX/064/GYs9MzLi8vMMFQ7xomkzFnJ3PQipAoUJqrq42MSi6gVCKmpNQEr/Ctlw2hNnRNgwuGPEnFgy5EABwIMXA6RFxIoZE6YiVnUhtMovA2YF10ZVU9KB7rhe6XA4fSYt0hds4kCoNgfU1j8d6Q6Bwfn+8DYvSYGjEPCB7jFEYFkhAlRFa27ntXYdKENBXfORtnpdZ1dFXHZDIeusUDuR3Q4JVGZylog9XxfaCFDK4U2igq1y+U9GDQSfAkwRCCkmOpQBcZX//gHpvdmrprGc2nwqfEYXEE5fBHC4CPpzMNn4D89/f9+38BHS90I2sblOpXrocD2/8dgh7GRB+En+OD3P1Rca5O0mixLHSDopCRKckSNvsdzjt++u//ND/993+aPsqu3yKGCEzLweiB1WMMQUnH9tybim8oDLMpQyWNXWUfHBzCQWh9kFgd7oIu0ijQkKUpGbmMmNaCIiYyh2G81VoJkBy1bFkuixCTmFj8pINDBdFeOqIYO+A6R5d0FEVOkefUdTV0WdqIMZ4IiP2whVJa0yWt8KKOb0ZDd3pwVehHXt+5KC5m+OySNCEvChyBq+UNZV3ivQXvh07TxnEkNYZQlLJq954sSSnzEpMorG1o6xofLKNRgfOO7XZLVe2oq4qqbWg70bjaEPC2IzEpp6cnzGZTmqal2u9pm4bpeEKe5aQ6wXYSAp0ow5MHT3jkHZe377BYLHi6WZO7Mo7jKW3b8vTJE/a7MUVR8sc+929IR6k1qU7pWuFcBedJjGE0zujahN1mC0qIpgBV1ZAGscp29hgicIjZbkAFIY/qMIi0DniZCLgioB7du5QA4eJfRgwdOxorD6fpC7DHC9dtjycosfHqrztM/Jy1EuG4D9iuxQVwoTdkGHoBHBplFCpN0AMfUp6QKFF12KOxd8h9UOAUWMCrBBuvK6MMyjBECAags3Vc6QXx8pefNLyXtukweYHXhoePHnCzWYuhQZFxfX3FbD6hN8gN+nClf6yYjccT6s2GLm69ho0HUoG15tCJ4QeMJRwVt+DlgzvEwx38xYyOPmVGk2UZZVEwGssoZVJD1bUYF/Vlw0E7LkAvtNv938PoySB8HzrCo23S8E2xMPYFTql+Jc3BBSB2Nv2HrZSirmqSVMitgUBnu8HbzWhDXVWYKE2RD6YRrAYZ2Y61eZ3tYkiuAlQ8AX2svdIxdW1LkphBq7bdbbGtjJhS9IgmiNFxoNedeksvLO5tmWRLpqOzSA8pxBV5PEa9LrfICm4tzujalq6RqD6twlECuacohRDrnWWz3UGoSExB8EE4WEqcJ9I0xXnYV3usa8nzjLatsbaj39SmSpPnBUYZLs5kqZGmKc+ePqVthHCLVmy2WyFTliV5lpNnOUmaUu8rnj57SlglTM/OKKJLh7M2nmclWhnqukJ5WfcnRUFZ5pzM5vTkzqatyTMxpGzj1rLtOoqyZLtr8AGaztNrBKHX88qJ1dNojjAOhpsJfXd1KErq6LmCr+oBaz1+HMuWXrYUOH746BzcXx0hKHSIXXqv3kA+dx88LqQo72P3Z6Uwe4XrUZd4bjkleZWN6/9NnDb6CBobPJ0XmZu1gS50pJHPp5WUGo8jqEjM9Q6vPCHWsSApxdjQ2xGJrleMNGV5UTcNaiO22oQgN9j4+Fgxm+YjylffYLte411HgnCLcpOQaEWRSRvtu06yJV2UKcRR7WQ8IkkzQFO3jsYGtMlJRyOSLAeTMJvPuXPnNienJxTR5K9uKp5cPeFLX3uXZzfPqHY1ne1oGxcJf5l0ZL0lEF4wKh+wToIt+l5aK4Mj4FW0gnaOMs25dXLCq7cuSbUiTxLKMmM8LsnLAp0ZlIZgA5vNlqZtJeBUQdcK0J/lBVprxpMxxoj/1r7ao4yibRturq+5vLgUHlJdSTFx4mXfda1gjtaxWJxQFgWr1Yqq2kd7E0NiMuqdSG/6k1LHrV9RFsxOZnzwwQc8vXpGZx1pkqGTVDAPrSnyEUVRPCd7kVNaum2TiLWycMegsx1dZ5lOJ0wmU7b7Pb3YeLE4IYlgbRqLadc1TMZTCELqnU5nGJ2wWt6w3VQkJqEowDsl3YzyjEYFo3GB0oG2q+NxsUM3Lek8WWSeG/I04+LyUjZZbRvt1SXMd7ff451IvwCePXuGMYa3336bu995l6dPn/Dw6hkd8N5775EmCZeXlxIEXFWMRxMuLy6ZjEZsNhs2mw1GK85Pzzg9PZUOu9mz3W7QStG0LXXb4ZGuJijJDuicFQJo0msl+04rtifq4wWr7/Q5LkhHi5hDp6NFXv8CJvb/59Ev4X0Ar4KIvhHKTHxF0pV5N2C2xovMDB2odw3GCjbogxPsz0gn1NoOjdhmaWXQxpFoec2uEwdlF5R4+ONIkkCWZqQmZmj6Lm4zY6F14L0CFbBGobzCK9isN1jruH15l7QsuXf/HnXTUo7HUc0SImZ/AM0+VszKJCWYhOmtgkQpytRQ5hllkpEnIr4NbYvralzTElyHIpBGsLnIcgKK1gZar0jyEePZgnw8hySDxPDpz36G7/quf5O8zPnm+9/gyeOH1E1OXqYs9ytC4tjkewm1qCxaabIkx8asPWstCk+SCvZQ1zuappG7YlBondJ6T9CaNMtxzjFKMhbzE95+/U0mqWFcFEzHBdP5mNF0TFpm6FSRKs3NzQ3bzR6tEzyKum5RylCUJXfv3qVuara7TZSftDSNuFY0bc18OmW33bFerSSSr6loW6EdzOdzZpMpk/GY5XLJQ+OxXc50OkEpRdc6QqdpKkvdNuR5RpIZmq5idjLjs9/5WT75+m2+8u5X+ej+A5yHyXROlhX0ttvj0VguCOviptix2++pqoa261B5IWc7UDUNSmnefPNNzs/Pef+De1RNF+GB6ErrJO27dz85OzvDaDlG3gd2uz0hwPzkBK0N+22Fd5LboHWgszX73ZYk1ZRlgXNd7MiOL3TpAoyGrrNsthsePxWb9PVqTV1JoXTWMpvNGI/HtHUzFOf79+9TVRWj0YjFyYKd7Xj9tdfY7/fUVc14NObi4haJNjTVDrylKHLGowvm8xlnpwu0hpvrG6r9jtmkjAEsirquCUFxs1xxs1xRNZbWRTDcJAQtx6h/xGHgaDrwHFZYPNddhZ51P0wIgkF+HLXlCPb49l3ZgKbH16Hia+hdDxXi2KucuLc21mK84GYG4ZBVnZhsGuUkPo+ATjTKQ+ui8F55yaA1ilR7cZi2IeYSaGzcfhoCVnmME3aD8CodZihmvWmoj2lsFm80TdPiA+SjEWXbELzCWkiLlCLXeOWHDIFvWcx8K8zqLEkospRJWTIpC4osJVUQOmFkOw0kiaQN63j3ThJWqx1dUHQBMIa8nFJOT0iLEq802ajkZrnki3/4B4Tg+fCjD7i5eYp1LfuuZr1eUu33Q1eh44fto35Ntm5O7EgivDeIeb1HaUkNEtzqwKFx3lNVFda2hKQQbkwi+QXOOZR1GDQmCWRZQlFm2E4kKsYc+DTX19ci5zo9Zbvd8uDhDdZaTk4WpFnKb//f/5rVagUhUJYlRZGTZRkhYkVGaXbbLffvf0TTNJydnnBxccF+v+e6XlLkJZqE1jY0TY1OSspiDCgePnpEmmTMZicUV0u22x1t14qLq3d0lcU1HaOyhCB21QGxHXddR7XdUZQlLvKAJqMR8/kJp/MFeZoxKgtA4xHKhzEG23aDZMpow2a9IUkTZtNZLMCWy4tLXn/9dbabHe9/831s55hMRjjfYXctPkioSp812o9MvYeWc9KxOm1kGZQkjMdjlFIsb5YxY8AxnUjRr6qKNEm4ffs2WmuWNzd88MEHZEVOMR5TTqe8FovZo0ePJEowBOqqYr1es1icUBQZSsFuu+ZxCIxGI2bTCYv5hPXqmq5rZTLoOpROWG43LDcbdnWHSjJaa2V0sl5yMpExycVORseteo+ZDRuaIAXMOTckivcYq47HWL/QrfXF7Fgl8NJHXN7YthHowvSbezVI0+RasHgdbyIhLqeUIlGyQAqAi7zHzqlotZOijWwk29jZa4lIIjVu8KO0KBKVivuyRsKS4/EQuEngFxtx3mA8Xgm04pUZbrTjyYxiPKVpO3Z1S5IJ8bdtHTqRu4V/oQP+eG6mls1lbgJFoslMwNDhO9F5pYByDoOLnCFNkqYUSYZKMzaVIyVFZwobFF4ltJ3F6k4Ijc7y/r0P6LoGk2kCDq0huI7ldk0dWtpGVvsy28s6t8ea+hFDzGEPjHppdXtsASRAVdFb6/o4elXVntL0yU8GlMY7cZ7wQdbEaZpQljk7X0Er+QNJmqE1FEUeGeKIHKYoOb8llki/+7u/y8OHD7m8vOTNKKN6+PABNzfXZKlhPp/Ttg2LkwVvvfUW3nt22zXL5ZKqqijKHFtJIEueZVR1jXOOLM+wrePxo6csFqfMZie8+eab3Nws2cdAEKMFtDVK5CrOOkKwGGUo84JxMWI+ndC2lvV2ewhGRlKtx2XJ5fktbtZb1psteNlol3khKffe03Utq9UGYwzT1+ZMJmOMSTk5mXNxeYfxZM96tZVOKjHYusUYRZYXKBXY7jaE4CKtQ/zLiNvn4AUSyPKC+WLB+a1bXF9dSXJTkQ8XuDgnyKIFpZhNp5ydn3OyWKCj0eP777/PvQ/vcXl5wfnZGbvNht//vd8lTRMuLi4o8ozTxYLZdIpS0Y67aQiuI8tEZdFEo0uUoo0mnHVn8WjRaAbBkENcmvQFJ8QiNgw//bjIATMj4pSHr8i5FLQGffDyP/6Zx0XtxWyAFx/edyhleoQuVjnkeBtNkuf4zornHAqlPC6AilJGr0zsrgI2aLkGtVyLDkMIRnJNnXSvnt6TMBAUdDHyToTm0sVqIoUpNiNKh8GNo6cIoQ0kSpYnBrIsx5OSJBkqrlN8gGbfgPExk/bwvj9WzG6dTgjBkygVcZyAt7V8gLahSBK09+jgCEqjvMYoj4+e4Vk+QWUjApp909JYS7vdEqqaoGBX7amqHUmekoWEut6hgiPPU9IkYV9VA9ta+4MNUf+h9hYqiVaDXZD3KXgvFAB3WF8/z1cT7tZut2ec5NixO4SPOKAFZwKkinFZoFLFnr24WZiUNN6xZtMZ18tr0sxw69Y54NnvdixXS95//z0JzEhT7t27J572tmU0GjGdiF/W66+8SgiBb35TNJJpYphMJgPOtb7ZkmclJpEIPo8XcXgI5KOCzWbLyWLBK6+8ymg05v7DR+z2++hmkZFqyQvddTuauo5RYI7RdMx0MmG93qAQLlzdNKyWK1595VXuXF4yn81JHz2K/vziOzWZTBiPx9R1xdVVzcnJiaSBJ0kMadFc3yyZX99wMp8zmowBz3Q6pqhSbpYO51q0VpRlSV2Le0mPkWmlSbRBaekku66jih3U1dUV6/WaNE0ZTac8e/IU23WMyhG73U5oMErjuo7NZoM2Bh+7m/nJCdPplNl0ymuvvMI77wSWyxvapiXNEqpqi3ftUahthw2KDCEI17VgtjpNaOo9LsC+6dBpIfiTlklEGS0LMSU5FCq4jxWgF7upfpoA4Ij42hsNHj//ZUXtWxcz2WyLs7EGHSPaIsEdK1CBD4HOe1p/iIHUKFKk2ARlJIc1Pk+hSHxcAniFSQuCFetMAK8lrIcobarqWkwmVULPm5TlU4rJNO12LwTc4X3JNWuBxEGqYb3asK9aLu9e0jjH1977Bq21TOcTlHFy7mgd6WHfopgp36KUzNgqWJSLy+TQge9oqoo0MaQE4c9oLZyoumFvd9TMKIoJIJvJfd2Q5hneduybSqxI8CSupWsAvOTiOclh7BnMWItBkeSZANZW4sCc7QOGhd3urcXGZOveRsQHJ4kwBJq2khWxF5D+0aPH5MqwmE3pWkuSGkxqBiyuSFL2+z3eCeg8ncgmLoSAMlBVO05PTnDORR0obLdrnj19wuXFJaPxiBACTV2z2WyECf/KK3jXcf/+faajMePxmOl0yna7JctStNaMRiNCqDg9XaDQkt7UebrWY62kVtldxXK1wQf4xNtvM53Oaa3l5mtfIxBY3H2Fbt+wXa3Ej46ApdesLgkERqMJTdcwm5/QWcdkMqGu9uz3u3gHlG2u7RqM0Wy3G6zr0BqS1Mj4FS16skwCmvdVxc3NDf/un/tznJ4t2G1W1HXN9fUV1nUxzSmNo6uibRPBRLwjKKHvpNE+PU0NVVVxfXND23WMJxOJtIPBWNKkCYvTBQR4/OQxIQSmkwllWVJOxswWJ6RZxm6zYbNZs92sUSjGoxHT6ZjZZMx4XNJHAzpr0YliMpnIRrOJdJgiZ1u1VHUbIwIdeaowaQZKE5SOihVJO3e+lzVlhy7tCOgXfEzkVr1jx8ABjNefd24wNzwOCzkuji8GlrxYAL33QrdRStLKYyB3CHHU94jLh4/JYFqD1tgIMWRJDN4OjqBlY24jzUKniYQda3FNkfen0Ukq7i9dSzAJmCTmZfqo35QtZVu1oAzWtfFaT9BB0XWtUFeMLAM9ktp1eXmJC55yNCIjUHcNaSZNjNFmSFN7aTELXoIkbPB0LWSJlqQaBUme4FuLMaIOkI2UkmBY52k6qEODaizo6OXeVDS+wwO7/Y7GdhgNLoZ7ag3BKmzXCkfKd7L+j+4S/YcwbIPiyllwMnHrOEg5oPcKEogixlahUEEKxHq9oZrvcV1vYSw/xyAfeNO0BNvhkDE0eFEmaJOSaUOz3wnlIBFSofMt3neApSwTiiLno4/uM5lM+OQnP0lV7fvTjvF4zGazAWS0mU6n5FkSO5ZaOtsiA69orcV46YzF3VQIolmWcXO9xIWvc/fuXc7Ozri9WfP02TPWqyUmaOh5ZKFnjUm7r7WJdjoz6qbhzTc/QZqmrDcbbm6upVPtGvG/Tw1lWWCtw3Y1SmtGo4KqqsWeyHt2+47lasXlxSVBwZe+8ke8cnnJnbt32KyXLE5PJHdTiX5xt9viXDrQXdrWYq1EF3oPWVagtRIssG3ZbDY0jUihZKwU+kyWZbzyyivRs20zSG/2ux373Y40zzDGRNxNKDFd17DeLBEbaIc2t5jP50wmI9q6YbPZcHX1DO88s9lUHDBS0c8GFfMwdYJKM5TJhZMVcdleiP2ic+txkYkEo4g9Hkwa5YvPh5h8K7b/typmz39vxCMjftb/YqGGyLgXg+FkTNag+oKm4qIiSQiRPuSRTk45CRJWPSFeiY1V7/Ua6PExdThuzonVvAso09O5pEsU7inQu0lHmhEKVJLgrcZ6hzEp+XhEVhR0weG0l3EWoS4dL18+VszAwQAAIABJREFUnpvpepEo4ANdcHglq6bEGMShPrauwdEqwOu4JhVGt4v+4SJdcjgX6JyjaevhoCYmI00ib817XOdoQ0cTarq2iye5l4h67+OS5iDW1ZGC6GxM/vaxgj2HL8Sjc0RC7GIIQy/2tVYImCqVUUdOBoOsERSNlQsujbuT0WgkC4jMUJY5zleUZcbdVy7Jspyu8/zmb/4Wo7Lku77ru5hOZZVcV1KcnXMSNR9PtjTLBlfN/b5iNppJknUvUzJ6sHx2znJ1dcNkOkGhWK/XdC6KuudT6qqia1p00Bgt9ucDQVYnKK1oqxa130mXo0SC8vjJE55dXTGfz7l95w67/ZbdZs1+vxP/r0awO5OkNE3vhZWQpKkoAEY5SZby4OEDPvH6q9w6W/Do4X1ubq7RBrquIS8ysiiwF/5XoGsdXWclsKLzGJ0ymhQoo7C1pW5qrBVqTq9acN5zdXMN31Qs5ifS2So5Rqfn59Rtw3K5jPZJFQ/u32c2m/HWW2/yxuuvc/fObZqmJssSmRZ2a/GqD44iFxVHXVd4oYVjvQTQNtYK9UIb6Tp6YXc0bgw9GK2IyeXPF6DBGktFeET1xgy9giY+7yVcsuMt5vEi4GPja9+lJQblxZU4glH0GKXWhtB10XFaCkyvElDxZ/T8RB+CvJfQP0/eQxaxtaB6uoXkYyiAo0JtndyoALwX26gAQ17tgOcpMCaGCCGXcds1bHY7WttFpxqNb7uIJ7pByxz8t6FmtK20uUmaxAxLL4k+XRvXtRqr4s4rQGaExayCVHwPuNARvOgmlYagAiDdS5+tl6WGIkkxiRLzOysnTdu00XQwHkTfj4+HD1k6R9FfSjBCGFbdzlv5EJQSEh7IWKrEsibPc5QSZX9dN8LjKjK0CwTnpANVGmNSgRmMNDpJkpBnGa+/8RrX18+wQTyVrBW3+9lswmg0Qumcz33uc4PV84MH92mamlvnZ7z99tssr2+4ub4mTRP2+z15lvL48WN2ux3WWiqdoGKISAgKlJE7KArvgqQ/mYT1esNytSQtUqbTCTqBer+jbWuMSiDJBtWE8xKUon3CdDZjX9d8xyc+gTZ6cE1dLpd8+jPv8MqrdwhYPrr/Abv1CpOknJ3NQWm2uz11vafrLM5XnN+6xZuXr7PebHFbEfn/wR/+AbkWEP3V1+6iVGC1WmKdparEm208Hg+Ym9aJdPpKYwexvnTU2sg5opWSm1AInJyIg8JqueSZtSxOFkOAzcXFBefn58xPTiJlpmQ0KimLgrZp+J3f/i2+fjLj9ddeYzIZiQxrPKLrWjbbNXXVUNcNk8kMk2TUjXyGVd2wqxpa50lcQCmL9RIyLxDEIYNU9aPdCwVGbsQMxawX9b8sUenFsJP+8f9Ny+gLn45sjyBFC492OvquBnwXtcOdpfMerQO+77iAgH8uFEVq1CEz4PhP/7qOx2nBMonHQ8rkkH4Xx9d+LQCensitvCzutJZusGpqGicYp0oUXdWC7pcsEp5yHLj98c6sQ7RiQZT21nZ469EkoIxIN0BCBnygM4g4FUUTYNvtaZEPtKobrG0I/QYDWbGrkBCMbFaUS1EWfOtoW7En6VW2Id5det/5EAI4STVyiHPs8CFH0buzVjpHpfG6JxBGsDDIqGdMQl037BLBy8Zxo6aAZreT9XIWlw0+jtkR9H786DGPHt9H6cB8McW5Bm08Rnucb/nau1+nKAsSbXjy5AnOOabTqXQjR84GdV1Hd4ImEkdTRqMRWHHaFMxLD+87ROnMaDyhbio2uw2z+VRwo8ePaLqasijAi+jaB0kUtzY6jRpNEqUzSoupYTkas91uabqap1dPmT2ccnX1lO12SV3vqKods5MTXn3tLtPpnCdPnvIgTbi6vmaz2ZDnKadnp9yslqzWK8ajMdZ2lGXGK6/eJQTHN7/5DZbLGy4vL7i4uBD3iaYZbGKyLCXLCowWd4a26dhVMsobbSiLEmVk5PBOzoX5fD4EgFzcuqCK+QDL9Yr1dkOe5xRFQWoSZue36LqGvbW8+tqrjEcFwVnausK1DQq5mLPUkCVjAp6bm2vSfIT1mt1uT9N21G2DC+IoG6yXG6WK6d2xoHnvhOHuBXzvL/bBiOG5gnNUyJ6rUWEImj5+7vGjXwC8WFiGYiiNlNzsQ0A5GZ6cC0MCkvL974l60dCPgQOSIwVIHzuA9L8jIMwPkSIpWe3LyEigbWrSJC7qjMAbqRG2nQsOxZE/mndRPSOv3UXHmKLMCHis7RhNRmJIOrxGHwti4PjgfayYWZtEMFwKj+tajNJMRmNGZUnbVOgAtrN0tsM7WZfqAI231M7TIfO0tV2MCpNWMjExqzB4UQ9og9fSmbnOYptW3AoSwbt8xCR6q5ShBfaSoHN8R5MLXgz6gpJQ1sMHGp04VAz95RDZ1nXSyRltMElGqC3BWbrW09Kz5aU7BcWHH77PZrdmOhsxGqdkuaa1mtX6mrpuefTgAZPpKR2yUPjkJ9/m4uKCb3z9a3z5y19mMZct29XVM87Pz7Fdw0lcKIxGI2xjMbrDJDK+WB+JhfG9rFYrlIHxZEpRlGy2ax48+oiA587t25RlidWeuq5punYY03TUxq13GwKKP/zSl1gsTnHOsVytGE0mWO948PA+dbWlKFLqJsO2DdvNBq01u92arqtpmgqUnJi73Za7d+8wGo9i95pjbcfZ2WLIVKyqvfiLbdYDTy9EAmUI4m2mIunXdo66rVEQuYAJaTZiPB4PneZmsxmkYbu9xMudn59jjKFpGoqiYL1es9tteeXuXbIsZY9neXPD0ycNJ/MZs+mY8ajEuRRrHfvdjqZuWW+2+KCxQRNURmtlPe6CjJi+v3kemRMmGHFx8FpE0M4PI1tfyIaxkgjQx4zMEHpnVuRCjZXkuVDtF8bM4+vhuKhJIVNyzgTFoBNSSpxkbUCbMHSGzkvwtIyHDEUqNdHq3T9PcZIOzKFJBowwvhRCiEziGENndEJqJLNCK8HG4w8U3mbk33nbSQqWSQRzi12eSTV1W3F1/RTLgs61tF0DRs4XlHD4zNE682PFrG5C5I9IoKftOjKTkmcaT0GSCQ1ChQ66OmZDJrGtBWhxrsWBzMtKk2gdMzJTlBdAXxN1fk7FjaTFdqKGxxxZCvsY0qvNc5YscmMULaLnIM/o5SG9kFUOzlHupZPnyAmhaNtWEnacI81S5vMT2rpiv6sl8qxtpDvUmq5NxUrmbE4xShmNc5R21PWa7XbJbt/ynX/sO+k6xWq5YrfbcXV1zc3NDavltWzLOIwRdV3joxFjfzImxqAzhXZysfvOAXYYT1CQZDImP368xuM4PV3QtA337t3j4vyCIislI1RrskwAbBcCXV2TZQVlWaBNS9M1Ivg3mk++9QnOz8+4feeS4E8pRzlPnjzl6bNn3Fw/Zb0RBvx6vcF7y8lijguWZ1dPuHP3FSbTkQi/PaxX1zx58oQ8zxiNRpydn5NlqZCAoxNrkqYo5anrFmtrfCr4oVI66lrDgN0YY4YwlyRNyYqC1Bh2ux2r5RLvPIvFgtu3bw8JR+PxSPh7dUVdy0WUZxkn86lQaoKjrnZcX7XClDKa2XyCSVOurlbStSjpiPviQQS8VUx+DxFo13G5YjAor7CuHTC0HuLogRJF9PdykeR9jHv1cAmBoD+OiX0riVPf5QGC9QXBe/vzSnNkhBh5lN57EtcNWPQA2gUdUfEwXEMRQI9fl/EuSYREHozgzGIyAd5bEoXkZEZDCMEFJWpOK5Eh6oBwO11HMEqcYuLvaVqRAq7Xa+59eI+zppJlUNugU0OaFVIkjTq8rpcVs1XdUOSQmoQuKFoX6FyH2TdgKuaTKUHJxaWMAIxCbjUE30DbDkJqR0DrQPAyrxul0EmCszKTd12HxdI1lrqtadoWn3hUiKZ1DhSWENII+sVVtfxk+VBlNSqLBy9FKkRw3UTLoeACng7ntQSLJAlpIdiTtZb9rqKcNuKCURbYtsG6lq4TrlWe58wmE04WczbrJZNyhE4FyByPCu7cvmQ+n5DlEz54/wn/+jd+g5urGxmHUkO130NwzF97jc1mw+PtliQxfOPrX+Pu3TvsdhtGI8GRmq4ZOs5h4RFE5pJmRrqAEGJ6UyQptn1IqmKz3dFlDqMNWVGQpCnOOTabDav1hsXilGk+47U33sAYw/0HD8nyLPrXa25d3IJgqfY71rEjq6oKGrmxaaW4uLjF5e07XF1ds1ytWa3X2M6yWJzw6be+g0lR8PTpE0I44GXOWdq2ZTKZMp1MybKcqqq5vr5hv2uwypFl8j61lwLQuo6uban3FTUInqZFQZHFsXy6WHBzdc3V1RV3794hSQzXV1cRK8vZbNacnp4wHY9ZrW5ompqPPvqIssgwGkajAg3il1U1tE2HViZa9Ah2gxYMygewziOSYNkwqyCnYBIZ9sTvVUgnNuRkRLy7F/b3ihWtDyPpEPEXn/ctqbFx0dXjYsTvU8TNo6wvowHpYcPaewkSAjoQMzKl0IsPnpDYOyS8dxhf6QszA94MeujO/GDoKddnlqbSfIQgXar2kY8qy4jEyPVMJwsoKcQyegbke1CGumm4uroWrl/TQAgkRkngSizSva3XS4vZXlvaADkBE1n8ddtBt0e3CbpNxNhNg0rlxEMd4qekgHiUl65JpB0R3AOsd0KZQOEQv/LGNtShw2rZkmZotEnwRjopH2xcAXtUojBBNo+eINsMLbZC/f3F99gAEpyiEUxPE2hdS1ak6DRlNJlws7zhyXvfJCtyzhentO2Ozu7xriZRnqyU7iJVsLm55urqGW0zwWSKzjaoFDrXcLO8pqpaJuUZF4szxpmQL713mCB3tOAd89mMIk+5e+cO+92GPMvZV1v2+4qmDqRZge08VSUOAU3bYb3HZAlZkuO9Z7las9vvxTixLGm7jlQHFicFIQieEEJAuw4fHE3b4oKNPlSOJDUsVzeMJxNuXZzR2obl8pokUTy9esats1N0kmJMik5Sggssr5esN1sZ9zA0VcNus6Xa7hhPppwsTmmbhqdPnvCZd74DwgnWCq/s6vqK/U7caReLBU3T0rbdsKEdjXJxq00S9rsdobWkRSkcQKOpdzVZljGbzNBKiexKwX67ZR03l5/61DukSULbVLz91mtyvIHVMpcOO3qxLU5OxCDTW9arJXVt5SZSt2y2LVppiqxAmZx959A6YbfbEIAkk8g8f9TL6HAA2oMVvWCZpQNupaLJpZwCflBeoEDF0d/RL3uQaKQIr3jnZZyNGLRCC98whrMoZIsqLjXiINSzA3r7Ifxhy6oj20BGRy9jqHfSfBlZevWGnyH4eBNlcLohyPWpTTKwGUIsrpEARJJkwmOzIqFCa9BBlAPx/bVB0pp0mpFoNaSWyZib4I2EnuybjtM0R8Vt9d3LW1xdPUWl4pOmorv0tyxmnfY4LChFahKsCTjtaXxLZWvS1mBNgvGICR0agpGxsf9Q+j/q0KEGkAPpo5I/BKxzdK6jDY5Oebzun9RbDh3hYbHdVTrQAwouyIwv5EBN0EYscNQh9diE3ldJ/qRpyq7aU+Q5l7cvMYmRjdWupt7umc1SxqOMrjY8Wy/ZbixtPSNLEpqm4vr6iraZgQps9huS0pDmCZvtluVyg58lJCjm4zFJmnJ9fU2mNXfu3pFjmhhsmUuqz2jMvtoCgaraCfO67nBeNpco8XgzsftN04zdbo+09BIUYuPJ3XU2noAdaWrIBhsb6TDTNGUynnKzWrFcLdnvKy4uL3nttdc4PT3l6vpm+PevPHuXDz64x927d3n99Tf44IMPCMBoVGKM4fr6mvV6Q11VZGnG2cmC2WzGkyePefb0MevbF9y5I/hdnuesViu6riPPc8pSfsZ+v5cx27uoMhAOGyGQJTnBygWdKEOWppRFwWQ0ZjIZkySG1WpFURQYY3jttde4uLyFNuB8R9eKuF1pzXw2YTS6IITA1dVVXDpkbDYt3iuqqmG7vR5i7bzzJOMMFRzWyk3BWie0DCKvyrnIBIp+YCr6kqloc2WECHo8Fh5bPw1RcUfXnRv+q+/IeyA+Uo6UIij5dxs8JghfDZDtaRAJlaEvZP2YSCwzsYAqCP4gfR9siY6uuedfW/ymF/7tZdtWeaoWmoaKGSFRohXi8Cq+fUnEpCyEGL/oQHmHdZYkK4Q14SXlSxZiVrIL2gbKhFSrSJ79Np2ZiitVH8S7XxtNMOIIuW8qUhOLWRBgWoyBUxJ0NGeT7/NI0VF9q87Bn8x7AR87J1yvzskGMsRi9hxJNj5etpbupbw9MbT/I86XUrF7rzHCAUitm2ZwYz07O8N7T5ZmLJc3pMmIIjcYI57p+22FCgGbZuwjAbatG6y3VHVFtdpzs77hZnmN94FnoxWf+fTnmEwmzGYzPvfZz7DZrFmtV3z44T3m8ylpmrJarTFGs9vuxO/LCRu+7iyohMQkaC1e+J3tqJsQrZV77WjBvqqod6IfHJJ3giXEEyRJDNqklOVkcMh11vPhhx/RdZbbd17hyZNnXF3Ja7++WfKbv/lbnJ8teOONN0iSlPfe+2bEv/K4kdbsm4q6qimKYght6ROANquKBw8eoJSkZ92+fZsvfvFL3Nysubg4i2TjdLi4++QmiBgrglM1bTMkYY3H4habZRIcXJZFFJkrHj16RFVVPHzwgGo/YzYZ07Yt3nmKNOfs9JzFYkFVVzjrefrsKdvtDQrFyXxB27U8fPCYk5MTJuMZbdPSdg7fNTSdHwqaj+C8J0SMSD6LEGcGQZe80EyMjvXnANAfA/jPncMv+e/j/6mhI+A56sNAheBQLPtrQnNQFPTX9BEq9rHf63me3/ay1/qiwuClxey5hzq8Zn38sz2YZHg1Q0bI0Ta1bVuSMsN7x36/JUlui7FnsJTjQrpWZXAalPo2ndmwKUFaaKUNOk1xnaVuW/KkxRtpp72Tvx2BVEuUVV/MpLxIJRbtmhAQQ5RROOeGYmaDi5X6cAd48a7Wu1+8+GG8yLbuU296/pnysr1JTIKOHcH54pQ8z8Xjqix5/fXX0Si26yuqek9wGm/FJkb5EIXbQs6dTaYEDbPRjLfO3mK5WfG7v///UKQFb771NvXO0nUtbduyWq344hd/n7Zt+MxnP835+TlNUw3uESHIEkIpTVmOIkHRCpicSMgKTkb4um1oW0sSU5lKU9J2AuIbk6BVItvjICaPkk9gSFJJqW5b0S8qbSiLEaenIxaLU959910+/PAjxpMpeZFxcjLnjTfeIM9z3n33qzx79ozdbsd+X3F2dsbZ6TmbzZa2bZnNZiilh63laDSi2m2p65qnT59yEvWRWgfKMhtkUPP5nLOzM2azGQ8fPhx0lrLl8zRNi9Y6dmwjJtMxOrqcZFk6LFLu3bvH1dUV0+mU1994HaMltOTJo0dMxiPGY9G8il2RnENnp2fUdc14PKYsS9q2Zb1aE0JguVzSNi2zyZzGOqq6o226CFvIeaejdGc474JAGopoqqme78iOMatjAfnx4/jfIlni2xaLb/m1INORGQrJC19Xh2vLh+f/fXgd4Vv/7hd5ZS8n9objb3h+uoqFuutaIRA7B94KTIRCGUOqPPuupZzlMZ/TMipT5tMxm+0KXQp0gw44J95y/ePjIcBKHcS/cbVMkkQbFk/jOtAysnlvCU5Ywr1GSusob1DSwurYjosJXBjE3dZZOhfF3kS6iDqwil9GyvtWAtzjv62z9PQTUQrIgU7TlCLNsFaoGD2w3TSNOClkOeNRimFLtVvibMNkVFKmGft9TVuL7q4sS1rboaJXv8ZgO0dTyyIjjQLx7XZNkiRcXl6w2+0iLysnz4W/571nt9uRxtc0nczovEOpGm3SQZ+X5jmT6QTnHE3bsV5vBFQ2mixLaW0SlQuHk7IoSqaTKSEEtrsdy+V6OKYmMXzqU59hfjKn7Sxt4yBoNustd+9+itdeez0Wi8BiMacoPsO9e/d48uQJ8/mcWxfnJGnCfrdnNBrRdR3GKCaTMePxiHq/G7qvqqq4urrCGMPdu3d5+PAhdV1TliXT6XR4zna7ZbPZCGXAMRhgnixmQzpQv7HbbreSfG4t9+7dY7E44ez8lLIsePrkMXW1Z1QIpuYJrLebyE/TLJdLzs/OmM3nTKdTfPAUo5JPfscnefL0KR/e+5CmbinLKft9w65qaVo5Xwg9FcMInhuB9BBcdEUVwXmI+PFxITtszxn4hi97hKP/81yB6xdCLxSQl3ZQHOiocRd5NMDGn92XzIgG9b/PhxCter7949t2ZPFLqh+Ujp8/fN8LRTOIcaRGkShFkRrmZc5sOsOHllGZMp2M2G2XoD3jsVi4d10XRfTy+PiYycG7q9dC9h+G1TISmjRhkC4hViA66Ih36OFFaq3RUbxlw8HGxwU36D970YWOeMSw8QmHbUpPp5CD9HzL/uKB7dtyxSEGK0kS8jxnVJTiIFrX7HY7YewrSeRJtOH8/Ix627Fe1lT7rZA5E5FPKBWYjsY8vP8AZcT65dmzZ5Tjku9461O88XpHXuRMZ3M+eP8eTdOwWJwMXUfXWTabDYvFnDRNoxttjTGKpmmYTqdkBEzM0wSFs57caIqiQCnJhswy6bKs84wnJcqII2rXdVHaU5AmqTiidpb1Zsd2u0WhGY3GKC2tfds6Hj9+zH6/j5mKFq0M1b5mn4tXmHOOhw8fc+vWLV599VVOTk5o244QPFUtUqMk+ordvn0b7xyP7n/EZrMmBM/p6SlVVbHb7ei6jv1+T1EUw3tPkoTpdMp0OpXR0MtCIM+zIRdCJGcSjCzkYsPJyRl5njOZjJmfzLG248GD+2RZyvn5GacnJzx58pibm5thDPbeDzKn2WxGOfp/WXvPLkmS7EzvMdceWmREqpJdM8BMAxgOljxc8Cv/A//xLs/hLg+Wu1igMdOiRFZWytAert2MH8zcwzMqqwfLQ+9TnRmR4crC7PoV733fENAhDeY8YaeD4/hEUcxml5BkJaVOVWMbbi4LYfBhB7rsSpkEh6pDOvlkEdeGrW7zec4IKaUa4yMMtOh4nh+vi/r1oYuAgwX5lp0xBQNl0j/1+drXUUvLPdlX1WGhfHIPX20G7HzYT2o8rTB5PKHhR5pNQ7c6IjVzB5aFqCQdX9BzbF6eziirlPmgT9cTBI72xrIipaoK8twm+zUNAFVWjaq5lFLTg5gmVGFZ2phJTecsNSqCSskGQVyVsol9rZalr2TVSJlV5nGhpac0f3jTHya+zpl963V7kJuxrF1bdZggtTF2HIdBv892pfnDiqLQ1aSqYrPZ4Ngl0XpJGu+pqgJZ2VhKl5ndQBMG9np9xpMpWVHwuHok7HaYzqdUsmK10YSJo+mI2eyELMu4vv7Carsme8x49913ZEWJ7XlUSuEFPnmhMXlFpfnYO90OliUMSr5ESBsM2UpR5vQHPXa7iGIf4/sOwu5QbUukFIRhD7CIdjH7/dZgnGx8L9CYNak1Hj5//oIQgmi/Z7XaUBQlYSckjhMmkwndjsPH97/w+PhIluc4lsOg3+VkMubh4VE/9ZUk2m3wfV+3pnku+71mQkgSjRPK85xut4uUkthUX5VSRFHE7e0t0+kUz/M4OTlpuiO26w2qUpoPrSzxfSOYLCA1/G5SST58/IXlcsV4PKJn8mTDoabIXq/XLBYrlFK8euXS6/VJkoTRaEKSxHheQBBoVPn19TW3t3fc3NwR+AFpmhHtUqIkQ+LgBF1c16GQFVIKTTUlVQtlryeznsnamNmWpTtljAd2DG5ta2nWP58YCKEjmiYP1jJk7QJCW0BY76YV1ZHya4+sXt/oa2/Y1AwkxJwIOKSajo1tfa5jnNvTtXfgY5PKaL0KoREUpqfcsiwNnDfXpv+kWWkFCqessPKMs0GfSgWcdHtcnIy4mI7Ii4RPnz8adTTtaNTbV8asqqpmFGqkcj3ACM3m4EjZsFJiaUCmZRL8lUmYgin3SqupAFVVrQLd3P4T8ZG6OfdbT6DntueMWX29Gs9zUItWSjUhUL3otEfjsdpFfPjwgKjW2FSEQYBtu1S5BlRayqIsC25vH0gzLfb7+LiguLvl5v5Wqw7Fe/qjAXf3d40qUJIm+IHPar3i09UVk8mE2XxO4eX0B30eHx8BwT7W0IXLFxc4jkOSJOR5amAntmH82NLr9SjLgn28N3J1djMG3W6H7WZPmuo8Wq/XQ1i2YZYo6fV6TCcnLJZLVus1i9WqmbhpoturVqsVnjsiCLTnM/EmbLc7/vynP/Fwf08QhPS6XWQlWSwWGgeWJGw2a9brDWVZMBqNGAwGeoI5DicnJ5ydnbFarfjxxx91qC+1wOxsNqPX62nPudPhy+cvbNfGSHp6316vh1KK+/s7NtsNeZESRRGdTshsNuPy8pw8z7XxzTJOT2a88QPSNGUymSKl4u7unlevXiGEYDgcEsdxQxpg2/pB5boeHz9+Zh/lJHmF6wf0gq4W5ag0rblWLDeCMGYOw8HrsYWu4ImWoTnO+T5nDNoPayEEx5ImbQ+sbQzbx6+LDrUhq4/QrBBxWGeK59dTff5vpXTa5//WfrVnpz3YCqXQ6lBC93Yro+FBrWRlCU2AattaSrHMsJKUvq07LryyYOT1GPoOaeYhJ1M63S6j8biZZ/CMMXNszf5ZlRWyruTIOhxUYPrkyhptb1gtKqV97bqIbe5eA11NsG7ZFnlR6kE3vZC1b10nPbUakb6sWm6+dtHbA3bswdVlb8eQ3WGQ28o8IeskdW6OXx9XSclmsyHLMqJoi0vMaOjRCbqaNrvIcFwbZUQ6SiMIMp9OGJ1M2CcR692W3X6rGVj7PZSQbHcbsjxjPB3heZ5u9kbQMYnn0IQ+6mFBVSmWywVplmA5FufnZ03/YhRrNg3bJJ2llJyentI3ikJ1TkeHbhnCspjNZtzd3VOWFePxAN/3mc81xXSvP6CsKgOI1UbUdVzQe3TIAAAgAElEQVRNSQ0MB31OJmOur6+xjR7kZrWkzHPWyxWvXw8pRUUc7ynyDN8P6HV72sAZaut6PMuyZDrVFcyaqWQwGLDdblkuVw31keu6zGYzI+mmOD2dNyBbgO12y2azptvt8uLykiSNm26P9XpJFGkjL4RO0K/XGzqdLp1en0pCtN9hOx7RPmEXxXy8uubs7JTHxYrtbkucZCxX2oBKhIa7VBW+bWsRZstht09037DjYQm35q9pWu4EmtRQoQVCaBmA2kM7zvu22/MaB6BlqI4f6scFhDriaBsXKSW2afSXUhpqbtusM2PCjDdWmjVTn7euelZV1eT72nCSes21r7cdSjdrUVSNBOKTTWrtANd1tKpepbEQdX7PQfd79sMOs36fbLVmMOiRLlbcbzZg6VbFYr0jzXKivEDu4+bwzxQADn1i39qU0tAK27Y1LKwpI9egWDPYsuVeGy+srnMKM6jHJeHyqALUdm9rXYD2E+x4a9x1UfecHb6AOtxxhNUYuCRJDOxAC/F6gYWwbErT97ePUpA6XKsqHQbajs0+icHW/GPng3P8pceHq4/89MtPAPQHfZPr0iHjYDDg/PyC9+/fa4/KGGjP9+kPBiRpyvRkyj6OuLu7a8LM8XiM63q4vt9U+VAY5SKNd6pDaNA6lbo3cqoT9GXVTMQoipAoXZ1MYpSUeI4WWplMJvQHfYoi15+rKibjMavVqmmyL8uSjx8/NUZmOBwS+AFZmrJ4fKSqKsbjMZ7nGciFoN/vc3Jyws8//8x+v+fdu3esVivev//Aly9fmM1mzGazJi+6WCyYjEacn5+zWDxye3urGRWM5+H5LkWpSSKzTHPdz+dzut0OHz9+IEtz3r75jjTLiBYRWZZxeXmJ5/v89PPPDW9cFEUMBgMeHx9xHIfxZMLV1Wccz6M/dPFCSafbo9fvooRDIRVWklNISPMSIVxqcozD41vQbnxu55lqj6b9MK7/1mbOaBvBdkTyxGtrFciePtg1iaftmI6FNrRDGIodXct4WjE1Y4s5TlmWjUNx7FV+K73TNtQaxlIL/baS/oYEX6DpwbSdqVCloLJLjZOTBaIsqHYRj1efiUIPiaQoU5SoELbWdBWObntzXZf//f/QZ/i6AGBbGpmMzovUCOJaSg5lEoiVHirjt7YqIvXXKpqfutm0xt7UX4SmFKn7xhSAklSmetU2ZnU+Bb7mRz/eDhWhJ2azOY6GY3RAQJZl7ONYP9XNblJClpfkecU22hPtYmwcgiDEcTz6/Q5+EFDKkmi/R24VQccnrwq80MMNXdbbNRJJ0NGJ+zhOiJI9n64/MRgPUQK8MGQymbDbbsmyjI4R6xBIPM9hvV5Tlgo/CEjSlN12z2azodfrYdsO8T4xjxCLqlL4vsd43GEf3QCKbjcgDEPuHx4oy5Lz83OTi0upZEmRa8EUzS8mKKuCJNGh7v/z8Se6nZDZbI4lFN0w4CaNSdOMbqfP5fk5Jyczon3E4nHJ4vGBONqBEOx2EeuN9nLqxP/333/PaDRisVhQV5Y9z9VtMMB+v3/ihQ+HOnTQdE31/JBUsiSOYzabDf1+33h9NmVZsNms2W63VJVkuV6xWCwJw5Df/fXvSLKU//M//keUUpzMZgRBwD7e0xv0CcIQUNzd32M7Drv1FiEclBIaS5hEYHtGfTyjKHS/sb4uR6uEK2MohBZ+0a08Xxeo2vP2GGbUrvgdP8iPjUa9tXt6j4sErZVQN/81oXA7maYLAGbumz83AkEcjFn79bFh+2oN1m2HwmSnWn+rjy8wNqSstCCxZVFWEkvm2IEFRUmy3ZLtIa9yqqowVHJakKUS6ivH7xnQbCteri022ijU7dtWY+JVA32oLb7VJuWGxgNxavXs6pBAbGL6VlAvRLsS8nQi1O7vt7a6zCulNJWlwzDWlVRMeCulVqqulaBc26EsJZYlySuBYzsIy7TzSFOxUhrqsIv3OJ6H5VpkacLDl3scz2U0GaGEIiszBsMB05MplrAbhlitk5nieT7braaW7vV6JGnK3e09JydTknjPaDhgu90zn83ZxXt836coK168eMlyuSLLElxXi99meQ5kgKDT6XF+fs5qveb07JTtZkcUbel0e1x9/ohSOgRaLhdkeYptW3Q6oYaLCDSt+M01j3fX/MO//1/5wx/+jjzP+Q//4T9QldpTm4ynvHjxwqDot5RlQZKk5FnWNIgXRUEQBPi+z9XVFa7r8vLlS0ajET/99BNCCC4uLgDNAluHmuv1mrP5HNu2+fDhPf1+nyAMiHZb4iTGti1OTqZYljD72Liur8PxKNEFlE7IcDgEdFrg4VEb89/89rfYts0//uM/Mp/PGY/HKKWIk5jFYoHneYhMz5dKSRzPx3F0X4ttKXzPJnWFBjXrngywpGZcNlagMQpKPVnA7fnZTvYfe2nPzfvn3j/Orx3ep3mIq9Y+bft1AJgf8nxPztF6fZwra/9+bGjb19FUQnWTKofVrr3HWkC5ZuKoqoqqEFSWph4vbJucSrcqCokmHZM4joUlHNIkMdHW02v4VWOmk4mquRSUwrFtbKG73i2FTuhpC4dEa+TVIvSA0Ts2mCxh4diyyZFRHYB0h6qQvulDheYpXud4MI8LBKr+EtTTe5Fm0Gp1p7IoqMqywZrhKrIix3YUSujO/N7Qw3I88kRT6RRpwj6OieI9buBxdn5KfzzA7XlUqsQNHB4fHuj1uvz2t78lDENub2+xLHj58iW/+93vWK02PD4+4nm+FgpJMjZrXXncbHagNIV0vE/YBRFfbm8Jgg5pnjEajSmKijwvdCUTG8vSbU5VKTW5YJYyHPbJspQ0i5mfziiKkvV6ZUSNLWxbMB4P8Twfz9e9bxo7VTGfXfDu9QvW6xXv37+n3+8hpeRv/ub3hGHIfh+z3qzI0kyLowjIspTFIqLb7fHq9WscVxuofr+P4ziNsag53WuPq8aWDYdDgiAwepgpX758Yb+PODmZkGUJm+2GMAzI84w4jgnDgPv7O5JkT3+gzyGrik4YopTg06fPeK7Hq1dvNPPCxyuUQjNrnF5gOzbL5ZrRcMIf//jvuL+7409/+hP/8O//N25vb/nvP/wrnh/gG4Fq1/dwpaKsSvKyIC0lgpK6yblhKDb9i1rw+elCb/9+HG4+MQyiZRX/wtauZDbvmf8r6vy1VjvSVNOtz4nD5+v3LWP16uutE/lf2YQj43a8SVG1nJ9WIaGpLKqmIFCZ4E7KEolWfaqkIJXSNJwrqqpEqRKngCw31VeltIfcOv6zxqw9WI3BMElN3Tyu/VMbzYShO700+liYXv/aamo3U+jKJ0Z+vmXImqqHVFAz037Dlf5WFej4emtj1t6nzgX0ez2dQ6q50pVqcGaO4xIlG00Tjo3v+gRhF4FNlSRUVcloMqYz6JLmKVES4fV95uczHM82BQIthluWJQ8PD9zdGcGN/hDP89jv9w1gd7lc4rkBd3f3PD4+MhwOeXFxQVlKhNDVUqUMkaPtmJyZTVUq9mUMsVbB8b2QONEEhVmeUZau5vbfbphMTzg7G5GmKbPZjOF4xHa7YxvtqSqt/JSmMdPZjF43JM1SHEvnE6+vPzffged5PD4+kucFg8HQJO8zg9cLGyzcZrMl2uuQOE1T3r59i21rpgXXdRshF91VEOP7PqBDzTAMdVK60Ji9IAwQljCdBD0eHx9YLhecn58xnU61qMx2S1kVDIdD5qen7LZ77u8XnJ9d4Ps+Z4bj7erqiru7Ozyjy9nrambgTx8/Yds233//N1oNKorQKPWMSlU4ZYlTFiglyPMEJUvN/mApTcRY54JsTX5gKVDV097i9hxs/zw2GuZNExE9NSDHx2jP9+P1W3tocDAgTTZIKdTxOhI88dCEdciRHRNBHhcsnvPOGoA2dutvGK/R3HvLMwWp2SPrv9oW0gbl2pq0UQqkFAfCiao0OXHx5KHxNTTDYD1qnFZj/ZWOcYUBxQopNchMgCOEIa3TuPiqFcw+50lhKEOU5lhBSOOECoFli6bptu2KHw/et3IJzxkzpZSuuLbUboSh767zaJ7j0u32WN/ek2YmPxTocn1VSKpKs4wGYci0PyWXJevtirRIqFTJoNvDDzzCIODL5xt++OFfKIocy9JDvFyu2aw3LBYr45G4VKXUBjSKCIJQo8stB8/1OZnOWK/XeL5FkibYjkee6UWlvQBBUehsSKfrk2UF+2yP49qcns6J4z224XuKkz2dro8feDofJXSnRCUV+/0Oz3N58/oV0+kESxYs728oioLPnz9jWRZ//OPfk6YpV1dXBvMlGAwHpGnG/f09l5eXTKcnZFnO9Zcb7QE7DlEU4bpuE1JKKbUXjO6kOD8/RynVFBn6/T6h77NeLqlkyf39vcH29ShNIaOWhqu9O4BKlnS7XXzfY11tcVyHTq/Lp6sr4wkJ/CDADwOCIGA8GjM5mZIVOTe3t5zMTqiUJE4SdtGObq9DmuqcYlEUWKVWFJKV0qpfUlAJRc0+IbE1X59t4Zg8siW+9rzahahfCyVriup2dHK8DurxbG96PEQDRK/Xj+YsVhyfUtU5s/p1/XvrXM85Es+Fn0+OSxs3Zz11NJUuHkoBjjrk0HQ3goUUim0W40nwlINra1YeRaVZaQ8n0Ywkre1rz6yVzD9OTOrX+nepNJuGlmrXlROpdFyrFFRCIGWltQSNpqZlgyz1E01fj/kyLBDYCFuz0trPfFFwSEZ+NXjtAW2Fm609daJcVUT7HVQlnSAE4bKPI6y1wLEtxtMhnheQ5RHbTUwc5diO5qcq8oKizHhYPDA5GXNyNmMym+CFLr5hPXUcB+kok+D28P0AKSVfvnxh8bikE3Ya4KdtO6RZSmgJk5dxcBybu7tbgkCHN8PRkNVmjVKKwrRQxXGMEILxeEqW5WRFgWVpuh7PC4iTiCRJ8X3dMlSVJTm6vzTeax1Pz7EZj4b0BwM6YcB+v2cyHmpNRSk5v7hktVozGIx0s7fxWieTCcPhiCjas16vcBzbgGIVt7d3ZFluVNz1fQ8GA6bTqVah8n1c1+X+/p7b21sGgwGuq2X9qkozZygp2e8jlFIM+gPWmxV5nnNyMqUsc87Pz02OTnctTCYT+v0eu2jX9I8GYYBUgvV6RRTteXx84M2bt/z+97/jH//xvzRqWZUsG3EWx9XSZZPJlE5PpwaqQqcgSlkgihJhOViOi2c7RpFJMyzrrLleNBp2aWldzfp5agxXZTCbdfWwNhh1BbEOnUQTgpqkvdCGUQkQSr8WZi5rPn2teYkFFrbhXjvCkbUtVnut8/z7SIWyvs7PHa+3J15ivfaE2b8egLrCoA55xKpSVLbmVJMKQ70NdZelHj8FVUkpW7k2WYJS+I5bL+sn21fGzLVspOmfVNXTtgzLsQ2NB/qJVJUox+RbpKCQ8kC2JgWWYWi1DW22VFVDA6Kl29HMAzVpHJrapM1R9NWgtZ5u8LRkbRnkNTYIieH1qvRTSTdragpvxyKTmW6hwcfOLHapT092KPKCJC6wLUuLYuxyfFeLmZRSstqu8XsB6ecrBoMeL16/oCxKNksNGB0OJqRpznh8QhLH7PcxeVYwHI64u7vDcVym04mWtDPGfzjs0+32GI/G7HYbBoMONzd3XF9fM5pMGI2G7JMYIWyE6ADC0OqUOJ5HlubYlovAIYkyqhwqIcmSHBB895sXlGXBp08fuLv5rBu9JyOm0wmBa/FP//Tf+ef/+l90Pi7NGQ9GxEnCy5eX/PVf/xVZlpKUKb2ezsXt9ztD16MX3mKxQDvvirHncX52ysePHzWLrnbBkWVBZ9BnMhpx7TogK64+fqTb6zIZj8izjO16j+u4vPrNC0bDQdOHmaQJX66/UBYFvV6H3XZLMJ8RBj7b7ZYvn6/J8pQ8LziZnXJ2doGS4LkOF+enpGnKTz/+iTxPKIqMwPe4uvpIWZR4vkccR/zN3/wNJydjfv7pJ2xgNhnh2TZXV9d4XshkOsT1AlbbPf2wxz7NKHMNJ7EcgSorCpVRCQsL3b4nDD4TqY0OyvCOGY9IGsNWY7xMw6cGjqKg8fw0dYMmGa3wvQBhRFQsai8Qnewx60IHbaKBKOk1ZNbKoRpgDFdrsQljPKQ0dEYHfJk6dhTaeTcdeevX8rAeNUWYjhAMKRCWbWkhGFlR2S7KF5QKSgW2KrCVwlEVSmkWaZQRjpFGp6OqubqfGtmvcWY1ercV1zfxuqWZWhW6aRxZokqdL9CN0DlWE1cr3VqBZo2sGZsUUvMyWUobMstwjRtvzTqy+scVnBrEVxuz4+LAwbjps9UDLWo/0NL6n76r5bJs2yHJE24fvpDmMbKssIVLnCRkaaK9DAeiRPOIdXs9PNdDoq8jjvQ+aZrw4cMH/u5/+nve//KBy8sXrFY6pLy4uNT0OEYUeD6fc3Nza4ChmXnf5uExI452WKICLObzE0oFy9UjfqCrdF++3FIWJXGSUVWSi/GEXm9AUVSs1xscx6fIK3bbiGgXMxoNGHT7hB2fTugR+D67aEe827JZLgDBsN/h/u6B9XpHrztmu02wLZtol5LEGVme4vk+2fKRn3/+CSkrgiBkOp2RpQXr9dZgz0yPYxiaZLxis9kAsFwuefnyJev1itubGw0cDkMuLy/oGaDtYDDA9zwC3+Xx8ZHpdEoQ+Dz8eE+300EZKvDJZEK8j/nll1/I85ww9JnNpqzXG6qyYL1aMhgMkbIkz1M6ndAQUypWqyVnZ3Nev35FFEUUhRaQvr+701XT/R4hK66vv+A4PufzOWUJ2+WG2TzkfHbKahdjKwtHgLAsKiytFlZJ/ZA2C1CqQ3FLHS1+Wadz6jlqZqtSEtu0ESql2WgRlubts0Aoq+Hwk4apoxGDMgauyUsJoYkbRc1cfMjRtdv9auNkio+HvBdPc86NQWtsyKHbADQVoQXYtoNtGThWMxbGyqFwbMuMjaHdFlazpmVlIev7Nh3zSunQXimtXWBbtdl6imz4ypi1DdjzwDzV3HBTHNA7IYyxOxgbC2lJ3YRej9qT0ePJ4DRhbMuIHf/+awnI+trN5dR7H00kHVaEHQ1oVZVkt4tZxxGb9Zp+d8DpfE4Z74nimKATklcVUbRjPBlqReUg4Lt3b+h1O/z888/887/8QLfboSgl//k//d98+nTFy5evGAwGDbYqNu1KcRwzGAwMw2qoOwaUpNvtslgscF2Px0fdFXB58YJ+GGovGUjThM16TceoosdxghB136umPprNZoCWpOuEHWazueFPT7CEVih/fHggTTO22y2np6eMRxNWiw1ZmlHka6oCpidTptNpvSwIAt+QPDpUlTC6lCl5VjbzoyhKXFeLXdSq7ZPJBMfRIjl3d3d8+fKlAa5Op1Pm87mGxrguJycnrFdL1hs9To7raFqhLKMqC3p2D9fz2RvixVJqz2owGKBQ7KJrirLC94MG41Zj2nQeSjQhb5omLBaPBsx8zna7Zb1eM+j3eXF+hu/7bDd7dtu9NrBhj04nZJ9myJaOhLBrz0HrFeg8lNXMtafVejOH65xzazkecr2akbguuNVz+bgQ1jY05mRm/4Mxe7I+W8vuyZppOyvo/LXm1tPY0uPuguNNtNeaOWz7Wtuf/Criav2Trd8POqLSGF39npL1bGwf4bA9a8zqGz6uurS9n3bZtkagCyFI4/irY7QT+e0BeS6R+NyA/aXtODH6/A2ZL6u+n9rLa4oC2tuM9jGTUhIEIUmQkhclRaWpRjw/YLVa8/n6C8ISvPvNd7heQBQnKCV4+eol7395T6/X45dffmmSuVVVMZ/P6ff7/Mu//EvDEVYzpdYezHK55Lfv3iGE4OMHzdX1/d/+LX4QcHNzT5Zl+L7PaDQywN6CKNqTpJkpDsjGg5GywvP1sT99+kSaxXS7Wkfy/PycNM0M6t5q+L4GgyFZVmEJGI0GvHz5krLKNZ6s8hkMhsxmMx4eHri6uuLh4R7QwrKaVVY1PZaxmQfT6bRZEJ8/f+bm5pYk0bi4LNMGtd6KoiBOUhwzZlefP/Ob3/yGv/qrv+Lu7k7Lx7ke0+kJjmPz8PigDVsUadLGThe/UmRZQZbpntvpdGoa/q+xbbsxnuv1GtALL8sypJRac3MwQCmF7weEoSJLCxC6P9bzXEoFSVEiqkpHGXWMJnXOGKUQtvtkfj+ZhkcP5sbgqIOPU6+v47XTFDy+EZ18Y9o/+Vlfw3PLRSmtrlFXPA/h6dNrF9bz56rPVFW6BFi3INYtV0I8Pd5xcaE2ZJrm3tCAUysT1KEzUJV8VYLlG8asnZdql4/h0LdVf6YyDeTtUu5zX+JxefnZJOI3qiPPHee5sjVotlJdbTV5PnGI6QUtw4uZTIa8sdMJcR2fotDI/8D38YKQaBdphsvQZxfFvH7zHf1BjyxLeFyujAFMWC3XnJ1f0On0ePfuN1iWxXK5ZD6f8/DwQJbpfNnvf/+9Qb4LM3YS39eU0fP5Gcvlmvlszmw2J8syPn/+TJwkRPuUbrfXaEbe3t7jej5C6MmSZluyPCHLM06mUzabNbOTGe8/aKPa6XR4+fIF+31Er9djMjlBmsfhcrUmDLv4fofdNiZNCyaTsWatCLRxQmiR3aoq2e/3nJ6est1GpGlOlmYIIej3Bw1po+u63N7e8vbtW+bzOVJKXNdlOBw0cA4pJdfX10wmE4Ig4PHxkXgf0+10GA5GnJ2e0wm7+l5mc8bjEVdXup1qNjsh8HXo3e10sW3bKGJpoeEwDHEc3UlR94UKIRpwLtC0fAGmNUrP4Y8frxBCh7OdTpfFYs16G9HtDbRCfa6JSmWZG31Ww/xSVVSVxa8Bv4/fe/paGS/MbgzaseESQjSN+seGrlkHbYPB1wHRN6uQR85Le821je6vmbLaA1McugVqh6fOxdUeWu1YPLluBa5l4QmFKw5VXVtoun0QjfjKsWf2FZz+uATbphlRSjXgx5o0r+6ZrCW+2srHz23f+nLbnlmblvd44Gsr386Vtbf6y29XcywTv9f76tBDV9fqyqPjug27aZzErDdbkiRlnyYmkWrzuFgSdrqcnV8Q7RP++Z9/IC9K3r37Lafnlyhh8de/+x1h2GG73fHDD/8KCE5OZqZypifq3d0di8VSKxPFSXMtl5eXDPoDbNvmxYsXvH79mv1+z3arDZB+eEhTZdN9m3XDej3++2hPnuf4vofGSxV0Oh3evvmOd+9+g207/PnPP+rwFwvfD3n75jsuL18QhqEJGbTW5U8//5kkiXn9+jWnp6emi2GL7/ucnJxwcjJhNOoThL4JCUNOT+fGWE4QQvDlyxfyPG+81NFoxOnpnNevX2vdUGNklFLc398jhKDb7SEl+IaSZ7PZ4XsB69WG+/sHQ+qYstlsSZMcKbWxnkymDWNHzcTx6dMn9vu96VjYNfMtTVNubm6IoogXL17Q72sGk+12x3A04ru3WnqvLAp83+XViws63ZD1ZoXtaB3ImpNLyQpkZRhna5S7enYOP/fwfs6w1J0zNRTl2dzVN9btE+N2/JOWpsYz6/I5A/nVv2/9Z7y5en22r6e2IWWrNfH4ms0FNHodGhYucLGwBbjCkDc6Fr4jCGz9s96epc2uLWq727/efN/HsqyGnvo5rEt74Js2otYFf+vLfe51ezC+lcOr/7WvpR5cPSksDfZt3VPTnG0Z5XLqhKPAsh2SNCVLUxBgOS7Ctgm7Xa6+fOHkbI7rBzwu12TFj4aS22K/T3DPPaqywrJskiTlp59+ZjAYGLhEYLxdDV+xbcdw5zu4boGUiiDs8PDwyN2dZnatSs3IICvJw8MDnVBj36aTCd1eyHqzaxD2vu+RxikPD/eMx0MWj4+UrS6HTx8/MRyOiOOY5WJNkujezNev33B2do5SMJ+fkSYpWZby6dNH0jTmD3/4WzzPxbYdHMel1+saUZOtQfD3KEuJ57vM5zPiODXklGN2ux1xHNPtdvn555+5urrGdTWmbNCEdIc+zjiJSbMMx3ZZb3bEyZ7lckEU7ynynOnJnLdvv2MfR+RFQVEW5EWBsGwuXrzgy/UX9tG+mQ91U38btKtJMbXuZm1k67lh2za9Xp+8KEniHVme0u8NGAxHFFKyWFamk0Th+w5UkrzSOTDHthGWo1kzxFd+wlcL+Jm/gpKG2aUmD6ApfD2XOz5+oKujNdR4ZEq1rkkc8nFH+/yakXz2+pX2iBpf6Zl00pOPq9pPPBzv6TGV6S6qtQxMhFXvKxSu7T2BqtTb1xRAhq75W95VPahN9cH8az5/ZMjaF/zrX+Tz7u9XLu43nlCHL7dOkB4MWS0ibFkWea75+UvPMfxqetLYIgBlsdruGI904juKIrrdENuxcVyX16/fsVw+sl5viJMUpQT7fUqn0yF0PZbLNf/1v/4Tr15e4nkef/zjH5v7Pj09ZbFYMB6Pmc/nDcVPURjqHqHbeyrDyLrZbLAsi+nJnPVmgxKC7777jqqULBZLXM9jsVgQ7RPSNOH161fYts3iYYHv+c3ilVIShh3d0rTRZIZlKdluFghhIUTGzc0dv//99wz6I05OJqw3OjRbrVeUZcHNzQ0nJ1PefvdGJ+aNIvbDwwMnJyeGH66iLHPWmxUC22hk6m6LIAi0jFwck+cpZ2evefPmTePt3dzccHNzg1KKOE55XCzpdrpEUYRlCbrdPmmqaZBmszlplpPnJZ2OTsrP5jP9kM1y1usNge/z4sWLxsg/PDywXq+ZTqc8Pj6y2+1QSvHixQvyPG88t5q9Y7PZMuj39cPEkJPqSip4ns1+s8O2bcLAwSoVVaJVslzbxXY94iz7i57Tc5swNiZNU3zfw7btxitr71Mb3jb9dtsYPXmoCy1UrNdP/X7L0B45Bqhatk48Oa55cbj+dlHTHFHbFYOpM9fQzpvVYtxKfZvgUZ+nLgKYgoZhq0appuJan6tly57BmbkuaZo2g1aHlrZtNwyi7cRkHZlijgsAACAASURBVGoeN4Efe2g1xctxo/jxl/wcO3q9X9viH3fuH1xb2QxIfV7QX44QGrnueV5zT1ppSWIJhS10CLqLtggBF5fn+L4HQFUVfPz4kfl8xu3NLXEc0+vqRujhUHsYjiVYLR64ubml39eh1v39Pfv9nul0Sp7nvH//nm63SxAETZK8ztusVitCP9SlbdtluVwRxZo+aDga6vuwNJGgHwS8//CBV2/emHtyUUrT8iip2O8LHMdmNBpxcXHJfH7C9fVnNps1AovRSOtXfrm+JcsqwqBn4CGPjMdDM24Sx7F59+4d3W6H5WIFSj/wwjDk5cuXeJ5LmmY4jo3vu+R5hm15VFWF53m8ePGCzWbTgGOn0yl/+MPf8fbtW2O89BgEQUCSJGj9V5s0L5AIRqMJnW6oab1twXA8NgIvOb7jkJcVHz5e8fbtG+Ik5fT0lIvzczxPayt0Oh2yTMNfdCO8nhe2rQG/juM0xqzb7bLd7sjTnJ9/fs9oNAQUZV7Q6/U1wYCrJXy63Z4WyN7FCCRCSU3vLWzjBT1dB+3Qqr0u6u2QlNd6GkqpJvo5Nmbfel136tTr4fC5AzRD59yeqivVl6E/gzFoOlwW1Iroh7VrG0ZbW9ga0KsUldT8h6VU2FbdEfTU6eHofmuG26dQMD2+ltBdFcq0RVpCII2HekwFVm/PembtpF07D1afuJ18bycnlYl324N8/KU997q9/Vu8t1/bryxN2dw4qJUZKNt4apPJxLCKuiiV6cSt1KZfIqmqgsfHBfP5KaPRgCja0+t1CIIhP//8k8m3JIzHI169ekWapny5vkGqit+8fUO/3weUwTDpilqv18N1Xa6uruj3+43IhzY0F02S+uLigngXExExm2lv4/5xQRh2GI9G/PCv/4qsFPf3D4wnY/JcwwRev9YMqg8Pj6bJWfDixQs6HZ8ff/qRzWZNp6PFcDULrW0wPYqylPie7rO0rBDbsplOJywWj7x69ZIoivjTn/5EGIZ0u10tK+f5vHz5UlcUHx7o9buUZclquUJWFtPpwFAmOQ2b683NDavVivF4zHg8BjT2bL1es1wu2Wx08j4IOygFWZ7T6XYZDIeMx2OiaIdSWhz56uoT3V6X87MzpNL9pbsoZrVcUpUVjuOy2+0YjUbM5/NmXmulKItut2vGImW71aSag8GA+XyOknC/f8APfM7Oz9lHO4o8w3Vt7EyQFyW9XogXepAWOI5FN9Aye1lpquWWZbzeoxCwZdCOowzzCe19iIMxq6Oe53LE7eJc+/2yqJrwWRgRYbPHVwa0fv/fltzX57TN9bi20a1UirIUlMbwyNY9Pomg1KFiC1/nE/XxtUGzbK0/WpM8CgssqYWEVf2weBqxPg+aPc5LtTmN2h5ZO1dVfyYwPO/f2tqG7zh39mv71Vv7nL+WPHUMFZGSVdPL5/t+EyIVRd6EmZZrIUstClIVOZaQlHnGevlIkmRMJ0MuL86xkDwuFkTbDbc310wnU8JOSJ6lDbVzWVZMxiOyLCPeJwyGQ05PzxDAoH/Hw8MD3V6PQb9HWZZEuz2+F+B7geZX22ukf6834OTkBGE5jEYjEFp4JQy7GjIhK1zPYzgaYjs2YRhwdZWwfFgyHA7xfY+yKk27UcX19XUT2m42O7bbHSh9TN0WNGQ8HtIfhCgks/kJlSz5T//Xf+bjx4+cn1/w93//R969e4fnOdzd3xIEAZPJmLv7W+7ubijLiulkThD4dDq9ZvKPx2PqKlyv12tETTITjtXqTFmWkeUlaVriByG2nRPvE05PzwgCnfvbbvdaMBaLWn81DELyLCcIO6TJnpubW7IswbIsbm9vm+rry5cvSdOU+/t7oigiDEOKokBK2cyNIAgZDkfcmiLNavVIrxPg+0PCwGUf7wh8F6UqPVeQ+J6LFFBWhWFgtpvw7S89nL8xy1HqUMyqHYt2IeBQIeSp8yErsiJvjqRzx20v6akFqD2x566zWZutY6EOjBd27a1JpYGvHDzB+hrr42haLn1fdThdVzOfGljdKyCFLropDNZND2kTQSlMHq0V6H0taGJCs9pYVFVFlmU4Rm6ufkocJyOb3JUxFs+51U8G6Nhqc6ikHLuj7Rs+3q8d8tZfrG1ruiH9hsBzXbpdDTStGVodS1+bJtJT5GVJFGl18YvzM5I4Zbl4ZD6bU5U5H375WWONbJvRaEhZ5BR5BsbFthDstjuWiwWT8RglFcvliiRJkZWk0+nQ6XSpqjsc28H3A1xXmqS6DnUWiyWqUgwHQ8qiJEszxpMJlmVxZwRAhLC5OL9gt4+4v7/n9HTO42IBKPqDHpYQOLbuOV0tl3i+Tno/PNyTJLFunK8qsiynLDQ7SFVKdlu9uB0XHhc3XF6ec3Iy5Xe/+2s+f74my3KWyxW2/YFdtCUIPIbDIUmy143qb16x3UZYwmG1WrHZ7BiPx2y3W9PfaRljETSpiU5Hi4osl8sGb7fdbvH9kpFwyLMN/f6AJE6xhE0YdsjzNa9fvzEFCRulcpI8J0lizZK727LfbZlOx/R6PZbLJfv9vvGAd7sd6/Wa1WrFYrEAMFCPGZZlMz89pdPpcXd/z263ZTwe0w19er0Om82Kssjp+D55VSFlAUoihARZIatCt9BZPsp6njGjnqO/vhl4w1HICHyVQ6sdi/ozlrQaJpLn1nZtKH8tWvo1wwZoeIUQpm3QrPN2Lks8xZA1xkweOhTaztJxAaOoJGXlUFiNWTSK6yCx0JyJGqD8q57Z8Y1oL6Z41gt67qa/xQhwbIF/bbB+7W/f8ubaP8tSc00ppVsn6kXU7XZ1X5yURjJNNcZMS6JFnJ7OGI+GJFFEVRUMBz0EivV6ycXlJdF2g0Axn50wPTmhKkvuHx4IfJfXL1+SxjFKKna7yPB9WaxWG6JIS869efPWPAx0s3iv1zUVPw3RcITDcDhmt9tSFCXdsE9RlERRrIVQ8ozF8pGT2YzHxweyLMXzHBYLTSF0Ojvl6tNnyrIkCAO2200DiciyjCRJybKcTtghswps22O/T5AyxvNcKukymfbZbNaMxxO+e/cdtu3w/v1H/tt/+ydOTqZ8//3v6Pd7eJ7DaDTky81nfF8z1iZJgixzoig2lcEeea4proUQDQ1SDe+RUjZhf5qmJoRxiKKYTrfDaDI26HrJeDSi0+0ymUywbUv3jKYx0X7Pcr3h5ssXXFvQ6+qq8c3NTdNdsNvt+PTpE0op+ia5X8N0Hh4eiOOYH3/8M0I4lIXEEhbj8YjLizOKPNECy7Kk3+vQHXQpSoVSAtspyUtFmuUG8KnJHZX6SwbrG5sQJg/Fk+jjeJ7Xa6ptLOsHeafTOcCl5FOjqI95vK6eGtz6Xcu8aHcUYCI0SwhkqcV4dbe4FqyuXa5jB+Y5Y1bfQ3tTCgoJeSXBVk0B1jJxZQVG5a0WXf6VamZt+RtL36Iiabu57Zt/zgof/63+/P+I2/1vCTuPt9qlrlSlySCNZmaNjxNKK6rnRUGR503S33Fd+r0+3TBktXygqnKmkxGWBY4tOD+bMx4OePXigvcfPrJarthHOwSCbqdDnmUsFguUUprNIst1rms80ZqjpVYqd10P3/eJoogvX77guq7JqXlUVaqfTEXBZDLFdV0KAxe4uLhEKUV3HPLw8ICUFaenc1brleHcz7i7u+Nsfq4hAkIwGAwMmaHug4zjxCDwE3wvpCy1Qs52u2U8njIajxmOPH7z21ds1mseHh4RQrNTFEXVdC7MZnPGkxEfP/5iWq8q4jijkpoMsD/oo5vidYhZGzOgUdPJsqwZlyDQFN8K8HwfEBRlSRCE5HlB7hW6WmnbdHs9iqKkLBXrzYblcsHj44MG3Cax7pdUFbYl8H2/MVyat00rmc/n8yeV7dls1kBmHMeh2wl5cfkCz3XYRWui7Zb7hxuSRENMBv0+pQRhuThuRrTP2Uep6Uu0qbBQz+S4/q3zWe8jvlrw9dqq3zsuLMABclRHVTXuSx9A/2jDPOow8+kFHD6rtDVr/lDnvmRVaV0EIRCm0ojJudXhY31Ndahs1UZZHAzjE7QCume1UlAhKDnwldWPhupwBl31/DXPrD1QbeWXY8PWBtPCwRU+9syO3exvFQV+bftLSf+nLryN9oA12rvT0Qu5LmbkaYKSUjPNVhXC0snhTtDFHg7ZbZbE+w2j4ZjTszMAiiJDlRbXVx85PTsjS2LCwOf+/o4wCHnz5rXWXrz+rPs5TSjV7XYbjJNSqkGfe57HdrtFKdWMcQ0eBciynLPTM8qqIisipFJMplOWyyV5XjShWbfbZbdeYtnai/jpp1/wXZ+XL1+xNZTWk8mYk5MTPn/+bNTENUwiibXB3eV78rzk5OQU3/OZzWZE0Y7hcIDn+RoCEXZZrdZ0Oh36/QF//vOP9Podzs5mzE9n3N59Jk0z8qIg2ZcEvjYg9cPPdXV7TxAEjShvkiQIIZqQaDKZ8PC4II5zsjwjDDsopfj8+ZoXl5cMh0PyvCCOdU+l52nyyaurK/b7qBGmQZUaG2jmY57nbDYbpJS8evWKsizZbrcMBgNDtx0BcHNzA4DvdbBtn1evXrEtczabR8LA1YUVobBsQVFmVFIHPY6jhYlt29LiPJaFMOT3/19zZs+laNprTc/zpzoZ7d8tDroZtm0jHK0aVZYH7dqn6+b5NarfPPxsGtj1h6mkkZFrVUN1ekc1RuZpAUAe8mXia2ON0t5upTSDhqXQJK4obOoUmMR2WqSPrTjz65wZEilLpNJ5oLanJYTGipjzUstZNTdiWciyPD7ks9s3v2T1fEj6LU/vufeU0qrKruNoCIZlURUlcVmS7fe4noNrOvtty8a1LQ1StAWu5/Bi+hJbWMRJRCfoNBxd0+mUjx8+8P79z1ycn9Prd3n98jW///73urIoK7wg/MobqSeblrOLGoT6eDxuKn7auIFru7i2Fr59XCyxXIvlakWW59pTKwpczyfwAx6WC7K8oNvVRmY8HrPf75u8ozYiB61Qx3Upoj1SKZIsxbJ1IrVSEtt1SdJMJ9WzAmfo8fLlnOvrLwBUVUkcR0YVKWY+n2HbEAQur1695p//+b+z3UQ4dsh6s9UdBUFAHCc4js1sNmM8HhtDX+eEJGEYmAbwHt1uyD5OsWxNNZUXeaP+LssKL3RBKQb9PiBZFgWLx0f28Z7BcMDl5QVClfiezexkiiaDTNls1qYns+DDhw8MhwNc12G1WlNVmgEkDLtYCEaTKbttBEiiaEe30yUMXDphjyxPmJ7MWK7WSCXIS4XEw3YcXN/Dy0tK4aCyygREopGkq4UCZJ3QftI1qQ1j7RK1BXrrxabbpWSTI6qFVGQdwhlvR6GoiryZc7ZtYzdKS4dqolItBXXV+Dp1lPjswpT132qnphX+WobZQ1OHG5D8YVFq569Oh4vmkOb3VrUVyKQklZZm/EC3JgqlFZ1KKbFMh0/N91ZvXxmzskixLIWqCoqqwBLg2jaqprkuNQ6m2+lgWRDHsWbjBB22HSU360Ft41T+UjXyW9uv5ewOeYFaM1C/doSNi42qJFVe0A87ZFlCVVb0+z0N7HQEWnlZEYQ+k+lEM+Yq7cH1RwNsyyI3Y/Lq7Wse7u91/gDJw/KBxXJBVubso4TJ9ITNVieae70e+/2ek+kJ641GzD8ullxcXJBmOS9mcw1yLSs8z2c00CHRl7sbLNum0+my3UW8f/+Bl69eUlUVodOhlDpNYVseQniMxjNOooz1YkmSJEynU6J9xGajNQfKqiLsdYmzlH20J6tK8qpE2RbCc6kEFCh2+4Q02SArgW1rYkbPc5iejAg7HlEUMbb7jMa6D9OyBGlSkmeA8nDdLpbtIyyfooTFco1lhKJP5nP8wG+UdvIsJs/2BjwcY1sS17WRRuovMcBbYbBewvNwEezzgrxIEbIiS/Z0Ap+L81POz0/xfYdXL1/w8HDHfrfDqiyKKicvUmbzCd9//9f4fsDN7S3bzYbZySllWbFZ7uiEIZ7j8/Dwnpsv17x69YLLy9f86U8/oBTs44zr63vyosTzQ2xPE3wWRUZWVERpSl6lWE5gbJdlmAcN1gsLSxgPhVrJTHsjYIQ+hGzMnKU04LUmQK2Njy0MhY7SxN1CafodlKSqoOYHtCyhjYDBeAq0UI+SJUposZaqBrGaJmYBWPYhLFXN2jKGWEEhpTFEB6lIKeqWJtNsr0xftMAEi+2Cof5ZiYMB1VkwC2E5lEKyl4KikEhLNTxpQgkqbFRRmnGp+zX19pUxk1VBrWsnpWp6GmWluZFQ4Lke49EYz3NZLhfs93ukKhsw25Pjya/Rvs8ZsMbLar33ddn2LxcWLMs2tEkKKv3UscwjQaqK0PNQZY7veZzOZni+x2q1JM1Tut2Ay8tLur0OnutSSckPP/xAbvob8yJnOpuxXC359//wDxRlwdXVFXEW0xv2GFg2ST9nv4+5vb0ziX59V7soQilwHJfxuEMcJ8znfa6uNFniw8MDp6en/PzLz4zHYzq9LldXV9w/PDAcalbY5WqF5wcI22X7uGz6NKN9yuPDCsfxNcg07CCEpunJcw1o7XS72J6LEFph3t7sKNNcV6UsC2WBG/i4vs4ZrsUWy7bo9bqEYcDZ2SmvXr3khx9+QAiaPFy027Na7igLECLAtgOqSgMz+5MReZZyd/+gQ0MTUnq+h+NYlEVBWWmgaafjMxh0idKCZKfblOzctBhZmvCQUqPefdtmMpzRCwN+/HEIls3/8j//Owajoa5exjFplnMyn2FZMJ2OAUUc75vzDwZ9umGPQW/EZrXFc3x225j7uwcuLs41fqyjQ/LRaEIc73lcvKdfSGxb89k5VORVxWYfk5YFtufhKYuyMoh4ns5pzRIL0Eroa3PR/KT+vzIMgHoaa8NSu0/2gZ1WmfeFEI3Ydm08pdLQFWpRX8C2LKRlIy398K5DRqFEc+4jpp7mDupzVK2wUtF2KgxDrLn3uqWQGrSrjop5HN7TBRPNCF1gUUlJYe7fRs9ZbdwVArdpn5ItI/m1MZOa3fJQkdA4lfr3stTus8a96N3b4d5zlcd24r/GxtSfP/6c3bLhz1Vwfi1/plTrG1eyQQ+jtGvvGGaFbjek2+2QpSlFqameu/0OtiO4ublhejKlLEpsx6bb0bTQHz9+wvNcet0+lrAZDIesliteXL5gH8fc3z8wHIxYrVZ4bsDFhRbUWK/XnJ6e0ul0mM/nJu+lMW1CaMm1KIrodDrsdjuWi0eqqmQwHJhOBEFR5iRpipSSfZJoAV+liSUVin28589//jNhEOC7LkWW4XgOy+WC+/t7Xr16SVXpRPtwOGQb7bAs23wXmiMfcfhutBdtE8e6ilcUGWmaaOxaWVBVZdOUvzbKUq7rkRcZrucRxxnL1YqLywvCwGexeCTPcxaLBfnrV0zGQ7JM55u6vQ6+79LrdrTASirZJGtEpck9XcPya9s2ZVXRCQMcW7B4eMRyLL57+xbL0d9rXhTc3d4ihOD0bM6r128RKPbRhqoq2W427HY7TSSpMNqdWpcg8H3CsKOLMZ5FpxdSVSVJmjIajQzDClSl1J0BjoPEojCiLlG0p0LgOEGTxzkET4eIqpVLr1f00w8c//2Z7XAM8SQBXufp4BhVf4hN21CONsTqad7q6Xtf5bbqjxrD2a5OHgoBqrml5yAYz6aZtKHRyvBCIJWtnVvNSkmlDMWqEChhlA3EwfI+Y8yOB+FowNCVqPV6jWVZrNdr8jzH852vKiztY7Zvov1P3/8BCGsJnj1Gvf1aWPpVQcAcy7JtXMvS6uZOZcrymnra9TVbhRe4rDdLwrDDoD8wFNcO7979BiklRVEwGo24urri5cuXZKmuXvq+j21p7Nft7b15rRfJarWiLEvevn2L53nc3d0BNK09vu9zfn5OFEUsFgvu7u7odEKyLGGxzI0+psdqtde0NcKi0+tSFAEnJ3Nub2/Ji4J9rLnubctiOprw23fvsG2bs7Mz6pxZEISEnZAszajJ6x3bwrI0BqwsCtI0Jol1ZVHn9ULyIqNGs9c9lrud5tzvGdbdy8tLgqDD/cMChE1Vlex2W+J434TynueSG9R9XRBp6x76vs9wMGTQ39Dd5ThWge9pRhPL1qmDNM9Jk1iHnWXJ9cfPJFnC3/7h73A8l6oocV2fbreD6/h8+njNYNClKjN63Q5v3nzH9fU1Simi7Y7dbk8cLdhtdkwnM4KgQ5alPCzWOK7u0AiDkDAI9TUGAXlRaBLNIMQPO5roMwhI0oKy0hz+VfXNxNP/r9u38GDHRYOG6IFDhdWyvm4tPDgPz5/vuTRPu/hwuJxvR1C/VhARxpgJy/l/aXvPJzmSNM3v5x5apC4FFFT3zuz06uXtLe1IM/7h921pt6TtkMbZvZke0QKNbgANUSJlaOHODx4RlZVIoPt2STcrJCorMjLCI/yNVzzv8yCF6RA1MabVhd/m7+0A/O1/zPhJnNnhcF23Q5EbOuQ8z4cE87760X71Zf+9j1n9YbL2DNkxj+yQ+7///6Ehk11y37JMIcBzHCSK0PNpW1PJnM1m+KFBo6erhLIqWMzmA4hzMpnw+vVrPM8b9B7H4zHv3r3j1atXPH36dCAX/NWvfkUcjwCLm+tb1uvNEHY/f/6c+XzO7e0tcRwP+KqeoqZnpDWMqA5SGBCv55m+xzgOadqKLCvwfQ8/CJjPp1zfXCEakyPZ7UxPYVvVTMZjPoufDpCEIAjY7TYUZU5VmaKEJU3RQxhtP9q6pswLNts1ogODIhRh6DOfTynLgOVyOYjn9tg8w5oRIKVNmhW8efveyNF1HP7m4WSapxGw2WyYzcbEUYhWBiR7dZUPuZM4ipmMa7YkCK1pmtp4sk2DAKqixJIdkWOWcXp+xsnJKQio6oqTk3OkZTGbTUmzhFEUcn31jjyvePL4BCks1us1r1/+yGppvOgeuuN6No5wyMuM2+Utjx8/IQhDVreGXnw2m/PixfcEoUJWFYHS2F5ojl21FEUFssWy/U+uof/o+FTha//9/ajI5MzuHJR+u2NOhUDeW4OHue3eg79vyMRgBH/Kjh+r8g6OkBRD+CxMCtEYyS5Nh+opuPcqId042s6034dpXvuY1pR6+zCp51vqtQj7ZvL9E+z3sd/hf3gBDo3Wp3Jshzm4D41vH1LKgQvKtu2OGcN83ixC2TGXGkBlVZeEUUCe56xWt4zHY6SUgwG6vLzEcZyOWDCkqqqhDafPeZ2envLu3TVpp6C0WCwGVgjHcRiPx4Qd/9jFxQVVVVEUxdAIfXZ+imorlGqxLcmDBxes12vTLeDaBEFIozWWZRNGAX/3d3/LcrUiTVNevTLQC1W1JtR1XZTWOI49PGyqygBXoyhkFEeQZNR1i0BhSdMqYls2CNOetlmvcd0TQAywir6vUmvTJF4WNU1jQMibzYbNZs1oNDFN27stliUIAuPZXF8bPNjlwwdUTkNZFlRlRV3VnUExzd/TsRFjKfKctmkQwsA6PMehcl2SZEvTtlxeXvLg0SW27bDerE3T+jbh5PScqmyYzxaMxzGObbPZrHn75j1Kt7StxvMDlFpR1zUn8xM+++wpWVFw3VGXn52d09Qtb9+8QwqB63p4nk8YxbRKk6cZRdXihbURLxEC3/MQtktVf3Cr/386juWh+2vSvx5CNpS64/RX6r5B3F+zpiJ6uP7vg3f3OxI+8Mw0nSf/oSOy72gcGtBhdLq6Whv9ALHngfWEQHcdpPfn4Chrxp0ii6l0GGN2vx/zLuY2O2zbdqCd6SfpcLL6Ezmc8P2T3E+OHpuI/b8dte50Hf22KUlL2U+OoTV2bIfRaIwQhqCvrA3DaBAGgDBismenbDbbrn3HRgjJbpfw5MmTDsmvcRwXEFxePmI8HnN9fU1dv+WHH15R5DXj8XjoPDg5OTFKUEoNC7tnP40i06TtuqbNqKkrLMsATxGCuq5wXRc/9PH9gLJuOmqfhslkyu3yhtE45tmzJ1iWzbsf35Gnedeqo4jiiPF4zMgeodEDSWRZlEiWbHepCcNty9zCSuF2+Ly8SLscUwkYPrW+BcgI+PpIYaMUxPGYIPAZj0cmnC0Lyl3BKI549vQJT5485ur6PdvttpuLlqrM8X1DA24eEA3ursR1jY6A7IzYeDJhPBl3yWWNr0Ic1+1gHRbv3pmUQBSNSLKMqqyoqto0u1sOk8mMtmlJkwTXc9EKRvGIk5MTPNcl9EN2yZZ379+TZDlhPCIIfNIsJ8tS5rMZs8nU8LRVTadqn6Mw60TVTRc6W+ylhP9/GftG6jBMPNYxcPe5vspp1rJSuqvg30/79B0Cn8pN98fxoUED40x0/IBHjltrPfC0Hd+3NFVgqRFaIqQG3RFna3EHoxUKo/DyiZzZPlDWNKb2pdQ78KzZxljjPv+BsD+Z69o/4P0w9APj9BFSt/1J+XAC7l4tyzAW2JaFNQB5TY5FaEXcQTOE0IxGU2aLGY7jUNZFl6S+4eHDh0SR4dNKkpTtdsdqZTyk7daQDe52O7744gFffPEFr169Yj5fUBQlju0hQ7sTHMlomoazszNevnw5NLyDYYzwPI8oigYuLc9zmE4f0DQVk9mMsiyZTAwEoqzMAnUdh9l83rFRGJ1JPwiYziaUZc1kPEFiOg3yokBIYfQGQp84joEEpQIWizlt03YFHYktBE1ZkiUCZ2pYbf2hOd9it9tyfX3NbrdDKcXNzQ2O43Jyco5juwRBwGSi2O4SmqZmu9sCRhk8DAOePXvGj29+pFUtSZIwnU66cBvSNAcElrRpm4YizaiKAikEcRwznU5xPJfV0hjS84sLttuN0dscjwiiENtxEVIQxDHj8QTHtdntElCGNNG2XYIgRKmWplHkWUGeFwigyAx5ZasUFw8ucfyQvCipmwIpbfK8JElSNtstSZKQ5TlaCYIowI8ikiwnzQrquu3u339nK9PPHPsP9v01d8xBuFsbuK3xQgAAIABJREFUd83r0Lc1fUh2KoQhAj38jv01tn8c99/b8/a473ntr/cPctv7QxgYD+wXUDplNW36MaVlGYOnBfskmEfDzKqqBkS6wWyBJe9EF3oPap+JtqcqOaQIOnYhjhmyfhwjdNyf2IG+RqmBSaD3JHtOfcex0JoOuGoTup5pYSoyPNvuFITUQOccxzFOZfM+fc92m2BZV4zHY3a7xFQMdUMUxSSJqVotFicUhSECBEFZ1jiOzXg84YsvRqxWq4Faphftdbpm917VqD/PPM8B44FMplOEaIaQIIoiptMpu92OujFzvdkaaqGbmxvW6/Ug7RZFEVl6AzCQIoaRz2IxNzTTWUZVFjiuQ+B5WPMZTVWRJglV1VCVGdZkhJAmR+E4Dn7gEgQenufiODaz2Yzz8/Oh6GPUlU7RSjCbTfH8gOVqzXZnVNINw27O+/fvmE6n/PKXv+Tm6orVasXpyQl+HJPn5sHQtq3h+1+vub25RqI5Ozvj6dPHhs9MtQjbED0Kz2ZXFrRNy+T0FFtr8qLAD3ziscEN1nWJ1i2FLXBsG6VaTk5Ohn7MIs9Zrzemf7a7rxaLOaPxmPc3K7a7hLLIqauSMPCp6ookSZFCEgYRRVGRZxkKA4ZumwZL2hxbn0An5GuWuNGLNZHCfrjXh1/HIE7746MhGn0Yed8h6NekyUPd5cz289p3TsbHjdb+sR6Lqsx6pWujUve6h/Zf+3TUsfPSBuvRSfH1k9XDOEziXwgTcvZV+H4c9fcG6hE+ZMfore8hw+zdhH2Y4N83Sh+j092fuGMTsD9ph1Z+f2J6IywE6KbBc22EFLi2A6od+tZmsylpmg4e0mZrlMN93wcMNEFKi5OTU+q6Jooi8rzg+voGpfSQS3McAyRdrVY8fvyYyXg2sLF6njc0uPeSa32erW3NsfQkkf0PGGZYpyMX3O123NzcEIYR88UC2/W4vTVMEE3TcnZ+zvn5Ob7v0zaKttYsbwy7g+udsF6vCQLPNJG3DaI2ObfA9xmPYuIwIFUZdVXStjVaO10PqTPMc57nLJe3Q4tWr8Q1mUx4+OAhUtoUhYFeNE1NHEdMZ1Pm8zlv3rzhq6++4uHlQ3OutsV2uyXr2o9c17BvxFFEGETc3Ky4Ddc0reZkMefi4pwwjinrCmXAWyhL8vDZ0y7VYfHu3VvSNOPMPQWhefPmFev1Etdxmc/nzKdThJDkWUYYhoapxHEQQhh+NwG+79GqltV6zZu37w0spa2oy4IqMnRNVVUP0QoC2qahqSraukUrjbCgqSuEHXRZ6737vw8JxfHC1qfzwD9vfMzj0Vp3y1bcGQw+LBoMYaDjfeDt7RfzPpa8N1obmraqDTiY+wwhh7m5Y4WALrFnPLuuKqS7XFwvENp2GDZzGf4D1cxjXpV5T9/Lhx2z3v+e8anPa33HYNsf152X1qBUQ9klmH3Hxfe9rleyHBq8W90alPztDUEYcH5+TpZl3N7ekmXZIBFXVRWr1WpoTjaUyi2/+c1vuLm5YTo1GLPVaoPruDx48IDdbjcg8D3PG2TP+kXc9272Hq7xGIpuHQia2giUGPYNgwnrCRVd18P3I5TWnbBHaRLX5+dY0iFNdswXc26ur2maijiOaNvaVEN9geP7xFHIZDSix2vWVUngLxiPRoxGI5qmHHKnSZJSFAVZljGbzbi4eNA1zxuptnfv3lM3rQnzpfGSi6LAdWw26xXv37/veidhNB5RdUUPAx1ojGBJuSHwA54+fkzdtFxcXHB6ssAJfKq2YTSbmhtZmnzoLjFsI95ohD8eoyW8ef8j719/T54lgODd+9cEbkAYRGRp3gmxnHB7e2uMapbhWNKIMacJWD5ZqSjrBt0qiqJGNVuKIqcsCixL4vkBju0YogilqOqqo8VRNK3GccQHxqz//6EB2V/sQ0P2T1AE/ZRD8DHDKO6BVI+jC+7nr7n33j5aod/mfs6NDtdx3xAeKzIcM7zDsXQel0lfdfvcc8JU/zT5KWjGsbFvyT+93X0rf/iZw5zase0+eZJHLlKPo+mNWeAbcd+2qU3+pchJsxTHsnAsgfbg5OSEzWbDZDLBtV1WqxW+73N7e8tiPh8akKuqYrvdEoYhUkpGo9FwUc7OzphMJmRZxsOHD/niiy94/vw5RV5iWTZFUbDb7QYKGiM8awCYvm8oavpqam+Esyy9uzxaDDqRbQuWbVEWFU3TEgQh0+kMz/NZrzdc31xTVw1hGOHYLg86IG4QekSRUWIvq5IoCkxfom3juR5t1TAex6Z3tWkJfJ9RHA90zbbjsNttmEzGPHhg8lRaGwqdzz//M8MLttyw2ewIwwA/iKjbhqIsAG04xppmgPKEYYglRCddZ+iCBuUmIVGtoWQ6mc1ASs5OTwmCgEo3KDReFIBl0QpBEIUUQnF7fcWbt28NyFlqmnSNbSvOzmeoVvHmx7d89+23jEcTyrzqQMDmuq5WK7I0xXOMt54XBa10sL2RoX8WUNUNWhkt0V4ftKct32wTNruUIstp2hZXGppnIfabd+4WkVnmd8wRgjvyhp+TIO/Xws9xOA7X17F89r7Xtb/PfYr7wxz3vjjRPo9aH6Ki73twhx7Yfhh8GL7C4EACpjpqtjk85p9pzA6rj4cTdLfNAXp4r6qwb32PTeDHQshhf922h2Dbw8/337Ofu+vVsoVq79SilQGtOI7DyWLBZDLi66+vB3WgzWbDw0cPcByH1WoDWuM6HnFswpAiL00zsW0zncwMfGO55sGDS37151+wS3a8evUjWWoods7OzoYG8iRJyLKMJEkGTvq+EOB0oY7Wxqv13ADbgqLISJMMjRGjtW3TWrXdptiui+v6BH5IFEe4XkAQhl0+L2GbJcymc2azGVJqzs7PaeqStq04PV0wjmPqpkZogWoaFvMZ88kU1Socz8AfNtstjuvgeQYga5rAI9q2HXJ/UkrCMOSrr76lyEuePfucsqrxPQ8/8PB8z0Ar2prpdDIshKZt2W5NA7dqFXVlcn5SWJyeL5iEI8IgpChL/DhmNIrImxpVVZRtTVmVLLcb3l1f8/L1K17/+IZtsmMynvBnnz3lF3/xS84ii8A2allxHBCHEWEQk+wy5vMZnucN90qW5eB7nUdpWveQllmU3WKqa4VrK7yuwV9rbaqgQUhVt1RNg920SMuhbIxx7lHx+2tB7d3nAjGI6fYGbR8K8anxKWO27zl9YPh6HJi43yN96A32JKyH62zfqB3z3Ew4q7rQ79POyKF9uGdfhrk3yX+t9w5+MJj/A8bscILuXg//3hkm9lzDg0n92Insv/+xp0a/zeHJH36uP+6qqoyxsCSu5+B6Hrbj4HQwiTAMhwZwx3HIS+MdvH37louLC0bR2CR0W8Usiof8oaF1rgac2Xa747vn3yE7dPyPr41itlHWMRTM47GpRIZhOEip9TxeYADIQRAYdafuta5LpLRNq5gAx3EJ/AhhSWzb9KSZY9JkaY5l25ydXXB2BsvbJTfXSxzH4fHjx8xmY7brFTc3V4YFtfO4XMdBIrBlTBSGeI5nqGGEhRICy3WM96iNoSnLktXqlqZpjOhux6Gfdzgw13VN4le1RHFE09aEodHgbJqaODJA4aIoWC/XHTGmwS+2rYECtK3CkpaRoAtHpHlGIwwwN2tqsrpinWfcbFb88dvn/OHrr9jsdmgBlmMjA4/WEszO5lzOfWLPpixqXMfGEpK60sznc85OH+C5Rkm+bRryLMdzDA123bTg+DTSJy8q2rqkyDPKPEO1DY3X4Pumw0ArY5wsKfFdD9c1Sua16kAJh2to777ujYE+SLbv54M/Nn6uV/bhH+7v41P7Ofzbfvrow9DyQ3sg5V0m6/B4DtfyUYN2CA054ux8aMp+Zph5eFD3DUpnVDqI7rHt+m33CwV3n79vAOXeZ48Zw2NWfn//jtMRMtoWnm2MGN0NooHb21tWq1suLx8a5H9nnJJ0Nygm1XXdaRfabDYmtGqaZtBclFJycXGB7/t8+eWXhsHWsnBdl/Pzc4QwPZ5FUQxg2V6xW2s9hJ37IsS9RoElJbbldDgzc+l225QsM0y10rJRnSHI84KqrphMp5ycLPC8kNlMUFX5ELq2ymhHhsGILNsZQLFt4dqOoW1B4PuBoZfBolKKeGK4vra79cAM2/N9PX78mDAMub1dkiQJn33+GdPJHKU0fl6gUSxXphBg20YoZBSPBkLKdJcSBiZst1wDaI7jEWEQmAeihrLIsaWkqktWyyVpW9M6Nq1qWW42vHzzmm2WID2jZ9ooxSbPeHt7zdVyxIk/4XR2wXQ6pshzvvv2OW/evuUvfvXXLBYzfN9crziKzBQrzXq9YpckNMJmnbVYlkK3LbLDGfa3ouO4jMcT6rplt92R5TlNq7BcB9d28DxNqSX6QDfz0MQorY8amP1I49joDcqnxmHSffj/sLqOR18fc2L6bT8W/u6vUymlkanTH+p97H/nodc42AB0F0npoeFe9/Qd3YTJjxjST5Iz3r8ChwnDvfe5k1zvP/9T+bWjT4YhYP54aPqx/NldDC+wpUR2eDmtoe7wVKrVrFYbgiCiLGuubt5T1xUPHlzw6PEjojDiq5d/QiC6aqNkt9sipSAITKN4X4XsvbDr62tapQiDAGkZcG2W57x/d8XV9XXXbL5hNpuie2OvAa27nF5hUO6ALSXjeEyjjMJQVdVUVcHN7Q1v379ls1lh2Y6p9ghJ3SryokLaDp4f0jQt52fnnC7mvHrzhqIukWgWp2c4lmA0jgdJNM9xu2MwLrtlObiuD3VNUxoMVlUaAK9t2zx8+HCQGvzhhx+4vr7h9PSUhw8fApIfX7+lrCqatjadA57HYjHHtsx5hEHIYrZANbrbr41AEIYeZ2cXWFLidg8Ez/No2oY6T6m0wvZsgnhEk+fkZc02KdCWh3QcqrYFywLHZZfnrLYbioXLdrtmFGuiKGI2n/PixY+D1FwUxWRZThiaLpamrrHSHX7gIxyfmhKlNbVQuI5NKQRNW5MXmrKq0BqyPCNNU+PRWhYoTdua/Jpj2XeeGHTVONHR3JhQTCPvbnPZ0+d0sVXHEHtsaMQRVov7Qwo55OSGV/q1BXAXQh5bh/vIhGNeWL8G90PjYV9C49gWSt05L/087LO49Uez//uQVezDdIzR33/VAqMA19VY9p8ZR8LM/qv1PVdZ9mXRIT91dwiIuyeJAFPZObDq9DG3Ms1WahAC7S6gNk3mHBirY2Hrfqy/XwhQShnSRSHQSlHURUe+6NJoqBpNEETYjs3tckNdKSzbpW01ju2xvFkynxgh3CzNWK3XnM6nnJyedsK5a5QyzcxZp/rt+S4Khet7tBqqRpEWOXnVkm12aKXwnBuDq/I86rpCNS3zmQnXQj8wXP3LNTfSYjye0mqQrmHESPItb96/pih3jMYW0lL4gY/nh9RasE4rrtYpr2/esdkmPCwzHlYZWZKweDdjHIaczuaczCacLabEsYfnOrz98Ufqqub05ALHDUh2OWVWgzRyakqZRvhWNdi2JAgWbDZrXr78geuba2zLpq5rfvjhe0BwdXVDWZRMJlPaRvH+9j1CCR48eIgUiqqoiYOYp4+ecLtc0dQNnuehWkFZVpyenuFYNpXWCFpWuzVlXRJPpvjjETKISGvNs2dfcPlnf8P/9dv/zh+//QbfsdCipa4L8rphudpQXEzI0wILD88NOTt7yHj0mjStqCqFbXu4boDtVIZzC43j2RS1pm0rdFtiywYnsMk8i8KVlIUmr3LSPGG5XrJLU0PNbnfV86ahbROElviOoTHv4R95VSEsG9uyKOratNUJaLWh6ZHSgk4rU7UKW/TicEcMDYaK666YYLbr12dvUIxR6M2FNAu/W293oawe1rtSd62IhyFyfyRib+Gbtdyadc19b6lum+GB3We3BtArRl1poKLsjVV32D2d0WDpDlJXSoNtya4l836fwRF1JjX8gDzwuA7Pc/9vnQVuD4zNvW6CD43cYZJRcGfIjrqh+r603T6VidYaqRQNGqEM+NC1PGqvJS9q2iZhK1JGUdQt5DPm8ynr9ZLl7YrpZMQ2z3FtCxkG3FxfYTsOge9hWw7r3Y63795Q1abxWVoWwpJstmsc1yXNClwvpqxbilqRZhVtXVM4NVX1Hs9xEEKj6gZdNyTejtD3mYwnlKUJSbfLhHfXt7RCc355TlpuiMYev/qrJ5ycxEjR4Ace2DartOLNbUr58oprpbFcl5WuaFbX5LuESgjKVlM1irwoWC5vuTiZcPnwwmDgPB/X83CcEMe1cKWF49tooYnjCKUaVqslmta0J5Uly+UtRZHzi1/8ErTgxYvvuLy85PPPn3F7s6RtNWeLM+Igxvd86rKmzArWtytePH/BgweXjMcTbm5uaerciPXWXWuKZVELTZJuuV7fEPgB5+OI0WyGof+U+MGI07OHTF5fg/0GJ/TIy4RtviEKXaquqFAVDaXVEEw8Ai8mCEYku5ymFUjpEAQxeV6QFyl1VRmPSWrauqRtclzHwnM9ssgjz23aVtIqs1C3yZY0yzuN1hbRIeotaeE6EpUn1FmBHUfYjotsSoTQ2I6kRtN260Zpw3cvW2MM2rZF1Q2hZSP1fYNi+hJFx+FlfBwTqupunYouf9s/9Hsx7Dsynn499Wro+wasX+d3QNf+Wz80qXrwxowxvDNkZs321Ny9J7V/DojuyMSd9dB7P+YgxN1f9vL+d/sxRlwNxtyMoxRA9ysrLaaX6zhi//BM96se/e/77+9vd2x7S8oPPvuxcey7GtUCXV+XEEjbRiMoi4qSisV8biAH2y1FWTKdzggCn/V6SZKmSCnZbLfoFppWcbu8IisKLi8vmU7n7HYpzWZL2yps1yEvcsqqoqxa1psdo9GCqm7JM5Pwt7qQUCnTjB34nmG21aBakz+bTidI0QE785r8+5fcrJfMTyeMxyP+/j//DX/xl89wPUXTpGDBapewqq9pLJCBhxUFqFqRNyWWqtGOhRX4RLMpkzgm9j1s3fD67Ruurt7y+NEj5tM5y/UGS1Y0jcV0tkC1NY7rmshJmZzW7e2Sqja8XUEQMp1NeXT5iN0u4f379wRBwNOnT5jNFnz/3feoFk4WC1zXQwhJkRekScb19TWu6zObzvE9j6qqOT095fLykensQFM1NdfrNa0UhJMY23fQoqEoa+q24OXrt7z+7W95t92hLUFeV1SGGZKmA3qXZU1RVISBmeMsz0mSFM8FrYwnFEURu2TLze0Vu2RDWaZ3EAkUgRfgea4pIFh34j51XbPt4CZKde1/wvQAO65jmuELo71qoXAdi0bZNNrI0Tm2NKG9AGXoYU0rohSARKieknpvcXUeiupjq06rSAhxt8jvpZXEvUXe+wT9OumLWn1eFfYxY3JIhxxzJoB7UdGnUA+aPccEPtj2nu3Qe793J9K/NbwKEzC3HymQfFQ302BNeoNx5xFx7KA+Mg4N2P5nP1XF/B8Zh3H8/kXppbfoLroUgsD3kZbF2cUFoW/z1VdfAYrRKOL9+3f81V9+YbyQqkDaLnWjWG8TvGBNWtTUTUva4aekJanqupufzHD8p2+R0sG1pWmbQnRN0CFRGBEFPgJN4LpEUciTy0uePXuKQJDsdmzWOx5dXrBN12RZyue/esZnnz3DcVy0qLBcj7qtKBpFrTR5XbPOUrK6whuFSO1RFymNbqhQuKHPeD7jbDZhMQpZvv+RP375W8qy4PzsHM8dMYpd1usdWktsVzCaBGjdorXFeDzh+ubKVFWDkAcPHjAaG8Hjsiw5OztnNBoNpJSz6Yyrq2uSpGE0kl2FtqIoci4uLjg/Px8W0mQy5vz83IgcA0mWcnO1YpXueHh5wenFGUjFLtuwzQvqNmOXL/n6u6/QXoQWNUVZ4bkWcRyh25aqqimKGsaCOBqjFGRpxmaz4fwsxrZN6BdFpgF/ufTY7aCqGooio2nuClS9QLBJuouhe8QUcVxs23DBtY0pEBVFgW5aPNeltu2hZ9qyJFmWo5sK4RiVMBuBUIqq1V3xrEVow9Sqe1s2uCUmf627OKzXbjCZoMFSdflYg3M7XEv7+a9j6uj3c1/WB5/fH301dn8/+2v9GLPNz7UZ7J33YU7tLk1//LiOGrPeoN2Lw3/m2O/P3J+4Q2zK4XcOhvInKjWHT4QPJ+rOoveCFgCObRMGBiv19t0Njx5e8vDRI148/4blcs10NmYynZm8V90ibYfpbA7SUPjmRcmLH16SFSVpnhuZNmGgBbbjIFuFZTtIyzJ0Ma5rLkZrGFWDMCQexTw4OzXMIqol8Ew/ZhSP8D3XGFohOD8/4fXbkOXtNWFoQMC7JMENBNLRLLc7kqIgmk6xNyWvr96zyhX/8//6v/Hk8pyvv/wd3/zhK/K2wgpc3DhA2wJtC57+4jPGo4B//qf/ne12xz/+p/8FIQST0QQpLKQwbKp+4FJVpvk+iiK22178wyeOR1Sloe2ZzWbE8Zibm1tsq0BIOSz6NE1IkmTgh3v06JKzs1OK3NB5G4k8I+AbBAFoSJKMqlWEUYztumTZBoQCq0aJHNdrcT1F0myN2lDdIN3QXHVlsGCWdNBYGAEekMJiOpkyHo0IwwDLMpRIURh2QsYZm82K5XKFtGweP3lK2xpYiJCi4/BrEMIYNssyPaKeZ65NWdSd523SClVp5q3RTSf+oWmaCiXAdx2CwEcJgaxrdFlSNQ2talAKdKOwpHPfM/vI8tuzY92drz5Y6PuVxP73ft3s/37/9UMs5yGE4nA/x/Z3+JlPFR3uVi8f5Mp+6jP9ONpovr8D87scjNRPDnHf4HzKE9vPfw1PwIODPrTwH3ta7BcC+uY5pRRV0xjUvOdj2TZaGLrp1WpFlp/z+MlTXM9BSMnp6SlZXiCkhev6VE2J7fjUZcF6u+bHt++wbJs0S/H9AEtaNK2pu1R1gxCGndQYBdEh7V1DydxhzxzXZTqZ4FqSIAhwg4C8KImiiJPTU6qyAFqiUUBa5l3TvGY8n2E5iqJOqRUUTUOmFElZULQ1VhCyeHDO3/3jP5AXCd//8ANpXZA2BWmVY4uWceQSRGNmv/ic5998ze9/+wdefP89Dy6eMY5PsW0PP7BoddkxWJjQOI5HbLdr02ERhHiuj+f6XSN50QGCFaqtqKuGPM/R2oBSLcvg7ebzOYvFHKVa/MAnCIOujxY8z0VrRd3UholWNQikofIpc2xPYNsK39ecLEIuzsZ8+/oduqmxpUWV7yiSjOnJgvPFGXEU4nshdaUoyor1estsNsf1PNMl4ZgKa9O0OI6LlJK8KMmzgtF4zGg0ZrMxVEWBH8BUdFKFemgC7w120zRGp8CycF3HJMVVix+4SNvBtiWO6yE9m1aDH4ZGyEOAowSV0FhCIwUoKcEyeTHZhZPDWqEPLvfveWO7RFdQM/e/xrLue0qHBuUuZ6Y4xI31CICfEyH16/djDeXHtv/U3+/OVd8zyp9CMuyPj5Iz3vesLCxpfTxvdv9IPrrNIUbmY/iT/ZM4FrsfurT7f7OlYU417CAm4dhzrVV1xWpV8dmzJ6TbHd9++y2L2cQAM7PEgEHrmsAP2KUlq82S3SahaiqqqiSMDM5MIxl1fGVplgF0XG42qmkQFtiex3g8YjadEkcxjiWHpm3P87DCkCCKCYKAplW0WuB4HrYj0brGtgWOtlBa0WqFlJYB3VYNtutRt5oXr17y+t0NwSjG9WJev/uRX/8/mj9++zWlanBch6Kp2GZbZOvSzCPKusByLP76b/+ab75+zr/9679i/0OERcho4nXN5GOUNnmo+XxBWaacnJyQZSmWZQgrpZRMJlOyLOfHH9/guT4nJ6dU5c6E6MJiNpthWTZVVQ3UR9vtFtt2DHDXshhPxoxGI7bbraEOf/ceOwxoqoa2rrDQ1HlKI1pGgc2D0wm32wWb3cY0ebcNWnXwkfmC0+mcKLQZj2coJXj96g3fffeC0WiKbTmDN5WmqaEvb0wPabJLsW2H8XhCkhia8qIoODk5YTQamzBfm/vN8zyjObpLSHYJZVl3t75GqwZbClzPoWlbsjwlFD39TkNdWwjbRkiJQGEJjS200duUEm1ZnfRn11VvVsK9JLnukAAaMaSCQO8ZoeOFs/710Jj1a61f+2374fo95mHtr8E70XABWEc/97OG1vfzZ3ujh5iIj7iqR43ZvqU2yU/LACz3mqI/fiwf9nv1YabWesD6DNXHbp9D8vXAOB1a4f3k5f52/asj7a5tpBNARZqG4bIwtDzCCO5GcUxRJCbZrxp832W7S9jskg48m7PerGmalqoqaVuDhvfDECyL2XyBECYZ2R9TEPhURclk7HN+dsZ8Psd3jYGwhMmd2KMRlpQkadqRDk5Aa6Rjo9DEo4hnnz3h+x9/YHOz7rBdmpvrW8KRh1YCISwc28VxjbenUeR5xvXtFavVFa+//w5P2tiejRLa5FqkpmpKqrpkPppxenbC6ekJN++/482bd5wunlJXDUnS4IXjDpfX4jg2VSWxLPtOSLmq8H1/6HZwHLfDxDVGhjAKaeq2U0KvSZIdWWaMR1XVXYuWzcXigiDwB3LKsihY3a5Y2C7JaktotYxGNqWCqsxx7YCJ5/DZg1PqoqLY5WzWKfPpgrP5KY/PLwgtB9/xkThkWcH3378kz0vmcyNw7Dh2R7mddz2ogjwvybKM0ShmsTjhZrlkt9thWRbT6QxLWtR10xmkO0NQ10ZFSoheH8FCSljMpkymI/LchJDRKEZIA8twPR8/DFEI8qJku9uRFYXZJ4K20SS7gvucaHLPkGmj2iTMou4N2f6a+ljRrXcODnNmH4aNx/PZ+/s5XMP9+0LcgWv3j+fw2D419soax8fPzZn1gND+xFWrugKK+sAz2wes3VnpD/FgxxKOvYHbbxTv3z8Wk5t9f7yKMhyHAGHJDjho+t+EMIrOddMgHYd3V++ZjsbmlQgHAAAgAElEQVQEgQPaAm2hhSYrcsMuWpTdjapQ2uTCLNtGSIvZeITruixOZuYp7TqotqWsDBBURZqzs3NOT0+YTmeURcFmvSEMA9OE3KU1pe1Qty1pZuAJdavQwjDMPnOe8uD5N7x4/Yo0SU3YalkUeUXdljSqwXd9HpxfkLWS97uc3SZFNRVO4DEaxeiyxrYkQihsW+J5DlJqHNcm73i6Hj665Ltv3/Dm7VseXa6QtocbhEghzTE1LnVthIHLsiLPDWuE7xvBkyRJGY8nPHr0mO1mZ0LEyhBKGtgCWLZRW/c8n6oyIFWtTZuTlKIjctwghCDLEoo0p85rkuWWiSsILQ9LKHxh4SDwHAfn5BQHF11o3r29ZRJNePzwCY/OzzmJA+aRD1rz2//+e17+8JrF4oQgCPjss887A2aUprIs4c2bN9ze3OJ5PkEQdlqgpnCRZRnL5S2+F3QejRqYPpTSnJycMJ8vkMLqwk5FEHrYUmA7NmXVUHdMI57nY0sLgaYpSxqtqIuKOPSxpDGSt8s1RdWglfHSjNfVMz4b7cz9NaHZh0jojjD1fjj2sRBtH860n8Q3uXL73jb95495WYOd2HMuLOv+Oj+27eH37/en2pbFfuq/B9D2X9uq4w7VB8as5/PvsSdtZ8wE972h7vCGuH344iNGaH/C6roeLHnbtkPOof/M/kkefnb///sX5R710NAgaxL3KCOaqoWZBGn5pm2lrolCD8+xadqKosio6oa2NQjrojRYMtdz9vQNYBTHzOZT4jgCNNPxqJtujWo1Zdkwnc46daOIuiopypy6MXz/bVUbjq35gl2SUtctJycL6qYhL0pOJxFNk3N6dsb5+QVFUbO8XXP24LRD2LcorVBlg2xaIs9lMYrZZAVlssXRIZ6QKCEZBz6R6+JaEs+x8Rwb17Eo8gzdGtaOsirZbTLevb8iHE2ZLmKElEgJlmVTlkVH8d1294KkaWo2my1pmuF7IU19dw37Fi3f9ylL43FEcUiaZCilu77OaphPIQwHXa/4tbxZgrI4n014MI9pshIhGzzPwpcu0vGRLVxMZsR/8/fkf9agypbADTifLziZjrDQ/P53X/L73/+J2Wxm6JI8nzzPiaKYsjQUUFdXV3z77XOSZDtAOsqyGlq4yrKkLEtUq7m5uaau23s0OJ7nD/edETI2osi36dZAOLY7w4irtck1BgGy461zPHfIsZZJSqsURbJDCRswOcQDYMKdERHWvffp8F5mjXxozI7lqQ/HvhPxU3nqj63xfr/HCn37UVr/Hft40X0HSnDfYKL14JUCnaLJh+MDY9b3DPaxc+/GfuCViX6C75/kx07+vuW/q3D2Jz8YqE+4ovsTvm/Y9mlIhDRAX9XddFL3hkwNIixat0bWXit2ScJmu0KrhigeY1mSNDeJ6+l02hEbuoYmR9UmbDSpODzfRSiHpqlp2gYtNK5jlN5d18F0/BsA8s2NER6xLduEqNIiSVPSLMP1fWaLBY7n4fgeeitYLE44v3jImzfvCH7/FePxCC/wCH0DEt3tElRe4gHTwCOyYLXbsEtTHASTMOZ0NGEahASWjWdJAtfBEuCFAenWYMRul0vK0uF6uWS+3XFaz9AapLRxHZfG8XAcD98LQCtzntowqtZVw/XNLVma0bZG36AndVyv16RpyumpYRBJ0h2e56O1GvpbjeGzu4daTZqk3N7cUGQVp7MxF/MRsT/BdSTUClUazzKQNq1sCMce7iIwDShNS+A4tEXFH7/+hl//y/+NY3vM5ye4jsezZ58P+buqqliv17x69YrNZoOUgqqqEcJgwHYdJffJyQkXFxekSUbTtANRwbt37yjLiuvra8qywrFdfN+Ey3lekJcZcRxxfv6A07MzmkbR9HnbqqIoTerAcVzqskI3huEl8gOE5bItmi590XstRj9S6L3qIH3dfn+t/IwC3c8YPzdRf+xz++v6Y0XAfedjf0gphlTGfuh8eFwfQ0V8YMz6G1JKOfBa9U+FwxYirfuKyvHY/GMnum/cjoWex050fz+Hf7sXdoo+1DXvO47xrCxp1F6kJTvOKROZK909HbpJalvV0dgEnJwsmE0mXWjlMpuNub25JUsTqiLDtSMC36cWmqYqKIsCP4hZr5YGry2MrmiWpQPV9OnJGa3SbLtwJY474ZKmxfV90qLAD0OefvY53/zwmn/78k+0SvDXf/M3xGFA6NrYKBLHo3YqHM+nVZpkl9BkJapRxF7AWTTmJBoxcjwCKfCEwBECRwocIflxueS7F8+5Xa8RYsSb9++Znz3gYX5GWdZY0jDOuq5ZqK7rUTdV15vq0NStoRRX5jxd1+ge2JakKDKKIqeuq07Orr+Z+2toChq9eI7VtfLUTW0ogpIdL178wDh08N3PTOeDlqi0pdElduAQOjEaG6Elrm0+n6yWfPfiB/7tt1+SZQWLxZzAj3j06JFpl3Ic3rx5Q5IkvHz5kuVyOWDJwjDsyABu2aU7Hj951CX/R/ieYQ65uVmyWq2I45gwNMZGDakDMww/m6BtTU+w1mrArgV+QBRGnbca47kuGiiSlCzPjV6BYyKNuq2NcpYQ2LYeBJvNmoF9I7a/3g4jpX/P+Km81seclrsw8r7R+ZhR6ynw7+iG+nk8Bhe5/z3HxifDzP2D742Z8Wz6ifvQMzOz+elJgA85m3qvrffMjiX/Pzb2iw29m621waxJ2yyaHvKRFwWOZYoOZalxbZv5fI7qcD7j8YSyy+20bUOS7JBCI+UECTy6fMjq9pr1ekVbF7S2QAiN7zm4rg3CoViu2G7NPKVpNoTTWmt2SYKzXBLHMZPJhNF4jJCSzXbHNkkYBYIwCLH9iKdPP2P+h2+4en/Dn37/JxaTfwTbwtWSqR+CaoktAwzO0wy71dQVRE7EbDxh7sfMPJ+R7zKLAmLXxdaa7WbFN998zbfPX5AXJY4bcrVccn1za0LhtkVrB8ty8H1B0zYdZY7q+g0hz7emtzSMWK3WJElCWVY0FiTJrtMAldzc3HB2dsZisTCQDTRBEAzIc6VUl6c1IGM/9FlvM75//Qopa6St+ay+JB4F2J5PvanRTs7J2QOTlypS2ionXa34/vm3vPj+JUUNs+mC+fyUhw8fsVicDmSZt7e3vHr1ipcvX3YqUWbxxfEIIYxq1YMHDxiPxx3v2d39mWUZRVEMlE2WBY5TURbV0ITfqhbLdijLiqpqqKuSsqwQwug8+J6hH2qqGt92kZZkEo9wpAF3YzskVUpJlyvrl+lgpXpyQu6eDkOP5X1j8O8dH0Zgx7c5/Nud8VGDY3II24D7oFu7U1DrPTKlFJaQ9Lxo+8F0/+8hfKsfH6XNPpaEPzQyxzyzvlP/cFIOE33HPLfeQ/pYshHurP0hyviwANELICitaFSL1TXv5nmOCHxkY6pTYTBiNl3QqprVas1oFCPTDKUUrm0Thb7BClmSZLvFP10QRyGClroqKPOUpq1xHZc4jtE43ZPb3PDTaUOW5VR1S5ZlZFlOq1rqxkiWFWWBZVukWU7dlPzys4cGcyQkn3/+C/7zP6z5b//tn/mX/+PXnE7GfPHLZ5zMIsLpAs+xSKoSKQX16Skjx6epJI7wCByfURSxGMXMJjGjyMG1Idlu+Pbrr/j9737H1fsbNBZIQZrnLDcbkjQ1gE/fxha9V2XwVFmWYTuS8XhEGEa0jQmdAOq64fXrHxmPAqLIBxyEkGRZxmazZjKZGjaMuhkwWrvdljAM8Lp8Vt3WCFtStjVpVfLizY8o0XJ1c81sMWMyW2B5IcqyeXudoLQmTTekmxvy9ZJks6JuYDQ+4+HDS/78z/+80wx1EULw3Xff8bvf/W4QlKnrGtu2mc8XnYDNFj8I8XyP9XrdLToL1RpITU+uWdcGMNw0d4u1acx5NU1LmhoQcOCHeH6I1tIIt2x3pDIFrfE9n/lsRhhHRGGAa9vYtkOp4TYtcYf0Ti/r2Hsu+q6dR1j3IAx31c3/2PgpT+hTnplZg/cjuEMg/b6+bm/M+vehw9N1+pn7WcM7w3L8uD/aAfCpisinPLNP+VL7k/Cxiog8Mkn3v/MuT9ZP1H7ODNFBMjoKIKOb2RUXhMCxLRzXwbYdLGm8Aj8IcJyo8+BsbMuEfpPJmPPTBbYtydOUqix4+f0L4tAnikJwbfI8o1U2oyginkx5+eMVVVUOeKS+eGDbxqX2/YDJZIKGLl+3QyNI0owk2eJaLQ8fnBEEEUEU8+TJM548esm3f/oT//xP/4woMqK//wsuHsyxpMZKN4gKmE6YBBG6dZGthy0tQtdlNh4xHgWgCnabJd9880d+86+/4fvvv6dpKiw7gi7kTtLUqCttp7iOxPNdQFGV9SAUkySm57RtVCcC3fHI2S5hoBlPxlRlRlkWRFGMZUm2220nljwljkc4ttt1Z5gEuykWlNR1g+XaWL6D5dk0QvPj1TW3qzWj0YjJ7IRoOgfbJStLmraiqVLKdIWlCk6mYxanD3j48HP+9m//nvHY4NeksHj56nt+/etfD6LN0+m0u0Y+l5eXQ75rPB6zXq9xXRMCa6W6IpUaOO5836dtTe7PdT2s2B5IQZtGIYWiLBvaJh1CKduycYPAiDPXzXAfF2mGahp2m60puACW7eBg9Gfbdp+51bTpdURCgwdkzJjZpn/4/HuHWWbH+zL3DdjHIqd9+7HvuOwz1+73hR6ua/MDR4FmPzE+bDRv2+7HNIgNhkPfHehwEncmcxiHhu9jJ3tovQ+3Qe81mmrTGaCBXihZClMKs6WFtC1TsUSjRLfIbNdg44SDJSWO7Zinn7A6T8tCWhqFJi9LfH/E+fkF2/VmKBS0Tc311RXjUcwojojjgKt3JWC0BdumNU3LYcR4HOMGQUey6LLZ7AZM3Xq9QkqTv0t2RhgkCCNGoxFRFFMUJdvNBmc+ZbXeEEchluUTRA6z2YK///v/CVVUvPj2j/zXzZIy3/Jf/st/Ihz52MImdABpE7ggdYCFhy1sXNsi9H2kUtzcLPn++2/4zW/+jS9//5XB03keZWsQ345jU1Ylm82GNM2YTWIz+9qAj8MgRJ4Ibm6vDa2R72JJm56C2fc9xpMRUiiurtbYlsXt7U33gDKsrkFgEui27aCVpmlMy5zrOjRNRV2XeL7LaGp0CmytEG2D1Ia+6Xa5YbUrwHFRQiOkxhI1nueymM149ughlxdPefjgFwPLr9KKP/zx9/zxj39AKcVisQDorrFRxyrLkrpqsC0bYdk0qubi4TkXFxdst1uWV6s93NkUKWUnZlwyHU/wPH+gRy+KHNs14em+Tmrb1lhSmkKSNq1Sqm1o26brsNgihCRvW+LTMyw0ugOAt02DWY6CfTVv0TsPPWXQYMfus130kFutOcqCcbD6urXbGUlxmGw/LDocrFt6Q9d7aHT70MMr9EgJuGPrEEO4qdVdW9YHEdqhrd47mQ89M6UGg4bShmNIKQQWtmUNRmT/1PZf733xkZPdt9pw34WVQhiMibQQ2uDFbClByo76xGDbFBqUviNq6y4gAtraULJoaby0VlcgpDF2lqSoKqxa0Dg2jm2B7qTXvIAotGnbBnRDVVbUVYFtScoyJ88z5osZQTjCsU1rSlZWlHlFXoAWPiMBl5dPmJ2c8erl60HNyfdcomiE63o0VcO7t29xXZ+zs1OqojA3PoLNeoNrwXaUY1kZrVphScmTJ49JN2u221ueP/8K+S+SQimefv6EyWKGFwWEjo3nSBwrQLVQVzVFWZAmWzbrJS9ePOebb7/im2+e8/5qh0YibQfdSoRSWLZGNyXb1Yp0m6DOTrGwEFJgCXPt4yhE6xlg2D7KsiTZJVRVRl03uJ6gaSpGccAojknTjKZuqesWW9qGmLEosXzTzWDaVhRtU9LUBaopsFBEnofvuniWBW0DbYtj20jL6trTGibTMSenC3zfYTTyubg45exkwWJ2xjiak6Wp6fxoKl69fknTNpxdnJOlGUIYYySkhR9EJGnWKWAJvMBhdrIgimN2SUKa51RNTZImVKVpmK+qiqosjZyaakE12BJcx2I6jnF8HyHMOdI9iNva5NNsS1JWBVq3Hewn6NIiCsd2mEQ+oecYXrymoK1r6CqhWlq4fmCoe3pQrTawKYEBuBvrcecQ9KSPGsOfppVpZjdRqzRtUFp3jovx8VrazuB8bC33Bqa3Jr3hEx96hl2xYojGhEYKix47BwqtZedldiGpNpyHQmvDlKG7jh7NIDs3/L53VB8YM0caLiXZHWjbh3/yQDKqO6lhZ1Ig+u7+I2Fq724eemSHpVxLmPyAQHQtH53B09Bj8QQdl5FSNErfsVBiLL1uNW3dYO41k4ezbQfbtqDVOI7pmfQ8Dx34eL6iVab6NJmM2axu0UIbJW3H6ai0W+rKhA9NA34QMhrHhFFrMFK7mk1yi7QVT5484a/+8i/48ssvef78O8IwZDyacDKbETg+3734nqt3b6nygqZqOFmc4DoOy9sb2qrCsTzQphdwPp8zHkV89ovPycoUN/JZ3l7zX//p/+T0D1/z7PPPuLy8ZL6YEUYxll12eaotu92G7WbD+/fvefXqFe/eGT1I6USgLZSW2MLog9ooRFOyXS7ZrrbURUNT1ghLozsvtFVGR1KjkMI0T9dNiepwenWTMR3FTMYBloTQdylFY55CloVEQmuS7IZW2yZLEsoiYbtZIoXC1gryGkvaTOIRvmdj/7+0vWlz5Eaa5/lzd9yIg8EjU5lSSaXqqp2ZfTNr8/0/xdrsdLdtV7dNSaU8SMaNG+6+Lx4HGGQypequXpilgiIjEAgE/MFz/A8FZZ4LQRuH9ZbN7Q0//Ph7ikVJkmWUK/F0yJIMRs/htOXTp88cDgfq9gxaJsjnc0VZLIgT8TS92mxompbjqRJTXhTrqyuyouCnn35ifzhwPp04VSfsaCXbDEqwcRRTVSfs2KO1IUtj4sjQND15maG8BCGFwtle2gypaNq1XUPcROJY3w1EiQw/8rygaxsSE4ExFGXBiOfxcOQcHOpNFON86E97JTd+BcrKzT5SGudlkqrQwYBYVusogoOzWuukyCHZ3OXUeQpYXwJU5/V/kcHNWZwCHZhYEi+nAtjPwz3rxG9iSrOUG3FeBWNkizI6BNgwApiCpn+KOfOvfi0ziyNRxIyMqF9aT8DJuLnx+bXNT3cEXq+z5+e9+NtlcLPWyhd1kbG91oC8JKdf7j/NBcg49oPsO/xpmiaWWY73Zt6HlAORkJDzAog4HXaMow3elIJcj0zCcrlmsViz3W7ZbvciBX1zw9XVhqqq2R92nA5H/u3f/hzcv0Vm+8OHj2y3j7z75j3LZclquWD7+MBhv2O1XNCVBc7B0PecxpGffvoZpTR5UchdWMHt3R3/7f/8b6yv1vzTP/8j//RP/8g//7//yi8f77m6umK9XrFaLUF52q6laaTk6bp+9ruEoLQbJSgi+m6k7x1GRxgDtm+pTkeq2SPTBC/IAe88dVVzPB5JM3FfkizWB36pZb/fkkaKMk+xfqBteoxJA/tBSnsfiNBoE3pFB6wbOB4OjP0gzjzWEqG5u7nl9z98x2a1pChSuTZjTZKlpHlGuVrKyokiRu/p+4F9c+T+42c+ffjA58/3QY7oTJKIy1VdNwz9yPv37ymDue/5fMZ7T5KkoGRIZGIjYpSPj5wOR7yHyES0TcOiLIEAxvZOykBGnBemxOFwRmmhOOVZinOOthUxR+8dNzfXjONAXVcMwwR3kV7cOPScDwfSLENHEVGaEUeGok/prZjrGg04hwgyhmoJhXcWaz0m0hdzwJBcTNXMHAUuhnMwP3ee371Siz4bCF7WrJf/QoV0ucsX2F9B8NvnscE4M7OBoigKfdxwvOoiIDKJTX5Z7H4RzCaEs6ByxdnGGA+9NGz/vdvXmv6XJ2fa5HlfGqNcbtNI/+n5X+7zC21ynt5/YiBEYRQ8BqG9cRgF2Ni1WOtJk4wsLYijGLxmGEbatpuztXE8Ujc1eZMTxYa8yElSQ9NmfPz4V06nI+/fvxfQZVWx3x84HPfBwCPn6mpFVdVU9ZnP94S+VYw24uOpjdxdP378wM3Nhnfv3nF1tQFgtV7y44+/59/+7d/4+eefeHx85PPnTzhnQTmUCj0TDUmckGYp15s1Wmu6dsA7hfeaNhrozIBzCu9HRucDU6Gh7Tpym+G9KJJ6pAdSFiVRZGjqlnEYiZMEpTRpMnBzfUMSKdpApk+ThCiSqV/b9dQOURvJUvpesrM4EtrOOPTsto+0TUeWlWR5xs3NDT/+/g/c3V6TJCZ4VyriJMbEERZPOwge63G3Y7ff03c9nz98ZByG4CZfzMqnzjnpUxYL7u7uGEfLp0+f+PjxI1prNptrvIPqfGa0A1VV0TYNQ9+zXq3IkpTdbof3nvVqTVkUYnZcN7I2woJOs+xZFTIMwt+cMHu3t7fBJ7UOTmB2dq/abXfkaS6KL12LGzpUFDMOPbHRREnM6BzOOtzo8cagoxiNxiHy14bQCpr614B1Ijk9Ba65slIXfbg5I3teKv5nTEgvt6/2yeGLOHHZg/+tfvwXwWwYhmcBTYxBRGIkjuOnNwSYppnKP0V6/yW38vLNX1PNuDxAI1DnL/7+slx9LTgC4giuL8rhAOAzRvhzgvRWM1ZFay2aW/EWZwcirYijjKIoAU3fj0SRlKR5XoSJZMb19TXWWpqmpq7PFEXJal2SphHffvd+bviPg+Pt2zdorTidjiyXSxaLgs31WkbPytM0MiHM8xKlRO67bRru7+8Zx4HT6U4cloqMNE15+/YNf/jDj9zcCKTgeDxQVWfu7z+jtSeK1OywnSYpRVGQJOIVORaWvh/pO8GORVpT1S3DMBJHMX3X8vHTR/76y19RxhPHhmHsgru5JkkkwHVtRVVXwDQljlmWJVmiGfpJjaKnrSvRfkOTxAZnB5r6LJpvaYRSntOpYrfdst1u6XpNURbYcXxGdYnjhDg2QXlixCtFNw6cm5bBjhz2Bw6HY4A4RFTns/RC84I0zYU47zx3t3csl2sA7u8fgopHxHq95ubmBut6uqHhfD5Rnc+zlFOeZmRpSpMkrFYrVusV5XQ9pFlwPA/E8iKZg/8EQJ9wVG3bst2KHeB6vabvhT61Xq85HA7865//lbqpWEQGExlhtChR4xj6ltFZlImEWjWI9JAGTBShlUJrP/tzyhqSjMb5icn5ROP2c5LBnI69jDNfg0j9Pdul1P1rQ8D/6Ht91Wru+RhVTsoXNILLID4POL/MlC4zs5dUppcHP08yXwSyaZuwKF+TIxpCL2ai1czYFT0pzz6XNwJmx+2uqbm+2nB9dUWeiYNTXdc4K2j387liuVyEi7KhqiqapiaKDGVZ8M03b6jrM8PQMoxS5iZxgjE3oayoOZ9PlGXJ1dWKLE3xiBLq+XzkdDqS5yVJkpFmKbpStG1DHEdstw80TT5jtIoi4927d2w2G7qu5eHhgb/+/BP4Ec9IXZ2o6gqllOimxSJJY0zE0I9U54a67oT0PDSMgyXOIvCO3X7PL798FHXcRY61PUrbmUxt3YhCNOfx0rv0olVJmiQkkeKw36PwpElMnkV4p7BWslvynDe3N0SxYb/fc9zvOR+OGKUw2tB3HV3bUZ0rDocjeZai1IIoKnAe2r6DoaduO3bHI10/cDyfGbqRWIuF3jCMjKOjLEuyLKOqarpOpIi01ux2O7bbR7quJYomy7+YvrdUVcXj4z1dKxzOKNCgVMi2skxaGV0nEBylFXkhBH3ftlgr4OzpBuq9n4NZ0zRoLUT+CZoweawqpSiKHGtHtEYcqnqLSUS+W0cFykRUdYtyI8rLkA43hOouiGtaK0O0kGm5KddQUn5OvE9ZPZdSQ2HtczFE+P9he5nEXLaMfg3l8FvbF8Fs+gImfEw/DkFV9SkrA34zM3t58C8/yGtZlwLcMIba/unvlxiV12hQzz5QuDPPjP9wwUz7KIoyQDWi2b9SwI4j537AW4hNEnpLYr9m7YB1jqqu8TjqpqbtO7xyoRx0tH3DuaqoqxNxIqXw8XhkGMaALVsx2pE//8uf0Vrx7t07iqKgbTsx97ADj49bjqcDaSJA2PM5YRhESy2KNDc3N7O35/pqzfX1Fbe3t2K0u1jwzds3DEODHVqq+sTpdMR7z3K5CJmmXCzjYDkczvzy14/YcaBtE3wGeZYz9Jq+73h4eGC5LLm6WhKnmiyNQlkZ4a3YxSVBsfV0PHE8nvDWsiySkLnV2NFTFAlZljL0I/vqTNvIhLiqToDndDzQd61k0E4KnsgYjBZC+/lccV4UxGkkEyw8o3U0XUdVNWwfd1R1Q9cP9MNAc24Yhg5j4tBCGPG+pe8H4jhmGAZO53sUQt0TMHATpH1GhrHj/uETx+MejSLfbFCxKKOMTE13H/plfm695FnOar2mXC7ZH85UdT1jz9pWlGen9bVYLJj8BKYyU7BrIlxpxwGvXGBMQLksyfICi8c6jx0GIqNwNpaGu1J4N4IbcR6sj2dz6sv1dwlaf7a9WEozQODF+v3PCm4vg9klXGt6nI738vG3tlcZANPCH0cbTrY0Fb/QM/uVzOy1uheeenLTNmVMOmBw+tE+y+6m47lk1X9tk4zvOSletMSe1DiiENwuNdSm4xr6gb4bcRZG69lcXxFFhtEJ5AOlOVc1zluWqyVJElNVFff3n/n5rz9zqk4sioyrq5W8bhTLNrFqK9FG8RDIycfjARlHa66vryiKjDzP2T7umGRfhGJlub//TNe1s1jgBDicNNTyPGe5WHG92WC0Jzbg3EBdn7F2lAyrLEHJJFE4o0e0gseHe4Y+J44SlIrZdi1V1XJ//5k41vT9DW/f3ZEsC5pagmqaxERa09Q1XdOK76RSeGtnjJDyMtBoqHA2KG5gwVva5sz9Z4FYWCcWg3EkFJ9+sERRCaGhXzcVdbsgbcQ8ph9EZruqG6zz7PYHqpZa6FkAACAASURBVLplHC11I3zQNBXA6/39PUqpwC2V8nB/2HM8HlkuljOtJoqkfH14fOB42EkwVGpWCcbDMPTgIYkd3vln6hlxHFOUxUx/imIhnU90na7rZmK9MYY8z0UY0to5wD48PMxsha5rsF7I92VZcnd7TV6U7I9HDqczZZYFByONs45hGEVRZRjoHTL2dxcJQFiiAse4GKw9W8gEvMPlKy6W+n9ymTllpZfUpqc1/How+63g9uoAYKIbAEEmWL6USxK48x6UjLKnCYiAWw1OuS/ecDoZLxn103YZRHllynn5QX+tSfgy49NaBSWNkJWEqeZs7RVS3NGODKNlUeQM48huL8EmL4rgMZugtGa7ewSc8A1vb7m+uSZOIh7u79nv96TxDZ8/f8ZoUVHdbK7lc6HYbK65ub3hz3/+M1V1pihLtFLc3d1xFRRvi6IQJ6luQCmC4J/00U4nwULVtRjQLpdLVqsV40oMjtMkZnO1YFGKRtZiscA5EViMY1GtLYsiCE72pOmk898Fmo6l7yXLUZVif9hTLFLu7AbvnXh+up48T8F76qoSocuyZHO1RuFp6wZ8j1GaLE1I04TIaAbvsAYiI76YduywowU0SRQYFGVJv69papku7nc79pslRZniGWm7lt1uy35/oOt7kiRjGCx10wUZoR5tJOs+n0SiaLlcUuQSyPd7CWTjONL3A13b0fWdeAJEEU3T0NS16It5h9UmaJAp6qoiiiLZX1HgvXA1p4U5DZLquiFJi1mgcHKsn4YA08JNkmSmQAGcz2dAskUTxYy9nUuuyEQC8kahncP2PUmWkmXpfBx9JwBcySscRksv0zkLgZYFLhDjweNQfpJ9JOC5vgwQLyuiX9t+K9G43M/LqusyI3tZeb3MDF/DqMJvKM1ObzB9IU3TzAHFOQfKidyvukgd1XOJj+nftM9JAuiSYzl9af5CDO7y9a8d+GsnY+pN/NpJnmg5+uJ5lwF2HEbR53IOEynSPGNRSBbmvWNzfUPT1IzW0fU969WaH//wB1brNT/99BesE5ybjsVII45SokgAlEM/8u6bd2y3jxyPR0CmfP0gZaVSntvbG7Q2bLc7zqczURRzc3MzL5iqqjgcDjMafbFYzlnH2zdvwDu6piVJJIAlSYTCBLS1R0ea0+lEXddiypGleBzWjgJydaNkcF1H01Q09Znt7oG+r2iaM2liqM6Guqro2jo0oL0kA9YKK0IJLS2NY4osm0uoqtJ4Jw5KeMtk4aq1ZrlYcHd7y2gfOZ4Hhr6jrkQPLC9SmrZif9iz3W6p6pqu78nzkjTJ6Dr5TlfLFYvlIsArWt6++YbNZsMwDNzff6Zpa5IkoSxl0NIPHbvdFoCylPZDliZ4a0mTjL7v2T4+ksRJYHvoOQBNrYzlconWck4Ph4M4vb95BwjEo+/7L3wDqqoiy4IZStexXq/n6zLLc5q+xUQxWSbB0I6Wpm6ww0is5XzboYdYJsYt0NQ14zCSFAtGb9F4FA5vLXgn007n5s8xeQZoFdaWZCNYgmeu+vVM6Lca9y//dvn8JzWeJwHISwXfrwXFy1gybZd9/FcHAJecqctm+nTCn/b+/AfJ2J5L+Lx2Qi6b75cHOpegr0wqvza9/Np22dt7RnLFzjiZSV8qjuNwV7o4fjwmEvxZFMd0Q09d16xWS/KiIE4S6qZD64r1esVydUVZ7ogjQ6QUaZqyWJQoZajrhtPpRNPUvH//nu9/9wOfP3/meDzSdWd2u93cIE7ThLdv34VRfY1CtNGKoqSqKqpKmvp9P/Dhw0fK8szd3R1pmnPYnxj7gSLPWCykeZ/bhDiBKJJzfq5amrZntJ4oTri62nA8nmnallNVgUpwfmToGoYhx9qBpqlQfsA5AbMqpxiHDrzDKFlc3o3YcSBPZeIqyP6p7LQYo8iylK5r6YPgo3eim0bQsVouFvSDJ0o6rIckMVjbU9UnurHhVJ2ou4bR22DEbImjFoVhvd5gtA4TxSEYjKTUdcP9/Wcet48sFiXr1RoTGbbbLYfDnrZtAsXKBGSFJ0li3tzecTwcONcVyivKRUkUiUrvYX+gKCX7SpLk2cLM8mzuw3adeI16L1i89XpN13Xz86cgXxSFPI8JTeBIkpQ4zkjTGGc9vR3wVqbTySbheDgyth2xEapYE7wolnHE0MrkUyl9cT2HNWRHjJbB07QkpRiSast7B+a5cPV/Zq9M3u953/vy8WXm9rLqmn5+DabxRTCbsq84jgVvlaZobWZF2AmecXGEoff/27iUZ6Xkxf/P04wpKr94/WvKmH/LNpXDl3cFMwEJnZsbtEoJ80ApUGHRleWCJEuo25q6fVId3e62LBYl79+/Jy8WWO/Z7Y9orbm+viPSivp85Hg4cjqdyfM8kMylALi/fyDPC3744ffc399TNw0PDw+0bctisZJF7ieQ5UiSCP1lIqyP48hyucRaz+FwQCnNcrlGKc1ffvqZm80NV1dr+t7RtANpFpEkEXmekBeZqIpg6PqRuumIkpS8XEDTcn2TcDr2HI8VjlEy0ywmTYzAPbwGZxmt9L6M9hjtUYzglAQ16xiHUehwOLy3YTqnwFsio4hjje8tFmERdGMnEBhjuN5ckeYDddsRx4ph6KiaM5nKiBJDXmYUSof+14nz4YxCUxQlH3/5QNf33NzdisN8JdPjvu8p8oIiDyq3zRgc5COurtYh+BicHcmylEVWyE0kjrm9vkEbjXWeJFQoAHYY52DVNA3DMFAuF7x58yZMpJN52qm1DG/u7u6CFPeWLMsYAhbudDqJXNLjI6OzRHECOoLRYUfP0I1ohfCKTcT5fEZ5sIMIWtphZFGUYnMYRySjx3uLswMusACclhITZzFJLMMrL8yAQL1Gis+pVfR8Lf89Ae1lxvZy/b8MZi9/97cexxfB7Hw+zzvs+55hGIXKpGXEfynfIRNNKY+m9/i1N39tCnkJAfFBAui19t5lTf1b22vTkvm4dOjdhUxwHAVvpSdDVzsg8jXQNBX7/Y6+F3K5NG8zjkfHYnGiLBdBF60TnNliiXcCSq3rLoBxHeurJVlWkCTJTFjWWqzmfvj+B/b7HdvtlvvPn9k+bnl8fKSq6lDGrASbtNtyPlcUeUGW5Yguv/SKfvrpJ/FsHD1xlKFUQlW1RLHGGEUUKxaLgvXVAqMV1luG0WOihOubO6wTEUlnHX2/JUki0jQiTg1KCZXNREqwTW4kUhBHGlEP8jjb47U0zI0GN46Bw6sDns/hrJfMzkCeJUTG0PeWpulom4ZxcERxQprnRInFRAavwfmBtq0wiSYvC/IyI0kytDbc3g0c90e2DzuMFrK8Ugo7WspFSllKUDmfTwyDSBg1bQOIookAaseg4TbMPbFvrm8p8pwPHz6wPxyIicnSlLEfqJqGzfVG2iVGC77LaOqmCVLbPVEUWCihkvFebPeaRsC1p9MJgLquub6+5nw+c3NzI96oKOquY2habDeSpgmLfImJI7lBjOIdkJgY5z3DKAT56414UozOs1ku6Qcx4unHICDpLQphXxi8IBFmVMYTpl7jUK8swL+lZ/a1tfjaa19Wai8nrq/16i6nnq9tXwSzz58/MwEVp96W6IzJBTDdaSTj0dI4DBOQCQLx8k1flojT316mlc45IvXU3Hv5mr/15L3cvgbreG2qqRR0XRN0up4CYRSJJtZutw8XZ8/j41bsyZYrkjin70bpU6AR1oClrtvQJG5FqXUcmdzWJasSfFHTCHxgu93OE2TnHNvtblZBjaNIOICZLGYZ+Y8cDge8gzdvvqEfHPcPe7SGONaYSICURXHmXDcoLGmWksQxy/WGOM4wUUo/WHbbHUoplqsCrRUmUjRtRdclpKnG+xGFRWktDWZvgjDBCJF4DExN6b7XWBvOh31CNGmQ1yYRyiv6tkcDkdFkaYaOYxywigpUZOjGnrprOFcOjGd9dUWxyBgHy2p5w9s3b/hL/BdOhxNxbDBRRNf1DMPA7e0Nv/vd7zidTnz8+IHD4cDQ96CEn2tMQp5nrNcrud6VIjURZV7S1Q3n0xk7TDpl0veKw8910xDFEYvFgtu7O4yR0rVtZOo8BtBvFIkb1JR9eS/VzsREmSAbdV2TJin9aMVvwXn8aFkUMYtyQRJ4rOM4UKSZUKOMMCq88jR9J9p4WlMWGabt0d6hgX4YGJ1FK4XREZGahgA+ECmndSO2htIz088CzNdKu9e2X+ulXcaI1/b3ssf+tR76a9sXwWy32/HmzZsgGbzGK835XHM8NXMTGkD7wPe6CGavTSBe26YgMkXhL9JQ/e87ec93/nSfmfd3kdpKRfx83xMnTCkoy4J+kOa3CtlTnuUYI33DYRjDhC5mvzvSNC2LxZHt9ogdOtarJXka4R0Mg+V02nI8nthsrlitFox6QOSoY4zJSdOEzWbDen3F6XRit9sGVHjKw8MDu92e9XrFmzdvKIqCv/zlr4yjJYoUdd1gdMRquRIYQ91gbYR3ohmfZwlpFqON0FZM1IrLFgqlDdYqRgdKRZgoph9HlFHkRUZkDHkWYww4P4JyJImRwOQdePne1SRn4i3ejxidy3fqrEBL7BgEBlXQqrMS/JQWmpRzREaDEQWTcRiIIs2iKMnLgmN9pvnc0DQVOtKCc0PMY7TSLMs179+/45CLacovHz4zWE+SZRyPMtEUpYvJ/8ERxYabm1uWyxLnpAxOkpgszYg8NMcz9x8/c9jvZTCgBIYi8k5pGGLJNfrw8EA39FxfXweviIjd7khRFHNCMC+2MLksioJ92HfTNAD88ssvlIsFTSsuX1EcYRJYlEvyrMAoxRD1gsUzUFcHvIZ1UaAjTT8MaCXXVVfXonirPJFRjIMV9Q2tiYxGq2CWopiJ2sLREJEhay1Wfb3n/ff20F5WTl/7/cuA9lvJzRfBrG07wf5omfhleQFo2jAxkuDjxCgkkEoFniKQClHB/BI6Mf2b7liX8I/L54UfZrrRsxSTFwgYdfHoQ5PzJf30og4H8NZNHc95v9ZarNYo62n7FmdHlFZEkQka9UZKKGMwOqKua07DSaZaxtB3PR+rjxz2W757/44sFgR633fhjl9SnevQW+nFGCVNuXtzx3r9LZvNFZtNLRd0WbLdbvnxxx/p+57/9b/+Ee9FitoEmMDkIG6tY71egofTaSvllxHqjh1HhnGg9DlJGhGnCq8MSZygdEQ/WKrqzH6/p2lalJaAFicxxijSJGJztSJPE5RyNHVFFOlAcg6wAWOIo0jMlr2wIoyW98B7IqPxOmL6ZpTyJJEAYp2F3ksvKEkSnEXoUcawvNoIcX6zRh81u+MW31uKIiXPU5QGgwTDrmuC+cya3//4A2jD54cdzln2+4Ng0qozxhhubq65u7shy1PROxtatrsdfdexWomG2tBJdmyMJg8TR5R4ntZNQ7vfkea5MAuKPCiUHEJ23QAKoxPyXIL6hPgvioKiKGiaZmYCTBPRqd2hlBJeZ29JkxTlRDn5fK7QgLeWOEpo6xaP+A0cDgeSLJXqwRiGvqeqK0xkSOOELI3wTrBsSiuxH0TaAzIECGoZXqov5SXT9rP0xZOm2bw+X8aRl7C0yxnhbyQ1X8vKLnejlXqmjIP3T8jei/f6IphVdcsvn+7BwmBH8qzAOh88E014QwlY88xDMlRw4HDyxhdDgZdR3YcDwwsVRk16ZVOAmYJTeHTq6XxJB2Zq0PHs7iKqHRfnVE3LiFAWP3346fdc/H10jv5cobQiiWKc8/TdgMaQRSIsmCcZbVVT1yeurq7YXG9QxnA6nxj6hNF2DCoiijRtazmejjw+buey2oVzd3d3S5LkZNkW74NBcBRR157j8UTTdNzd3fLu3Tt+/vkXPnz4RHXuAgTgyBB4eUVeMklAp2lB21aczjXVueLm9ob1lUjj4KXnlaYpzqoAwu05n+sANzBkWYLyPZGOKYqM5aJEa2jqimPTY4yiLHJirVEOvNYYFaERwOzY9dioR8fxPPL3PClLOC/I+choLJCYgT6KpD85dJzOJ5zS5MsVSZKQZxmLsWSzXlMMKcvVkqLIAzAavBvZbR+ITcrV+poffv89cZZxav5vHrafA28yFW5nIiV3sZDP1TQVjw/3nKszy8WCsshx48D+sOfcnEnjBJ0YwZthidMYjKIdpJzL8pyqqkiShOVqNVvsHfYH0AJ1GceBtm3DfTNHgNACQTBGB2aHaOhlkwoJBm9HlE9ReJqmZuh7YiNlfJzEtEOPisT5/Hg8YYIZ82hHqqYGFUQiooisyKUSSFP6YQwiCePT92EEyDsBbb0WddtRTa2BQPAPworeT0voKZMIII9nccSHpEaFRTjBtpTSPAt8U1yaBn8++G7657kKIWHyCryVQCyGe78yzexHz3Z7kOmeVjRN/6JBJ70QaYdNNvFiFmwnI4MZthJe4wWkOlllaa2fgLEw01jM1G+bcqwQEF8GMQHsym/1U/h+ikw8/9GFE6V8kG25PDb9JK1tnUdHEd654KwDbgTtNWMyorwnNoa76yuaPMVEGvyAwrIoE4riLcpbxn4gTWNGmwo40w3BBTwnCnf6JM7oe8tf/vfPfPr4idV6ifeO3W5H23Z8+vRZLhQXLq7B0TQtWZZTFItAmpfeUFEU3N7est8fGOyJOE7Ics1ymbG5Xs1uVEM/MARvUKWFvG2t5XAUJdw0McQqZb0sKEuhWp1O54BWd+RZFkrYmCgKKr9KyOog0IxYG7I4FRpW12DHAQLgljlDDkHOOnCecZAmfNO2HKoa4oRvvntPFEWkccxmtaK3mUjbjCOR0ozOh2llwzfffMvbt29AGaq25dvfvaM8FhwPh6Bwm7BcLMiyiKapgJGyKPju22+A0KgHHh+3nKsT3kBrOzovwShx4u9gkojFakEcy0Tz/v6BsiwpiyJIaCdz8/pw2Ae2jBM+7NBTVT4gBUTHTLw2B+I4ksDX1Fjr0SbF2zH0qy0jkuWOePq2oXfinWoBr9Tstj5lNVmRg9YM1rIK7l3FYhHMiFP6vg8afQNS4WuRFpqClnUMeLz1Qc3W8aTIr3BKPf0c2ktPozsvOLjQL9dGNM6sCwKS+oXwBGEphyWtL4LUlIVN63+u1vQkcOSfrfdXPABkxyMuaA49R/Mr5Z5J7LzEgaiL7MpPBxoiyxcl4Pz30HvzooY57e/ZY/h5zt6YAtxT5J4UMr74TIpX0c2vbSKTomZTBc3k9ylOO998+x3jaklVV9RtTd93WAfFsmC9XjO0DefTSWzFypLFYsn5VHM+16xWK4yJ0VpxfX1Fucip6zNd1/Dw0NJ1LavVmuvra+Io5nQ807UCK8jfF7x79571esP9/T1iU7dgv9vz+LjFGCG7R0nM7d0tRVEEgYCRqm7Is5w4yVAqZASxCX6XotCR5SmLZYl2MauF0JvO5zPb7SP7/YE4SkjevEVhAA1+uqDFPV5HIk8uU9Z2zkCsFXiGCzpmCoV1YivXdj1N29GPFus9JhINsf3hID0xLQJ+WhvyKENH0sNM0ozj4czQezZXGcvQM1Ta8e2333Lz9paf//pX/vznf2X7+EASx+JnoMC5kSQuQ+YojJe+a+m6LogECFzHeZEGz3IZejVtTd+PAaC8ZrfbMfkYTC7oj4+PXF1dkeWCCZx8ILTWM75sAu22bUsWpIIEgyi9szzPiSNpFUz+opNfpwyerEw2hyF4v/qgKhJLO8ULr7rt+2cDuGnQILAeO4uO+rDgJ5USNMLc0ApvJdDb3uKtxehYMsrRhWCjggJ0MAMPwUzxHMM2VYVP9dCL1OwZfuEyG3neqpq2Z5TKi+1VBsDLUei0s9eQwC83pS6F1C4P8ULC9+WUZD72LxuDvwbt+I9OO1/7HHPDkQvxN+9xOMZxoOsa6lrzv//yv8FbCb5G6FIY0dEfx5E0SbB5Lpr5OiKOU5JEBArX60343LBcLigXBUWR0jRnTucjfS/GsiK3/IhzPmhyleR5wdu3b8mynE+fPrHb7Tgej3z+9Jmu6/n++++5u7uTJnkqqrr7/SGwAAzaKJpG+l5VdeJxey8LO4n49tv3FGVOmhi684HYyGefYASHw1HI8l1PPw6STSlwVoO1OKOJ9HRBD6I6rGB0IhVtR7kRaK2J4giPoreO3o4M1s4u7coYykAXaloB98ZJRrlY4r0NbAU4Hiv6UYClZblitbqi7wTUe/f2Ld+sSvph4OMvH9j6p4s/ikRhOE8zztWZtq15uL8niYXdcn19TVEUgEwXJw1/gVW08yQf4P37bykKgX5MPbA4jrm+vmG723M6nS4m4dHsYmWM4eHhYQZrp2k6P0drQ1EsiKOM0+lMG5Q3pmA2ZXLwxHGeJqaTOIRyFuvHZ5zn6Xkv6UJPbaMnIYbIaEycggk8Z23o3cCIlSm8Cspp6sJyeLK+CyFORBWnJMiHgDa998u1N/Wonh5fW8t/y1r/IphNlIvLf69hPqb/n/4mJ435A3yRhF1kZl8byf4twfLlcb32IX9rP9Mw4bV96ak2D4R1kee2NI0k1UMvKglJElMuS8qyIE5ToiQiTiJcoCZZN4Z+ieH6+opxsERxTJ5PvRNH01SMozhct600hne7HWmaoZXh06fPDMPAarUiz6Xs+9Of/g+urq5omoYPHz6w2+/ZXG24u7vjxz/8nrZv6XpxZN8f9mijSdIUow3nc8t2+4hSkKYJq9W1ZE1BYjlNckxZiOJsVVNXFUM/hPMBbdvSdT0m0+AUg7Mo63BGERkpIbTTeCPNaIeUQdZ7ulHUaiMboVSEBemDGIMdLb219ONImmXkeUlVC+zk5nZDkqacqyOiLzfQ947FYo02EXEs56rrqtnmzRuRtF6v13z+/JnT8UQSx6zfvWNRBrS98+RZxtu3bxn7Aa00zsp3BMyZ1CWFTGszB5+bm2tRpA2QjP1+LwDpOEapJ6rcZDLsvZ/5zdvtdg4mco0oiqJAKY0xMYrn1+/EKX7pQzld7zM9UIlAwWgteZ7Nw61hlIGT0oqu70IWJo5h0soIHGUfDHmtxVmpZiIPUZwwascwWJk2K5l7jkwaabLeXVhXRj8d/2Uz/2vphnrxeLnOX+7n17ZX6Uwvg9fLf5d8yunfs5Nx+cYXZaLiy4B4uf17R77/ocwM/8XFMr1eTVPOkL4Tghl4xtHT9dLXM0aaocPQcz47TNeSlyVRXGK04e7ulqIohbJ0OJNl0u8Bi1KOLBOrtbZtUNoTRYbN5op3795xOp15fHxktz2Qpunsqt11A03T8Oc//5lhGPn06RPH45G72zt+/FGEGpM4oW7PHA478jxnsShYLAqaumF0lvffvgs9sDNKJzhnRQ0FiOOE9dUaxpTzYSdUn6bBezdrb0lJJQtjEma2bvIMkqmX8w4dNL681igVYwAz9IHM73C+By0YRa8Vg/NUbSeKJBjSfEQpI8j1QHmKo5x+HLAW8nzJcnnFMDrapgN6kjRnuU5BKX756y8zTWi5XFKfK4aux40jPshLTYj/ZbnksXlgUZYCl1jouUaaANF9KNmMETeqcRx5fHwUoLK15HnO9fX1PLls226GYdjJjMT7LxRnp/IvisQNXqafPdW5mt9zmlhOgfOZrSLMyhwTHa7vexyWJBVye1Wf6fuO6+vN/JpTU8+v1Vpu3KMdRLfPGbyOpJ+JJk4EMOycp/YdbgyAZjTKOwYQ8ru/SA6CW9TU55rX1tS7vmxbvViHl732r631L3QVw/al1dyLXtj0u0vpnimgXUIengWElyVmaL57nrK5rwWflwC9L/f9vD/3H87ueD0zkzuVB2tRxoQJjEIbJZO9tqHIU+JE+Hx939NVZ+lR+JGrRTnLyuTBJ/H29g3jaDmfz5zPB4xZk2UZUVxgjChbgGe9XnM6VRRFwXq1YbvdEkURm82G87liu91xf/+A1gIX+f777/nh+x94+/YbVqsVV5s1dXciy2LyIqOpG4ahJcsTtI7I8xRrB4ahxRhou5rr5YYkMUSxIctSIm/QfmS33RLFUZDFFoHOru+pm5rIGGITkRiD1wq8LArtBZSJFtkkbz1RpImShNjmqOCsZIcRj8YGxHo7DDRNS911oCJQmizLGUbLw+NOMGB5jo4S0lS0vTxQVQf2hyN5b1mvpVcVRTFGGay3eOdZFCXJd9+RJHGAT9QsyoKu72jqht3jVibBSUJZFERGuKJlWQYvTwG3LhbinZBnIrl0Pp9ZLpd8+PCBOI7nPpg2Zg5el6DzqdRVSs3lIxC8GZ6c3cdx8luNsbYMrxUqYZLEZAE/Z+2IUlIhpGkSxAxE+05HWioLKwBYrTR5lkvpP46kSYo4yMskcxgGyaSdJzERTV1jQ0so1RF5kEaPlCE2BofCetCIXwNK4QL+0F3AEC7LzLDyvogNX+uZ/Vpm9rU1/qoJ8MuMa/q9jJSfq1K8VjK+FqxeHvLXwHDqxZ3n5fP/I9nYFwfya392Qv/Ae7SfsFSGNI5Js5hIR6RpLKoUSYzWBofcdY/HI8Y7iiJltVoSxzHH44m+72jbFhB7sX7oOJ72WOu4vb0hyxL6vg9qFh1KKf7whz/wX/7Lf+HTp088Pkr/7P3795zPtQA1A++v72XanKYp+/2OLE8ph4JxHEA5mrbCWei6Hu+lqSwl5g0eWSDWDvR9S9t2lKkhSwWHdb25Zhwd53NDFeSpz1WN91AkKT5JIYnDNNiAjogTIeb3vWRxsdNi6acNSnuSNMLEXug2bmAYRY9rcA6vtBCsEwHw1k1LksTBaMTgnCUvStbrKx4eHsPEWa7Rfhg5HI6kmSz4OI548J6+6yjynNvbW5qmZrfbYkdZvFmaYgPX1YmsCFXTkGRpgMkIhmwcR7n5RBFt1/LTTz/RNC3fffcd3osKxjiOcxP+5uaG7XY7v24KaGmaziq1ImWezGKbk0ZdUSywdmqtE2SFwuTR6KCGK/4JUzDL8yyUrA3OWrzyjL0wK4pySKu2WAAAIABJREFUgc0sSfAYsOMo+n6Egdsw0jUtbd3I+opjtAOQm5OyDjeMxHFEFseCZbOWwXu0k0DGKK5KAMoBTA36qV922Q8TDb/nC9K9ePyV9fkra/5VccYJKDplYNM2pbSXHgFTdjY5HU1Zz7w/ngeyLxD/F4cvMI/fTjMvj3V+3W8EuSkz9OE/WptXnzd9Xq2kiS2iehFxYoTXpv0sma20EdUHo9FGJKX37sA4PpUfxkRC5PUu3F1FPfZ4PIQSQ7Ner8ONQ45nsViwvlqjUHPzdyL7T36Dkw9BkiSzkukwDKjIBiyWpaoIUtHSaBbxADvT0tIsxXtLlolxcRIbtO2w3lMUOZvNhqbtadupTBIaD16h3FPJHRmDgBAUSktmZV0Yz4+hLWFH8KJVb+IIr0fa0WGtp+sGIPT2TIRHMgjnPXGaYaKE4/HMdrcXByo0u/2RuhZliDiRftBut0VpWK+XLBYi6SP+lkr6YaE/d9zvKUuREi+KgnwKVE2DMoa8KOiHftaQ8xfXy2RsY+1I3/esVqv5Oloul3JjyfKLNSF/myV+gqHJJAEEPFOizfOc/f4oGWZYK8MgDlvOT1magLpjLYo2cRKLyXUSk8QRUSothCyVwROeIPF0Bh8Mn7sOpQjlqQC8nXW0dUMcHJ8UOtjqqeAvYDAa8nxB3XeMbQN2ZBykD4cW7TXrJGApRWB5TNjUi57602qd2z7TwPPlgPDX1vXl9mrPbEqPJ97ihNafEMuX2dprwcC9yALnepmLYDYf8HNlWC+GgM/2/zLre03692tGKRdHIQEjpPuTQsfLia1ACATZLqTolMWioEhE3K46nTmdT2KjFsfkWUlaFCzyguVywdDWnM8Vnz7dh+mjoPfbtg3YMAE4fvfdt4A49wh+LMOYmOvrBYA03weBAlxdXQVMlTS5f/e73/H+/Xv++3//7ywXSx4ftwKDcIYki6maNkjb5JTlkqYWx6kJkybfrbQJrHVYK3d8/JMWexRFFEVBmiZ4xH186Ae802jVh0Bmns3gPQrTDxgn/MJJM64fR7x1wpYYRubk3gv+qO8GQBFHCWOY4F7f3JAV4lJ0PJ64f3jk55//SrkoqRuhexkTM9iWppOscbt7DFnnArwnDecujmMZuNQN4zAEyZwWn7nZ2DoyhnKxIJ97pxKcxnEioouZsdGGsiyZHJWUUnM7oW1b8ryQDD3IY08yPyIeKY7x33//vQBsDwceHx9n6W3xLRCxy0mdxjpLFEdENsI5S93UjHac+2QmkhtKmiWsWAk7Io45ng4Yo7HjIMfW1Bz2OyZhyOPxGDxZr+m7lsZoRu+o64beCWg8iiPJBsPgahxH+rYhzbIZeybc2xE7dKBFLTrSehZEteMwUyDFQOe5kuy0D8Kj4jk381XA/d9aZk5vfNnov9zB5YFMz3kWPF68z8vycpIT1iEDFJmYC2K6Vl/u5N+5/Vrf7JIy9XLzXkCzWoskUJJoFmXJ9fWaZZ6ilOeYJfSDqKQuVkuKfCEwg37Ae0iTjDgpWS4WKIQ/KSTyJzyS6GClAcAoCqkgag/VWMmxBLDstBBASsmqavjhhx/4r//1v1IUBY+PjxwOxxB4UqLY4yppVhdFGfbf4hykqZjMTtSbcbCMoyOOhRjfDz3G2XlQY4KMc5KIZ2XXjwKC1JphdDRtL70UHZEkcjkOg2UIfTHCxensiLcWp524atPjvKLvLUbLuRjbjn4YKMoFd2/uyIucKI7QytAPDcMwst3uOVU15WLN73//exarJW3X8uHDB/qhp1yUxJEhjqO5PDudTuA959OJoeuelWzTd951HU1d03ZC5YuSmLppRHm2aeYpotDXIrSOnsxMlAiOtm0bWgkqcFF10LRbMAzDHBAnPNpqtZqVNAQjKB4Np9OBm5s3NE0jgaXvA0j9iT8MwgxIkkkzT3pvSsFmc8W5rujaljgylEUuih9DTxs+SxF+t7m6YrlYcA7nSG4AYupC6HXmRUFWZEEGXqAhnz7+QpTE5HlJWWYUbc7xfKIdetzkSHyx6VBWTr4E0uSXz0HoU0+rXimPupjY/lp//eX26gBgala+LDMvI+YlRuUpUnyZRn6BKgn71EFHjGdl6tMH+3u2lxndRKl4Or6nyer09+mzSYbnGe2IHjXWyaIED8phjGK1WhDHYlTadS0qEoT49fUNeRJjtASLXz58xLkxSC2XtG1DWZZzdjRMyPe6w4f0vx86siwlS4ugkHrP4+Oj9KmKkpubaxYBzS09l5zdTuSgN0F6+3wSmzXnHOfzmcPxRJGXpGmG+AuICq70m8DomKJYoshpjve03Zmh7/HOEccyaesHR5S0DKOVDKK3jIO4lcc6IolTjJma3IGmhgPvZm8Ar5/OdT+I8xAooijGuZZxsCyXS27vbsnyBOe8QDXKEqUNdSMKrIvFkvvHR3bHPUorPn76ECbpSvpBCsqAF0vihLqq6LsehWdRlmQ311KqeYcbLdYTxCGXRHGEiiJMJPLaIrHdzzer1ncsSpEr19rM4NjVShD9x9MxTGAlM5le3zTNM3jF6XSi6zqSRGwMkySZy1Bp1A94bynLnKIQzbPz+Yy1lsWi5OHhAWulBeKc5fHxEWOMCBJkGUbB2PcMXUe0XLJaLLi93jCOI03TsFgsWJYFaRyRRIbYaDQGF0VY3+GNxnpHN/ZUTcUYMiyPY7WWclrHBqc8kYZYgzcGjKEZniTBjNJo8wTf0ijm4mueB4Qn+2lYKKodl2v4tfX9cnvVnelS9vpyB1OT8quThWny92x0HAKFfhoUPGvmX6SOJmCT/p7tMsA+P3551HMwewpklx8gzF8QLBihbyYUFG0cYxpTlrk07M81WVbw5uaaq81N2Ifmn/7pnzmdTvzpT//AN9+84eHxM4fDkevrDSrYoGndE0cJaZITR8V8LHHfkqRR0C3LLu7+PtCW7lBK8fHjR4Zh4N2792w2G3a7HafziWIpzusysJE+5mq5Zrlco1XE+VTPPVGtY8RnMyWOU7wVHbb6KHr0aZKKMa6KQEeo/VHwX27EuZFhhH608z89iMxMEvweCSWIVxplpPRQSNO5b3vqWtD/XdfTBdqcgIlzVuulZDgmFrMVrVlfbWjbnn/8p3/mf/4//xM0rNcrqvpMnovLukFRn89sNgJF6HuxrWvbJkzwRO/fDgPDODAy4qJIfFHjmCiJafpulnGexRitcE36vsd7z+3tLcMwhsmiZbFYiCDjp09kRc5iIe2CSWl2uVzOwW1Sn5kI6M459vt9AEgvZijGtCama3kSCJ326ZzjcDgE68IrlJqOT24qk2gkwHq9nsvtyceg7/uAHexmfu/ohQs9WssQuJ579qRpSpGJyssUH0S6yUCa0HWGrmvp2oZxul5mqauntX8pmT397ovhnnMyJf+Vtf3a9kUwuzT6/drOLkvPywO1U7CDWcZHQgTz42W56r1Huademvz+darC37NNGZ8E1ZDC8jqjwXk/I9XTVPS5ijJnuV6QxAJJEECloywL7u7esr7a0PUdHz9+4vC4w46Wosxpmo7TqWKxWJFl4oe43x8QYcZS5F0CnEDKSpl+OS8lUhTFwgk0hraVRVTXNUVRhknpUcwu4oQ4jrl/+MRgC9J8ysoqurYjihLGwfL58Jk0zYjjOChvyHhfK40xEU11kCZ46CHFiUwnHZq67jgeK+JYZG6GfhQcnnf040jbiXdjpCDRwut1Vgjm3jnh+noYAgj1eKo4VTXdIIq3Xduyubnh7s0tq6sFZRnUP6yi7TrqpsFZSNKMKDgaZUWGiQRSYiJDnMRsVmuGpqM6nWUx971AFJxjcJa6qtFenL+jENwAurbFW4eKDM3Q0YSyccKDDUOP1tHMyhC5bGZ1DOFYxsTBvCRJkqcAEaaaU1aUJMlsNydeCw3H43H+zler9bxGJqOUKbBqLabVZVnO/Nwsy8TYZhzZ7XaowN2bdNikl5fTti2nk/i2VlXFbrebnaPmtYKn6tpgcCKSTG4YSKKEm82GNE0wRov6SzdgnEEpTRpHuDRFG0NX9Tg9reknV7WpBz9h8OZ3vGQQePl//8r6/Hf3zC7pD5cB6zLbmUrRiRM2fZmCa/FPqhc+4MumR76EZPy9JeVr22sf9mUm9rVidhp2iFmwkOqHQWy8BDAdNOLf3FEUK6I4Y3888rjd0TQdu/stZVlye3tLWS5xDk7HM6MdiGPD9fU1SmnGwVFVNcdjLfJgXqOUJy9EfyyKYor8qaRfLEqyLOd4FOeiJBE4x6dPn+fvoq4qPn7+Kzd3V5TlktPxRNP0IQBJKVuWK5q65Z//+V/48OEXjNH8j//xf/GnP/0JbzvKckGsE/qupR3k4tNKgJ55kYde14DS0hB/akMo8BmREq07o8Rqru9a8I4oMkFdVgL1OAyifmBlmpUmGd99+y1//OMfub294Xg6UZ0bkiRnGHseHx/5l3/5V5arK0Zr2dxseP/tO3788QeyPEUbhYk0eZLx4adfOB/PggF0T1m/mMr0DLHIEkXBENqOozg16RadxBCqD6XUrDIBagYPp2nK+XymbbvZZenx8VFQ/DwN0KIoEq5lkNue1k7XdSyXS4AZHDtBNvq+mzMnMY0+03UCwp2yr7IsZ9mhN2/ekKYp260MgQSHthfLvSyjSPIgmClkf+89f/rTH9Fa8/nzZ+4fHohDcHEzgd0zeicZUpjCO9dRNzVZmgYoiAyA0iwNHNERN45gND2awYoc2DNHt+n/5/56GAxOj95/FZnxt/TNXsWZXTb+X2ZgL6EZ0xeslBKO3TTNDLWwuwhm0zaVosYYlJf62jv3tc/x79qmXp6CWQNJKcHUzEFuPpdfRn87DphwR/FeENVVVZPHBuek7Lu6ukJj6AbRzNrvjvTdQKQN7969p+8HTqczfPiA1p6ua1guF3z3u2/ZbK4x2jAMlqpqOB1r+l4EG2XCdcTaTiRdliNpKhdOnhdcX1+zWEj5NQEzJz/NLMsZraXtWuq6oesGTqeK5WIV7vSaPCsBHcq6lsP+wDAMfP70wPt335KnJkBJBF9nHCSJEJ2zLCVNYnFwGnvGoRNql/No7ynSLGiVgR8HvPLYvmVoavAelcToyKA9ZEksprlLaPqBuu3xaP70xz/y4w8/4IxkdJdmvvvDgV8+fOBmGPiHf/gj66s1m+srvv/hd5hI0Q8tQ9/RVS2Ribi9vSWKIu4/33M6HRnb4EvgIyFnazUHmd5JptYPlsh7oiyZr48pmGVZKi5Kg0AsJt7qVMlMvbFhGMiLxTzsmW72PmT8IjZgGIeBJijSpqko/6pQdp5PJ7IQBKegGMcxHqFZ9UNPVdcUeT63hSYc25s3b4jj/4+yN2uy5Dzv/H5v7svZl9qruhu9YQdJUNQyE77xjGlLE9I3sC7na2juHWF/D3scY1/YEYrxkOJIQ1IUCYACAQKNRm+1V5099+31xZt5+nSxAVIZUdFA1amsU3VOPvk8/+e/aCxXc8JwRavVoihzVoGSzVm2yeOvv6LT7TKfz1guF7TaLVzXw2/7dGyHVDdYRSFxFFHVVJBCQhAF6JqSP3U7HTrdLu2Or24QeUqMus63R2OCRN201ouS+tpkY/Gyntg2Jjgh5TpkXTSfbHYKa3rVH4iZbWZJvu6btGYlz0t8rUlBt0yToixecalQNe0lILgujg02J+W6kJXy1YImb/zbvMF4zfNqCm/jPS8qZQYoNgpX/cD6nHLtOvAS05NQFmjSwDBMbNNQLhfoCGFg6CaVUFyaIiuJgoA0CnFMDdtwKAoVXJskAXFcoRsSyzRIYrXJsh2bJM5otdp02h16vQGu7RNFMUWhJEDdnk8cK9udoijodJz1XT0MArq9PmVRssgy0kQlHZmmgWUaLJYZlmljWg6L5ZKrq2vyvKTb62HoOkkSksQpVVWyu7OFQK3i215LOaRIgzhJEeRqG9pS8izLCUnSiMVCYOk5OSmVTEArMQ2TtmvR8x26voOjgyELdAmurVEIE5BY9QWpaTpSaGCY5BUEcUIQZ5iuz63DPXptn/PpGUWWYJuKa1XkGVmSMBwMGY+3ODw8YjgcKuIoknCpnEfiKGJyPSEKIvr9Pp1OB8/3SOKY2XTKbKYCfgtZ4nlthltjHNtWFkdCEifxOnkrK3KSJAYBSapIz92urLPZ1PupwX8sy8KqeXxpTdItiwLDVvmiZVFA3QG2fB/HcVSYTRiCUJF8eVXh2jamYTK5ntAf9NF1nX6/T1EWatMqK1ZRwDIMcH0Pz3WVaWftmNJutzFNjb39Xa4nV/Xvm61tvJWQ3ef8/JysjgUUukleVJShghc83WRvPGIy17hKU8IypaokVVkSJhmGmWPYJVaWE0SRamDq5KiW5+HrBlg2RVlSGspavSheXq8Ke1S8S4HA0Aw0oYxGqdS2VgoNKUqE1JCUqLCCuo5sCNHFDdH6t8qZXsfSb/Rlzdzb2JQ0XVoRFaoLeuWsYr0AaLq6m/Io2ZThP6CdpN6CqqLUGC/W4+PGyldr2MeSdeRWc0ghQCoPtiYIVUdimTq6UH9YXSjDPdOwMHUb03CIghVBvESWBY5hYPU6Ktk7y7B1hRO1WjbUUtw8V1KlsiiYXk/Jk4KqkDiWS99r41g2AKtVSJGlOG5LYVzFijCM8TwVKhPXRoCmoey7ZVXW/lOSIksJi6zuRkGio+km19MpX3z5JWEUsLezx8XFOWVe0ml36Xf7eLdvUZWSfm+IIUx8t4MUKWkmqERJKXUEirW/NWqjM8LWUiakREKg49Bp9+i3+3huC9s0sEWJLcDUBJbh1lsyNXbqmoEwTNKiZBFEzMMYvYzp+DaHdw7Z2e6ji5IyiVnOJrhuG6SGZZhsb2/jeW36gxG25SArSOOMKFyRJKqDCIMVq8WKy8tLvnr8WLnP3r7NaGtMt9ul0+2wXClGv2YIVlFIEIdouobTdsllQaurlBthFKKbOlmekWYpaALbtXGERlVK8iIjKzIMDGSu3FVM00TTdeW4oel0Wm0uLy5YLVf4LZ/lYolpWQwHA1XsNJ0wCjE1HdO2cCxb8bLylMn1FUmacOv2bTRNY7GcY7uOMgUQklIWSKEu7jzPcOvOWdd1FvM5nueuE75U/oCO5big6Wzv7pFkKabjsNMbEIUx15Nrda0Ija2WB60WVZyiIyjyiihKqKQA3SZIcoSRgh7iZjltx6bdalFmKaskIQqWrBYLkBLPcShLobIiipIwSXAdR1k+6Rq2XdeRMkeTBZphInVl8iqrqjZBYKOjedkYadWrGs3Xjpmv+++XdUT8jlaz2Y7ByzH0ph5tM/h38/x/iJbyX8I12cTpNtnEN481b6gmAkupAlAt06YqcsJVRlXktV22iW072JZLamTk+YosinAcG0vXVB5CkVNJVVy6Xb825FMZiK7roAiyBVdXV7XLwoL9/X1c1yWOE6SEbq+rnHoriWEoIPnk5ARZSXxP+aM1kX+WZdHyfTRN1P5mJY7r4rQUpWO1XJJmKdvbWwwHfQxTIwwDzk5OMQyTB/cecrh/RJFXzOZz0iTH81xMW0c3HCqZEYQhpl7huQYt38U2xrimwLcM5pMZspT4TouWZ+PaJp5t4xngaiWWrraaugBRjxlK+gVlkVHmCbLK6XU7HNy9x90338Zrd7iaXLNcLBl0++QlXF1fsbW1z727d3lxfIYUGmdn5wSrFZ7n0vZ9ri8nhGGApkGv20NKSI5fcHV1xenpaZ2C1Oftt9/mzbfeQgjBfDHj5OSYLEtrcqtNUZVcT67Z3d2tqRIKbG8kSUEQMh6PiaOUPC/W08tLYNukrBTmOhqN1ounJonLsiyuLq/WqWdRFGHoBnlR0O6016/rphzq9PRU5QmYKmIuzXOou8IsyxThVygtZpakZFlCkRf0er1a4jYnipRhQMMh7fS6rKKQMFZefGmWk5dqoTUcDjl58UJdG0Kj2+7UVJOQKE7JqxIJRFGiJirPwbUMHKFGc1kWtet0TYSVFYYQGJaJsG21dNF19feqoRUpJbqUFLqu+hRD0TdU4dLWk5tW04pedmavXtPfWsx+X9G42cW95Gnxyph6U/b0TT/vJtt3s4j9oQWtqqrf+SW/6WdsPjcAWUriKMJzbFqtDu22X7fu5vr5O7bSwYmaSe1aFlmWUxYleVkhDEMVBVPFyqk3kKQo8jVoWxRlvTKvcF1PaRhNC00TFNXLbEyA+XRGXuY1m185OzRAb5ZlvHj+nOl0SqvVYjjSKZOETr/Dd95/n4O9XTRN0Gr5NZbj1KaMIWEUUpQFF1fXPH78BN9tIQzB9u6YdsdFlxCGSzSUlMfQLFzbZNDrMej2efHsObPJjDzJmMymGNqSjucz6nhYng3olKUky1UildIVWhRlxWy5IkozesMR+7ff4PaDhwy2d5gulqRZjm6YKJKwRbfbwzBNgihCora5L16cqDHS7DCbzYiTuC4+AIKjoyMQcH19zXQ6rSkJks8//y2fffYbjo6OePudt3n48MHadfW3v/2cXq/HbDbj9PSUVquF0hC+tOeJo4Qir9b0Giklum6sZWJSSkxTaUMlksVyQZzEZEWOBHr9PmmWkeYZeZErW3pfkZitOrGpqMqacKxkbdQTj+e+THRSN8B4zUvrd7qMRiM8xyVKVFC0YRm1uiRey67QdOVPt1rSbrfpdPtkWUYQzEiSRHWkQbDmyVm2gYEOQiPNSpI0o8xz/JajFgpZSmEpDFJdTKqBsiwT01Dic1kTfoVUmZ86amIyDE2ZFdTbZEODojAQskQXyvxRyNqYXzac1mZw25zCXh7/4mK2yQnbJJ0Ca4bz5mNuFozNInKzyLzu/28WtN93NB3Z5nk2j3UXVj/XhjsHikhaVDmGqSgRpmnViUclxrqQaTiOh6mrwA9T1+ssxvr313UkpcoRsJW7qV4XeMtS3BvTNBGaZLGcsVqt1uBtWRa02oq46XkK3A2WS6IoZD6fE4bhGnBO03S9Wj/Y3+f+/fu0221W4UrlTZYluhC0Wi2Ojg4BtWYvsozVKuD2nVu8ce+O8hH74rdMTqdgwLvlW9y//waWpaEJvd4SmjUYrtHyHKzabaHf6zGbzLm8mLCYz1ktZiwnJlvdDjvjEW3fp5Q6WVkgypJSlsRZTpgUOH6HW2/c5ejeA/x+n6yqKCSYtkNLqni0vJIKQ5pMuJrMyQul1TRMk+vra6V40DVG4zGmrrO1NWYynXBydorruOzt7ZHn2XrT1+l0cByHJEn4u7/7O6qq5OHDB9y7d4+HDx/ieR7X19ecnZ0SRXFtYZ5gGib9fp9et49t21xfT1+R+DXRcXmuLIaCIFDxf/LloqYxEjBNcy1Mb8iyrusC6kYcx7FaqugaSZatg1Dslkp7Oru4UMuhvCHjJrQ9X3HGopjJ7JrRaITruqxWK5bLJbPZXEnZ6pBpoSs9sGE5TKdTVqvV2mvt/PKS/d1t5RCT5kqDayqJmNp2qrFWxQtqVJWrbvSGjqU5tASYnkcpS7JEcd6iKKWoUihL8jxF4CgirW4gKdGFIu4aQkerMgxKJZeSrPH3amOZuL62/6XF7CZBtsHMNsfGzVZ783Ob37vpgfa6n3Pz8ZuF7F8yZr7u2Dxvozlt3oyNhbBaZsDWeB/HUgZ8YRhSFjlV5SkQF5VepWkabquF32pBVaGblgqDLZW7gCLZaliWD/homr5OUloum/xERWpMc7V2zwuYzq4JgiVCaMxrA7+mE8tqIudwOFz7bJmmusgGQ0VlePTVV+R5ymg8YjDsc/eNN5RguKrI8ozD/T16vQ7zxZLBYERSJCxWc3TLYGt3zN7BLq7v1dtlNRYqWKGBCsxa/mRxdOsW4vCIxXzJ86cvePr0KdPLay4ur4gXKyzDQtNMqrJUQvWqQugFUZoiLIfDN+6yf+sOpusTJDmlVoKh43g+SZoxmc7JywrXa/H8xTHnF1fsHx4RxRHzxYxBf8ju7i5FkTOfTri8uOT09FRdRFmmRNVCUVy2trbW71tqa5pBf8BqtSIMIn7+85+vA4PH4zEPH76FlJKtrW3m8zknx2eEQcTB/iFZlq+hCdVNa7VGU3VueVWyWqoRcTwe192bgmGaTNpmedDE0fm+z3KpFAdZmtJpd3BNl9n8GNM0VXclS9U51kXSskwKqazGGyvuMi9YhS+VBK1WC9u2SVNVSG3Xp9VqUVEHYNdWRN2u4rUFQcBiNmc06FFVBaswoiwkbqtFUSrr+DRNSeMUWZW4to3vOopzVpYYmvq57W6bvFBE6KLIEFLZpGtoyErD0Kgj71AyNyHrTE/ljWZRYFDWy7y6fiCpZIVsYvJ4uRdojm8sZptFZ7MYNDjYTaXAJi9t83teEZFL+Qph7veNf687383v+52jHjXWa99vOO8m7aTZxgop1V1SSrI8p6yqmpBqkRcFk+kMWRXomnpD5llBUWT1eGFRygrLVsGyjQEjgGUpDpFhaOs3dlE0krEIrV5353nKYjGnCVDIa3yk2+3ieR6GYTCdztYkyO2dbfq9/jr4FqDVapNnOafPj0njGMPQ0QwNTdfoD/rKUUNKXFcFXYzGQ961LFzXY2dnh16nh+u4IAts2wVy0jTH0CssU8fQTRzXoe37aAgM017TgzQ0tELiagZ5JbieKyJolmUYuo5hmsRZxtb+Pru37uD3hySVJC1LsjwnLQqSKCKIEoIwrgO/DGbzBULX6Q+HIASu52FbDmmWMp/NMA2dO3duc/zimDRSHv6WbXNxccZqFeD7Hp1Op8ZIdYR46Qgznc5otX2EEEynU548eUKe59y7d4+D/QPefec9ZrM5i8WCdrvD6elpzawvahH8sk4xN0iTTBFbdR1DqKjGhvGvG7UbSD0qNjf+ZlJoaB1pkmKZCf1+n729PWzHIYpjlvPpugheXFyoJUN9GawclRRmmxZeHa5ycnKytoryfW/d/Ukp6Q/65HlOEK1ux0DtAAAgAElEQVRYLBbM50sFQ7guO3vbBLF6T+a1nEvP1JieZylZmiCAosjIkKSZMjVYreqENSocz0UWJboGwjRwbEu5EAuheIi6pnzjpKQsSpXUpen1xjdD1yrMerppruQStWSRslSvn/IaesXU4rWuGZsF49swpk232cZu2/O8tTj6ZvDATZfMm8cfMmr+vkPcKGQ3R+HN56IE39aaWS3LivPzc0zTxHNc5Vem6xRlyXy5VD7xva4SMpcls+WCIkvJy0LFq1UlWZGh1R74Yai0dP2+jmGYpGmC67lrENNx1NazAWYNQzm4Oo6SS83ncwDKQslm1I1AkCQpyyBgsVoxGAw4PDxkPB7T7nS5PD+jzAtc2+Hy/ILZfIYUFTt721i2wXK1Ik5ShYvoBn7bx2u16v9u4XoelBJZguv6CErKMkHTK9BMdNPGsl3QDJUOrun0hkPyUplPFnGOJXVKYRBHGUGYUpUlmlZRRhmtbpftw9vY7R5JBZWmuG3hfE4YRWRJSp5XjMfbCN1gtlji+T6HozGj0ZAkzel0eiRpSp6mhHFIsFwhy5LlQrnzllVR35SUsqHXU0VcCe4roijCdV06nS6TyTVVVTGbzet08y5hGHJycsrpyTnn55fs7OzUY5jq1vK8xHFUZubZ2TmrZbDGzZquLcsylsslbi1XahLNS6FwIN1QVjmmZZGkKQhVPDRdp6zdMVqtFo7rKqZ/VbsL18oN27Iwa1uoq6srNE2B9VmhOv2T0xNOTk6IogjTVMuMLFO2RskGMVdxFfM1vlZVBbY9xrEdhKavixyoFKeqKgkWIatVjmzSz1HypyxNSbMYwzLXWaWmaSq9bM2Z04WSXFWiRFSSPFX+fbppKThGquxOqBQlCnVv15qFwFrQKesb6MvjG4vZzWKw/oaNjIDNwtA8riH6Aa887nXnuvmYm+dtPrf57x90XvHNYvXNztI0zfWbDxTPLc9y5SVVB0yUpdrEtXyPbq9Du90BWZImMUkSUWY5jRN6o4RIEnXRNLQVTRNrlwY1bst1Z2bbdg0wi7pVN2j5LdJUqQ6axJvlclGDn8q2OSty8ixne3ub4WhEp9NDVhWXF+dsjbbY293ls88+5fH0MV7Lo9PuYVsOF5ePefbsBZ7fZnt7G7/VIU5SJtMZ7XaX2we3MIVOmed0ui1aLRvPbmOaYJlqUZVXAkvoaIaFY1jomk4lBdPJnKvTKRdXMyxDAdRZmpHlNYPesXnj8DYHbzxAGo7il3kuwjCRQsOwHFzbZ1mowtZrt/H8DotVSJJnLFdLkjTj6npKWTtzzKYzxYEKQ5azOb1el/HWFrZtsrW1zXQ6IQxUClGv160tyFOCYFVfqN5ab1gU6rXv9/vKJLGoSJKUs7MzXrw4BgQPH77J4cEhW1vbICV7e1ecnpzXmGaAEILlaslisaDX7bF/cFAXnEuEUO66tmVhmCaVrMjrrWRzwzYNA8d1WcwXOJ4ix7Y7bXZ3d3ny9OlaC2rVxazp8qbTqQLn2z7dbpckU9zGIAiI44S8yBG6UiRkhdJrNphbM+o2LiOO4yA0gWMrk0rT0FWWhGlCVRIHEbIq0ISxVoc4jkNVlpSlQRiGdUfoY5kmslYGmYZBIhWmaBgGVY37WYaJNFRxMnSlH63QoFLXzlqVrqE81poy1ny+Pl4rZ7p53MTNbhaTTbVAY5lyMydg0yOtKX6NP3qDXzWt/2Y39bpi9rpDCKF0l69wUn63GDYYxppVLSVJkqxxP892kBKWqwAhQBeCdsvHdlzlimHbrBZziqJU7HzbqeU5JbbjMtwakdSs/ObuB4I4VqOhWgBYOLby6Wo2k2VZslou6Xe7OLaDJiWubSkRd6782fO8UJo1Xcd1PYbDEfsHhyxWAdeTOe+8/RbvvfcBo36PIs/RDQvH9rj7xj2Ojm4phw90nj8/Jc1SfvCDP6I/GHFycso/f/op21u79Ns9bN3k4vwMv+Wxt7fN4cEumq4RxRGWqeM6BggToSlbmEpWOF6L23cfkCaSxerXTOdLpKS23CkZjkf88Z/8K955/z1026VEB0OjqAQUFabtIkVOEWeUFUwmc84urtg/OqTf7/PbL7/g4uoSy3FYLlb4fouyKImThDRWBoy24zBfLJgvFgwGAx48uM/u7g4nJycAxJFKWTo6OqTdbiGlZD6fk2UZvu8qXzEp6LR79cZZjYjtdpc0/Rrbdvjiiy/5+KNfMx6PuXXrFvfuPsCxfZ48eVK/J1w8WTGdzxGGTlbk6oZVC9GbRHPqrfbl9dVaiVAhWa5WGLq+1tBOplOWqyVbe7t02m2CWlOZZzm6hL3dPfqdTp0JsaLTUxrNra0tOp0OV1dXHB+fsLq8JAhj0jSlN+gTxzFJ9lK1oAi2Od1um7QOOh4Nh6r4xxFp7RkXrJboQlEuHMdESOV2bNs2eVFgOQ6aJtb6U8/zVMZAPX0URbEW7yd1hoJR21JVZYlhW5RZPdFVatLSBAhNRxiqSSmLrL7oX73Yv7Uzu1kQ1kXjxvi2Weya7c1m8dgsVM3nNnG2Bkcyamvfzcc0P6MpgDeVCb8zBnOTsPvq0ZjaNR1m2rS59XMIa8KjbVtrCUmSZsxmC3RNw7VNRVK0LZVKbhhMpxOWywVoGr7fxrKtGqS1kVLWPB9Zb6ZaSElt8tfm4uKC589foGmCvd098iTD1Iu1TEwvK8bDEcauRRBGJEmmeGFphmVb6xDjw6Mj0qyg5TpcT+ecnZxwfnnNO+++x9GtQxzXw3EdWq02B/t7IASdThdD1+l0uhwd3uJg/5Dbt25h6SrEQgjw/TaGobR9WSZr3p1LXqgutMiVg6uuWThel8H2PuPDOWH+lKurCQiDo7t3uXf/PuOD21S6Q1KCrApKWYFeklcFy1VtO1RUREFCWWkIzQShMRyNGc2mZGXO9s4up6dnzGeLOkOgxHYdLN0gSzPa7S6+73F2dsZPfvJf+fDD7/Hd736PFy+ec3p6CkgeP37MYDBgd3enBsiVq8ZicclgMGCxWFJVJZblrG80TUc/m81wbBfbtnn27Dm//vU/A4K3336bd955p6a7XNDv99fv18VisT7HJj5mGMbaSCBNUyoplUWU7zO9nqDpGr1ej8Vyyeeff04cx9iuKrqNo0oUR7Q9j3arTYhy6fB9Hx19DfU0LsPhMlAqE9lTQcaagZRQlrKWbZVkWYFpW0RRtA5tKeouTwM0DW4fHWDoalwMo4CT0xNc1yPPctqdFodHB0o6Vo+UQtPqLahSluzujpS/W16o7FZZoZWKjpGXFYZQdBANkPpLPZNseKtaU7Z+TzG7WRy+iTi72XE1Lxq8VBA0x+tyODc/mqNZKDTd0uuK1s3C+Vo87MYC4OaxSfDd3MA2v4cQOpVU7g5lpfy4VEcjKcuco8N9DMMEIQjjmEhK4jTDqI3yojhkFawAGAwsBoM+3e6AMAiJk4TLi2s6nQ5ljc8VRcnDh2/SavnMZ3Mcw8KxLOYLxeJ+6603abe7PH76jJOTU+aLFYPhgJ29ffYPDnj67Dm9Xh8pdfKiUOvvJGU2meB6Du+8+wGHh3s8P37G6ek5ZSG5f/8Bnuersavbw7F9DM3EdRTeqTsuW9vbKk7P9+n1OkCJYVgYuo7EQkoNUXtThXFOloZEUUSUl3RGW4xzSWk4CKGz98Y9xge3SEqNs+u5cgkRKvxC6MoTbhksERL6nQGtlkmn02O8vUWcxZxfnqPrBpauAkmKUoHXQtYGB6LC9ExcX+kET05OcV2HVsvnF7/4J87OzvmTP/ljfN/nH//x55iWwd7eHufnF2v/ujiO2draYXt7W3XJdTCvaZo8fvw12tphVhGr1fsZqkoynU746KOPkRJMS6fT63BwcMD29jYAX375ZU1ejdYwTDMNrFYrptMpWZZx5/YdHt6/z2qxWMMfDYNAjYsxaApmcGwbU1Nqg8RXCwPPdYlTNSp60luPmA19BAJkJescCLumjmTrBqTJ/+z2OyRZShgrYjhSImUJQsPUBf1elygMiJNE3YTKAr3I0UxTJacniQr7qUdYr47g0zSdQU+RmlerJcvFiihJkbUGR0oVlFJVAl0XSj2ia+tAcVkpJYGhKaOAxvyzOf6gzqwpRJtFpflDvy7H75uKz+YG8ebjmsKyGZhys4P7NpxsvSz4A8wdNxcTN4ubUXs+VZW6iyorHFN5VLXbJEmG5znYdQhJUotpqWUlErVRdBwXgWA6XaBSw5VBX7vdrl1gnRpEVunfSZLT6/Yp0wTfsXlw7x6u73F2fsFXjx9zNZnguA7fv/+AW3fucHxyyn/72U+5dXSHg6Mj/uvf/wNpnCBLGPWH2I6JMEx+8ctf8dtHXzCbTQiDgP3DA/b3D4jjmNUyBAw6rS53bt1BSqX/rMqK0XBIy/MBJbUqC+U44bouuQF5XlGUFUlSEoUxYaj0pGESU2gG7dGYylAju9XuUOgmZVmho+FZKhdByIqqVALvspR4jgsIojhSIPl8QVHlpHmOFHB2ds7l1ZUSh6cZruNhmgbBMkSg6BaGroOsODs7wzAUUffLL78kiiJ+8IPv8yd/8qf85jef8pvf/IYHDx6wXC5ZrZbrxVUYhjiOw2KxwDBMut1e3cmblEXJ5cUlhmFydXWt9Lu2vY7+E0Kj1fJ49uIFn33+Oe12m7fefJPBYMC9+/fXSUxJHPPkyROuJ9e4bp21EMcEYcDHn3xMv9ujkspYc7lakdc0jyRJCOO4dqVlvbxqxkTXVoHRQRBg2mb9Pn+Zot5Jc0zLXHPb9KKqC7ak1WopHAtJKV+6XRimjiEMqlLZYctKZ7GYkeUppqnjuA5ppoqhwnIroumcXr+jvk9KilKqYJi0QCLx3JZqFmoeWVVBIRSPzKh1oIbQQAcLlcO6NozQFCSl1Tj1JtfsGzuzm4B78+/rNoub33OzWN08R0O2vbkVbYqZlHKjS3r1Md/WJW4+j2/rzDbbfctSo2RjWpfnOZgaEomuqfSelq+8/YeDPoNej8VsUhdwwWK5ZDmfo+sqVi7LCizbZtAfYZgmT5885fnzFziOzcHBIUdHt5UDQVGRZwWmaWFbit9TapLWsMOHf/anUBX88pe/5JMf/zOVVJmWg/6A23feoNXu8tHHn3BydsbDB28xGm/xH//j/4nrtSjyAsdyVcqR7aObDrNFQBBFVFXJYhViXV4TLFecn1+wWq3Y3t7l1tFtXMdltVoyW8xpd7vKusay1qNA42mvGzZJUtaBHzlZmhEnBWkqEJpDWsTMgog0zcgqiWU5JBVERUXLbVFJQZIrB9uqLCmylCzJoJBoCJaLFUmUYdkmXz16jDAgTAKup1dcXF1ycnLMYDTEsV3yssDxXBaLFasgoN3uIIRgd3cX13V5+vQpQmi8/fbbPH/+nB//+Cf81V/9JX/+53/BP/3TL7i4uOD4WHG55vMF29tbjMfj9fbS83x0Xefg4IBnz57TbncBQZYV+L5d461KiG3bauyrpMRv+TiuGu2m8xlPnz9DSllnPxwghGD/8ICDo8PaHjtSN1JdV8uULCeJYoI4Yrlcomkao50tirIkuLrC933u3LnDoNvj+uqKJIwYDod4rstsPlXBwvUYmKbZ2oRRSkmapLh1OnqWl2uxfAO1mLVVErD28UcIXMeh5bvowHKxwraV42+rLYmSlDhOmS9WzBZz1VWuQiihlAVIQRgGuLbLWG5xeT1Zu8nquqVs2KsSWQlKSyCFWsZpUlDIElPWhhaGjoYAWaJJRQX6gzCzm0Xr5qj4unH0Jla2WWyaj81i1oyWTVVvPjYXB825mi7w5tf+pcdmMWsKZ/Mc1Ici5em6ofSYjrO+K4e1oDzPCyxdr0f5ukOtf58wCLm4uMS2bXRd5/bt2/R6PZUDmSvWt+/bSqyc5wwGA959933lFZ8l/MPf/wPnZ8csl0ukrBhvbXP//kMGwzGrOOTjjz8ly3P+hx/+j0ymM/7Lj37EZDKllZeARhznTKYLdra3GI+H7O1s4XoWpqlwwLfffBNNCKIwIU+Vjk4XGgd7+2jaAbPFHM00GI/HWJZFsAoIw4iylIBOVWpUZUVRlAqUFRZVmZGkJUVVECYFy1hF0xm6CsLI0JgHkXpMXmAaBp5jQ5lTVTmiKqGCLM6IooQ4yqhWFYvljCRPOb8+w3RMfK+FaVu1aYCKeGv5bSzbZrlYcXF+Sa/b5f133+Hw8BCA09MTHj16hKbpDIdD/umffkm32+bw8JCiVCLsfr+v0raEtsZSe/U4NJ/Pmc8XLBYLTMNSW8Unz2iCQTxPOQLrmo4U0Ol2sF2LvFAWQKPRiIODA46Pj/nqq694+vSpcrV1XcqyXI+e29vbjIZDHNtm9+CIq8tLnGCl8LU6xs62LLWpTBLCKGLY66tkL+UusL5OGtPG5hpTyU/Kzmg2m0FtnCqFXjcRasxO0xTDNDEdE9t18XxvjfG1XJ9uy0c3DJAVmi6UnrTScNOMqb5isQxI02ythvA8F8d1KYsS23LxWi100yZNF/h+i16/vzazjOOYikqld1kaQiqzz7LKIVeEal2r82sBHdYGEd9YzF7Xmd0sWpsF5mYhe102wGbhWlf9jW6q+VqDEzRFrYm2h5fWQ5uYnNj4oClu8iUH5XVH42S7pmPUsV3quWvqzlgpV4qyZmTLsiRLE4JghYbEMkx812V7Z5dOu8tsNiWKFE7Q6/ex6nzE/mCIQCVcT6dzgiCi0+4wHo84OrrN7u4uILi4uOCzzz5nMZ9w8uxr2q7FaKwkKQeHt7Adl2fPnxHEEd/57ncwLZunz4958uRrkiRma7xFXLvfBlHAaLhVu9/mvDg5wXEsdrbH3L17n063T7BacnR0i3feeldtlurw3jiOyfKCIku5urpSnZhmYtoOIivRhEmeK8pJs+mrZEUQJiyXMYUskELH7/Yw3AzTsOl0+hR5RZzlJElGmZf4jouQFWWWIqhwLOXvtZjPmc9WTCYzprMZ6BLHtYiSmN1+l9HWiKzImM4m9TZQvUdarRZpnBDFEavVEmTJX/zFX7C1vc1Pf/pTrq+vSNOEIAyxbItimmPZNvfvPaDIS548fYym6ZycnHJ9fU2702F3Z5d2p02318cwLHpdVfAOD49YrcIaWtAUfoqs9aEq2s1veazCJXGcsFjMGQzu0Ov16HY6XF1fIwR19kNMnMQ4thprizxH13TmkynT6wlC1wjDkFUQMFnMFJ5pqtH2008/ZTmdce/ePaSUPP76Mb7r0R/2cByHVbhaG0M2U0hW35CbbFjXU4ErTXBOmqZIIC9Keq5Lp92mKnLiMCSxVAaDJsC1HYIwUoEruVzTkZIkQWYZjX+/57cZDEa1r1nGbLFksfote9s7tNpKCmiYFtPpjDRbKtxOVvQ6Lco8Jk8zyiwnr3JkKZT1uqGSxPQKBLLmn31DMXtZgL7xK6jWTrx8jHy5VWh0iFXd5WhCqyOrXtoTV03BkXL9vQ0fy/VcZGMXXBSKT1R7L0lUwRHw0tBtTUNpCmi1oefafLbq0HQFGWqaYuLlZUYhC0xdeZgJWSr2toAiT4mqAtNQZoK61OkPBzi2Q5FBkKdkaYmhu3RGAzrdFrfv3CIrMlbLgOlsxtX5FVGSsLO9yx/94H0O9g/I8ozZZMKvPv6IYBWQ5TmGruG4Lnv7+9imcg8wTJvpbMYqeMFgPOa73/kOEo0vHn3Fl198RgUc7O0wmUwYj9QINJsu2d0dYdsapydnXF1d4jgWz555PH22zd7eHlJKfNej1e5TSI35bEGQ5BRlQZantfg6o91O8Tyf1SoiDGOWQUzL96lKRTxtBMZpkpAXBYZt0vY8us7L7ETTsEiihKpQ+YuZloIoyfOKOAooshhdgzgKub68Yj5fIISOXo/76j2ls5gv2T845MHdh/zjL36BqJRWtixytrd3sEyds9MzHM/j9PKCv//pT/nhD3/I//QXf86PfvQjZrNJnRcZY3sOL45fMJ/P6Xa7tFtdKilxHOUasYpCZosVaAaWZdIbDpVzw7UgjEKWqyWddqc2rbTo9jqKMlBW5FlMFOVYpk6WVEwnV5R5xnQ2oywyHMdib2eHIIqVgsIwcCyblt/Cc9015ut5Ln67rTqXIidIIhVi4zi02x3G4zGmaXJ5cUEaxfR6XTQ0ri8vaXc7LBZzylLF1jm2je04aGmq8g/KEmSFqauwmqIs12O/REKp5FKWYSI1QYJgMV/V+FTF7vYOAv1lelhZvJLpqmlKqZJlylQzS1XCVMv31l1jJQuliKhy0jwiSQNAIy8sNL1HVZlIUZBXIAsJmlTW63mFaYCKSdaQG6CZ/jd/8zevlKr/7X/9X/6mIXmqTmXjQ9cwDB1dUwGfqpC8ZORqQnU+eq27AolRe7QbuiIlrs+tacg61LSJlc/ylDzPEJoKthgM+vT7PTzfU/mGmsD3XKV/rAuYQPnlafWoB7Im1Sn5g3oWEoHyTDMtk5JGf6OMIfNSZRP6vkuZZbiOhevYVGVJHCdUeYWhW5iGRVlAy+1gag5hEGMYNm8+fIfvfvf7vPHGXRbLBZeTK4JVxGA44oPvfI/vfOd7jIZbhEnCZ5/9li++fMTjr7/m/PICKSu8lo9h6sRJiAb4rRaTyYyqknS6Pfr9Pm/cvYth6Hz8ycdcXV2wWi3RZEWv22E4GuA6JtFqxXjUR1BR5AnXk0uCYKG0opbN+eUFj5884ezikjQrOb24JEwzojwnSBI0S20pZYn6VwqSJCNOMpV+pOusVgGT2ZQ4idXfsw6hNUxDiY1tG9tSG1nfcfFsR6UFIcmzBF2A71i4to6QJUGwZDabKvVAWSI0ELpE0xVL3rabzAEVSzfojxBozOcL2q0WeZ6yvTPi1u1Drq4vSLOCNC95cXJKfzjg4PCA/rCP49oITbmlqhSsjCAMeHF8Ql6UfPd7H+L5LcIoptvrkZfKJeL07JyvHn2FZhhsb4+JE7Ww6fW7QMVsNkGIkk6nhWUbJElAXiToBui64M4btxCyZDabsjUaI2VFHEWYuo7vqHhAXdPptDu0XJ8iS5lOJiwXczzfUzkMpgpOXi4XpHmmOhNdwzLUVtaxLQxDpyxyRqMxnueyXC3I0hTf83EdlzAMWCwXKMvASp1D09A1ga4J0kTF7ZVlgWkZ7O/tUGUZtmnS8jyWi2VNHu/w9NkLZF38u70ejm1TITEMDcMykdJAaEZ9U4zXiwgECF15s+VFRhwHhFFAWWZIUWAY6oYeRzF5VpFlKluirCSGZiJ0g1LWJgRSXc+FFPy7//nf/wf4PYEm3zZSbo6TDfYEvLKRbLCtBrxvUp1fdwihgL84jpUWbbnErjGJdrvNeDzGcRziIHylrc3S9KU7rlSCVihBClTUmUBoCu8RQmku0yxHVgJH07FdH91QDhFplmCaOuFqxaoCz/XotFo101mnLCpa/RZ5XjIedfnehx/iex7Xkyu+evQ1jmdhOhq3bt3GNGzm8wUff/Jrrq6u8P0u+3v75IXSnHmtFoah0277+L5LXuRUUlkQlUXB/sEBnueRFwW379yhAn72s5+xqreGg36XPC8YjwcMhkN+85vfcOvWPgKds7Nz4iRFExKE5PadWyA05k8WjMZb2LbL9WxKeHyM4bj1BrckryqlkSuU73+cqDu5buiYpqUE8XmOqEd+x3XWgbVCq0nLGlSUSFmhGzq2qYTPhpBQ5QSrFWmcozsOKiFekOUZcRKjCbBsZa9UlrK2ohEqszPNmVzN2ds95P13P2A2maFpBrdu3eHHP/47/vW//jP+7Q//Lf/7//GfeHF6yqA35KOPPyaKA/70T/8Yx7PoX/eYTCd1II3k5PQEgYZlOiRxws7OHi+OT2h1uliOy4sXL7i+vgYqrn854enTAQ8fPuTNN99kcn1Nkaf0+0o3OxwOMQyDOA6oZMFyOcd0LDotn52tLS4uLplOJyzmi3UO6SJcEawiDENnWN+4oWS1mLMKVpydnWG7Tl3UbcXST5P1RKNej2y90bRsi6rWLrb9lqJXhCFVKeu4vIxwtaQ3GNbRdREjOcJvt9A0tR01bR3TVmTY5XLFkydf8+bd++zs7HB1qQJxDg4O13BR835wHJs0tSmFBmikWUEUSYoiJRMC09QBidAkZZ6xWOoYWo/BYIBuCC7Oz5jPZiRJRMfvItDRdInQDKqyIMlLkiynKnMcS8cyDaQ00Y2XJP/XFrOGDrGJT22y9pvi8zsBwLAuZJtJ6JuE2U051E1cTQol76nqF0tKBfImSaJIhJrGwe4evV5v7TOWpSnz+ZzJZMJytaJjdShqIzoVWlpSlrmy/q3U3cRxKuXRpRJK1Eq5TMkFtGwXv92pE5CsdVCu7Tj0uj1u3bnN+++/T6fT4fj5M5aXC44ODxCa4KvHX3AxnbBczljMA/K8WK/vNaHCXx3HodNtY5oGUpbkeUYYKQwmzzLG29sc7R+wWq4wDJN33nufJ0+fcnl5yWAwQjctTNNWGybXZWtri/PzcwajMUVR0Ov0ODy8xRdffsl0NsPzlSfb1UT5et2/fx/Lsvns898iUeLxMAoJwpA8TRn2h3TaHax6y+u6ykfNtm1lqBdaWK6zTjxvUoLWNy5ZkpcZZVGueYMCSS6URXqZ56xWSy7ShLJoOE4luq5Ge0lFkSlTy6KUSCkwdBNZSSbX13z11WPefe9dBqMRXz99jN9ucefOfb748muG4x3+/M//Hf/v//OfWa0Cnjz5mouLM4oi4823HjAYDPg3/+a/59mTp3z00UcMh0NkBb7X5stHjzhKM/76r/+aLx894pcf/QopJUdHh4RhwPnZGdPplEePHuE5Lnfu3KbbadXyIhVoYtQBxFEcYJo20+mc2XTB/QcPeP+9D/j000+5vLwCxFojapgmtm0RhEuyPFEB02VJWVbKc67GePOaLtEQbI16g968D4qiqK3Rk3Wmqm4qHNQwDHZ2dsiKnAVDBqkAACAASURBVGfPnhElab0gUDeO5jo2TROECilJ0lSZhzoOtm0zGAwIg4DZbLZe4ummWW9MUyaLucoCLSVZKokT5VKibJp0QI2jSVLhWIqUKyrlzttqe2psbDIThEATGpowakzXwNCULrQqFAZdSaGeq/iWYvY6A8VNbtnNjutmilPjhrqpN2vO29AgXtf1NcUsSRLlOlAz9ZtC2KTYnJ+frwl+Qgi8mqdz584dur2ecjQoFZM5zZJ6NZ1QFBVVVRDHaZ1SI7AsA99r4XoqN1IH0jBiXPtBJUlGy21xdHgbx/WYT6bs7e5zfHLM4rM5i/mM+XzGj/7uP2PoAr/t0x908P0W3c6gtl4JaucETRnidTq4ntp0RlHIdHqtWNBViWUoLtbJySlvvfkm3V6PX/5KXVRBGDBfLNb+VkVZcnh4yPVkwmQyYe/ggOlsitD02qhxRJwkHBweYloWF5dXfP/DD+l2OhwfnzCbXtPu9MjzjNn0mjzLKbOcNE4JOwHdTnedBdncoNJUBa00oRybX1vbKmkC07DQtRKkpMgL0iRiMZ8xm00IVkuWiwXLxYwiT9EN9Vo7tk273SJNYuazKQKVKq8hsEyTTtugLCuOj1/Q6XV58OBNTs5OOT4+5b333+PHP/kJ/99/+Xv+6i//kvff/4Cf/+znYKrtn5Twycef8O677xCFAXt7e1xfX/PZZ58hKzUNaJpOHCc8f/6c0WjE/v4+y+WSqqpqHliELmBra4vp9YRP//lT7t97gzt3bhMEAVdXV7URos7t23dYLBZcXkzpdNrEUYo3VDY/o9Fo7SFmWRb7+7u0Wm0WizlZllCWBQjodjo4novQNSbTCYsgUB2xqVQyLd/H9/31NZnEiSI0S5XRGQRLdF2n1+tRFmpbmRUqAu/i6hqAdrtdKz3qgphXLFdztFwlN93aP+DWrVt89dsvlWbTtnnx4gWu6zIcDhl2u5i2xWw+RyxVhKJtGMRRiIKdJLqhoWuKCCulwr69Xg/XsxUjAAURdbtdZbtuu8hMaS8bCZSmaeimhSGgKgyyrKRAkBYFRfUt1IybHdPm5zZHymYLuUnBaPhWLwMUtFe+/m2FsilmKjeyllfX46xlWRieeuGiVbB+HlVVMUsSJpMJjx49QqKcHtTGx8HzPXy/Rb8/wnN9zLp4CQ10XVmTuK6PrguiKCZcreh3u+RpRr/f5/DgFqvFip/97B/58ssv0RAkSUKn06bXbSuMQNfo9Nu4joWuKz5NEKzQNBPbUok+rVYL21IazUaGkiQhWaYcDjzfUyN4TZS8c3iE7Tr8828+XYt/w1gFnLieR5pljMdjNE3j4vKC7b1d8iJXFBDb4enz5+R5zmg0Yndvl8l0yng05N69N7ieTJkvpkDF4cEewXLO2cmxMi2MI6ZXV1w7nvJJGwzUHb+OTFNCbHW3bDbPDcE4z4tarqKtO7WiKIijgPlsymRyzWIxrZ0rCmzbxDJFDR5nSNvAa7nYpk4aRWhCidelUBSIlu1gmDYnZxf86lcfYzsuH37vB/yn//v/YrS1y5/9q/+OH//4x/zqo1/zwXvv8/z5c54/e8Lx8QvOz084ONgjDFZsbY25vrzk+9//Pq7rcn014fmzY5I4oColn3zyCYZpopsGd+/eJY4jLi7OAdZ5o57n4Q4GhGHIZDLl3r277O/vc3p6ynK55PnzkzU9YTZboGmmoveUEsvSa1cVie+7GKZOr9dG18H3HdJEjZ2u6yqrI9chzTKCWo0gNXUdGbUHXzO9qKVEG9dV+Zyj0YgKyXQ6Jc9KBoMBxBGz2ewV0niWZXgtn93dXXwvYLma0+/3KcuC6XRKv6/sgiaTCe++8w4AV1dXa3qTris+puM4JGlKVkra7RbLICKJctI4I0Xh67ZloBsmcRgyGvTodNqqyTFMLNOCqiRLc0zNUOlVNV1E0zQcR9byQoHRSLmK2iTym4rZOtfuBrdBSsXMbUJy16nlUtbGe8quY5MA2xSrRjLUZBc25/udglabu1X1OZuuTyUtq++zbXs9/xdFoVrSumhWJczni7oFVeG2Qgi0mnKBYO3pbpkWRVlQldU6RNX3XD744AMG/R5nF5f85Cf/jWdPnlOUyohOcXjUeaSQmLaJbRnYloHQFUHQMFWXYZkuKj5OEXM938e2lA5wuVrWDgsS27bwXI8ojkiikPt37zEaDPn440+U22iNDXbaHRzXVZ2PaTIcjjg+OWY83qpDNEJarTZZlpNmKavlku9//4+wbIuT01O+973vAoLVcoGha/R7PQaDHo8ePSJJQhzHQqBCfZVOUW2rlauHWrqoQqycGGxb2cpU1cu8hyzLlYWLpkHtf7UKFsznU+bzKWG0UiJk08A0LJA6ZamrboSK+XyCitSuAzsQ5EVJUZZAY3CguvNPPvk13/3wQ9rdHrPFive+8yHvvf9djo+PeX78gg8++ACBZD6fcHV1wWw2VaC657A1GvO3f/u3vP/++7z33vt89eXX5HnBRx99wqeffophmliOzbvvvsvh4ZGCAkKVX7mYLxgOBrx48YKD/V3a7Tbz+RwpJf1+n3Zbib5fvDjGc9vkhYpa++ijj1nVlk3dmpQ8HA6YzSY4jollmcrQMFd4XlILu8eug+9565EySuJ6FHspawqCJWmaUrreOlMzCFUn1/DOminKtm06QmM2mxMEgbK5dmwG/QGu43N88pw4VtvR09NTloslrusqNcJySa/XW1+/8/mcMI7W1uPz2YxlGGLbHZUmVRTq2tPVwlDTFBOhUWY0Vkm6kCrdyjARlSIl53muYvVqEbthW7i2C7pQXWaqoIys+JZiRh0FpaGKwXocrOrOrKpqHonSSck6LRoEotGKbXRmm6TY1x2vMP2FeqKIVxn8su7UGsPC5qN5odbctFwJtGWlvRyBa8V+gfr5OjqykBRSEVjHu2Pu3LnDwcEBnW6bzz//jK+++oqL8yuWyyW6UOtr3VB6sPHWGNdxEFpJksXohkPX97FsgyxJSaKEJJEUuawzKLX1i9gkWhf1c1/ji1K9yVqei2nZvDg9RWg6k8kUoWkMRyro9er6ilHt1nB2eUEpYX9nl0ePHtHtdkmzvA7wUC6rw1Gfrx5/je///6S915Ml95Xn90lvrnflq6sNutFogBiCbnaoIGNnN0TuPmj1oIh92QeF/pPVPyATMn+E9mVDoYjZl9UMxRlwZkCQME0Q7buqy9f1N296o4eTmV3dADgR0o0odKOAqrp1b+b5nXO+Tk7eR48ecXF+hreULILVYk4cBnRaTXrtlpgnBjFBEJeGe7K4zfOE9XpFu92uk7BsywYF0iQlqzh5cSKjoaJKhmUYEEbyEUUheZqDBoWuim1yoZS+agp5nrJcBigZFElBHKegqERJSqEEpJlcmoZhMBwMeP7iBcONTX70w5/wn//mr+n0vuSHP/gRx69OODx8hfWOwfvvP+Di4pzd3R08b8752Rlff/01xd2c3d1dvv76a1ZLj/fuv89sNufly0MUVfbCqqHz6aefsrOzzfvvP8A0DQ5fPKfZbBIEAc1Wk/l8zsuXL7hz5w66rvPq1TG+H9Bwm2yMtsog3zXtTptOp0OSxKxWS4LAJ44jdvd2CJ56pYtwUvL35NDO0owwimhHkYBWkYzkVR5nJSLv9nokZRhxFEVomsZoNOLLLz8HVeH27dvEUSqHVhzJisBI8TwPRdEE6AkC1v4ay5TxMUrF72xzMJT7LM3p9/slo0DE71EU1fZFVdGpCO1h5JPE4hRrlIVLUUBXNXRdZWtjs0yikrR7yV4F13Zxeg7n55fkeYFhmaBJ3muz1RF5napQoJKjo2eQ5n9iZ1YXmdfV5htjZvWkr3dP1Ye4cArZtcp7vK6vvL53e7ugUe7c4iQhjqJaAKvrOoYmqFqRvk6Fum72GMcxfhDiui2ha1RWRoXIMaqfZ5XUgUazycZoxNb2NoZh8Mc//pGnT5+CUlkXlfIdReLnVU2V+K4oxFsvMXSVwaBLt9el0XCkE1QVyAvyrLhG8i1K2xdhEKWp8GsajQaappKmMYaq0Ww1aToOy/mCdqvNyfEJcZLUwmdd1zEti8FgQJymnJ9fcOvWTZbLFXleoBsmi4XkBMxmMz788EPW6zUPv3zIRz/4iK+++opnT5+iAJ1Oq1Q1xHRaTTrtVv2uO9brjAHICEKP9CpiuZzhuI6MBKYpyFleEEdRTXKOowjHkv9HkoIi6VZ1SWs3DFX2l2pOp91FIWexlKKnKAIOGaZJqqTCYcoLKKRbTJOIrFDw1ktQoN1u8ejR13T7fd7/4AOePX3GrYPb3Lhxg1/99X8m8tf85V/+nF6vy8M/fMH5+XndYbx8+ZLbt2/zve99j0dfP+Lvf/OP/OVf/gv+1S//Ff/P3/6a6WxWagZzTk/P2NuTpHVT1zg7O6PX67FcLjBNEY1Xfv+r1YowDOl1B1xejlFVjXfeuctyueDFi2fM57PSBkqAjlarVYb26iyXpdur7ZJqOY7joGdpqRIQgMjV3ZrRXyWNt5pN4k6H1WpFEsX1gdnv90nzjPlsDqjs7O4SJ2L4qSiVB5lQQ8IoEhCpJ7vi2JNO6+6t24zHYw6fPqfX60FRcHV1Vcv/BL1WavCg1WphNxrkuZB9wyAkSSMoCgxDE+qOqdd+f7quvkHsFU5pgWU75KiYaSamjiVomANRHGFaDoWmUwh/57uLmfa2y8VbBUtT1ZqbVRWLioUvth+vEcvresrr1I23C9n17q86carCV/mOUf63JIyA1yaQFcKqKAqGblDWEyjEV9wwTaxypq/QvI2NDVRV5fnz5/ztr3/NcrWk2+kyGg7wfE881tIy1wD53ppSEMdqmb4jPDTD1MizhLywURA3Ase2ubi4Io4yBoNhGVRSRpGVST7L6ZzlMqPTadHptBmO+kRRxJdffkG31WW1WtPrD5jNZoxGG4SxILY7O3vcvHmb/+M//Ac+/P73sSyXrx89KV1Oc8bTGWvfJ4wiDm7e5Pe//z27u9s0XIfPfv+72i3BMDTa7Sae57G3J8V8PB6jKCp+EOE4NvN5TtOViLU49AnWK64uxOFU3E81kTVlaXltCAiwUjXIczH0U4X46nTb8v/nCpqSgSLXUZZndQYDJffPNA0atvjHLRdL/DAkyXLSfAWKiq6rRIkEZeRZwWw240c/+hFxEHH08iUPHtzn+PAFT58+5ubNfX75y1+QJBFXlwL9D4cDTN3g1atXdDodbt++Qxgk/PaTT1it1vz4xz9mOpvx+ZdflO4mGX/84x8Zj6/Y392h3+/z/OkzuQHzjMlkWkvXkiRmNpvRcNu1+PtXvzpCUaDX75S7Rgm8sUKL8XhcdmNanfztBwFxkuE2Gpi2ycoTVHwwGAiAZkkXNBgOiWP5eRVQ0+926Xa7jMfjOulJURTiMgVdLQ1Jw7VfU7CqddB8Pme1XBOnITk5rWaDi4sL1us1gLjm2rYUVdctR70IwzLp9XoMNjdYLpeMZzNm0wWGoaFgoaiFWPqUrjO+nxKHEa12k43BkG67Q7crluZpkhDFKePplLUfEIcRzU6bOPAJYznMLsdXHBzcotfuYJjeG9uwb3ZmNSv/2t9f82KlcF0rTEVeoCivnVvTLBO/7mtF8O3CdR0UeEP/WXYEWplnWbWvRSEiZFkwa7UtN4BRFjvbtlEVXfL8CvnelmnRarXoD/r0e30azQanJ6c8fvSIs/NzKAqGoxHbW1v4vs/JyTHtdgtFKUmbteSqLKq6TqfdkgTnosA0TGy7ygyIUcqC2+v1MA23vmAqy5fZdEYd4aXLDkouvglHR4cUikKW50wmM3q9Hgc3b+KHAfP5HMMw2Nnd5Ve//jX33n2Xre0tPv74Y87Pz7l37x6Pnj5hMpmgFAX/+l//a6bTKa9eveKj73+fTz/9LbPZjJ2dLQAm4zHbW1u4ro2pq4Thmt3tDRRFJclysrRA15Sy44rQVTnkTN1EURXSJCCJxMJYVcTrXlEgJxWJSbngVhSxUMrSmKJQyfKEKAplPE0FbZ5Op1LwbFucPLQ1TbdNGERSmJOopOqIP5ZlOfT6W6iaQZYrBOsVx0eH3HvnDk+ePOX87Iwf//jHzGYTvv76ax48uM+HH36Pdtvlq6++QlUgDkNevnzJw4cPeefOXe7fv8/J8QlfffUHHv7hIR/94Af87Gc/44svvuDJkyfcuHEDVVE4Ozvj1q1btNtt5rMZo80hDx48II4jnj17xnq9xnVdHj78gq2tbQaDPnEsLq+C/Arp/IMP3uf07BTPW3J5eY6uV+7MSom65+iahuu4sg8NfK6mU/zAJ05TDg4OaDaaJEFIGEU0bKd27/B9n+VyQRAE4vTSbBHpMWogSKiu66zWPovFgq2tHVrtNqdnp3LgOzZXxxd0++J3ZpV74jwSA1W1nHCCIGBvb49Wu818uRCQIQpRFCG7X11elmoeKfIa1WRUoCgq/W6nliYKsruQbl8XYqxhWhhZXsrrEpRCyM6KJilh3toDBZJE3Ji/u5jlr4tZUYIBSoUuIjIhvVysZwWk5FCAoQrqSCJL4Le5ZN/lxvGGRVD5p+u6aKpat+2KoqCpWuksoNdoWfUcxf43IElzBu0+vX6Xzc3N0iBPxoDDF8+ZzWa1C0HDNtE0ncBb4S3mEhCyuUkcrykyASxMTRM1g6rjWDau69Bpt/FWC9kH+RGxE0GulciOzuFLOfF3d7qYpoXvCyITR1m9KDYtnbt377K3t8d0OuH09KTuRC+vrjA0o/SDV0kz2W/8xU9/ysM//IHlasmf/7M/5//+67/m448/5vs//AHPXr7g8PCQe/fukSWCxH788ccMh8NaYRGGPkkSCZWl28Z1TGzLZjabUWQpRZ6haqArBUka0m469XVgmIbsSJJERowsl5TvAgzTxCg78SxL0VSt5gkVFGITbRmomkqeqxR5xtpfs1gs0DSFLMul+0ozkbplPsulvGZVoK4ACpUxHzTVJqahEMUZaZpxcXbMD3/4E/ydLabjMR9+/3v88Ac/5Pe//y0XF5c0my5RFPHh977Hxx//LaEfcPfuXeZzCSppuC22d3bwg5DZfM5vfvMbuv0ev/jFL7h79y4nJ68IAp/lQkam73//+zx//ozVYs7FxQVhGTVYubC0Oy3iJGS1WpAXKXEccnV1KWJxy+Lzzz+XqL5el52dPWzbLmk6M5aLFc1mC0VRiKKINBM5XTWhGGWsnWEaKKWcSCvBt9PzM5arBdvb23jeql716JrBcrnk+PSEleehanL/xHFMGAR1WE6apNilw62ma/U1KT5uUsxcV5B3QVBXrNZigW07NpPplJeHh9jlcr/io+nXLOo1TaNhy2hplkW8KAqUQojrmq6jqCrNdof1OiDNC1RFuGXz+Vx0p8sFuqaQZznqNXHmN+RM/8v/+D/8+7d3ZG8XpLc9wKoZ3LSt0qco/9bO7Nseb9v3FBQ0W603UEtN01CVMtGm7NDCMKxTf1ot8bPf3dnmndu3cV2H9drn5OQVz5+/5OTkFcvlkjiO6PX6RHFAGIRYlkm/36fZbJDnBVGwLqVRObqqYhg6pm5gGgaOY+HYLq1mkzTNCNZrAn9NlgqCaZZe+JUn1mq5Jo5jsgpQUY2a1nBw8wZFUfDq1SvSNCEr3Rtct4HjyM/oD/p4nsezZ8/46KOPePzkKYeHh/yXv/wFn3/xBU+fPqXf7+M4Lmt/zWAwxDRNNkYjNE3jD3/4A9/73vcIw4DpdEK/1yXLxWZmNBrRabVqhxLT1InjUCLBoojVaoll6hi6iqaAXTLCDV3FNk0gK1OqwNBVQUGzBCiwTQPXtjB0DZS8HClFwqZqAg5URGgxCIQojksgwCDPYe0HeGtfCrou2l7T1HFch2arwdbWBo5ts/Y8hoM+a29F5PuMBgMODw9ZLOfs7u5iGAar1ZIsS9jZ3Wa1XLK5uSEmmGWI8vn5OXGUMJvNMQyTMAq5vLzED3y+/PJLdna2+cEPPsJ1JBDlyy++4OjwiIMbNxgOB1xdXdUopa6L/32v1y0leiFFkdccSyjqgGfZhQW8ePGSMIiwLIeN0ZbYF1mSRRmEofi6aRp+GLL21iSZqGgG/T5ZkhKFIaZusFqtGF+JiL3f7zOejIlK6k8cJZIiFUcoiPHozs4OUSS7seFoSJbJyNxut9BNuU7TOBFL+NJGqEJD4ziu1zYFsq+OkhgFsF0HQ5eiVNUAw9BLfqSDaRi1L167vM+LkkC8XC6ZL5ZkikZ/MKgBBlVTS+v4vFQRIWseXWgu//bf/bffLmd6e2H/XSPh28Xoumrg7SJWfZ9/0rqnRMfkhJZ8gMpvKS8LRcUUbjabOI5Ds9HAdd0a6n305GvSJCGOMoHFM5H0qIqOpqssljMgp9Vq4DZskjSWYFJFo9lyyTL5GoUMpRCPpbQoiCMVXVFJoyaOaZE4Dov5jNXSE76MLfq2wUCkInNf+Dqj0Yg8zwl8iSO7efMmKAUvXjzn6uqCbreD60oU3Xq9FmjasMiygouLCzrdniSlqyr/1b/5N1xdjXny5CnNdofBYFB2Fk100+Do6Ij33rvPF19+wa07t8qg4TmWZdHrtFEUGctVBRFdhz7NhoOqKhSFQxrHKEVGy7XQdVVkZaXrqaZpuA23DKiATJMTU1Xz8tBJ0HWNJM4psogsk6zOLBP0WzdNcQ0tChRFVhcyUkkUn2GItY+qJkRpTuL7kGVomYapGNgNm9FwJJF1mmh0/bXN5sYATVG4ujhld2uTG/s7HJ+d4zZc7t+/z+9+99t6bFv7a0bDAc1mk6urK8m2HI44PTnD0A329w/44IMPUFSFs4sLNE3jyZMn4qHW77G1tUWe57w6POL45JiD/T329nZJ05STkxOWyyWet2K9XuI4bnX147oN+v0+vu9zfHxMEDSvoZKiQZxM5rJrjWMGnT5RHAv62GkzGA7JFMSoMQxqNQbILsssl+DdbpdWu8nXX3+NqsLejX22traYjGdcXl3Wi/rBaINOp4uq6qxWq5LmlNWuNoVaWntnIp2ySkeVKjt2sVhIarphkObiyuuVz6vd69BouhRFThqndcZFs9lEQ7rB4aAn+QBOkzzPicrGxPM84izDaKlohoXjNln7Yf28iywVSlORErcb6GoB+Z+gZryNWl5/1HyuCs28NiYKz0jQlJw3vcy+rfi9/e+CZgp4EF6Deq+rAFRVpeG4dRqMaZooUI8L6/UaDcmglI6olC1pOqZZdZPyxkVxiOIXOI64lWZ5jh94OJYulkC5dBtZmokTZhSTRjENuylM/labPJFTQsmFYlBkAqm7rkuradZqCIHMTYbDIev1mvOLU7IsY2Njg9Vqycpb0ii9owa9Aa1mm9l0xsbmBsPRiL/7u7/jF7/4BZdXV/zN3/yNtP0lYdFxHMaTCZfjK95//wGKKojbe+/eYz6fkWc5g0GfLIkZjTbQNZXpdEIQrHEcG89b4TgWtmUxnguhstl00RUNXYUkEHWCrqhoFBiqQqGrZKp0TIZhoBY5pCmGrpHmMVEmjr9FkaGqClmeUBQKRa4TlTexZZmyR5zNsWwb23Lw1mviNKNQVApFoVDlIwcUVaHRsGWZPz5HHW3h2gZFGtFp2gSeyWo5Y393lyhNWCwWbG4MGQ6HHB0dkecpt24ecHV1wc9+9jM+++wzzs/PicKY/f19JpMpJycnzBZzDm4eUJSk38PDI+7cucV8PueLz37PT378Y+7eeYfz8zMZ0Ytc0sHLgp+kMZCh6aDroi0uyFitlkLUth3p3Mt0c1Bw3QYUEPgRq5WHkgnAFceR2Fh3OjQaDXq9Ls2sRaslNJpeb4BpGCiFOItUe6iDgwMWixmLhfiwzWfCQ+t2u7iui2HZzOdzGg0JCT6/vKDVarGzs8PxyRFaptFwHRxT9KD9VofpdMqyVKB0u12uxmN838d2Hba2tvDjqKRkJTScJrZtYWiSt2mU10kcRCLFKrNzK2DMMAw6nQ7tdhtF1zm+mrP0vFLUn4sVUJ5haAKeLJcLup0WKpI1+53FTM4SGfcKBKWqk4UVqKRE16oRIAvdrJDUYZQ3fcrekD1d+5o3illJBnEchyAMZXwrBbbCjpc32HVl/1EhQcU1s0btWviqqpTk1rwgSWN8f02aJVimg2FqJcM8JQx9TNMAVIoshULGJgClyCmygiIryJSCVNFJk5gsEZZyw20IVyYr0BSNRqdHkgudIs8kVNb3fbrdHjvbe5imzeMnj2k2BWJfrRYlaCKJ04PBgLW35tXhK0abm9y5cYMnT57w4x//mCRN+Prrr7l58yZXkwnDXo/9/X2+fPgQRVXY2Njgz/7sz3j45Zd88MEHLJYLojBkd2+H8dUlnW4XTZP9oa6V9i2qdMGGpjLzPFbeChVoN100VaHXaaOrarkTUsiLnCyJiYKAwPewHQfLMDB0jURVZFdaOqMYloWlmuiGSZxIHmSBQprlYuqoaqwXS9ZrH1Sd9dpnufQI45ikyNBNg1azQ7vTQiVHUQrC0BdqjWUQhh66qjObjUnjjDQOeXX0kjgTkfzz589oNxv0+z3G40tA9ntRFPPJJ7/lxo19wjDkyeOnRGEiuxh/jR/65EXOjYMDrsZj0jSBomA6EQfX3/72U24eHHD3nXfw16vakvv27dv0ej3yPMEwVeI4Iori8nmHLOZLXLdR8i/FX2wwGNHr5nS7XcIwxDBs8RDLUtqtFpPZlMViQRTHrNZr1t5aMk5VlTAIcEabKIC3kPi5IAxYektMw8APvFoWWBQSpqMbBr4foKdiCmnb7hv296oq45yGGDZW+7Jer0cUhnirlRiQrtc0Gw06nbbQJaKoFr6jFgRBiOs4OI5dht6k4iqcSKd2fn4uul5X9NVco2wVikQSnl9cyFSgafhrjzgKGPZ72LZNQihrB9tEV183XN9UACiUUQHygSKfUxWFQpHbvChtjOBLOQAAIABJREFURKpuqihK56lUFPQoChpyqip5QaZKiGdKjlpQ+41V4Z6FQpmGXCoG0lSitFQN3ZTZu0jlSXueV0K4saS1lMtmrZyfxd4oI8vTWrAuF7KOiU6SpuS5LlHveY5qKOiq5ARGmXhSKaXbrKLIwllFQVM0NK3Ul2UJSqHVQEeaSgfYaDSYzSdYpoFmipC81XDp9QdEsc9Xf/ySW7duo2kqURTh+x6XVxdsbW2xvbPJ1dWEPJGU7Q8/+oj5XEa8e/fe5T/+n/+RIAgwDZMb+zdwbAtv5WFbFqEfcOfmbY6PXpHGEe+++w5nJ8f4WYJhiBWybZnEcch0NqbTbtM0XcZXV5LmraksVit5DQs5ypIsRkkhL8TjSlEVsiQjK3RQVcTURcTGjmmi6AZZmhCmMVlRYGgahmWVSe+lCVOhoBo6SqHgByFRnIAmcWzBLCIIIwk6KTJcy2Jrc0Sv2yWJQ+IwIEszzKaBYjusVx6aqkNpUyTLYJ35fMKN23fI8oTlYkaj0eAnP/4Jp6eneMuAnc19PvnkE3qdAQoS8KwoohTwvBWmaXF+HrFYLnjw3ntsb23w5NFjJpMxvV6H4XDEfD7n0999ilmuDbI8Z75YkKUZN2/dYrmcY5rGNQJ5TqPRQtV0ZvNzbtw4QEGh2WyzmM+5vLxk5a2wTItGw+Xq8gLHdepFexRFeOs1URJL95IXzCczZs02RZ6zKB1mzNKkYb5cYJg6lm1DAf56RRSBmQniSeDTbLYoiowoSSXbIsm4mp4QJxkWOWkUEaoqke/j2WIfNJlOyHOZRlqtFromnXZRpDimThjnrEq5YcO20VSdMPSJfOHF5XlOnuVYlmQQCOdOqbXXnuexDiPszpCLywnr5arO84wCn16nTcN2cLpdBoMBrYZDEr/uzL4JAPzP/9O/p0SQXsuMVLF4UdTSxOy6i9k15dM15n7FUStKaodwtdQ3mP3134sS5SyK8jRTStZwqTLICymKaVq7aYjRiCKFRlWFolHeGNIdKmWhK0+d8qdpaml3XZS0jrLlLTKhmOS57OVQVPJCqBKKqgr7WdNoNF1GGxs4tsV0NiWMAtod4cmcnp3R6wq1oygK2q0mFDlPnzzi/PyUGzd2CaMAyFgsZpyfn9If9tjb35VADBVmsxU3b91BLR1v/+zPvs/5+TmBH7D2fNZrn4bbYDTaZDqZ0m13GQ6G3Dy4SRJHbAw6+N6Cs9NXOLZFkUlgsWWZ4ienSHsfRiG6YdDvD0rScYpmGPiBz9pfo2oa85UsZKNEwieiNCUpCuIsI84ygjhi6fmopoXbaDFdLAjCCNOyaTRb6KZJnKaMp3PCOCVHxQtCwjhhufYIIklEz4E4TcnJ0fUCQ8khT2i7DnkSkYQh7VaTVrNNu9nG0A1syxH5lKLiOg2W5U00HA0YjgZYpsFsOsWxHFRF48XTQxazNQ2nQ9Nt8Zvf/D0P3nuPmwf7HL16jh8sgATTNIijuDRIdNne2mBzY4SmqhwdHomkL88xTYur8YRmqy1KhThmONpguDEiimPanQ6379wlCGMePvwjhmkzGG6QZWVuQCEp4IVSsFqv8MM1mqGJj99gQBiFHJ8cE0VRDRiEYYjruAz7fQLfl5DoomDlrSgo6HQ7Qr8wTZI8x1uvcWwH2zbRSl/BIPABySloNJq4jSaFojKeTlksVxiGzmqxoChyDFURP7kSVU8S8Rx0HUduoFySy7IslvfHtUmiGNdp0Gy20FVDJhingYJKEieoispotMHt23fIsoyjoyNZbbRadVHw/JhOp0uz4bJeeaRJTKfTLr3tFB689wDLMknKIv/f/Lv/7r//1mL2v/3v/+u/l9TNqohJAcuLQuxIyiJRM2Xf+vhmhPCbjz+FblaOsPWYex1NvfZlRVG8ll2VELBectNEO/q6ba40pG8L6CuVgKFqpQSnsqCp9nwSpaZpmnhJuQ1c12HQFyKsqkmYQp6/9mlrNBtC87Aldi5NE9ZrjzAMiOJIsginYy4uzvF9n3feuYNpmXQ6babTKYvFiht7B9y7d5/JZMJPfvITJpMJs9ms9pOXPAGbMJDE8K+++grHliDiV0eHbI2GzGZXeKtlffEWRY7rilBc07Q6C7Lf70sqUZ6z8jxJrC4KwsBHUVVBv1SNJM3IigJF0wRd03Vs2wVFZe2HZLkk60xnc6I4rlHZNBNL7fF4ytLzSbNckMwowvdD0kxO6yhJyIqcNEkwVHBMjVZJg2k2GqIySFICP2C5WNVhvvJ+KjVPa7Q5KjNL81p25K9Dmo0O7957jxcvDjEMk2azXaJnc3Z3t8mLhCQJ8APhiZm6hes0UBRYLpZ1QakW75ubm7XPXlEUHB8fM5vNuLi4II4jbt46IMsylkvJFVh7AYZhcnFxKUG97Q6GYZYdesh8PmO5nJPnGZqu0XCb7O3tYVkWnleRuFOSOCZLU9JSzjccDhkMBvT7fZEkrdfMFwu63a4YGMYxTsPBNs2yG7LEmXdrmyiJWa8DVFPHtBw8f4238lEUhXbTYdDroWminVQV0UyLkkOpSeOu66Agtk5qOVF53grDtFFVvQTzomuouQQDV/vC6XTK8fExSSJjflEU5eSkYFm2NDdlbF2n06bpOmJqECegFDTdJu12i1/+1//229FMwzC+4b1/Ham87oTx7cXq/18xqyvNtf+36sQqaVWe55B/My290mrCNZ5b/mbwsHwbhest4nU5laqUZo7Fm0Es1R5wtVqVOsqSm1O+UbZts7O9TbvZIIp8Ts/OmM6muK7L1tZObQPuNFzGpa9Zo9Hi2cMvieOUu3ffxbFdpmORJP3zf/7P+fzzzxkMBjW4UhFtNU1jMhF/t3a7zb1798jznP39XYpMnD2HwyFpmpYqghGu63J0dIRt2zVXqEqU98u4PF0XrmCoG3iesMQVTSXNM7IkwXVdFBRUVcGyHVA0Vp6MUoqqEiYxSgFRknF5NcHzfVAUkqxguVwxnc7FItwQmZteCqbJcjRVwXJtmo5Bp2mXYcPaN9432TkJf8kod0BRGJe/S4qiqsRRzDSZ4jguD7/8I5eXU+7dvU9R5HzyySf86Ec/4P0PHvCf/tP/xWx+xb/4lz/n1auX9PtDihysRotup1+7RSwWCzY3N7lx4wanp6c8f/4cVVXZ3RUk0/O8+mDwPI+/+qu/4sGD99jY2GZ7ewd/HXJycspisaDT6ZRmBxZg1VPIdVlPmr42VTBNAUqqQztNU5bLZS0pyrKMra0tzs7OWPt+nbEJ4kADcs3mec7GxpBev89wOGR56Mm13HRpt/q0Wi0mYymoRolSKqXLi3aNwC73l0xmiqZiWBZuuZNTVZU4zRhPFoRRUtM3rv+OjuPg+z7Pnj17A9xYlfu4ra0tklzn8dNnTCYTcShxHObzOYGhMRj06lBjcTl+HU357QBAueSvitfb/+1P0Ssq3/3vLFj/VGemayjXqBw1peOtr89LMu/15/f6uSlvFLProvO3f5830dTSelsRpnL1+WqmT5MUpZB9F3nOfD4nz/PXF/Law1stMA2NdqdNf9BHVYVXlRcFg06Ho+NXdLpdHMfh0aNH6LrOYiEM6M2NLc7Oz9nc3Obzzz/n9PSUZ8+eiV41jlmv1+zu7tLpdGopy+3btzk/P+fx48d0Oi32doa0W22yPGU+n9eHT5ZltUtBlaY9mUwkr7G8+IsCTFP8ysbjsYRdFNJ1+X6ApunohkEYRqD4aJqBZdsSzlHuhrIsh5WEZqzXHrpukCQZ63VIFMcYho6iSNpQaVeASk6RQbPRoNduMBi0UYEgCMXZoShwXQfXbcoyfbEqgQAJ43UcR9CwTpvFckWaZaxXHklS0Ol0mM9XfPbZ7/npT/8LFos5r14d0W7f59133+X49JCHDx/ys5//nMeP/8j5+VVtRul5Xn0PXF5eChl6d5d2u11zyxRF4fDwEM/zymIT1fyz3/3uc1qtNu/ee48HDx7Q7faASvcKuq6UNvIauqESBAHz2ZzIjzg/v2A2m5WLerteu1y33RL/tJjBYFAXvHa7LRzMNCGOBEiL/HVJwm2hluaObwcEVYUzKK2mPM9j2Ouyu7eHY9scHh4yn80ZDgdsbm4RhkHNAy1yiWmM45jxdIKqmgD1wVlRrHxfcgyqz7muW/PzKmuiOI5ZlK97o9HAsk3m8ylnZ2e0my79fpfRhki5JpMJURzW9/I3iln1g6/nWFaPGnH4EwVJ+aZ70BuP/E98LZTTqqK80QEKQ5haNP5tz0GeKyUt5E2+mxS1b/q0XS9kVTHLsgyl9J+vmNeqqqIWan1TBUFAsJYLpF8iLOJ0u8BQFTY2hgw3RvVplSQJ3lpY73rZXjuOIxIsTUXTddrtNpeXl/yzP/9nNU2gKAqiSODsTkd4ZRXZdjwek+c5rutyfHxMo9FgNBoJqmToTC7HpGnK5uYm6/Uaz/PY3d1lNpuxXq/rHcx6va4zMUVZIfItzTDQTBNDVclzmeot20HXDRYLj5UX0G53UBSNdSDfJwikoDluQrvVRjOckqu2ZLn0cBsu3U633CPIXjPPc6jci8kxdXEVNUwRsnueV3YhRdkZiNC9KmZ5XtDtCAcMRWE6m5EkCcvlCgWNfn+IaVicn1/xh6/+wK1bt/j8889otV329/fI8pjJeILjWLz33vtMxr9BU+WGv7i4QNd1dnd3WSwWzGYzWcCXHeLFxQXD4RCQwjIaDVksppxfzPnzP/9zhsNNnj17znw+ZzyeSCp7yYcMQzEONQyt7H56tS15vyvGnp7n1YXFNE3yXEijlZddFQ5S0Zgqhr5lWZxdnDOZTCSwxbFxHAdVVVkuFrIf1YVsniQpszJtPU0F3W6YDo7rYDeaaMZrswi1PMxsx2W5WuGtVkJgdRwa7TZmGGIvlnQ6gzIs26XZbLJYLOppz/cl43NjYwNFUQStjaJ69fH48WNmS587996l2+1ydHTIbDbDsky63Q5pGbsnr19Uk9LhW4pZ5RB73RH2uj/Z9SLwbY9/ivH/Tz2uk2uvi9Mrt4z6ZxcFSrUXq62GcqFKXBszK8Dgux5vWxAVCHKrlfIsy7LkJs/lxhPhrcKg12M4HBJHYrsThmHZjQ3odLokScLp6Rm2beM4ckItl8uy65lwcnJCs9livRK/KE3TuHHjAFVVePr0KbPZvMw7LEqE7YybN2/hui7n5+d4nsft27c5PT3l6OiQra1t5vM5s8kZ29vD+gKvWn2gJCBL9xWGAUUh6HCv1xUpGkLcXS0WqJqK7wc4tkOcpBR5UWZnZiRpih8EWJZDkiVMJzMmkwlJmmHaLlGqEETCMQsDnygUUrJafagQlNF8pqGjYWAaOpauk+cZi+WSfr+PXVo2K4pS5ohCt9urxftJktTd5mw2kzCZqytQVC4ur7hx4zbNZpM8h26/w9eP/sAPP/oRu3s7oorodxiNRlxdnXN6es5oNOL+/fc4Ojyp5UPVGuG6j5dhGDSbTcbjcWmlI9dG5QbhOC55nqEoYuaocEWSpGxsbNa7LZGHRagqmJaJriu13Gk02qjdXB8/fsx0OhWDT0v88KqfX42gQGk35AuSWFqlh4GP4zhYlllboCdJwnK1otPvYZom87XHaukz95Z1qEyWZXVq+vHxMZZuoOl6CUQUXFxccnl5QegHWLZRd3imZdFut7Bsm9VKVB6VVKrX69UC9el0KqTbsnj75XhcdZ3V+H58fMzV1SWmadJqNRiWhOfj42N5nU1DTCfLxzeKWZqm5Q3o1PqwIAjqN7W6Mb6zGPH/fcwEcd64HohSf74oY9vfCkuppCLyQuQEcURRvM4pqBb913+s8N2+netmWRYoeX2BVg4e5EKIzbNCwIBGQy6G2aI8OSyGw4Gk4TjC6TEN8XP3Vj5BFGGaNkWhcO/eu1xe9siyjAcb7xOEIbblsr29w+9/95kYGJo6ilIwGFTuGUMsy2AyuWI+nzIYDLh7907ZHQxwXbsEGyICP8CyHVarFZeXY9l1KbJnkwKty35JUTDNFEXRKAqBXnw/IIoTWq02eQ5RnJYGiTn+OqAAkiRD101BP0NZxguXzEDXTXw/ZLlcY1oGWZpi6hrtdptmw8V1HLIkIULQbdu0UC2RO5mGThgEEMrNpKlafQPHJbAgO6G1hLr0+wwGshucTqd1hxHFSSkpW9JoNvH9EIqCu++8Qxj5PHhwn9/+9hOxBFovcFyXra0Nzs4u2d+/gbfymc/lkFmv18zncw4ODsp9Vspisag72dVqRbfbpd1ulyafTbz1gqurMbbdEMfXTOzgpSDL96j4cr7vMZ1MyHIRozu2wxmn2LZDURT1qGZZ4lpcdTeVa4xhGLXAXVHEJUZVxVqn3W6LDVcZBVd9Pqh2jHFSri/C0koqkzDqLCVOUlgHsnIpf9c8l1GxKO8LVddI0pzx1RTfDzBNg8CPaLX7dZGt5E/NZpNmyZG7urri8vKyliHqul6P6d1ul95oyPOXR5ydnQEFrVabZtOtQ10eP/oa13XZHA5qAi58hwJAGNpWXSmrE+f6Huu7HkpRUFzfcXFt7qyaqm/9Svlsdg2lvC6Vql5AXXvTqrtqt6tiJh2ZTKMVSbeil7wxtn5Ld1kAlmmCkpNn1D5dgp5Kh7i3s0+j0SDPMi4vL/E8j1arJadenLL37g2Wyzknp4cYJfWh6h5UVWNjMOCrr75ie2ubzY1NprMZuqHzzjvv8A//8I9Mx1POzy/Y3hbXhbOzczqdDrZtMxqNWK1WbG9v0Wq1OTw85OjoiG63S5qmolvceJcg8BD1gyo+WXFSXvhmnd7daDRIkpRu1yw7W62UO5k0mqooI0xTEE1FYueSNCNJE9IsxykDX/JcoUDBsm0ct0GBTrqWrzFNiyAVNUaz0WRnZ4tWo8FsOkFRBNRxbBtFKfNUE4UoCbBcs/RJy2pTger3rygrlRFho9Eg8EMWiwVhGNJoNEGRjsS0DJ4/f4qhW5ydnRAEYk64vb3F97//Z/zDP/w9Nw72eefOXV6+fC6csCRna2ub42NZ2FcIZhUkUrHqayeU2awep+bzOf1+h1u3bvHy5QuSJGF7e5vFfMVyueTy8rI+HPf2dsuCKFZIeVnMet0eURBzdHRUX+Pz+ZyTk5M6CX1Y2v9UE1O1L2s2m1ilbX2lXy6KArvMWUjTtO6W4jL0N8/FPbpa/Hc6HbzljNXKo+m6DIdDOt22dJRxDEhj0W53UVSwTIugvJ66vTZxlOKWUsOqGao60SAIxHctEYv3YZm1EUXyvgBM53Omy2fops329lZpQU+99Jf3uFEDA9d34N8oZhKXFdQjSpX2UhRF2bLndZd2fSSsdlSapqGpWl08sqwyOxRyaxmoWReV13+nLGavgYdq9KlG3epPXdeFTV4mWldi9ySJywvg9ffPq29evO4YhbLx2gW3ch5QFWTh6ohxXbWzUhSFTlfkFuRvZhm0Wm10XQwp1+s1X375Jd1Oh0F/VIpwE+bzJWEUCwFwHdDryk4hywqWK4+f/vSnfPzx38s44Ta4f//dekG6vb3FYrFgd3eHOI756qs/sL29zY0b+3z22WcURc7Nmwfs7+/z+MnXvHz5Etsy2NvbYzjYQEFjtVpR5JJylCY5Chq25UIhF3uv1ysXuQWbWztcXl6w9gN6loOmGqxWHvPFksFwiGlaJKm8r4ulRxhJOEwcpeh6JlbilkEcxoS+j64IV8m27TqAVgEsQwT8pilAgK0bKGqBjYHbcqlsgqqOREY+IScfHBzUS/iz01NUVQre/v4+jutwfnEpNjJl99JuN3FdB8psx4uLcx48eICm6bhOgywr8P2Q4XDIeDxhvfZ5//33uby8ZHt7G9d1efz4sUh7+n0cx6kF5r1er+Tpxdy+fRtFyQmCNXkuZo2r1QpvFXB6esp4PJWC1euR5wWTyQTLMtjc2GRza8iLFy/Y3d3l4uyS09PT0h59wPPnz2sAxzCMMhFKNJEiGBdFzO07d0g8jydPnmDZFtvb20wmEy4uLhgMBjiOxfbOjiSypwl5ruBF4nzsOA6aasq9X4AfRnK4rZYkmXR1hmWhlPdvu9Phyy+/5Ec/+iFZlrKzJ8DU559/ztVEJoeqIRqNRnUx6/V6TCaTevc4mUwYl9IowzCwSvPKNH1tjW+aJrqh1oVLRPo+53HCZDL77mJWoV2VK8V1jeb1xKW3KRFVcav/LHdVefX1iiL+V6VvU9UJVT1R9XfbtuudXTXSZllWx7Bf9y8qiqKeucXn7Nutua8/3qZavKEfVRQaboMkjZhOp3VH0Gq16kg1b7kmCAKUosBxHFyngaZpBEFAURRsjUaEYchgOCSKo3qBv7OzI4EYpTvAnTt3UBSFn//85xwdHTGbzTg9PSWNE+7ffxdQGI/H5Q2pcHp6yvHxq9oCWy8XuHt7u2RZxvHxKwI/qDl31YVQHQ5RFNXi50of12w2ARErLxYLDg4O6i7NsiS2bLaY4wdh6aowQ9XEPlzVdIpCwVuvy/BeIb/meYauquiOiWmYMjIbBrqmEARrgjwjTaQLURXI0hSn0cC2Lda+h2ZY7O/vkxcF3sq7ZiWj4jgWpmmh68Kuv7y8rC1zRqMRW1ubxImMgWmW0+noMp7YJt1utwafptMpjx8/ZmtrS865XGE02uTw8JCG63J2dk5RwC9/+Uv+8R//Ec/z6HQ6dLtdfF+6vuo19n2/5lClaUKSxjiO7CkXixmglNSXuO4kwjBkOp1w+/Yt4jgkjiMuLy9rZNTUzXofWPELJ5MJ/X6fGzduMB6PGY/H2LbN3t4e+/v7TKdTnj57xvmFKEoux1d1gRiNRiVvzWC1XLL2QwzbRi+pHtU4G4Upy+WCG/u7ZEmMpigkiagQhA5VoGoKrUaDJJW09Ol0xp3bd7i8uuDy8hLHaXD48jHmTbtugjY2NgDodDp8+OGHfPrpp+U+7KoGn6rGKQh8JBFCrSkquqHWoEtV3Kp7v0KGv7WYVW11ZYBYmS5WBa2CdSuU721BuqprdYEocmmHFE2ti0XdNl17FNcWbVWxrGBapUQYr4+41zvC6xysujz+qaUdbxaxtz/ER0texMrqKI5jxuGYLM1Ik4yG26DTbpcFzqjRwUajQRhJInWcJjSbTTFMLJ+3X7K2t7e3AYkty/OcFy9e4HkezWaTXreDbkghsewGjis7vDzP6XTb9Pt93rl7W1A+U8O0DOIkJAgzFssFDdtE1w0uL6/qDhsgjhMaDZX12ifLcnRdkYwBRSkJoXaJuEmWqG4YLFdLrq7GpElGUagsV574/GsapmWTphlLz2Pti3ZTT0UMbJsahm6VnDgVU9ewTZ00EkscuaZeE53dZgNd1/AjH1VTqTIjBS3ulwTUVU2XKcqDpNFoiLYT2UnN5yJa39ra4sXLQ168eMHLly/pdLp0Ol3u3LlFnhWcnJwwnU64f/8+liVeYs1Gm8AXoGI6W3A5nrK3t0uv1+PJkyc4jsOzZ8/qgjYcDnn58iUAt25JtNyjR4/Z3Byyu/tOaXYAlmUzt5YlzUMaAjEc8Es0UezOz87OaLVaLOYLtja2xKa6PGiqDrO6HqvDyTRNXr58ycnJSYkmB4xGIxRFYWdnh7PTE/z1mg/ff4BlWRwdvazBKacpXWZCwfGrM+aLBapi0O106vsvyXPSPMcoCeJCMteJkpTZYsnu7j7j8SXT2YxXr06wLQvXbRAEQa3LBpmEquQqx3HqOtNsNuv7t56wNFXUDeXKyLZtQf3LVU0YhowGYlmUlgf2dxazStx93bHi+u6qWiJWkWkVpFsVPVV/0+Wi6tquF5Lqe73tvAGv052qjkLGxwQlfzNFvSqo9QiriotnWI6af6qQwWth+9vFLAh8dFOrT9HKU51MnnPTbcrvWnY2zUYLhde/41mJbF5eXfHeg/cIw5C9vX0CX0AU2xXi4O7uLt1ul1//7a/rN3Jvb4/VcsGjR48YDof1WG/bNufn50ynUz766CPyPOfx48d0u10ajQZff/01vu/T6bTLPZZQGuIyks6yrLqDqDru9XpdL2GHwyHL5bLkqnVotVooccx8Nmc2W4jhom4QRTHL5QrdMGihEMcJa29NnCTYtoNpmDiWiakrWIaObVslEVfoFFEYoZaGlyBFtNVqoqjiXqrpmjjZpvJ6rL01RVHUO8kgCBmPr0rAQq6BVquFoZulU/AphmkQBGGdhSm7LimicRwSRQmu64jQPsvodNosl0tWK4+NjU2uri5ptxpkWcpnn33G+++/j+M44pHf77NcLlEUhcFgUJsxuq5bG4ROpzM+/fS3QIHnhRwc9DF0i9XKI8+L2iDh9PSMR48e0e93yQtxVW00GrUR5uamIJ8vXryoC1mWZSwWizLH8/UqyLbtGrhzXZf5fE5WCPcrjKJ6TyWNiGRUrn0fTU1xy+vx+PwMQ7cYjYZkSUr+xmJbpIzS9Uss3Gq14t6dd7i8vOThw4eYpslwMCj3hhJR6LpujWK6rstsNuPFixcEpSHk5uYmnucxn8/rIBellNslWVqrVVzXKW3JBVCbz+dVsXizdr19s1coxHVaxHVqhuM4tFqt+g2sPhRFkMCk9oR/3bG9XcCqz30boKBqrxm91f8rRZE3xlqFN9PTZbz9p8RU/9SjELM4Ja/Tn6pR0zZkdGg4zXqpGccxnR2JDYvjWKD6xZJer4dhyTg0Gm3IBbZY4DgOBQrD4QjLsiVrMs04Pj7ho48+4uLiAsuyuH//Pq1Wq/apv3fvHmma1tkFk8mE+XxOt9vl7OysTq1er9ekUUSn3aoT37e3t1EUhaurK0zTJI7jNyQkjuNwdnZW2z7bjks6XxLHMcvlUjSPKKiqRpbmRFEs6ntFQ9EKUFQURcOybVrtFm3HRFdSdE3BdmRVYJomlm3jOpLoVECdiG7awvSXZO2CLI9ptYXi8vLFS8bjMUVRlJIdmEymgp6W11yz0ayX2yCdz2RwiUaIAAAgAElEQVQ6BQoGpW+WdO4iHVoslhiGSZKkXF1dlQvomMVixXC4IbZJRUKz2QHg6OiIjY0N3nvvPU5OJA9TUOLLOuz6iy++KAGbAZdX52QrmV6CQA4NVdHLrkqpd2a2bcnrbZs0W81akpTECa7l1rQQ0zTr9626J2ezGZUapRq3KpVC9b5++vvf0esKN/HVq1fcvXuX9957r+bKXYyvGI/nGI5Flgm1I4mFaD3o9dF0MW2Qe6xqMPLXmQ2FwmQ+Q7dMzEwCqaMoQFFV3n33XU5PT+uDqEpBHwwGTKfTev9XWeM7jiOJ6es1l+MrNE1FQ2yiGg23bpwqJUHFXrCuraLgO3Zm122p4fW4VX0uLxm/1wm2FTk0LUMqro+GaqnvpPgmA/86wgilNvMttLQizVa7tAqAuP58hFeVCcDwJ8bM+ufx+ue+/hCybBQLc7qCgm3bJksy6R5UXdw8yrZ3tRL+U3WKpHGCZVls9LqsViscx+HJkyeomsZwOGS0JVD01dXVG6dSUYgZ42qx4IPvvV+TA2/evAnAy5cvy1SgpdinNKSdPz4+ptVq0e12ef78KaGi0mw49V6h4kFVXUzFKq86sjAMOTk5YTwe02q1yPOcxUKsiyrTRdHLiSoiv/Z+VSCNpolDg9ABbAwlRSWn0XQxTatM4jZQVa2MkIN2u0NeQBhFuI0G7U6HrChYziM8z8NxHDY2Ntjc3JRl7/kFeS4HZLUAdxxHbIjMcjWSpjTbLfb29piVFuXV5OA4Lru7u7hug9PTM6JICs2rV8d0Oj0cxyHLCgaDES8Pn5KmCXt7e3VS+f379+vCcXFxgaqq9Pv9ev9YBZqMhkMsW1QdcTwTXa2ilyCTXiOft2/f4uTkWG7Q8tA5Pj5mvV7XwvIsyxiNRrUsrcqQLIqC/f19ZrMZrVaLs7MzxuMxBwcHdHo9PM+j3W7X13Wn08F1ZV+q6zrz+VyKpGEwmc5IYpnCdMckzmRHVpCVBg4G4hQj/w4CkimqUkccvnP3HVzH5Xe/e8ZwOBS0v6wRYRiyWq3qMOn1es1oNKoLVLVqsG275poZpoGey/tbUWDCMBSDUNelyPJ6N3y9Tn2jmDmOU3dV11HF6t+jMlqs2ldVJ2KFMBiWSZbnZGVXppWFTKkIqdeoFeVdUf+pAHmWf6PAVYWmombkJSVd01/vEF6rFd7M3HxDoH7tH8U3Clkh9IOSqFoBENUeLfJFKJ7EQn6sjBcvLi5E5qPrbG9vM7dslosFqq4xn89rs8mDgwNM06TT6fD06VNarRYPHz7k/OKc0cYGv/rVr8rAXVENnJ6dYVsW8/mc07Oz8o1tsVouOTo6qgm9o9EIz/OYTCZiCR74NWdIqBdrOp12TTytrJQloTytgY7RaFRbF/t+SJQkKKp0XEaek6QZRQG2Jp9TNXEpUVQRnzcaLo2GS7NpYyoxGgXdXhvTkqTrJJNoMVUzATmU4iQRo8woQdF0cSnQ5HnOZjOGgyE7OzuMx2NOT8/qQ6PT6UjknWXSbDRJU7m4V56H5YhjR14U9cUuYEiCqorDrqDkcv3OZjP29m8QRTEvXx4yHHbLHaJBmmbs7u6yXC755JNP+Iu/+It6NK8S38fjMaPRqERXl9w4uM3p6avydfRJkhTXaWKaBooiB97p6SlZJkBFqyW6xDSTg7O6Fit1hqqKB11lB1R9br1e19fo5uYmiqJweXnJq5MT9vf32d/f5/zslPV6zc7tWyRJwtXVBaPRBuPppaQq9fuEWcLVekZa5DRbHZyGy3Iyk1Sl0uZdrcxWDYNCgcVqKSNx+fP9MCQoUf8sy+pdYtUxV7rMqitdLBZ1Iev2eqRJUrr0itLBcGz0kvPmOE4tl7J0U6ZC25HDq2yovrOY2WWcVMUgr614yhdY03SS5HXIb/ULy6mZvfG9FF4TWxVFeb2fv/b3qk9Sys7tOvO/+lPTNMjlv2V5Xo4jGaqio+gamqKgll7r8hwK5M7IUFApyBAXjBwKQVVRFCmQKKXThyqSp7xA1010QydNc2bzpVAJLJv+aIM8Scv8vqQ0qVRptQTVtCyDvCi4HF8xOz0pn0/Bvbt3efDB+2i6zsnpCYqi8Mev/8hsNuXg4IB//OQTVEUhtmwCQ2PprclyaHf+X8rebEeSLM3v+9lu5ua7xx6ZlVlZXT3NqVkAQZwBySH0ABKEecDhQ/BGNwIl8EIAwSHV4uzd1V1VucXqu9u+8uI754RnVFYNZUBWVkb4ambnO9/yX2Z8vLnj5uaWf/Ov/w0//PADq+UK24ZoErFer80QxrIs8QM4JBz2a+ljui7T2RwviMiyhPc3HxlEEbbrsFwLgLZqaqbTCW3bsd5tqNuOvpNz7yvKkG1bNI1sXH7gK8s0T9QqnBjLhjAMRJ008vFsOafxcIgfBjR9R50XtH0rmRoWTVth2eB4NkWVUdUFXd8JWyKUHtP9/QN5UeG5HpPJlM1mS9O0XF5c4fkBRZ5T19LTdD1f0c88LHqiQJyzbMchV8OBJBFX8mgwwHNrpfgh9oiWbXN3f8cgDqT/57tidAwq6Iz4m//23zg/P6VtxWUqCFyKMuHDxxTLfknbNvSdmIKEQcR4rD0lZaiSpgdR9zhdUFWFKaOSNOH27pbZbAI9PD4uDUawbVseHh8J1DoUrwjpi8VKgSIIAk5OT3m4v8fqe/aHHYfdlsD3sXtxQKqHQ1zXp0cMRvaHlKqqCfwB46nwjKuyZDgYEISemPpgiQ8sStShkwqpaSUuvLx+Q1HkfPf77xnEES9efMF2uyVL98xnAgq/u783QPc4jrl6cc3dwz3bnQiTXpyf40ch9W5Lkmd0Fgy8EV7vEfkBvuNStRU0HbgQqH5dWRTkWSb97J8KZlXV0DQdrvvEcteNR2meejiOh227JmsSmgvYtktVNgqsqgx6W0UKVz2vqqg/MUTp2yOUPQJa1WltXbcqoNl0dDTKNclRllRt19NWjSKIqxLIUrJFvfR5rN4CXEkAu05pp3V0KrxZtvMkNtj1dHUPnfYacHFdX01hbYWMH/Hh9pbZZMZiNmcym1MXNcN4xHw+ZZceuHrzhRjw/t3fcbpY8Ks/+UOavuM//af/B9/zWa1WrJcrmXYul0zHY/b7HfEgAsclzUsG0YD/+uu/YbcTUOaHmxt2+z07hThvmp48E+zOfr/nZD5j+fhIWda0Xc94PCTLMm4fHnl/c0sYSlBarj+SJok4jCtg7Wq7pOskW/Udm7ZvaauazraIY8F8OZaI6oWK59c0Fa7TiZ2YbRMPI6qyJMtzLi/OZHDStnRNS9P3xOMRWZ6z2iyJB0OiaKB6nw1R4DAYxKxWa7BsbMejaTvKujE4oq7rSNKMIIgYTSYMhyMDYF2ttyY7nk/nWFbHw+MD6SEhGkRMplNGccxwPGYwaPnw8ZbNbkdRVVR1zd/+w9/z1Vdf8cWbV2z3OyZxjEWHY7kMwoCT2ZyHh3viYUQU+CxmY/Lcxfd6zk8nMgFvUsqy4O0P31JVHXXZ0FbShmnKir7HEK21rNTp6SltXdNWNYMgxLM9vMDDCwSeVKrMI1SqEVdXV3hBQKc2iqIsWakNTdADco1cegLXIXAdVsoHtlQ4uM6yKWpxn9/vE9pe+lBxPCQvStIkJfA9yiolr0pcR7J41xMHqKoQgOt4OEFYIxbz+Ql1XbLdJ7h+wMXFpcFoBmp6uVqtuPvhB95+lE2+tyxa4GG9BqBuG2Ug3FEVQp6P4xirt/BdcRKrq5qd4hbreKHL588GM50ZaXCs7ufYtm0wYPo4hkjo5z6ffuob0bbtT/pon6D7+/6Txx4TxI9fV1NBDJVJB8S+p1GqGJ5taYtFkcg9rjNtW3YXy1FZo7oJVGlqYROEoZS76pn6xNdtS1nXrNYbhsMRcTxivdrQNz1fvn7N+dk5++Qg2lxtx3qz4Q+/+YYX19fc3N1RFAUfbm7wHZcw9NVIuqAoMzGIdW0pwR1H+lfjMY+rJdvNhldfvOS3335LXVU4ts1uu8X3XKpaYCR+4LLdbdlsNxRFRRCGuJ4E+8elKGOEkc9wOCDPM4pKsiDoKNUUzrZtptMpjivZbRCKI1UQ+gpN4+EHLlEUqmxQppVCtfFxPYf9vqWqatq+p6pbXF9Ay7YrgoF2WeG5vrqOgqPSbt3TaU3fC/d1vztQ163hE/a9paZ1teKVluR5yePDUkEdTonjIX3fkxcFVt8SK05gWVXc3t7S9TDYbpnPF8L5DAOiYczJySltJ9n05fUl/7Td8PXXX7PfbaVH2NXsdlvGkxFZlvL27fe8+eoNVRXTtjXn52ccEnGAenF9SV5U3N0tCYPY9IGenI18ZYbccXl5aVgFMizIVa9wQBBFpi+q+0aC4YrMOtAOV5ZlqYGCvFfX1Mapvu077u5uaBrpP2WlcFh7pSpTty1t29Mh10PWac8g9Akcm6aJDLNgNBqZPux2uzXT1CRJ2O12AAbloCfnemhhaJFFgds0pHmmlGYiM6mNbFGUPWx3REHIy+trFmo6+vDwQHo4yGaQJMb67nlv/WfdmfRF0MFE986ewymOoQ1GXVYdx89//nh4QvzrQ/9bP0//7ng62ve9yeyeB8S2/VTnTL/f8+P5T4Qy1eN4Sqa7640UkGVBqwKpLCahzwwCWdjr9ZrQC3BcyTCcxmUymvD6i9dcXl3wN3/zN9x8eM/pfEaeZbiOzenZHOgJfdl1XAuKqqZS6hD/7f/7f3EdlzDwefnyBR/fv2ezWTObTtinCX0vyp9d0xD4gcALkgOu59M0FY+PdwgNSQyNy1LMH6IoZDgcSUmkJLE9TzB6k8kYqwPXsfG9gMEgNPAX13MYDhXXsGvJstRcJz2xHA1FdYVeJpxt0yNVnKMc4kUiyPdtoihmNBpSFJVyJaoJgpA8LyiKCsd21VCpoWlaw/LQCxksRcRHUZSuAIumLlitH7m/f8DCZnFywi9/+QfMFws22y2DeMiJqhb+4Z/+Ueg9sbg1/dm//DP+xa9+RZal9H3HarWkroU29fr1K9Uf7phMxpRVbmwCy7KkArregt7GsV0DBNXEch0UbNvm9vb2qG0jPcDD4UDbtiwWJ/zuu++MvJRu5AMmsdAIA61TpgNJ3/e4tkXfNYi8tQOWY0RLNQQoiqTnFBaC77IcGWC4TottyWDL1T64YHqsGgKi4T56HUorQuSHhsMhnpra6hiSpim1GqjN53O8/U5EJzsx9WnblpubG+q6ZjGd8fj4yL1ys1qtVlRVZQxXRqOR+Vme5z/fM9MBTJ9AfRMdL/rjoPQ8mD3Pyo6DzecOvSD06xgu5LPX+LnnPf/58eewnwcy81meQLfyRPl5WVUylLDFA0AmpLK7OI7DaDjm7u5eSrtXc4IwYLPZUGYli9M5wWhAkif8xV/8BZZl8Xd//7d4nsdXb95QVgXxIKSqCk5PLsmyjMuLc3744XuwWixLJsO+79FZ4IcuX335JU1bMRgG7A89YeRhWRFVJQDctm2wrI4gdJi5IxwvoKwq2rKh7WqlhfVknGzbFp7vUlctZdUQBgFRNJUFWRREYcBkMiSKQoIgpO87mqZWSg0DhsOB4vxFBpogk6sGsIjjEX3fEYYeXSdDA8uyqapa2aq12LZI9IShaJRJcG05qD6ObTlG/6ooSmVj97SgBFIyIAyECL5cLo28jg7A52fn2LZDmmXc3NyQ5zleENJ3PR8+vOf+/s7g7hxXBEmXyyW/+oOvuf/4gel0zHJ5T1nlTGcTMyh5+fIF+/0WrJ7z83PevfuBtoXxaEaSpIShZFQabH1cWuqFrzOpNE3Z7/doVQ7btqmbmrOzMzUNrcxmoXvZbdsaJQqd0ekFHUURw8mYMkv58P4eyxKOtZ6Oaoqc+Hz3NJ2sF2wBuGZphu95lGWOq9oAeuinBTzFeEU4km0rE3490dUZflMJNUwHPzHBll7l5eUl8UgyzbTIWa1WJEmiBBFCFtMZi8WCUuHjtJ6bBuE+KeT0TxhQdfzY0KR7InofB6KfC0jPs7SfC0QmgzOZ1KfSPseQi+PM7Djb0s87/qz6T6dwbvpx/bPP0ret9M14wrDJLyTQ1cooV2c1VVXTW5hRcJ4Xoo4ZSX2fJinj0QjflcljuXrgf/qX/zMXF1f85//8nyjzgi9eviDPM7quwYkCBnHAdDLEtjoct8fzLYIGGUpYDk3X8cfffM12u+HkZMpvf/Mb4njA+fkpi/lU+HnbLfQ9eZ5R1SWT0Ywei6JqcDKb+WwiEj5RRFWLzdh8PjXO2s74Sb7YDzzSVEC2fuASxwJU9DxfTaxtQ3JOksTAQ8bjsYGQCAcXbNuhrnvFf7WwLAfP9bDtjsDvqYOWsqi4u31gu9kTDSICP6LtWvJsR9f2+KF71E5QN6rCpbVtx93dHbPZnHggsjir1YqHhwdOT0/xfCmvv3j9mjiO+fbbb7m5ueX27p6z8wvyouD9h/d89/33XL94ied55FlGPBgwm06YTybYXUNy2DGbTxnWA8PN1LJA6/WKs7NT1PiVIAiZTRe4bkDfWYxGnQmUMgDwFORFpnh6kqzLzLqu+fLLL/E8j1//+tdMZnMDpdHl3W63M25fWkjRtm1Tikqg9IkHA3olBtA0lfrMAa7bP4HAi9J8Lh0Um6qkrRs838N3beJBbIC6GqmgierHlZbOEvXUOMsybDojISbO7cJftRSwPssyrq6uSPKM7777js1mw3w+ZTKZkOc5L66ucRQMazKZ4DiO0oRbGmkiPe39WTrTsSiiDiA6CzoONMfB6XPg18+Wds8yuOOS9rhM1YHpc8+Hp5JTZ1XPy179s77vfyQG2bXiyuRYT+wEyxJjFN0j07i4rutou17co9TPHdtT4+EBgyAiU/xB27awLZeTxZQgCPjrv/5rNpsN08mU29sbwshncTJnv9swnY7p25po4HE4bLm6uiA57EjSjM7yWO/E3Xw4DCnLlFevX5ClKWfnZ/RtK9mH0+PYNus1JElLELrUTcsinhAEHo7r4bjC62vblvF4RBwPSNMDo9FIqTxslGKD/JnNpgS+i+vYBKEr4MXOwg9cgiAkCHyyLP/kmgV+iO04DAY+nuuTJClJkZmsoGtRDtkuYq/ZQW8zHI5pmp7tRuzmLAt83zcEZMlQBcMmFKxWqbf0ijgPtmICjEYj9X5S1iVpYqALZ2dnDIcjVusNXS/0oOvra0rV/Bf1DcnMxuORTFOjC/7LX78jCEQ7H+Dk5ISzszNFXevw/ZDd9kDgD2gbKePHoxm3t7dm4WtsVRzHZhKpndzX6zUvXrzgm2++MUFOB5j3798bSWmN7NcYrMlkYhazzrQGAxFFyLKcIi8oC2GbNG1LWVYsFh0TJVMkRHope9veUhuFZlSA59gyhfZ902LQYhOistF9gvXU5PfjhGc8GZt+mua0dl3H7f29aPXtRKvPDXzjOarpTYMgVEbEar0peMf+cKDIc9O37yXl/2Tdf3YAcBwo9BM1OFZ/8OcB63MB5fgxnytFnwcek039TFb3vCw9fo3nZednS109XOB5D8/G6ntc5brd1LUKboqOo0QaXUeaoPRit1WkGYckYRyPuLy85Pz6nL//+3/E6jt+9ctf8k//9E9st2v+9b/6c3o6XKsjigJ2u4x4GBL3PtPJhHuroqPB8Qa0QJ5mvHz5gjQ9cH5+wfLxkSgKKbIMz7E5WUjPrcgz4kGI74uqxHgyw098iqIiDH1OT09wlQ1f1zUMRwOiQSD8U6s3ZrwgwoeDyKNrxfsSBL4iN6wYsMZD8UPUChau60s25QmVRkT5RO7IshwF6egU8LEkSVJs2+Hk5Mw0/MuyJstSPE+I5JWyD3Mc6duUZaVIyDmz2Zwvv/yStpFFpcUKbduhKHIOyR7bdWh72O4P2LZDVlSiydY0lFXJ5dUV52dn3NzcEA8igiBiv99zd/MRi4avvnzFYBAaJRRBqYcmw5JJ6p79XiPwG1UKZ8rrYGUWuwgLjozIZxzHvHjxgiiKOD8/Z7EQiSgtD3SsjqGzGE1uPz095fr62mRK+/0ewJSCepiw2e/YJanJvHrbEteytsdzPZXhtrRFQVXU1JZFXeZYfWuyT51t6cxNl7K6hE7T1KjHHicnIPAuzSDRKrLL5ZIPqi82GMZ8/PiRwWjIdDo1JWoURfzql3/Adr2hLEs2mw2b3U4CdZ4r4YMd4/FY2RdC/3PBTJ8crZihpxE6mB2LoT2fNgI/KuuegsWPe2rHfz4XCJ+/BvBJkH0+BGjbFseyPt/cP+rDWdYT4d2UqDwpzbZtS9M2YIsGWBAE+KrJuVlvGI8nxOGTLv9sOmU+F5ec//Jf/iu/+IOveXF1xW+//Ufm8zlff/2G3W7P1fU5oe+w2TzKxND3OT87ZX/YUlXiBo3dEIQew/iEOB6wWEz58OEDk/GENE0YRGJ1P5mMyIucJNlzsjgBBTYpypqmjQjDyFCamqah61uCwOfVq1cARpFjsVh8QofxXMizvRmmaOQ/SAM6jgOi4YCqahQeKyBRva6y3Io/gHK10vdMWVYms9JClm3bqobwgtlszt3dnZKm3tE0Rz6L3ZPptEaVB0FAT29KNEA+u+eLaGYoFJe7uwems5mZ+GVZRt3UFHkh1n6Whe3YPC7v+fL1lwD89p9+w/rhjslkbFogssnCzc2N+Rzff/+9Mu8tCfyQOI55uF8yHo9YLE4oy8IoqWjtNU1LevfuHa9fv8bzPMNV1IyOtm1NJjOZTAx1rWkaxuOx6Z8d66slSSKeqr7PfD7H94Wu5So5dltNyNumI4xCFosFWfaU7eogKq0Uuda6d30slKqny9pJ6Tj4anC9VjbW6AVNDtctjel0Sl4WRkF4tVopjuyEs7MzZicL422gM8LRaMTJ6Qnr1Zr9fm+03J5PND/bM7Nt25DNdbajd4rn0At9oxnepP2pg7kpzxQd6niw8Jz2ZFkioqh//7kAeJwZfnZC+lwGyLI+GQLohWkfDQDkfaXBWJWF4Ov8mKZtaZqWLM9JVekwmy7kxHkuNx8/slqt+Fd//uf86pf/gr/6d/+Ok/MTyqxgvVoxm0ypqoqyKAl9nzovKIqU8XCETU9dVfzw/fckyZ7xeMRoNGG5TohDsTrzHBcLi9FwyHw6YTQY4CpKh5ZzCb1AOHSuKz6MfcduuxFA4tkJruuSZQ2O4+HYDqEnU6ZxPMa1XCI/Yjqa4loutL3yChQNMOHOaYxUgUBaZMJYKwhKXTVKQmhP1/VcX7/g6uoFh724/+hs+vHxkXfv3klJ4Ufc3z1iWZYZ+4fBgG7UczjssW2bxeIEx3FYLlfAUzDyPFckrHv48OHGcA13uy1xPCIexri+R1lVDMcTsKCoStNsf7i/J/DviKKIy8srgsBnNpkyiELieEBTC7n7q6++4uzsnNFoomARA6GlqSwljoe4jo/rCNQnSTIOh5Q4HuA4tsrghlxeXppJ3eXlJW/fvjX4Ta2NnyQJv/jFL0jTVEjtD088Wr2oh8Mhs9mM5XLJfi/naLvdmtIzTVPFLGjpLRiNxwILqRvyvCAII8q6ous7PNejyHKiMMB1BJz75svXnJ6esnxc8eHDjSln9aZ3dnbGZrPhH//xN5ydnXwiBKEHG5onqWlHeloL4pEQKnaLF0i/r2r1kKcwqIDb21u+++47bm9vRRhyJoORKIpYrla0fSfPb1u8ZzHgs2Xm87+fB7Cf6lHBE1FdBzj95TR/Tf9el4XPM7Tn08fj9/upUvRzA4ifOhrFMdPW9YadoN5rOpvRqilJ3QpifRBFeGpMbSFNzLc/vGUQhPzlX/4lbV3zV3/1V7x48QLLtSnKgjR1se2eru3Ii5y0qaiqnGEcCUZqKdObq8tzLi8uSbOE3/z2W5reIRyMKAtxgxqPR0xGM87Pr6Dv2G7WNI2Y1U4mM+lZWZZIZucFtu1wfnbGerPhd7/7ndGur+ua8/Nz5RCdGz5jVdY0dUuayE5tOz2eZ9O2vSJGi258GGhC9l7JvMiuOZvO0UCXwWBAmmZsN3uTPeiJV9uKT6emsWjt9jzPjRBhGAZcX19j21DXlbLpEzu5LBNPRw2STFPRyhqPx5yenjKdziirmoflirqpcZWaxcPDiq5t2GzXJlNxHYFOaN9K13UYj8WEVqoPn/V6wxdfvGK73bHb7SlLMabVKhOHQ8Juu8dxHPb7gzkHlmqy73Y7FouF6TsNh0POzs54eHig6zrevXtnsGPag1PTq3Qf8P5epubn5+ecnJyY3pKW/9GaZ7pUC8MQz/dZrpaEoSi9bhT39+T0FKvrWK3XbNYbXry4VpJJgVJ8DZlOp7x9+5ZXr16S56VRpe06UfWYTqdqgvvOrG9NVRJfTVlPh8PB/D6OY4UnzNgniXhlKGOSuqmN18Hp6Snn5+cMh0K6185YOl4cJ1bb7ZY4jjk5PVFViRw/G8w0ePZzTf/PPc+yLAOsO86ejmvr458fP04//1jm53Pl4efgGM8+iOF79r3MLI+L3rZpsBXr/jhb7BWlqigK+k5czD0VOLM8JwLB0LgB7969w7Ec/vf/9X8jCiP+j//wH+h78Hyfqq1ID3u6tiYMAxwg2e3JsgODOCI5+Pi+mHhYvU1VttiWT1v1tHWP5wcEXoRt2ez3O+pCJlKuLQT4KAikHO1EzcMficR00ia4jk/dNux3exzLZrY4AeQ7RMMB++0Bz/FJEunBjOIxWVaIS3fTU6QVw8mAMAgp8hrb8sWNKq1EGjuI2Gz25HlNlmZsNhuauicMI4qixPcDWsUhBJTsjChVpGlKEARGqcNAYtS1125LQeBTqUxKShvfOPfoiWpZliSJLJgnOeiczXbH/XJF2/XCPZzJ9FbDDsLAJwhCuraFvqOpnsrUvSubVOD5RnTwcEhwHZc0yUWp13Wht3EdH98TWaXhMAaEyucHLm0rxs+WJXLXWkpKQz/0/X11dcXr169pmoZ3796xXq5WWMEAACAASURBVK+Va9E5//d//I+GLtj3PTuluKKhGJvNRiAOccx+v2e1WpmBg8AVSoLIBtvFdjwcLyAaDBnF0nqg75jOpnhqsxHRS5/ssOd0vuD2/kH1RG3Tz9IuWePx2ExjAQP90BPVvu/ZrpfmuhxfW1tVN30v6jSD0dBUbnme8/btW96/f89wOKS3LequpUVKXTfwmcxneGEgrlmNT9N1gov/qWB2HFiO4Rn67+eZjD70z7VG/zHAFX4M+Th+H/1exxiSY6zb/59DmvM/LkOPP4dMOT/Fqdm2EmdUO4HrC3UHLFOmVFUFvc0f/dEf8ctf/JLf//737Lc7zs7O+PjhhuXygflihmVLgHQdB8fWpa0AUfMsw3NHjMcTETfcZTR1j9XbLGZnZGWL74krUVGUuF5A4PvI+rNx3YA8L2nqgr6zcRyboqjoe5vZdMo+2WNZO0ajMRcX5xQKGNn3Pff3j9i2y253YDSUsf9hL0YSURQzHMbM5jOquiBNV1hWhYVHWcpGNJ2OCPw9+/1e9Vo6DoeEJElN/8q2Rabb20jm4LhSvk+mY2bTGU3bUFe1gFFdF9sG3/cYT0ZEYYRtO+Zm1xlBGEqzXA+i1us1h32iZJSeTIyLqqbIc1w/UI/bKMBtiW07Zqo7Ho2x6Al8aVlkaULbtsymM8KTAR/ef2TbJ/zw/Qcl4CiAVN3Q1hLQ+/2O4WhAPIxYrVaiJJzIUODFixe8f/+ew+HAq1evDHxkNptxc3PDDz/8YOAVd3d3VFWlnJREJlwT2H3f5x/+4R+YTCa8efPGKHZcXV0RBAG///3veXx8pK5rPN/H833OVDbddQ2j0cioTkSB9Kx8z+X25pbxcMj9/T2jkSj9vn37jn/7b/8Xbu8fjVS5BiZrrujbt2+NyIJOVHSSoZMfDbbVQxutjKGJ9FqTbXyExxNlE8lSf/jhBw6HgxmEwJPKdaMUbruu4+HhwbAP4Gcys2MC83FgeR7Mjh9v2zaVEVl7yrT0c/SHOX4f/bf+wloC6OeGCMfB6UdZ4rNA9jy7M4C7uqFrn2p+y3ENSruqKtq+Uxr0oTl5XdexWJzyzTff8P3vvhdlC99ntVziez5JknB+cUqgsDq+K/AGz/OIwojxcEQ/iGX20EPfdYThgJPZKXVdkxc9bV/guwMC16dvLBqrx6Yn6wtsy8JGsrqu62jqnrqsKPKKvnWIoiHRIObkVECXj48rfF/I0pvNFtfxCIMB60ZKp7pu2W53ylnqhMXijI6OJCmxrYBkX7LdpJKRej5N0zMaTQGbMBwwmy1MpgA9222BZSkVYsfC7oQYpjFQfuDSZBV1U+IHvhJJrMmLjL5vGcShKndGxn4sz2Vz0TSZphHk+26/ZzSU66rvqUE0IAhTHN9nOBrJglDSPF3bMp7NCIKQ4TCmLCTbqDVI2oLtZo2zT2lbm8X8jPV6S5YVTKdzbm8/qlaJLK66FqK8ZWEywDgek2VPi1cHK33/V1WF7/ssFgs+fvxoBAw1dTDPc7a7HX4gYpw6s9Glqh7AHQ4HkiQxGd9mszHBoO07ilL6Zn0vUCRozDRWP18+ew+IdFKetTR1iec5vHnzJUVRcTgcqKrK9MR0kNLJh/5susLRa304CD+p7PS60yVpmmdGF+44AGp4hu+LzLkeSmh1Hj1YOD8/NwPKn3Vnet4re55ZHQeWzzXgP2dF97ycPH59/Tx9Uug/xZ3pQ5+8Yxzc5zK349f93O81n1OXOVroTb9urcwdsC3SPGezWROosfh0OuXliy/49//+33NxesF8Pud3v/ktruMwXoxZzGeCik8qbKAqK/xAxutFluKoYNm1GmgKXWfRtRZFXtO2Fo4tk6imalgtt1KaRdLkHwwiZlMb3x8gDAZB1PedKH7URcvZxQmHbG+uV1EU+J7PfLagaVoznbQsmzCIGY9E5TVLS1bLDav1mrZrlbTznvV6zZs3Q7xBKAHRdRmPJ+rciS7bfr83/Sc9dZvN5Fzo3kfXtUbiZTgUHqX+f2kYR2bDiCIxVdbXJ1UlbVmWRhZGZI6eWiBN02ArDmdvuwyUC7zGpBVFyWAwIAwDzs8vaJqK929/IM8yLs7PmUzGLJePfPx4BwRcXl5TljUf3n9UOnIpr159QRB4tG1FT6fknC3iOGIwCLm4OEP4nIJne/HiBY7j8Pj4aJD72kVe973yPOfx8ZGu63jz5g0Tx+HDza2ZdNq2bVy/v//+e+NJofFqWthQBzNsh+3+jsXJgrZtpYHv2KZ/t9lsqMqCb775hs3ykevra1zHYbPZ8ObNV9zd3jIcTVku19JTU6YkesKoA6KeWup+2fG614FNwzyO11ZZlkbmSkO9tLbZw8OD8pMdqsFVZvjhWtzRtm0j9jgYDMygAn6izDwOTp/DkRwHqOdBSn+pY6Ct3lX0Dfy5IYMJXpaF1X9KRn+e4enX/ySD6+WPZdvYR5+P5303hfTv+6cGpud5WAhgt6zkJhPDjh7P8424Xdu2/PrXv+bs7IwszTgoK7JRHPPFF1/Q1BVVU1A3pRKvrBjFQ5qq5nBI2e8PnJ2c0dMxjGLquiJPMoq8pK4qRInEpshKdc56JqMpk+mIqqpkzG550Hc4lkVZVFRljW170NtkWUFelNzfPZAcUkajMYXquc2mMw6HRGSwLWE4xFHMIIxFrTbNqWu5+YejEUVeU1diubffp1Kebg/UdcVwFBMEPmUpjliOI5tYHA+U2GRJ29Y4js1wKDfcZDIxWnjymIq+F+9I1xVV0aapyDKZyOopWhwPTVDW91AURZwsFgSB9M+2261kmJFI/ri+q+5XMcA5PT3FYk6g8E+3tzf4nggo7vY7JpMxYeix3Wx5fHzE80acnl4wHI64u7tlPJ4SBqEqL1OqqiDNdJAWAUisDse1OT87l5ZA3ys7P5lWhmFoLAGDIGC330slYtsGkDubzcjynIuLCyxLzE32e1EuHo/HxsW7KApTmpZlabTAxuMxo8mUV69fMRpPyTMBD9dKiLKpBDUfDyJlqqKkomzIsoQoWAhUorfNsEW3fHT/TNOTNNREczC1THvbtkJUPzIsdhzH4OXyPMdW/UnNIgmCwJwnzceM49iIOmqxzpOTEyxLDGIslLnRUb7yz2ZmzzOknzv6XsizlsJsydPEHfxHpO/+KAD9xOT06e+nMvW4/D0OZJb6f8dx6emweuhRWV4vIFesHrvraTvxbNSlgOsKSLBtGuLRmLKpyHZ7wijiF19/xdWLa5bLJX//t3/HH//xn/LDDz+wXm64vrzk6ze/oCoK3n94x9nZCcNhTFXbZJkgq6UxapFlKYfDHsu2yFKF1K4KfCVW2DQtgR8wOz0RhY6iYhAFDOIQR43h0yTh6upK6YG1HPbS65GdzKFpOtarLZblUlUNy+WayXiKY7s8PDxiWarkjWKSJOGQSIM7z3NTRgwGA3xfkPwSOAZs1hsFxRAxynATMBwOaDstkS60pcl4xng8VSBdW6HXHS4vr5jNZtzfP/Dx4wceHh5VRuZRVZk4aTetKmc9s3B6xSHUJhh6Z2+axlj/7XZ7JV5YMw2njCYTcG3Eg6MlDH2uLs85XSx4XD7QNg3r9YqReq/xeIzrCU0NWxyvttucw2FPGGoD4oKTkwU3tx8py0KVRg2OK9ctTRMO+5SH+yVROGS9Fpma9XpjFnjf96RpxnQ6Ybc/YNk2g9GIrmvZ7vYkecZ2fzCepo7jcH19Dcj0brEQN++3b98az0hNdeq6TriuqueW5blRroiigEEY4nkucSzBoyoLfvOb31DlBVVVEIURvh9S1jUX51e8+/CB0UTwee/evaOqKmazmcG2aTHK6XRqICSHw8FknEUhfMrD4UDTdVRNQ6KyrI6egeLX9r0ocGhf3iAQ5ECRZgyjAbRSfrdVjd2Dg4WDxZ9880cmMB5XjP9D08znvbHjQPKcduTaTzehEWO0bZxnWVXXi1CihSWCiS00itPVWR12b6FdiQTPJjAHz7F+pKwhfTZoe+G7tRxnjqhsTB4zHI5Zr9d0TcMgHoHlkKm+zEgRnuum5c0vvuYP//APWa/X/If/8/8C4M//7M/5/rvvsLF49eJayp3DFsuymMwnuK5DmiVk2Z6u6aBvWW+WdF1DEPoMRxdM5xOwW1bLNZZt4QYOeZURBCH+wOX+/obzizO82GM0jPBcC2hJDnsc2+HlixfUdc3NzQ1d1/HFF68kY8gysiwlygIGcURZSX/lYbkyZYnreCRJaqaN2e1HKXVKMXFxHIvFYmY4mfvdhsfHR+nZKHBkFAzIsww6VMDp8f2I+eQEGwfsnqLI8TxfKWx0io7UK9DkjLpuqaqSum4Yj6f0vaXEEzMury/ZJSnbg+z0kUK/A8bdfXvYMWgHWK6NH/m8fP0Sy7KMq3hZC+i2mQwo8x3r5S1dk1PkpQSAYcTNzQ1lUXF2dsYXr74kOez5/fdvKaoSP3BI0i37Q4dld9w/3Kk+TcnFxYVB5C+XDySHjNPTU3bblIf7NScnFxz2KcvVkizNCEKf0XCMrVyNlustVdNiuy4np+ek6YGOR/aHhLq5YTwacXNzJ1pgQUCWCe4xywrluxkq2Z2DrIlOwNJxPKSuGm5ubnB9nzQTeeo4EteteCgB5OOHj+Jf2jTMZzNse2rWcVmWZEVBh20yQK1+rHtX8/ncNO11uad7fnpd9/QkeYrlWGRFRtVUWLaF7Tn4TkBaZBS1VC9XV1fGE7brOtaPK7qmJTtk9G1PmVe4tkddVXz3u++5uLigLhtc2yP0pZXzk8FMHz/Vj3r+7x9NDHvrR489LiOf996eH23b0tF/UqICUgbyRDA3TIAeg+a3LDnJvfUpE6HvJROjR2SFr6+JQ7mhtV+k67q8ffuWi4sL/uBXf0gUhvz6179mv99zdXXFIIp4/+4dRZqZi1tVFbZlGZxPVmREnisuz21GVYmRqlZ6kJszoe97JtOx6SsAtH3LPtnhey5lKen2ZvuAZbXKMSnEcVyyfE/TNvQ0FFXBcvnA/nCgLErKMmc0GeKHFwaHpCdwmn6ip0L6vW1HfAkd16JuciyEFiTTug1NUzEYhFhWT9fV5HlC23ZE0VT1yYR6JNSfhMEwIoqkqb1ei7BiWZY8Pj4CYq93cnJiMFJaG2symfDhwwd2+z1BJDJLuh+j75uiKCiKQhr3atCkKUa+75MXGVii7LpcCil7PpuYnlOapixXj5yennJ5cQkIlKisKpbrDUVV4XouWZlzOGxFwcO2cd0pDw93RJEMUxaLhZJNwmj6R1FMVTWkSU4cD2maFt8LFOBTDErEV7RnPJlQNY1Ikzsevhpu9EVBlueUWW5MdE9PT9nv99zc3PDixQtmsxkfPnwwDum6kd73vZGRn4xisSFU5bbnuxR5zoYNdVOLMoaS0NGZsGV1NI0AyBeLBX0vUlQaP6avgeM4JEli1tWxTZwEw5TpbIrl2KZnZvrvTYPVd/zpn/4ph8PBQFI05ens7IxxPOT737+FroeuJw4jGtdjW1bURUmVFwTTmXzmMKKLfyYzOz4+hwN7XhL+6HH8OIj9c8dT6ak4oH1H3z71xCxLaEaWJbLZn2im2dZRdqiC3bPvoF4aC3hxdcXt7S1t23J9cWmcsbuu48WLF+ZCLpdL43kpmUmn3M4jY/axWCywHYeHhwcGgwHX15dEvkueB2w2mJtMI7617pMQoy2SJDGTqPF4zHw+YzSOqOuSouqx3Z5DtsFyG6o2oylbmocS1/XIy4TtbkuW79VUSAYbVSucT7n5auq6UIOCJ12t6XRqvpdtQxAGuK5FmopzUZ7n5HkGVsN0NuD0dEarvv9sLlle15csZqdE0SmHw56myRmOBnSq32VZlqGp2LZtJnlaTkaXs7e3t2w2G66vJdN9XC05pAdF2ZLn6sXTdZ0pcQBDwNbN9KZpWC6Xprk8GMiwoK5bikIc5bebLbc3d0ZH63A4GPT8drslHojNnAhPhlxeXjKfz3l4eABibBspsRXhP4oCLKsnDH2KwqMsC1P+6aa5RsG3CCTI9Z+mirqhro9DkjDwA9MLu76+5uLigrKUrNB1XSaTCZvNhtVqZUClk8mEeBBjObaZ7hZFYShOURQxHY358ssvWT48fiK1BRimQVmWYLsGs6b7YjpgeZ7Hy5cvzRBHb0Zi6FKT5smTMcmRe5LGzTmOw3fffWd8T3VfbrfbsVqt2K03nJ9emiRGU6R00ARRrdW97p91Zzo+fgpp/7mMzODIrJ8WRPzcwOCT36Mys17EET95D1VO8uzzHI++bQscx6KzPiNdJCJOrNdr4jgmDgWEuNvtcByH8/Nzrq6uSNOUd+/eUSgreUHMZ+RZjqf9Do44qsnhqc9xfn5BmR5o2/qTk6ybnWEYGpkTvUC1qJ5w004JApsk2+F6U7BEmz0vEuX5WEmPI4qom4qyyuhp6WkJI9lB67qiKBXJ2IPpbCTeg3HIqIjoe8ksZKopAUIyTZf1egk0tF2B48J8MWY0GrFYLEiSA47bsVic0DQNh8MeP7AJI4e68ciyGtvpaWtR4Oh7TNagNzWdIVqWKEjofo8mLkdRJGV0K5AEDcDWjef7+3txuFKORX3fk+eFaUi3bUuapgZRLx6iMkXt+57T0zOKoqQspMR5eHgwgXW5XLLZbKirmsALzBRPI/O1M5bnecZ1SAcN3QoZDEQDTDfJtRqslvtxfE/JuMPpyQmpYkZous5gMKBpGq7OL0wPSnufalNgrQZyDC7Xvc7BYEBvQRxHlE0tevxKDyxJEsos5+XLl8Z301cQkr7vDX4uyzLysjbfSwdbrZihp4wgGffDw4MhiTuOQ1nVxvNA9wr1OtYBTk8hNRBan5+u63CQjW+g/Bf0Y05PTw0vV2eKOuDq43+4zHw+0TzGe+k/XddJ0LF+rPL6OTjF84BmXuszn0c/0rJt6J4Mhh1l9GrbNh3Ck+usJ600U6qqF2gaARI6rktVVUynU16/fo3jOHz8+NGY5B7jauRzSV8gTRJOT09xXZf1ek2ldszLy0v2+x3pbkvb1eYm0+Nr7TWqKSti2jvh1atXhkBd1w1NKwFrOIyxrClNI+KE3mSoMpxGSesEuK7QRYqiwHXFTcj3J8IxbDTRWGcJBW07JM8LhsOIroNeuUZPpxPVJ6uo6hLLlvOrTWXDyMOyYwZxoOSDYpIk5f7+nrv7G6IowvNtbm7eU5YtQTgwe85qtcK2RYU0SRKDWbq9vWW/3zOfz1WwfDIb6dqe/X5HU0sQubq8pu97fv/735OlOeWoMouNHjEPGU2EvHwhfE+Dv6pbzk7PDfQn8ANeKh0z3RfSzWTBteW4tmtgCN9++y2e53F6emoGE2mamgnfMVlb/ztRtB1d/kZRJEIFrqP4s72UqknCWmng64Cky+sgCLi4uGCz2fDw8MB0OjWO4Bpvpqldeh3VTUPbNQJA9lxDdNcI+10jJHbPkZ4ZR1ZtJntsOxO0nvfHwzBkPB6bDaiuawPT0CyAjp7dfkOlhDt1RiZtlhDf91kuVzRNa7JWLfKp1WvbtjMbmB5OaVpe13VcX19TFIWZ4urjs8Hss+Xjs6D0/PFPQeMJ+3OMCfsfOtRkUw1BzSGQjeP/f6Z4oTMzLHq6T+AY5jXk5Tk/Pxe0tu3w9ddfc319TZZlfPjwgcPhYOSOfUVozzKZtnWN4IToJCU+LqN09rbbbfAdC89zTK9Bnx+d8mu8kS49AbM44jiCvjdj706N94OgNbiatu1kYttJYB4OZXqWZZLtnZ+fKxyWEL+jKFI3hvRDgqDH8wKAI8lhy1zDMAyYzycqc7PUjtkQx4NPpJIHg4jRaIhtWwZh7roOddPj+4EpATSO6PXr18YnVJcvOhOYTqcm+EvWdzBQBsk2G3rlvuU4rrn2bduZjHc4HBLHQ6M/lucF2+0e2265vLzEtm3Tb9OTMy1XvdlsTMBt1SIDmM9FzcP3feOBulOSNDpD0Atf+8jqvqScD5fRaCR6ZrZFWVUUZclytaRHpLt1mXa8zpIkMTQhLRGtTbePOZk6Q3tyK7cp65KqKonHI3W9gydl2l4yneXDo4E36CAspiYxQRCAShB0j1KDYkW12DfBXytY9Ko91Pc9nu9hW7aRKdK6dPo+lPshYjKZmKxby9C3rUA8hoMnnmxVi2+rfqzneRTqvjkcDoYHCj/hAXD89ycB4TMDgOPndV1Hz5PM9vO+2uee+8ljFA9LWv3dJxfYeu6HyY+xbj09XS8DBI4yRMkq5TH7/Z6XL19yfnJK13X89re/JUkSs9NpBoBOYYUHmOBYCpNmO6xWK1zX5eLigtFIXIK22y3j8ZDRYEQQuEbdQ/chtNIBYJraenFruRfPc3F8l7ZzsGwX2xF9sqYRWk/bWQxHEzUir7CbVpx8HAdsBz+IcFxf6YKFqlHe4nkhYThQgn6iCCuIflmIMhVrqGvR6J/NBmZz6ruOQ5IyGtrYtvR5wqBQgNcxriOTs67tuby8xrI9omhocEbD4fATDKIu0RxHTJE1IFOjvrseXE+04ieTiaKsrNSAIcdxPEXf6pUjeEgQROx2B/Jc3LH1onccVy0yi81mp0q+jrdv3zIajdjv9yYjGylhxvVqDV3PcDRgcTJjNhdQ5sePH2naisel9EexOopS7inXdQlCD6yOvneMvLUOsmEYkqTpJ6KN79+9I1Tof9/32W63rNdrZtMpdicAUw2E1WTtIAg+MQnR1YfgIaV8cxsx7dWDkTzPzQbSNxJgAs83JZ8u8XSW5wcBSbYxDX/9PXS/V09ZNSxCb9q6XJUExvrE/8H3QgI/wrE9+s5iNp0T+LIxbPO9KcXFaEZK9V1yMBLdvQW75GCYKrf3dyaj1hvPZ4PZTwW1zyHqfypLe16CPj+eB8Hnv7MAVDlpfq6f94xo/klvDJQn5tPn0H8cJfnzJ3/0xyRJwsePH03/QZN4dYkwGo1obFvG96VYa/mux3q9JvKFcqG1pfROr+WLQSZWelrY9083JmDSYp0mxwpwOxgMWK9XRLYPlktdg4XHZCLZWFU9UhYN45FP11rkWvmzdUjTmr5zGA4nlHmLTUXXOpRFi211jEcBYTDEImG/yzm0OcOhS985+N7AZDyDaMx680hyyBFxRvku+11CU3ew2fP4uOT6+orLyzGHQ8Ld3YMpyaLIoywrIDd9MV2+aI9PHWySJDEORHoxymSxNtrvslu3BmOlF6WGAmg0ued5JkuwLJskSfE8X8DITU1RlGZyJqR1KV8OhwOlMq+dTqbEQ4GdeI6n4BAZL1684OHhwVCLtBS2VgPRyHQJnspCsX9SytUZnG55DAYDXr76gtvbWyLVAJfN7sBqtZOs3Q9MFnx2dkaWZTw+PhqZcj1N1xuO67pGbaJD1E5C1Wo4btD7jmsqGNd1GQ+H7Pd7k/WAmHZrSR59vWYzcXw/5lrqe1r31LSYxDESQQ+Z9FRaQ70+fPjAfD7/kcKObYvdcNO17LfigXB2dmZ6qjp71wbEWhdPHz87ADiGUejgoBf9MXFcj1+lh/UpreFzPbLnQU5/EceR5l//7PHyHPWfZ6XvMRm+R8bTTd/RqNS7V6Xa6ekZ8/mc29tbacwqCZljkKbejbbbLU1dG+ngtm2pVMnW1Y0RHXQch4FKn6Vh7NLUDX3fKA19z6TCDw8PfPvtt5ydnZmLfNxHk92ppW2h6SErSnwvoO16mqYljme4U48sz7Ftl3g05/J6wm67Iwik2UsPwSjAsSJsqyJLawLfoSp7yqIiSUrKoiNJ9hz2JXE84PZGCNJXV5cM4xl11ZJnjcKB1UosUDTc7+7uyLKC+7s1TW0JavtQqt6Vx3q1I1Lf5/Hx0VBtiqIwqO6vv/6aNE3527/9W9pWaFMa5b3b7XBcmZbpDE4rqp6cnHxSmvq+z+XlpTh3392xXC558+Yrri5fcH//IKKJQYTjuBwOGZ4bcHJySp5nKrOQnmXXdxRFTtPWptlelxVFkZNlKSDZxmAQqbI+NmDbtm1UA7smz9Ug4XFDVUn5r+lM0+mUx8dHNbDIRXliMFCO7o3KYGPm8zHbzYbxIDbZqtC5UmazGaPRyIhYvnjxwqDuT05ODNvg/vGB6XRMpYYow6EwKIRkPyZNU5L9gTiOzb2tB2Gz2Yz1eo3v+2w2G2G1qGuTpinz+ZybmxvjTaADlDYNb5oGn0AJMJYmi9Q9NhAAsEYLaB6mHi5st1tp4SgEgB8GjKcT/DDAcmzmiwVZkeP6HnuVuYVh+PPB7HPZ2OdwY8c/P+5dPX/884D4/D2ev+/PHZYt8tbPM8e+72n7jjIraJGdRtNAfN+nb1oeHh54uL+X3bjrzcRKZ1HH4L/j4A0ypbUsi/F0anoHTdOIpJC6oJ7nUOUpto0BHGoDCN0g1dgv3cuwLMs4/9iOw8SbUaQpeVHjDAP63iI5lOb7dK1Nnhckh4QsbUiSA2VZEwZDJpMpDlAUNVlW0DbQtVBXnQo8OVXZ0DaQHHICP6JrwQk86B206XGa7mnbniwr8T2fIBjge5IR7vd70izlw/tbmaA6Aa3V0nWW6vUJl1RPyDRhWluu6ea1vl/2e5EHShJhMwRhRI9lSit9D+qsajgc8s0336DH+R8+fCCKIq6urri5ueHduw8s5qem1CwKAedGUYxtP5G9XdcxmyeqvRGGwraoXe+TcirLMvNZtAJJ0zQsFgtDT1oul6RpSpqlVGVjEPhxHJtJrRv4VHXNer1mOBwKMFk1sXUfMVL31mKxEIyYCmZpmpqyc7Va8eHDB25ubgysR5eLWi5J9+E0Qr/rRD26Kisz3YzVedMcTj1hdryAOI6ZTCb4vs/79+9N9qVZCLr81FmS7/v0KvBHUWR6e1EUGcpSmqbKTFxAzYCRE+r73ohRjtS1L9W/Z7PZJ1Nsfb7W6/UnMeMnfTOPA9fz/z/uRel/m1S1/7ERyc/14fTPJdP75zFpLsCOkgAAIABJREFUtsKc/ahfdjSmtr2nCYuWMdlu1uy2O+MH6NoOkQKzWpZlcCzHCiGfBG0+Bf7qrMU5kimxbUf4k7ZcVM1H06m4BhZqIwrALBYAzw8pihsqVdJ0rSSjWZapaZw08suyIjnkuK5M3+T8Q1FUeLYlzuJZRt9bdJ2oxEpZlSrLt1Z9V3Gh7zrY7Q7YjkVZ5uz3gh4vyxrX9WmbHjyb8XjKeDxVXMgtnuubwKTPc99Z5jpqdVeRGIrMZ10sRGJcL7ynXotsIHle4nmBmnR1SltLFCuEP7rm9PSUL754zcePH/n48VaZZ0wZDUckSaaa5pip4nB4geMIwV5K4hGe55IkB5q2VtSzANdx6fzADGq22y2r1cqQnI9LqMViYVRkdYalUfIaz6cb9/PFgqIS8PDdneDcUkUy1xxL13WZKh6rFiiMoshM8rRixO3tLev1mjTNGY1ihsOhmIHEA+LRkPv7W+q6MmW4LsW0IKLnuJSqzXF2dmYGCsdA5aZplF7b0JSyuqUi91ZvKgwNk5ABoIXr+IxGEyaKEpXnOU0jgp2Xl9e8ffuWoihpmoPpfz5BTSxBRDgOXd/T9j3hYMDi9JTheMzj4yNnFxfkZclecTn18ZNWczpAfK5Xpk/scRDRAa7vPs20PpdB6eNHeLVO//8T0NU8Vv276z91dDrujdmWTRSF+FFoyKvb7VYMViuZ3Okg4qqG/jEuSO/Gn8fAiUlwqbh2BuzoefjqorquQ5YesKzOMAS6rmM0GpkM8e3bt0btYL/fm2mMTGmWVHVHPBQ4xc3NDVVVEccC/pS+k5DEq/JJq8pxHA5Jyu5mQxT69F1DXdZYFpRlRlmJ/2TTlgSBh2V3uJ6F51m4bkhZlazWjzR1RZIeiKKAuq5YLOY8PNzjODZZluB5AsCcTse4rmOgDQI9GXJI9pRlTt2WlGVhJk5BEDAej02zf7lcGsE+bRjSdR1d22NbLp7Xm8Cgg9Hp6akBo263W9I05c2bNwazpP0TBU7ikRxSurZVkj0Nh0Oipo2tCWi+7xkVFd1qcB2Xtm/NRqgBoHIulQiBbYnJje+z3+9p29ZkYLZtUZQZ89kJ19cCKXl8fCTPM8q6Zr3ZYDvCfNjuNeDZNj22MIp4fJRp43K5/ERZQqtvnJ6ecnt7y2w2YbFYmM9qYRGGMqnWJbpel3oiqUHItro/9eBB9+jquiYeTYzMkO5RHZf4erh1HNh0udp1HW/fvcP1PeVHEKhN/YlHORwOKYrSDMe6rldOWIHBFmpYlG5h6TL0+++/ZzAYcH9/r87rz/hmak38TxbyUSA6zlY+wZf1Pw5AnxsA/FR52fe9ZkI9OSc9j6G9OIv3R2Vgf0xCV6+pR+P6T9d1RCqA1KXsBB2fBmK9y5jv+fyzKdCtZzt01pEuG5CrssN1bdqywHZEcPC4rNbZnEAIYkajkaEW6YuGZdH0GXVXUKQ5m/VGkXNbOlqjx68b0WWTEQYRw+GIpi1I0i1J0kLf4FgO8TAmKxrzOcLIZTweUuS5bAxIyZUVBVWdKV5qSpcVlNWQqs7o+pqiTPB7H8vuWK3vTc+wLEuyPKOqM2xHbS60pGlmCNbPp2J5nosbvLFHy0xmVveNULV66DuZhIWBwAriwQjH9rAtl/FICM625ZJnB8JgwGJ+KtSYouUXX/2SIi/ZbPYEoW+YBklyYDqdUJYB2sW9Vd+laeRzNn1D37ViGJPn2DYsFpJhlGVuMkzR+ReH8yxL+Prrr3h8XIps+HbPYi6tBY0jK4uS6WKuzlf9CexBY636XnwhNpuN0YnbbDaKPuWZYKKzoclkYjIn3Vu0XcfAQ3TGqGFEOkM7NiHRYGNN4NfDMI38LwphNOjApvFf+pp9MmRT36VrW/b7Et8Pmc0WzOcLI3V0e3urqhcHkEy57xuapkPjzD3PM1hBrbixWq0MJ3k2m9F1Ypw8Go3++WD2HCSr/34uznh8kuSHT2Xm5yaVz/tmx++hPS57S3EunwUywPTLPjdtxZYTmhZPqgH6oti9BDlHlzM9pkemm5N6+qiDj95J+r5HDUnJixzHftJq6tR4W1J1i8V0hO9rtQzHjK/1d9bIZ73Li26+4MyGowGd1VGUBa7n8uarL0zPJEky2q4ljkfYlk1RpGy3K7oOgw7H6qirDPoGO/CxncDgjwSP5RCEFm2HQsvvacOQps3w/J7Q8RjEUk5htVR1xXAk7uEaYlEUuVDK2hrXsxjEAYfDgds7KU2DMKRpG5rGNmBJLSJ4fOgFpzM0bZDSNi1gMRqNTfNaXxdNbh6NRpydnRk6k178+nz/8MNbdrs92+2G2WzKZDJWxOxK+K1ZQds2tO3QLMq27czi0Vn7cVaZpqnBS+msUYYj4qS0WCxo2844Vv339t6rSZIjPdd83ENHpKrMUt0N0TM7GCx3SY6RP//Y/gAa1+wc2+UNIQZAK5RMnRk6wvfCRUVlC+7tOYYwK0OjUlREZvjnn3iFXYyPj486g600yf0lL3n7/t0ziIXN4JVS7AyezTbZbT/KNtGrquL9+/csl0vm87lbp9agebiBW1WY4TDFQiAOhwNn06kLDB8+fABwODLrd2l9QB8eHpyqiQXLOl6xEU9su46mbRmPp/SHvZP1sRubpWJZdVrbe7Z9SD2MC5jN5k7yCHBAdtv3++mnn9ykeOjQ9tlgNoy6w+BxGuDshbgMhydrqtNA5uLSl7IzC60YBDAwXRiFU994Bsmw59c/lSfAEzOg72kbXUqeTTVAU/TPr+H03HozsbU3uyeMkm5R0tC4gYGAwWeF2QkVWSYdt9CWpNpg494hxq0rs8XZlFWB8CGKPSbTMVdXGrn+8PBA11fUdU8QaB5gr0ZuN25avchmcYokRBrgchzHtOsCzxOEkUDIlqo+6DJTgvQ6pNeRjcJnU+kXL15wd3dHls0N1scninVACiNJGMXUtUaVB6FP2/nmPAriJHb0qFOkut397WahlHKmv7ofo6gqbWE3mUwc2twGf7s5pWnKw8PDMypLXdeMRmME2lZNMx40vckPtG/oZDInCAKKMqeu9eJJ0sjBDtq2xfd81zOy9Bm7qc1mM9eGsH0uO0XUoOgApaCqtLP3Dz/8wLt371yGVLd6Ea83a/d+1iF+Mplo7uhmg49wZiKPj4+u37rdbtnv9yyX2sh3sdBOYdpp6gXL5ZKbuxtqQ5gfj8ecnZ1pWtMxdxv8dDxBmkokz3NXguq+Z8MhL9xQoR5kinVdu7aGveeHawQw0IwnL8/lcknXtggpncPUZrNxLR4LXfE8z9AHE3dNDq6hlPv7dlMcls+fDWbDLEliMyj7o5DyKcLY7KXrni5ICKNyoT6eeOpg9bzN/6yBb5FiNgtzzxm8QAgwE0374XWq189RsNxuHAbF3uRd1xFIz+1ISil8E5yGsr/2PCxYdAg/sT9honWYbP7ZK0XX94iuw1OCOEnxpW6CCjxtxbY9aFBqqxv/1ttwv9+iVMfZmdZbX2/X+KHPaJKRmp22qWravmM2ndH1Pfd396hekaUJaaLNf61DeRyFxJEHtNRVhQCOxwOeJ/F9abKfxiHCoyh4NpzQG5J2Z//uu++cC84vv/zi+iarldZIE0Kw32np5jhK6NJesxKajiDUC/541AFSZy2dg2dYTNZ0OuXrr78GMHioktvbB3rD17DTrTzPXU9nOp3y8uVL8jzn5uaGutHCg0VRGLG/kSkJaxDKeDeUBIF2YOr6jrZtUKqj71uiSEMsmqbVAxHPJ45TMz3sDfG+BKRraI9GE969e0dZ1oBASp+Hh6Urneu6QfXai9SCctuu4+eff9ZZFkq7EV1coJRis1oxPzvj8vyCzlCqDobDulyvmM/nKHQrY3/UQo+d6vWCt0KHo4z1es3heKRDkZiMcjbTdoer1Uqvhb6nUz1xkoCU/Pb2rc6eg4DMAMDbVj/n4eGBotKDgsl0ys4wX2y2bdeKBd2maYof+FR1TatqQi/k6sUl42yCkoqmbNjne8ZpQlke6JseKXsCKUiyiBeXF5xfXfLu1zdI6RMHkiiNUW1HUVRURUF+PHJ9dYX0fQJPIIMvyGYjPEMXEijjaD386XrIiyeMllLCcri1TI/LugZRSH1cplrSuCtpPYkHug8xHAqclKB1azNHM1mkp+5qMOqeFrhqs8XhrtE0DV1jel3S4zTrtIcUArzngpI9mp/pskYhwdM/wtfuMVEQooTECyKquqPf6axAnzNcXr1gMtE9Mz+QjPYpUaKb4L3SPQApJX4QkkQZSZohgc1ux/3dLY/LFUkcMRlPmJ/NQQhevYD5YkEchvx+e6NNOugoy478eCRONK1FyIDReKwzjyhi+fCoS88g059DW/P4eM/ZYo4QeqBwMDpkk+mcyXhKkqTc32/46ec3BIEWlQz8kCjKyLKUMApo2obb+7f86fVrXr685u7ujratqeuG62ttmfbmzRv2e03ZWa+01M7hcOCbb77B80J++vvfEUJ/xGEYMg3HLstt25a6KfF8weL8jLdv3xInIbt9x8PjHW1Xu1KqqipG44gkidluO8JIkmVjrq8XjEYZy+WSw14b6E4nU62/1nX88ve/0zQNl8b1vK5rptMpq9Waly9fUdcV4/GE6XTKw4MWvQzDiCiKyfOSbKyzrLwqafc7pvMzBzidz+dUVcV+uyOfTLk6v6CtKo6HA4dIG/Te3d0hfZ9jWeD5Pr/89iu9UtRVRd22XF1cMDtfkGQpcZoiPB2U7m9vORxzzheX3N7e0rWKs9mCJM6oyoYir3j9+jVVXev7R2oHr6qq8PyOdDTmbHHO7e0tN7e3NHXN42rFYb+jR9vf2UFZEAQE0gNlhmmeTxyEeKHHcnlPGCi6tuB4XJMlgRmy1YxHIaKvmTQRTdUSpxFJlKJEz+G4onp3pGsFx21J4PusVrcc9wekL6nLBtX3zGdnJFmMZyhinw1mpz0zG1CGzX5bltmJol3wdipjX3OKKRsGqNPXfq5Hpz4R2PSw4NN4NjF8zrC3ZgOVfO4h8AQLGQQy+14n2dqzaxB6YOGpXvf4PInwJUVR0TU9ga8lsIMgQAqPtmvI8+LJQFUZwrvn0XUapDqZzJDySePs8f6B1WrFdrs1VKSGLEnpmpaDmYK2bYsAXrx4wZ++fc3D8kF7JbYQRBlJ5us+o+FaxmFMEicsFs8bz54X8fLlt7RdjVKCMIzwvJyyyBFCGvOTO6Io4fXrM1QPRVG5jKaqGrpOEUaCLE1oWg0LGI0zttsN4/EEpTS9S8vqaD2tH3/80RGLpZRMpjPOz88dC8DyVO21WqMT+2/bS3maCAaMx6k29z372pXpbZsyneqgqPtDWuXCZk/2c766vCJLx9zf37NebQmDGCl82kZnIKoHTwasV1uKXIOyL86vmM/nZlr6SA+Oh2tR+N9++y3b7dbRksbjMX3fc3tzw+ODli0qi4K8KPTU1pjqKnhyBkO3TnaHA0mWEZhe44ebGzf06rqOH374ga+/1tf+7//+77Rt6wxC/vM//5O//OUveKbbXtY1G2NGfPXiBbP5nOV6TWomlpXBRZZ1zczT0CPLU478wEEyfN/Xdo1K8f3339H3LXleojF8kjwvKIoSpRrSNOHu7oZ3735HKbi8XLBYnGkZ9KJEEHPc544e2bYth82epq2Yjifc3t4yHmf4gUdZfgGacdof+1QgGQY6+wHbacan+k+n7w9PPRMbYE4HC5/67+d+d3o+n/qbPeoZu9MugNNhgpCfljA67Q3Yz8E2gu2NK9FZToWVTLElrHCAP6UU0tNGGHoMHbn37/uOvvcp8oL3737n5uYGKbWpxcX5mH/6p3+i6zonaieFT1U23N896gZpW1OUFd2AYtW3nRFRFHhhiBKCKEmpq4qlkcSO45irqyuQkuOxZH/7YKhXgjTNaJrWyCLrJnoUxYz9wJUddd1oTa/qSBhKzaEsapI45bA/GvCw5M2bN8xmZ66ncnZ2xna7cxy/tmsdTssOELruifxt7xerYW9t5obQAE2qbwnD2EBBVkblZIPvh4RhzHK5dtLdr1//me12a0om3zXarWad53nPtO2sQGHbts9YCvP5nDRN+f321n3PVpPMUnMeHx+ZzWZcXl5SliXr9drBS5qm4WjgLAujUGGnh7ZXFIYhl5eXjhdsDZYtQNc21S153/b0rEz1yFCYbBCytDxAq/huNs5PYDQacX5+7lo19nrjMHLrXinl+KaiErQt3C8fSNMYzwuQEtpWUVUNdd2aSalkOp3R9wIp4erqBYvF3FDn9jw+7BBC84C327Wesjc1io4s00Df2WxCnERfhmYMA8TnAsMQmT1c5DZAnAab4fsNm4bDwGYfEx+NMf//H0Pg5UcDAp4gFxpm8ZRZuublAPJxeg2nQX6YSdrsoJIeaZggFNrcpK4MNy0yXEfBw8OKLIsJo4DHx5y2bRyVS/tBal5hXbfGoUhwfX3N69d/ZjabsF5vjCGIxpEJ4aFUQVFU7A57RuMxHYq26RGypetK6krfjEmSsFrv2a43eJaLutN2bHleE8UZ5xdztts1f//7r04VBIQLJpZPaXXY7GdTlAeKUjcuz881IPb3328YjUZcXFywXK5IkpTz83NAOKs0LTiouZWr1Yq6aRlPp8/0vTREQiPLh/fYsAqwY/osSymK3G00Fpv16tUrjsejcxyyUz9LKbNDgNAY2HzzzTcURcHV1RVlWTo8oGUDxHHsNM7KsnQGLLGR6Lm7u2M0GnF1dcXl5SVWTMAOfTRx/uhUOCzcJQgC4yeq2QnDxvtoNHIqHG3bOscqC3rVopq6lLWQFxvwlsslVi3E8kuV0tmp1RSzJaRlBFgesVP7NZtxF+ngdTQep1YGSLePJGmScXY2I/BD2q6ha3tymVMUOw6HI6vlltE44/r6peaRRlpnr64aM7wQXJxf4Hlwf3+D53m8fHVtpulPbAxP+iRJ6tboR8HMTgc+6iOZG8jqEw2zkmHZaKP8lxb/acB7nlnpqeAwKJ1COYblqMOYmZ0cpZ699ikY4QLZMHha9QAbzDozIftc5jcEH9rAbFN81fWUfkFk+J5xHJnxeISiM1MgOxHuDTxBy05rl6Etj48bwiAiDCOybMzV1ZhXr14yHk8oioo3b94RhiFXV1f0fcdyuTKMghFZOqbpFMdCT67ax6Xe1QwEYnE2Z7Vc8ea3X/E9bco6m04ZZyMNOykqQyrvSNPMfD4+x2PushIN0YCua929oKeNJVIKxuMRoJ3INVWlYzyekucVSZLy6tUr7Qjv+fhewIf3N2SZzpBsi8OKMFpajuXgCSFcJmYXphUKuLy8NFAFePPmN6dNZoOFfb7N5sIwdMKcv/zyyxOLQwgmI12OWjkiO3G2wFEbYK07+2KxcNnZ/nBwiP2yLPnrX//KZrPhr3/9K1VVMZ/PnTmwDTg2GFo60osXL8jN7+7u7qjrhvn8jPl87oQVh1PeIcLfTh/tMMJ6X1pIxWaz4eLiwmn5W7GEpmm4urpiNpvx22+/OZ6k3agnkwkTM41sa817zo2suGU62A3OCwNG4xFdpygPFV2vENLD83VPNRuNCKOIXsFxf2C92eN5AqX0FFco2O7WFOURP9BA+Pl85uAn0+nUSB91z5KfTwaz01JumFVZ3IueGFVOz8g+57R0Gx7DoGAD2mngAGUyjo/9Lz+VLQ5HtJqB0D8LVu555gfz3lY7bRjMuq6j77qPzn8YvCxgcXjuNs3u2w7ZC1JTIiwWCxaLheFkViAUvi9N2VAjPX1W6/XKSXdPxmPSdEwSpwRhYOSlYx4fVtzc3nBxfs58MSeOEg7Hg6P9xFFCXmq5l+1hz26353DQdBEpBZPxhKbuaKqauumoGy0rnY0mBIGWltkdCt69f8/8bMLXX71GCJxaaRj6YKAnVn6761omkzHjcUYUBaRZiic93r9/z/F4xPM8x8ULAj0x/e2337i/v2cxPwcsqj5yGVLbdeyPBxdI7P1V1zXb7Za7uzunDjt01rbPt8+1oNOqqtjv96xWK1dSDX0eh9aHeZ5zPGhrtomhzlhljaIsGRs5HzHAh1mxTysjrYCyrtzAQl9/5e4Xq/xqAbOLxcJlfaPRiHSUPRMstCR3m1GtVisWi4XTerPUsqZpXGmohxLCCSBEUeSkp22ZbNeI5Qjb7M5Oj4UQTp3D8kdjA5oNU+0F2xozEft3pJSUTc3D7ZJDPnbKJ/Yzz7KMLMt49eqV+17qOqDvdAlqM8soCPnw4YMpqc/d+Q7NVawwwRclgE4zn9PfW9iDzUpsRnZadg5/hmXpsD9mbyK7yzwFxeeYto+zrI/7d+79xJOr1LPgZ15j8WEYmZ6hVM/wNfbc7fvaBrXNXJ3el7mmIAjADxglI85mC6azicNX2Zve86XbSYWAzmR1uomuQSuXVy+Ikwyl9PR1d8hZbfQAoKxr9nnJcvOrUVUIGI8nxGlKUdf8fnuvdaCamv2h4HAoTJ8kww8iqrrB9wPm5xcoBdPZGXEyYr1eOTzZarnGk5KLi2tzThqlHcUxQkCSxIOhhTKfi6DtaopCgdKwiPV6awYZguNxbcq4iv3uwGg0dooWX301MdlSQdv2ZOOMr776itvbJ4ciWxIOA5UQT0BRpZQrjSxQNI4FVdUY67vQBJWGJMnw/ZCiqPD9EM8LHPBVKZ1RxmmCDHyE79H0HTLw8Tqfsqnxo5AkjDi/vDATzhVVq3GHc2NC8h//8R+uhHt8fOT8/Jy7uzs9vTUB1pLP7YR2CI7dbreuJ2epbxbEa0HDw3VhLd6GmKvtdvus/LM9NwvaHmqI2d8ppQxGT3++FutnQc32vM+msydivlk/tq93LEviOGWUTZDCZ7c9UFctga8cDKiuWpq6w5MBUZjQNj1VWdC1Wvzh+vKCtq356quv+Pbbb7m/v0cI4cr10WhkBDX9ZxvFJ4PZ5/pdgENJ2/LClho2gJwGsmHwGQZFl0kNvhS9W6jnQWhwfG6gcPr+w7/zrBw1wazrOhDqGfhv+LzT87eZ2VAmaBjM7BfqCZ/RaEqapUjhufIEerPTJbRtzWQ6Jo4jdrstZVE7itN+fySMYkByPGqzk91uB8B4PGY+P3ecPaUUV1dXhGHE8aipIu8/fEB5HiLwQeiFGUURi8WCLE2pq5q+6whDfRNGSYIC1usNXdcxHo+4OL8kiSNWZjAAxom61O7stsTDYAKlBEVPnh9p2w6UVrw4HI7c3t7y+vWfHN3GosY9zzMZxrnbsYtCQxems6nboZMkMUq6KyeJk6bpM5FDK6tsv/ejEUG0i3+xWPDVV19xfX3tXNJ3u50WZHS4qtbg1DJDsC8cSt2eb55rV3VL64nj2JHPbda4NabQFswrpeS7777D933n+2DpUE5cwNCUHOykLAgGXpRDFyp7/1kV16FTVdM07nPxPE+r25rsUZspZy7w2cGCVXSxPcnz83OXKdof2x/VVMDKTUx93yfyg2eYTts2yYuS6WzmhCVt0LSSRlZDzmIHrW7d+fk533z7FYfdlldfveT7779nMplwf3/v/o6V8NLk9fZZAP8omA3LxM9lZzb9tR/aMCOzad8wW7KBYAiLGNKFhpPN0+NTwXWYNZ2CXhHamdyWHy4YCeuvaRD9A1enISbNnpN9T3v+tpSxIEEbFC1MwPM8gjDA8ySH/YHSLwlDnzRLnapBkkT4gcf9/R0fPrxHiCdcnNYUe0lZ1mx2K8PDVPgGlR8YGe31ZkNu5F3ef/jA23fv3O4UJTFKCjb7HWWhyyJdjkiU6jibT9ltthwOBVk2I01j9vstSRI5IGvfajL0pGsNXUhLxvR9z/FwIM0SQxEL0JLZgdvQQGeYUkoWiwWxMY2ZTqd40nd0n8lk6vo86/WGqqoJw8iJNkpfOskka2bx4cMHt0PPZjO3EK+urui6zmUsaZrx9s07oihiNJrw/t3vpIkm6netMjSmnq+/+pZvv/2WH3/80RDMQ169esVsPuH//Y//h/Vaq62OpxNubm6omprxdEKe5yymE27v78hLTaF6+fIlddvw8PjIZDx+1lPebre8fv2aOI6dZpi9r2xAskHbvqY0bufT6dSJEtqyz/d9ZrMZd3d3zkZw2D+0WZq9761ruA10Nku002MrxJgkiWYf+L5jXtiMcL3WE8XAVDJCCM7Pz0nCyBmvBEHAixcviNKEX9+8o6g1bCVKE1KTxQFObtvSw4IgYDyb0vQdH25vyEYJx/2OFy+vWa1WTj9NKWV6rZ7LvrtOPaPJfbZndjpxtMHCNmmH5eMweA1f7wLJ4HnPJ5fPcVw6UDxJyHwqkJ3+TW/wAX8uQ5NSIg0wIww0K18ongVBew4W4zV8n+E1DMtMG4RcmQmUZYUME5IkIAwj40NZ03U7iiIgjkPSRIsU6t0T9vsdy9UDt/d3CBFQlCVN3YEwpGBP0PcdbdeAgCgOQUFVl+y2e4qyII5igkj7IfZdo6W7Q4+ub+jqhratDDFa+0rWdcl+qx23pQdxEiKFpO5bNDq+w/MknhfjeZqTWZS58y4VAsbjEUmSGrUMvcFlWUqSpPi+Dhrue41906CXPD4+uund4XBACM/t3EVR0Cnd2LYYsCzLnPWcXdxXV1duExFCy/FMp1OqUutf6XPJHKTClk9W4aIsS632apRv0zSlrErWq44kyXh8fOTxUQOLrf6clQXSyhnSlbdWbmq329F2HaEfOPHDtm3Z7XY8Pj46mMloNHL6+DaQjEYj2q6jqDSvsh1UPZbgbTfeu7s7pw03LBlt5vby5Us2mw3r9drpoNn70/Yhh4Bw+77WHNpWXXb4ov0keiIzRJjNZuz3eza17kP+wz/8A4vFgvuHB3788Ueatud8lDlO6/39vWsBpGnK999/z88//0zbtq6ktkF3u99D3xFHiVPuGI1GHI9HHh4eHHvmqS31tFY/m5nZcuo0QzvtRZ1OJk9Lx9PAcpp/ZHnEAAAboUlEQVSxnfamhkFu+NovBbNPQSuGX5bneS6YOTJ5/3yqahedPwhwp+dt/z3kow4zw171eFIyGo+YjCd0nZ4edX3L5eWCS9MLyEYpUaRluD980JOtNE0Zz6bsdjnHsqBtWhDQ1K3ur6kemUu8QCJ6yW67I8+PCCmZzmcIBIfjnqquCAbSwsosmND3iaMQ1fXQd3Rdy363Q6BL2DSO6fqW0E8RoqVpKvb77gkKIZSb0GqzC737d532niwLjaNaLM4pipLtdmd2bB28m7pltdq48kcbpnh0rUKI3oznFcfiQFmXpuzV0kl2J3/58iXn5+eOmmN7O4CbKgaBdv+xpdhoNHJSz0opB8Gw32OSJK6cE0JvELPZjIflA0poba25cWa3w6LISEnbBndRVVRNQ900dG2Hl0g39bRZ4+PjI69fv+bx8RHAAX5tkJ7NZjpr2Wv572IAubD8RXufzmYz5x1qhxuA29gtsHhqtNGeTG30kMTeszbLscYoFs83GlknsNr9TgiBZ9y6lsulVr4900DhN2/ecHt7S2SMfNf7HW2vPTSVUPihR72t2OzWiLXgh5/+k/V6zfX1tYaENBXfff0XPXS5u2c2OefXX98YX80xHz7cMJ/P+dvf/uZwcMPK7rPBzB6f6n0NA8xppvW5552+57B8O31c/w5Ofv3Z9zwF2vZ97xr99vf2S7PBzJ2nOpUuegqqw2s8zSCHSrR2Z7aP+1ISxU9EZB3wIAwiPC+k762ApaCuW0Bwfq4DQFnq6VvddnRtTWOkW9qu0dzBtkbLNCd0XUNVl/SqY5SlTKeaFPz4eGQyyVhcLBhlI4eSl0IwGqWMMy3HHPgxmGsJTdmSpglFXtC0OlPrm4a6ad3ub7Xcy7LC2u5tt08mJUIIylLTljTS22C/RhPiOOH+/oHD4ch4POb8/JzjMTdO3yFlUTKZTAy96Mj+uMfzpdutbcC8ubnh8vLymXKDlZSJoojb21sW8wUPDw+u/LJDApsNWZqT7/umV1c4Xa3JdELfKw77g8uglFJcX19r2pR5rVXSsA1zm0EqoO17J7hoMWFxHHN9fc3Z2Zkjig/bMfbebcwE0yqK2PJxqLtn4Rj2nhvK/MATmT+KIr7++mum06mDgPi+tsmzLR+7hmwws4M42/Oz5wFPE8ssy0hjjffbbDau36czyxYlNIj6WBzMRNued2My44btds/V1QXffPMN2+0aITzm8xlN86TAczwW+H5IkmQEQeRsDy0x365t339CRXySzvSpqeQwOJwGomFDfAi5+FQjf/i7TzXZ4WNYxacOG0hOBwgOrSaeICC2+Y+ZEOoyU4H3pAAw7Jl9KtAOr9X2C4dfvud5eL5H38N+d+Aoj4ZUfqali5uSn376kSSJePXqFReX53ieYL22AoQ60Hd1pctJIAx9o7ulVXirqjSu4wGzmSZIa5ybtjXTf8sjTULi0OPYljRVTts01OWR3VprXSVRTBLHSBRSeHRtyXZTsD/sXSkWhiGTyZPzulKKpqlZr1f0vXI9tiRJnFjfw8Mjy8c1bavBtrPpHM8L2O0ObDZb8z1pPFtV1gR+5NRMyrKiKCrqpsEPtNDl7e0tNzc3xiBZ8w1/+OEH3Sg2+vS3t7duZP/jjz+SxLrpbIG+da0HVnd394Yg7TEeT5BSMh5PaBqt0qElm3yiwGe5fCAMQyetZCEPFrtlN6shtcr2p/quY5Jl7Pd7h8K/u7vj66+/5rfffnODEOtqL4RwpZ8F9do1YR2XrGChDTJ2AKSUckHbyozbwYbNPFerlZMysuXZUJLH3s9WFttCYawSiM3K7L1uS0V7zkII50BVVRW744HR2diBZcuqZbPest6sUD2Mxlpo4J//9k/4XsDd/e+kScbD4z3LxxWT8ZjVcsNfv/vfefv2Lbc39/zrv/4r9w+3/Lf/9n/x1VcvTWZtrBC/FMzsB/apTMsGidNsZfj8odKE/e8pFMMGomHAfJ4JfT6Y2Q/2UyWlUspxK4dBTilFrykALrOShhg/RJHDk3AdPA/c9v3sLmb7CnbnklLiG2uzJIhJjMP48ahdgKQHvh8AnpNGWa1W3N3d0hsxwOl0StU2eFIShr7LOGy/xAYae57237Zv1/UtTV2gupq2BU/2ZFlI3/movqdpagQdQQBhKJBCS0UHgTQ+ii1BgLnxI+I4RIgneRg7frcwkq5VdK1iPErdIEBPo3WAWq1WBgZRDLxGey3eaJr+VqlVOzn5NHVNL3qiSAdLu8ABN2yyk0XLILCfjVah3XJ2tnC2bFZg0H5/diK63W6dQ/loNOLy8pLlakmSJlR19UzD3srtWIT+bDZzCH67Zqzk+uFw4HJxznw+Zz6fs9lsHCxjt9s5WpHtg9nJrZUdr+uawDTGbTYipXQNfHv+NgOzen0WXmIzJRvIlFIO3DvEkJ3iPO2mPEwQ7KZtg2nXaR33zWZDFGqmxOFw4O7uzvUtX768pmgriurI/nCkaWoQMJmMUEozfPK8oSiOVFVDWeYsFgvKMmezXfPVq1dk0RSBz/FY0nUNj49LNuud2zh077GhV8+1FL8om/2pADZ8/LT/dfq4DWTDYDYMevaDex44wKJ6T7O4YZ9tiHEbBsNhNjl8rpX2cI/zcSnd91rD/Eul5rC0HZ63hqZ0dHTISFu31XVtmqctZ2dTo3sfGjHD35FS8tVXr5yS583dDVEUMV+c4fme63dYEKUfaKNa2xzX2cXY6VLtdlvGoxm+hL7viEKfxXxGEsX4ZjAjECRx7DJYK0VtG9LHPKftemqzOAWSONbpvl68+lr3u4Ph2mkPAaUEUZQwm00QAu7v742E0MhkchF13bBcrnSZVdVI6ZmyFfb7I4vFnLqp2Ow3jEaZkws6HA7s93tevHjB9fU1x+OR9+/fO06jnbbNZjOqqtaqE1LLQ8/nc+q6dngrm1lYqzlrEyeEoKkb6kbDLML4SRDR8i6H0upPXE6P9XpNWZZGUFDb2s3nc5f5TKfabf3ly5f0fc/t7e0zYUObXdn7yLJQ7CZlv297/9my2CYe9u/kea69GUzAtfet9VuwGZ0NUHZaPBQltRu0nWTa7LDrOjwhiOIYT2gjmr2xg7OYMys1lJ5lIBVIhR9q74jRKKWuWw6HHf/HP/6ftG0NUvC/ffdnLi6uuL+/RfqCumn49tUF//e//XfyY8l0Nubt27eMRhn/+I//SNvWZjOpqJuKrvvCNPPZoTfgj3tcSj1rbEkhYBA47JcyROafZjjDiPq0C5ihgfszz6eTNkCd9umG7/3s9AfnQ6/ltqUNvOr581wmehIcT/tyQ4Dg8Fr7vtdyJL7gfv2IWiqSOGY8NmTvvuP9zXvNdxyNmM2mSE+w3eoeSpJkvHrxChn47IsDnhBk4zFR4NP2nZ5INjVVUaJQTMdjkjQl8HwORU5dVkgliIKYsipYLTf0XeME8bIsI/B9zmYzkijieHwOyrTN7TQv6DpF3ZgMVnp0ndasu7/XvaA0yfC8gNEowfdDmrZju10ipeR4zPF9z3kvhOGOum4QQjonHrCDJGkWa21wWEemsykdPb3qHPDz4uLCqYoKoeWi66piko1YPTy5Ch3FHnrltLWs2/zFxQVlWTqxS2sVZ8UXoygyLuoRVVUyGWscYJpmCAkjYw7dK8XPf/879/f3juDseR6r9YqqLLi+vtbNdimZTTRxPAgCLi4vabqWMI5YLh+ZzPR5eZ5HWVcgBUhBj5aFb9oO6Xn0CNq+51jkzisgCD3y4vAEEfJCty6Ga2Q+nwPap7VuGqpGf8ZBFGqminySxvaEBtX2bUtrsjff85FKggepUfS19/92vXmmlCylpO20n0DZlMybcyazMdPxzH3fWZpyvkiQ8gV//vOf+bd/+zeK/MDZbAG9QiipFWHajre/vdElblMipdavU8YS0Pd9Q34vqeqSZiDQ+fE0c6BJphvlepd3j9vF3VsxRRMQLIBU6jgX+Jo8reNAT989TR8sbUgKENIGDEXf9TorUwOBRhdUBEJIGtNY75rexFTt1tT3TzvL8HCvN//2g8A9R0ofTdTWhO0w9IlC/1lz/3SSa18rxBNM46kk7zlWDUJK/NCn9WqOLZR5aXp2PbPJBD8INJpcaSHAIJgQhxon9LBaghSEUUgSpsznU5arB+42a6RQlMURz5f4XkDXNPhIRKcIvYDxfEzboalM+840n+eUpeBxeU9dlsxmE9IkpjUllO8nrDcHIzE9p216uk5peIWnpXcUHXlxIPAjxmPtiXgwWDcLfMyrnNVyjS9DLs4vaJqW46EiFxVtp6ejesOS1HVDVTdIoeEbVVXTdYrd/kBeFyC1ekjg+/hBwDHPnWUZSuEhWMzmHA8HPZVGcNztqcsKhWC3O3DY7hiPx7y4vNJBB0FTVohQIaIY0SvqouTud01knkwm3N/csT/suby+pK5aJDWer8vxwAvo+o6ZUWDt25ab+xvTtmhI0pi///ITi/mCF5cveHjUE1cN2I159/sH9scCL4gIooROCXb7A7u8QAUBvWnOb485RdEyP5tSlDXb/QEpFGeLOU1Tsdlt9GBhnCERtG1H39Yc91uapiONY/oO9tsDcZrQ9/DwuNR6gdLDDyO2xw3CKwmCCF/45MbXtMr1dDj0I+hACAm9osyfvrvSPCeOY/L8wORMT5ujNOZoYDTXi0vCMCYIdDXQtDVd0VDWB4Ig4Pdf3+J3EPQe5S4nXx+1Im3R8/D7HUl4oCy2/PUvf0JKycvrK3755WcmZ1rGqChr3n94r6lSBrD7yWB22gP73OOnPbOPgt0nHvtU9vT86F1QspQmpT4N2fj4sDiw53/P/tcGM4uDE+pjnJx+7GP9siHM41Nl+PCaatXgCT0t6mRP3VbQostcYLfZ6N7C9QutE1/V7Hd79mqngbG98f8sKw6HHR/eQ1nmNG1NliWDTK+nqhrHm6uqmrju2GwObHZH6qanXB+4f9zQNDVRGHA2m3J3t0RKQRT6ZGnprudw2PO43EAvyJIR05lyygz7/Z5OGRbDqEGh+2pVVbE3Y3KlFML4UFog7OGwxwgp4/u6DwiCKAqdzltRVLRt776XIi9oaQgjTSuK0wSJLj+apkEKQd1UHPZ7jocjtSnR4ihCIPCjiMX8nMJgr2xGY/t+lkFhDWUAR9vRpbzPbqNLutnXU/q+o8hzft/teVg9EMcxr//8Jx4eH/BXvimxdKlmByXCCIeWZamJ4p1Wm4jTBOFJdgft9J6XBfvjgU5pqk9Z13QKgihgd8jZ7HfEccR8NiFOApptRZ63NM0W35cszjSDwkMaeSkN95hOzvTnVNfUrRl4AQjt/zo/XzAZT5iNJ7RVzd3tLZvlitAPGE8nVFWDQDIZT7Tbk7D9Yu9ZxSWEztSiOHTDEZQgkAF1UbNb51R1Qd93+L5WGglCj/dv35rvMkSEgqbu6eoOOuhVz++P7w3E5GCUjn/m7u6O9W6rXb58LXu13R8Rhy/omX0uEH1pAQ/LNM97ImL/V/23zwU7pYaMAHHymk8HyacMzh9WwE+9rsG/9Tk/Zx6496d3wew0cNvy+FN/H2D4W/s8ib6BAk+XNy8vrzg7O2M6mSKFoDP0sMBAH6QX0HSNAafWlFXBdDrj4mLBZDohzw8GA9SQ5ytH7Wiall5pvuJsfkHbNcaM4sjl5SvO53O6rkH1nZZdCQPTZNflxnQ60eVgD6NsQhjrvlBVF5SVll2J4piyzFHoSWvb1hTFkbqpTd9J0TQV26pGS6l3QI+QEMchni/o2tYYfpiRe14aGZoYPwzoVAeewvc9kig2GUBOkedEYUhqJHasWKVtglvxwcPh6FDtlvxsy1M7xbOBWCktkWMFDfu+Z3G+QAgchAH0/bTb79hsNszOtG6/Va7QbRQ9WLC0NG2fpyeDD48PVE1D0zZaxFMIN/2sqoq20yWl7Y8pJc0gIUe1Ct/zDNtCmumxriCUemLb0Cn3nvP5nKvLFzRtS17qUtj3ffwwcJVEHMfUje5vqVbfp1mWIc05gXTwlTTNKCsNaZnNdGluyeOWswmasD8ej+ma3lDKSvb7LWWVm8c9I2+krz9JEsIgcYMUe/5SSmazidkINBRHI/59jsc9VVUQpxFVXWjxhsHxRdWM4cTw2aId/O70GD5n+PpPZVanDXYbqJ6VuSiXnenXyE++3u6+Un4GEmJ+XKnY6f7Z6TRUiI/P4XQy+qUjjmJ6nsbogecTJRFZmpLEiZPG3m42ejGgOWphoD0cdYboE0YhkOAHAeNxwmJxDigDNZBuGuz7nlE9aGlaRZwkFGXJcqV7WN9//z3ffPM1vpQ8Pt6TZSkCRRxpw1llMi7f1woXh+2BoshZrddsttqfMgg0B69XirwoabvWNZ3tBE0bAIMyyqJBEBLHEfrr6plMRnS9vnHTNOZsdkYcp+R5iXYc1yYoreoIogA/8BmZ3tdyuXR9KonQpaanG/mV6f0sFguElHS/31BWDWND/ekNmd9uRJbuM+QUW+bBdDrlfLFgv9cT1oeHB6bTCbPZlMl0gvQlx/zIr7/+6rIvwMkS2Yb5YXdECt1kb9qWABz0weLjLPyBQUNeSu0qv93uSJMUz88Iw4CqqinLBt/zuL6+IgwDulZP8uqqdll/HCdk2Yj5fK4z6uPBrWmHt5Qav7daroCeQGpK4mw8QSjY7LaMx1OOxu2q6zszxOoYjVKnx9ab6Xie5yDUkwtX25DvtZWh7/ukXmruEw31UGhtuTTNUL1kuVy6YJqmIzPllc84mBbiYj9Dq0QyZN7AJ4KZcx06ydA+lU19ruk+DGSnzfTTYcCzgDMIml8KGnbqOfyipLTI/aeA9KlDPb3JSRB87sR+GhA/B1M5/a/nBaiu1cMGKQn9iDCI8WRA38Pt7T2BHzC2QntJiu9pr8GmaUmzlE71eL40Gapu1m63Wx6Xj7RtpXmGnT4fba7rs9vt2e5ydtsdv719T1Hm/Mu//Av//M//TBD47LYbXr58QRRFbNYrgsB3OKrDYU9R5Gw2W+5v7siPJUVZass7A6qN45jWTDvt9NCyDCzHDyBNUrI4dQFSCIXnC6azEV3XEgQeo9GI2WxCFMV4vqAstEQQQlIW+oaPI62jf3V5SRxF5MYdfVNvdANbSOIoovV9FCA9jyzVemlBqBfWbrfj/fv3Dixrrek2mw3z+Zz9fs9PP/3kVFujKCLPdSPfqsJqQrOecjZtw2a9QaEIvMDxG5umNoyEUgOkpafPL47xu47EBL6dsV+zgOq8NI5D3ZObWFU15n1CpNQLVWc/LckoNkBeTbuLAg258IVnwL81x+PBmS8f8yMI4dQ4nrK7hOlsShJE9E3L/d0dQRRzeX7B4uKcIIxZDdQ5NCVrjxBaEsoKOYJW+AhCXW5HUURV1gbSkpFlE6SHwawdaTvtM+pgIC0us9Yu7ueEYciHD+8cJ3e9XmN9T5NRRt/37nsBnKQ6fIbONJw62oV8mlmdBh4bAE9Ly9MFf/rYaWamM8MhYPf0fSRSgpJP9Ckd0KyiRfvRuT+7PtMb00a94TM9K8/z6LvmWWAbfh6nmerpNQA0dY0S4okmYgjpFoU9ilNm04zFYqFvzK6nqWuE0vI6o9GItu+pGw2kTTNNfarrypRVHUKUhg6kJ4QaC5bTtVqscT6f8823f+P169duNwv8QBPO+56iyNltSkqDfj8c9kipy7CyqhACslFGGA0mZVISmhG83fDiONajev+pTBolGVmiy4eq1pNX3w8JQo9QaD9OP5AgeoTQ5armgWI8TUOEASQ/Pj7SmYVj1VrKvGCzXpOlKXLwvez3e4MRfPJQsFPmoT1ZkiRuOjrUPdvv98Zpu+Tb1984G7eyLNlsVqw3a+I0plc9Y8NK6NBwkCzTyHRrVBslke47CUFZVUjfwzNTcFvythbw3DTURgFEB67elKId2qo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" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + } + ] +} diff --git a/docs/zh_CN/tutorials/config.md b/docs/zh_CN/tutorials/config.md new file mode 100644 index 0000000..9e9c87e --- /dev/null +++ b/docs/zh_CN/tutorials/config.md @@ -0,0 +1,417 @@ +# 教程 1：如何编写配置文件 + +MMClassification 主要使用 python 文件作为配置文件。其配置文件系统的设计将模块化与继承整合进来，方便用户进行各种实验。所有配置文件都放置在 `configs` 文件夹下，主要包含 `_base_` 原始配置文件夹 以及 `resnet`, `swin_transformer`，`vision_transformer` 等诸多算法文件夹。 + +可以使用 `python tools/misc/print_config.py /PATH/TO/CONFIG` 命令来查看完整的配置信息，从而方便检查所对应的配置文件。 + + + +- [配置文件以及权重命名规则](#配置文件以及权重命名规则) +- [配置文件结构](#配置文件结构) +- [继承并修改配置文件](#继承并修改配置文件) + - [使用配置文件里的中间变量](#使用配置文件里的中间变量) + - [忽略基础配置文件里的部分内容](#忽略基础配置文件里的部分内容) + - [引用基础配置文件里的变量](#引用基础配置文件里的变量) +- [通过命令行参数修改配置信息](#通过命令行参数修改配置信息) +- [导入用户自定义模块](#导入用户自定义模块) +- [常见问题](#常见问题) + + + +## 配置文件以及权重命名规则 + +MMClassification 按照以下风格进行配置文件命名，代码库的贡献者需要遵循相同的命名规则。文件名总体分为四部分：算法信息，模块信息，训练信息和数据信息。逻辑上属于不同部分的单词之间用下划线 `'_'` 连接，同一部分有多个单词用短横线 `'-'` 连接。 + +``` +{algorithm info}_{module info}_{training info}_{data info}.py +``` + +- `algorithm info`：算法信息，算法名称或者网络架构，如 resnet 等； +- `module info`： 模块信息，因任务而异，用以表示一些特殊的 neck、head 和 pretrain 信息； +- `training info`：一些训练信息，训练策略设置，包括 batch size，schedule 数据增强等； +- `data info`：数据信息，数据集名称、模态、输入尺寸等，如 imagenet, cifar 等； + +### 算法信息 + +指论文中的算法名称缩写，以及相应的分支架构信息。例如： + +- `resnet50` +- `mobilenet-v3-large` +- `vit-small-patch32` : `patch32` 表示 `ViT` 切分的分块大小 +- `seresnext101-32x4d` : `SeResNet101` 基本网络结构，`32x4d` 表示在 `Bottleneck` 中 `groups` 和 `width_per_group` 分别为32和4 + +### 模块信息 + +指一些特殊的 `neck` 、`head` 或者 `pretrain` 的信息， 在分类中常见为预训练信息，比如： + +- `in21k-pre` : 在 `ImageNet21k` 上预训练 +- `in21k-pre-3rd-party` : 在 `ImageNet21k` 上预训练，其权重来自其他仓库 + +### 训练信息 + +训练策略的一些设置，包括训练类型、 `batch size`、 `lr schedule`、 数据增强以及特殊的损失函数等等,比如: +Batch size 信息： + +- 格式为`{gpu x batch_per_gpu}`, 如 `8xb32` + +训练类型(主要见于 transformer 网络，如 `ViT` 算法，这类算法通常分为预训练和微调两种模式): + +- `ft` : Finetune config，用于微调的配置文件 +- `pt` : Pretrain config，用于预训练的配置文件 + +训练策略信息，训练策略以复现配置文件为基础，此基础不必标注训练策略。但如果在此基础上进行改进，则需注明训练策略，按照应用点位顺序排列，如：`{pipeline aug}-{train aug}-{loss trick}-{scheduler}-{epochs}` + +- `coslr-200e` : 使用 cosine scheduler, 训练 200 个 epoch +- `autoaug-mixup-lbs-coslr-50e` : 使用了 `autoaug`、`mixup`、`label smooth`、`cosine scheduler`, 训练了 50 个轮次 + +### 数据信息 + +- `in1k` : `ImageNet1k` 数据集，默认使用 `224x224` 大小的图片 +- `in21k` : `ImageNet21k` 数据集，有些地方也称为 `ImageNet22k` 数据集，默认使用 `224x224` 大小的图片 +- `in1k-384px` : 表示训练的输出图片大小为 `384x384` +- `cifar100` + +### 配置文件命名案例： + +``` +repvgg-D2se_deploy_4xb64-autoaug-lbs-mixup-coslr-200e_in1k.py +``` + +- `repvgg-D2se`: 算法信息 + - `repvgg`: 主要算法名称。 + - `D2se`: 模型的结构。 +- `deploy`:模块信息，该模型为推理状态。 +- `4xb64-autoaug-lbs-mixup-coslr-200e`: 训练信息 + - `4xb64`: 使用4块 GPU 并且 每块 GPU 的批大小为64。 + - `autoaug`: 使用 `AutoAugment` 数据增强方法。 + - `lbs`: 使用 `label smoothing` 损失函数。 + - `mixup`: 使用 `mixup` 训练增强方法。 + - `coslr`: 使用 `cosine scheduler` 优化策略。 + - `200e`: 训练 200 轮次。 +- `in1k`: 数据信息。 配置文件用于 `ImageNet1k` 数据集上使用 `224x224` 大小图片训练。 + +```{note} +部分配置文件目前还没有遵循此命名规范，相关文件命名近期会更新。 +``` + +### 权重命名规则 + +权重的命名主要包括配置文件名，日期和哈希值。 + +``` +{config_name}_{date}-{hash}.pth +``` + +## 配置文件结构 + +在 `configs/_base_` 文件夹下有 4 个基本组件类型，分别是： + +- [模型(model)](https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/models) +- [数据(data)](https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/datasets) +- [训练策略(schedule)](https://github.com/open-mmlab/mmclassification/tree/master/configs/_base_/schedules) +- [运行设置(runtime)](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/default_runtime.py) + +你可以通过继承一些基本配置文件轻松构建自己的训练配置文件。由来自`_base_` 的组件组成的配置称为 _primitive_。 + +为了帮助用户对 MMClassification 检测系统中的完整配置和模块有一个基本的了解，我们使用 [ResNet50 原始配置文件](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_8xb32_in1k.py) 作为案例进行说明并注释每一行含义。更详细的用法和各个模块对应的替代方案，请参考 API 文档。 + +```python +_base_ = [ + '../_base_/models/resnet50.py', # 模型 + '../_base_/datasets/imagenet_bs32.py', # 数据 + '../_base_/schedules/imagenet_bs256.py', # 训练策略 + '../_base_/default_runtime.py' # 默认运行设置 +] +``` + +下面对这四个部分分别进行说明，仍然以上述 ResNet50 原始配置文件作为案例。 + +### 模型 + +模型参数 `model` 在配置文件中为一个 `python` 字典，主要包括网络结构、损失函数等信息： + +- `type` ： 分类器名称, 目前 MMClassification 只支持 `ImageClassifier`， 参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#classifier)。 +- `backbone` ： 主干网类型，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#backbones)。 +- `neck` ： 颈网络类型，目前 MMClassification 只支持 `GlobalAveragePooling`， 参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#necks)。 +- `head` ： 头网络类型， 包括单标签分类与多标签分类头网络，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#heads)。 + - `loss` ： 损失函数类型， 支持 `CrossEntropyLoss`, [`LabelSmoothLoss`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_label_smooth.py) 等，可用选项参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#losses)。 +- `train_cfg` ：训练配置, 支持 [`mixup`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_mixup.py), [`cutmix`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/models/resnet50_cutmix.py) 等训练增强。 + +```{note} +配置文件中的 'type' 不是构造时的参数，而是类名。 +``` + +```python +model = dict( + type='ImageClassifier', # 分类器类型 + backbone=dict( + type='ResNet', # 主干网络类型 + depth=50, # 主干网网络深度， ResNet 一般有18, 34, 50, 101, 152 可以选择 + num_stages=4, # 主干网络状态(stages)的数目，这些状态产生的特征图作为后续的 head 的输入。 + out_indices=(3, ), # 输出的特征图输出索引。越远离输入图像，索引越大 + frozen_stages=-1, # 网络微调时，冻结网络的stage（训练时不执行反相传播算法），若num_stages=4，backbone包含stem 与 4 个 stages。frozen_stages为-1时，不冻结网络； 为0时，冻结 stem； 为1时，冻结 stem 和 stage1； 为4时，冻结整个backbone + style='pytorch'), # 主干网络的风格，'pytorch' 意思是步长为2的层为 3x3 卷积， 'caffe' 意思是步长为2的层为 1x1 卷积。 + neck=dict(type='GlobalAveragePooling'), # 颈网络类型 + head=dict( + type='LinearClsHead', # 线性分类头， + num_classes=1000, # 输出类别数，这与数据集的类别数一致 + in_channels=2048, # 输入通道数，这与 neck 的输出通道一致 + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), # 损失函数配置信息 + topk=(1, 5), # 评估指标，Top-k 准确率， 这里为 top1 与 top5 准确率 + )) +``` + +### 数据 + +数据参数 `data` 在配置文件中为一个 `python` 字典，主要包含构造数据集加载器(dataloader)配置信息： + +- `samples_per_gpu` : 构建 dataloader 时，每个 GPU 的 Batch Size +- `workers_per_gpu` : 构建 dataloader 时，每个 GPU 的 线程数 +- `train ｜ val ｜ test` : 构造数据集 + - `type` : 数据集类型， MMClassification 支持 `ImageNet`、 `Cifar` 等 ，参考[API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html) + - `data_prefix` : 数据集根目录 + - `pipeline` : 数据处理流水线，参考相关教程文档 [如何设计数据处理流水线](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/data_pipeline.html) + +评估参数 `evaluation` 也是一个字典， 为 `evaluation hook` 的配置信息, 主要包括评估间隔、评估指标等。 + +```python +# dataset settings +dataset_type = 'ImageNet' # 数据集名称， +img_norm_cfg = dict( #图像归一化配置，用来归一化输入的图像。 + mean=[123.675, 116.28, 103.53], # 预训练里用于预训练主干网络模型的平均值。 + std=[58.395, 57.12, 57.375], # 预训练里用于预训练主干网络模型的标准差。 + to_rgb=True) # 是否反转通道，使用 cv2, mmcv 读取图片默认为 BGR 通道顺序，这里 Normalize 均值方差数组的数值是以 RGB 通道顺序， 因此需要反转通道顺序。 +# 训练数据流水线 +train_pipeline = [ + dict(type='LoadImageFromFile'), # 读取图片 + dict(type='RandomResizedCrop', size=224), # 随机缩放抠图 + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), # 以概率为0.5随机水平翻转图片 + dict(type='Normalize', **img_norm_cfg), # 归一化 + dict(type='ImageToTensor', keys=['img']), # image 转为 torch.Tensor + dict(type='ToTensor', keys=['gt_label']), # gt_label 转为 torch.Tensor + dict(type='Collect', keys=['img', 'gt_label']) # 决定数据中哪些键应该传递给检测器的流程 +] +# 测试数据流水线 +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=(256, -1)), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) # test 时不传递 gt_label +] +data = dict( + samples_per_gpu=32, # 单个 GPU 的 Batch size + workers_per_gpu=2, # 单个 GPU 的 线程数 + train=dict( # 训练数据信息 + type=dataset_type, # 数据集名称 + data_prefix='data/imagenet/train', # 数据集目录，当不存在 ann_file 时，类别信息从文件夹自动获取 + pipeline=train_pipeline), # 数据集需要经过的 数据流水线 + val=dict( # 验证数据集信息 + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', # 标注文件路径，存在 ann_file 时，不通过文件夹自动获取类别信息 + pipeline=test_pipeline), + test=dict( # 测试数据集信息 + type=dataset_type, + data_prefix='data/imagenet/val', + ann_file='data/imagenet/meta/val.txt', + pipeline=test_pipeline)) +evaluation = dict( # evaluation hook 的配置 + interval=1, # 验证期间的间隔，单位为 epoch 或者 iter， 取决于 runner 类型。 + metric='accuracy') # 验证期间使用的指标。 +``` + +### 训练策略 + +主要包含 优化器设置、 `optimizer hook` 设置、学习率策略和 `runner`设置： + +- `optimizer` : 优化器设置信息, 支持 `pytorch` 所有的优化器，参考相关 [mmcv](https://mmcv.readthedocs.io/zh_CN/latest/_modules/mmcv/runner/optimizer/default_constructor.html#DefaultOptimizerConstructor) 文档 +- `optimizer_config` : `optimizer hook` 的配置文件,如设置梯度限制，参考相关 [mmcv](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8) 代码 +- `lr_config` : 学习率策略，支持 "CosineAnnealing"、 "Step"、 "Cyclic" 等等，参考相关 [mmcv](https://mmcv.readthedocs.io/zh_CN/latest/_modules/mmcv/runner/hooks/lr_updater.html#LrUpdaterHook) 文档 +- `runner` : 有关 `runner` 可以参考 `mmcv` 对于 [`runner`](https://mmcv.readthedocs.io/zh_CN/latest/understand_mmcv/runner.html) 介绍文档 + +```python +# 用于构建优化器的配置文件。支持 PyTorch 中的所有优化器，同时它们的参数与 PyTorch 里的优化器参数一致。 +optimizer = dict(type='SGD', # 优化器类型 + lr=0.1, # 优化器的学习率，参数的使用细节请参照对应的 PyTorch 文档。 + momentum=0.9, # 动量(Momentum) + weight_decay=0.0001) # 权重衰减系数(weight decay)。 + # optimizer hook 的配置文件 +optimizer_config = dict(grad_clip=None) # 大多数方法不使用梯度限制(grad_clip)。 +# 学习率调整配置，用于注册 LrUpdater hook。 +lr_config = dict(policy='step', # 调度流程(scheduler)的策略，也支持 CosineAnnealing, Cyclic, 等。 + step=[30, 60, 90]) # 在 epoch 为 30, 60, 90 时， lr 进行衰减 +runner = dict(type='EpochBasedRunner', # 将使用的 runner 的类别，如 IterBasedRunner 或 EpochBasedRunner。 + max_epochs=100) # runner 总回合数， 对于 IterBasedRunner 使用 `max_iters` +``` + +### 运行设置 + +本部分主要包括保存权重策略、日志配置、训练参数、断点权重路径和工作目录等等。 + +```python +# Checkpoint hook 的配置文件。 +checkpoint_config = dict(interval=1) # 保存的间隔是 1，单位会根据 runner 不同变动，可以为 epoch 或者 iter。 +# 日志配置信息。 +log_config = dict( + interval=100, # 打印日志的间隔， 单位 iters + hooks=[ + dict(type='TextLoggerHook'), # 用于记录训练过程的文本记录器(logger)。 + # dict(type='TensorboardLoggerHook') # 同样支持 Tensorboard 日志 + ]) + +dist_params = dict(backend='nccl') # 用于设置分布式训练的参数，端口也同样可被设置。 +log_level = 'INFO' # 日志的输出级别。 +resume_from = None # 从给定路径里恢复检查点(checkpoints)，训练模式将从检查点保存的轮次开始恢复训练。 +workflow = [('train', 1)] # runner 的工作流程，[('train', 1)] 表示只有一个工作流且工作流仅执行一次。 +work_dir = 'work_dir' # 用于保存当前实验的模型检查点和日志的目录文件地址。 +``` + +## 继承并修改配置文件 + +为了精简代码、更快的修改配置文件以及便于理解，我们建议继承现有方法。 + +对于在同一算法文件夹下的所有配置文件，MMClassification 推荐只存在 **一个** 对应的 _原始配置_ 文件。 +所有其他的配置文件都应该继承 _原始配置_ 文件，这样就能保证配置文件的最大继承深度为 3。 + +例如，如果在 ResNet 的基础上做了一些修改，用户首先可以通过指定 `_base_ = './resnet50_8xb32_in1k.py'`（相对于你的配置文件的路径），来继承基础的 ResNet 结构、数据集以及其他训练配置信息，然后修改配置文件中的必要参数以完成继承。如想在基础 resnet50 的基础上将训练轮数由 100 改为 300 和修改学习率衰减轮数，同时修改数据集路径，可以建立新的配置文件 `configs/resnet/resnet50_8xb32-300e_in1k.py`， 文件中写入以下内容: + +```python +_base_ = './resnet50_8xb32_in1k.py' + +runner = dict(max_epochs=300) +lr_config = dict(step=[150, 200, 250]) + +data = dict( + train=dict(data_prefix='mydata/imagenet/train'), + val=dict(data_prefix='mydata/imagenet/train', ), + test=dict(data_prefix='mydata/imagenet/train', ) +) +``` + +### 使用配置文件里的中间变量 + +用一些中间变量，中间变量让配置文件更加清晰，也更容易修改。 + +例如数据集里的 `train_pipeline` / `test_pipeline` 是作为数据流水线的中间变量。我们首先要定义 `train_pipeline` / `test_pipeline`，然后将它们传递到 `data` 中。如果想修改训练或测试时输入图片的大小，就需要修改 `train_pipeline` / `test_pipeline` 这些中间变量。 + +```python +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=384, backend='pillow',), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=384, backend='pillow'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +``` + +### 忽略基础配置文件里的部分内容 + +有时，您需要设置 `_delete_=True` 去忽略基础配置文件里的一些域内容。 可以参照 [mmcv](https://mmcv.readthedocs.io/zh_CN/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) 来获得一些简单的指导。 + +以下是一个简单应用案例。 如果在上述 ResNet50 案例中 使用 cosine schedule ,使用继承并直接修改会报 `get unexcepected keyword 'step'` 错, 因为基础配置文件 lr_config 域信息的 `'step'` 字段被保留下来了，需要加入 `_delete_=True` 去忽略基础配置文件里的 `lr_config` 相关域内容： + +```python +_base_ = '../../configs/resnet/resnet50_8xb32_in1k.py' + +lr_config = dict( + _delete_=True, + policy='CosineAnnealing', + min_lr=0, + warmup='linear', + by_epoch=True, + warmup_iters=5, + warmup_ratio=0.1 +) +``` + +### 引用基础配置文件里的变量 + +有时，您可以引用 `_base_` 配置信息的一些域内容，这样可以避免重复定义。 可以参照 [mmcv](https://mmcv.readthedocs.io/zh_CN/latest/understand_mmcv/config.html#reference-variables-from-base) 来获得一些简单的指导。 + +以下是一个简单应用案例，在训练数据预处理流水线中使用 auto augment 数据增强，参考配置文件 [`configs/_base_/datasets/imagenet_bs64_autoaug.py`](https://github.com/open-mmlab/mmclassification/blob/master/configs/_base_/datasets/imagenet_bs64_autoaug.py)。 在定义 `train_pipeline` 时，可以直接在 `_base_` 中加入定义 auto augment 数据增强的文件命名，再通过 `{{_base_.auto_increasing_policies}}` 引用变量： + +```python +_base_ = ['./pipelines/auto_aug.py'] + +# dataset settings +dataset_type = 'ImageNet' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='AutoAugment', policies={{_base_.auto_increasing_policies}}), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [...] +data = dict( + samples_per_gpu=64, + workers_per_gpu=2, + train=dict(..., pipeline=train_pipeline), + val=dict(..., pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='accuracy') +``` + +## 通过命令行参数修改配置信息 + +当用户使用脚本 "tools/train.py" 或者 "tools/test.py" 提交任务，以及使用一些工具脚本时，可以通过指定 `--cfg-options` 参数来直接修改所使用的配置文件内容。 + +- 更新配置文件内的字典 + + 可以按照原始配置文件中字典的键的顺序指定配置选项。 + 例如，`--cfg-options model.backbone.norm_eval=False` 将主干网络中的所有 BN 模块更改为 `train` 模式。 + +- 更新配置文件内列表的键 + + 一些配置字典在配置文件中会形成一个列表。例如，训练流水线 `data.train.pipeline` 通常是一个列表。 + 例如，`[dict(type='LoadImageFromFile'), dict(type='TopDownRandomFlip', flip_prob=0.5), ...]` 。如果要将流水线中的 `'flip_prob=0.5'` 更改为 `'flip_prob=0.0'`，您可以这样指定 `--cfg-options data.train.pipeline.1.flip_prob=0.0` 。 + +- 更新列表/元组的值。 + + 当配置文件中需要更新的是一个列表或者元组，例如，配置文件通常会设置 `workflow=[('train', 1)]`，用户如果想更改， + 需要指定 `--cfg-options workflow="[(train,1),(val,1)]"`。注意这里的引号 " 对于列表以及元组数据类型的修改是必要的， + 并且 **不允许** 引号内所指定的值的书写存在空格。 + +## 导入用户自定义模块 + +```{note} +本部分仅在当将 MMClassification 当作库构建自己项目时可能用到，初学者可跳过。 +``` + +在学习完后续教程 [如何添加新数据集](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/new_dataset.html)、[如何设计数据处理流程](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/data_pipeline.html) 、[如何增加新模块](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/new_modules.html) 后，您可能使用 MMClassification 完成自己的项目并在项目中自定义了数据集、模型、数据增强等。为了精简代码，可以将 MMClassification 作为一个第三方库，只需要保留自己的额外的代码，并在配置文件中导入自定义的模块。案例可以参考 [OpenMMLab 算法大赛项目](https://github.com/zhangrui-wolf/openmmlab-competition-2021)。 + +只需要在你的配置文件中添加以下代码： + +```python +custom_imports = dict( + imports=['your_dataset_class', + 'your_transforme_class', + 'your_model_class', + 'your_module_class'], + allow_failed_imports=False) +``` + +## 常见问题 + +- 无 diff --git a/docs/zh_CN/tutorials/data_pipeline.md b/docs/zh_CN/tutorials/data_pipeline.md new file mode 100644 index 0000000..bbcf9d5 --- /dev/null +++ b/docs/zh_CN/tutorials/data_pipeline.md @@ -0,0 +1,148 @@ +# 教程 4：如何设计数据处理流程 + +## 设计数据流水线 + +按照典型的用法，我们通过 `Dataset` 和 `DataLoader` 来使用多个 worker 进行数据加 +载。对 `Dataset` 的索引操作将返回一个与模型的 `forward` 方法的参数相对应的字典。 + +数据流水线和数据集在这里是解耦的。通常，数据集定义如何处理标注文件，而数据流水 +线定义所有准备数据字典的步骤。流水线由一系列操作组成。每个操作都将一个字典作为 +输入，并输出一个字典。 + +这些操作分为数据加载，预处理和格式化。 + +这里使用 ResNet-50 在 ImageNet 数据集上的数据流水线作为示例。 + +```python +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', size=256), + dict(type='CenterCrop', crop_size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) +] +``` + +对于每个操作，我们列出了添加、更新、删除的相关字典字段。在流水线的最后，我们使 +用 `Collect` 仅保留进行模型 `forward` 方法所需的项。 + +### 数据加载 + +`LoadImageFromFile` - 从文件中加载图像 + +- 添加：img, img_shape, ori_shape + +默认情况下，`LoadImageFromFile` 将会直接从硬盘加载图像，但对于一些效率较高、规 +模较小的模型，这可能会导致 IO 瓶颈。MMCV 支持多种数据加载后端来加速这一过程。例 +如，如果训练设备上配置了 [memcached](https://memcached.org/)，那么我们按照如下 +方式修改配置文件。 + +``` +memcached_root = '/mnt/xxx/memcached_client/' +train_pipeline = [ + dict( + type='LoadImageFromFile', + file_client_args=dict( + backend='memcached', + server_list_cfg=osp.join(memcached_root, 'server_list.conf'), + client_cfg=osp.join(memcached_root, 'client.conf'))), +] +``` + +更多支持的数据加载后端，可以参见 [mmcv.fileio.FileClient](https://github.com/open-mmlab/mmcv/blob/master/mmcv/fileio/file_client.py)。 + +### 预处理 + +`Resize` - 缩放图像尺寸 + +- 添加：scale, scale_idx, pad_shape, scale_factor, keep_ratio +- 更新：img, img_shape + +`RandomFlip` - 随机翻转图像 + +- 添加：flip, flip_direction +- 更新：img + +`RandomCrop` - 随机裁剪图像 + +- 更新：img, pad_shape + +`Normalize` - 图像数据归一化 + +- 添加：img_norm_cfg +- 更新：img + +### 格式化 + +`ToTensor` - 转换（标签）数据至 `torch.Tensor` + +- 更新：根据参数 `keys` 指定 + +`ImageToTensor` - 转换图像数据至 `torch.Tensor` + +- 更新：根据参数 `keys` 指定 + +`Collect` - 保留指定键值 + +- 删除：除了参数 `keys` 指定以外的所有键值对 + +## 扩展及使用自定义流水线 + +1. 编写一个新的数据处理操作，并放置在 `mmcls/datasets/pipelines/` 目录下的任何 + 一个文件中，例如 `my_pipeline.py`。这个类需要重载 `__call__` 方法，接受一个 + 字典作为输入，并返回一个字典。 + + ```python + from mmcls.datasets import PIPELINES + + @PIPELINES.register_module() + class MyTransform(object): + + def __call__(self, results): + # 对 results['img'] 进行变换操作 + return results + ``` + +2. 在 `mmcls/datasets/pipelines/__init__.py` 中导入这个新的类。 + + ```python + ... + from .my_pipeline import MyTransform + + __all__ = [ + ..., 'MyTransform' + ] + ``` + +3. 在数据流水线的配置中添加这一操作。 + + ```python + img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='RandomResizedCrop', size=224), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='MyTransform'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']) + ] + ``` + +## 流水线可视化 + +设计好数据流水线后，可以使用[可视化工具](../tools/visualization.md)查看具体的效果。 diff --git a/docs/zh_CN/tutorials/finetune.md b/docs/zh_CN/tutorials/finetune.md new file mode 100644 index 0000000..efaa88f --- /dev/null +++ b/docs/zh_CN/tutorials/finetune.md @@ -0,0 +1,222 @@ +# 教程 2：如何微调模型 + +已经证明，在 ImageNet 数据集上预先训练的分类模型对于其他数据集和其他下游任务有很好的效果。 + +该教程提供了如何将 [Model Zoo](https://github.com/open-mmlab/mmclassification/blob/master/docs/model_zoo.md) 中提供的预训练模型用于其他数据集，已获得更好的效果。 + +在新数据集上微调模型分为两步： + +- 按照 [教程 3：如何自定义数据集](new_dataset.md) 添加对新数据集的支持。 +- 按照本教程中讨论的内容修改配置文件 + +假设我们现在有一个在 ImageNet-2012 数据集上训练好的 ResNet-50 模型，并且希望在 +CIFAR-10 数据集上进行模型微调，我们需要修改配置文件中的五个部分。 + +## 继承基础配置 + +首先，创建一个新的配置文件 `configs/tutorial/resnet50_finetune_cifar.py` 来保存我们的配置，当然，这个文件名可以自由设定。 + +为了重用不同配置之间的通用部分，我们支持从多个现有配置中继承配置。要微调 +ResNet-50 模型，新配置需要继承 `_base_/models/resnet50.py` 来搭建模型的基本结构。 +为了使用 CIFAR10 数据集，新的配置文件可以直接继承 `_base_/datasets/cifar10.py`。 +而为了保留运行相关设置，比如训练调整器，新的配置文件需要继承 +`_base_/default_runtime.py`。 + +要继承以上这些配置文件，只需要把下面一段代码放在我们的配置文件开头。 + +```python +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/cifar10.py', '../_base_/default_runtime.py' +] +``` + +除此之外，你也可以不使用继承，直接编写完整的配置文件，例如 +[`configs/lenet/lenet5_mnist.py`](https://github.com/open-mmlab/mmclassification/blob/master/configs/lenet/lenet5_mnist.py)。 + +## 修改模型 + +在进行模型微调是，我们通常希望在主干网络（backbone）加载预训练模型，再用我们的数据集训练一个新的分类头（head）。 + +为了在主干网络加载预训练模型，我们需要修改主干网络的初始化设置，使用 +`Pretrained` 类型的初始化函数。另外，在初始化设置中，我们使用 +`prefix='backbone'` 来告诉初始化函数移除权重文件中键值名称的前缀，比如把 +`backbone.conv1` 变成 `conv1`。方便起见，我们这里使用一个在线的权重文件链接，它 +会在训练前自动下载对应的文件，你也可以提前下载这个模型，然后使用本地路径。 + +接下来，新的配置文件需要按照新数据集的类别数目来修改分类头的配置。只需要修改分 +类头中的 `num_classes` 设置即可。 + +```python +model = dict( + backbone=dict( + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', + prefix='backbone', + )), + head=dict(num_classes=10), +) +``` + +```{tip} +这里我们只需要设定我们想要修改的部分配置，其他配置将会自动从我们的父配置文件中获取。 +``` + +另外，有时我们在进行微调时会希望冻结主干网络前面几层的参数，这么做有助于在后续 +训练中，保持网络从预训练权重中获得的提取低阶特征的能力。在 MMClassification 中， +这一功能可以通过简单的一个 `frozen_stages` 参数来实现。比如我们需要冻结前两层网 +络的参数，只需要在上面的配置中添加一行： + +```python +model = dict( + backbone=dict( + frozen_stages=2, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', + prefix='backbone', + )), + head=dict(num_classes=10), +) +``` + +```{note} +目前还不是所有的网络都支持 `frozen_stages` 参数，在使用之前，请先检查 +[文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/models.html#backbones) +以确认你所使用的主干网络是否支持。 +``` + +## 修改数据集 + +当针对一个新的数据集进行微调时，我们通常都需要修改一些数据集相关的配置。比如这 +里，我们就需要把 CIFAR-10 数据集中的图像大小从 32 缩放到 224 来配合 ImageNet 上 +预训练模型的输入。这一需要可以通过修改数据集的预处理流水线（pipeline）来实现。 + +```python +img_norm_cfg = dict( + mean=[125.307, 122.961, 113.8575], + std=[51.5865, 50.847, 51.255], + to_rgb=False, +) +train_pipeline = [ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']), +] +test_pipeline = [ + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) +``` + +## 修改训练策略设置 + +用于微调任务的超参数与默认配置不同，通常只需要较小的学习率和较少的训练时间。 + +```python +# 用于批大小为 128 的优化器学习率 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# 学习率衰减策略 +lr_config = dict(policy='step', step=[15]) +runner = dict(type='EpochBasedRunner', max_epochs=200) +log_config = dict(interval=100) +``` + +## 开始训练 + +现在，我们完成了用于微调的配置文件，完整的文件如下： + +```python +_base_ = [ + '../_base_/models/resnet50.py', + '../_base_/datasets/cifar10_bs16.py', '../_base_/default_runtime.py' +] + +# 模型设置 +model = dict( + backbone=dict( + frozen_stages=2, + init_cfg=dict( + type='Pretrained', + checkpoint='https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb32_in1k_20210831-ea4938fc.pth', + prefix='backbone', + )), + head=dict(num_classes=10), +) + +# 数据集设置 +img_norm_cfg = dict( + mean=[125.307, 122.961, 113.8575], + std=[51.5865, 50.847, 51.255], + to_rgb=False, +) +train_pipeline = [ + dict(type='RandomCrop', size=32, padding=4), + dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['gt_label']), + dict(type='Collect', keys=['img', 'gt_label']), +] +test_pipeline = [ + dict(type='Resize', size=224), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) + +# 训练策略设置 +# 用于批大小为 128 的优化器学习率 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# 学习率衰减策略 +lr_config = dict(policy='step', step=[15]) +runner = dict(type='EpochBasedRunner', max_epochs=200) +log_config = dict(interval=100) +``` + +接下来，我们使用一台 8 张 GPU 的电脑来训练我们的模型，指令如下： + +```shell +bash tools/dist_train.sh configs/tutorial/resnet50_finetune_cifar.py 8 +``` + +当然，我们也可以使用单张 GPU 来进行训练，使用如下命令： + +```shell +python tools/train.py configs/tutorial/resnet50_finetune_cifar.py +``` + +但是如果我们使用单张 GPU 进行训练的话，需要在数据集设置部分作如下修改： + +```python +data = dict( + samples_per_gpu=128, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline), +) +``` + +这是因为我们的训练策略是针对批次大小（batch size）为 128 设置的。在父配置文件中， +设置了 `samples_per_gpu=16`，如果使用 8 张 GPU，总的批次大小就是 128。而如果使 +用单张 GPU，就必须手动修改 `samples_per_gpu=128` 来匹配训练策略。 diff --git a/docs/zh_CN/tutorials/new_dataset.md b/docs/zh_CN/tutorials/new_dataset.md new file mode 100644 index 0000000..86782a1 --- /dev/null +++ b/docs/zh_CN/tutorials/new_dataset.md @@ -0,0 +1,230 @@ +# 教程 3：如何自定义数据集 + +我们支持许多常用的图像分类领域公开数据集，你可以在 +[此页面](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html)中找到它们。 + +在本节中，我们将介绍如何[使用自己的数据集](#使用自己的数据集)以及如何[使用数据集包装](#使用数据集包装)。 + +## 使用自己的数据集 + +### 将数据集重新组织为已有格式 + +想要使用自己的数据集，最简单的方法就是将数据集转换为现有的数据集格式。 + +对于多分类任务，我们推荐使用 [`CustomDataset`](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html#mmcls.datasets.CustomDataset) 格式。 + +`CustomDataset` 支持两种类型的数据格式： + +1. 提供一个标注文件，其中每一行表示一张样本图片。 + + 样本图片可以以任意的结构进行组织，比如： + + ``` + train/ + ├── folder_1 + │ ├── xxx.png + │ ├── xxy.png + │ └── ... + ├── 123.png + ├── nsdf3.png + └── ... + ``` + + 而标注文件则记录了所有样本图片的文件路径以及相应的类别序号。其中第一列表示图像 + 相对于主目录（本例中为 `train` 目录）的路径，第二列表示类别序号： + + ``` + folder_1/xxx.png 0 + folder_1/xxy.png 1 + 123.png 1 + nsdf3.png 2 + ... + ``` + + ```{note} + 类别序号的值应当属于 `[0, num_classes - 1]` 范围。 + ``` + +2. 将所有样本文件按如下结构进行组织： + + ``` + train/ + ├── cat + │ ├── xxx.png + │ ├── xxy.png + │ └── ... + │ └── xxz.png + ├── bird + │ ├── bird1.png + │ ├── bird2.png + │ └── ... + └── dog + ├── 123.png + ├── nsdf3.png + ├── ... + └── asd932_.png + ``` + + 这种情况下，你不需要提供标注文件，所有位于 `cat` 目录下的图片文件都会被视为 `cat` 类别的样本。 + +通常而言，我们会将整个数据集分为三个子数据集：`train`，`val` 和 `test`，分别用于训练、验证和测试。**每一个**子 +数据集都需要被组织成如上的一种结构。 + +举个例子，完整的数据集结构如下所示（使用第一种组织结构）： + +``` +mmclassification +└── data + └── my_dataset + ├── meta + │ ├── train.txt + │ ├── val.txt + │ └── test.txt + ├── train + ├── val + └── test +``` + +之后在你的配置文件中，可以修改其中的 `data` 字段为如下格式： + +```python +... +dataset_type = 'CustomDataset' +classes = ['cat', 'bird', 'dog'] # 数据集中各类别的名称 + +data = dict( + train=dict( + type=dataset_type, + data_prefix='data/my_dataset/train', + ann_file='data/my_dataset/meta/train.txt', + classes=classes, + pipeline=train_pipeline + ), + val=dict( + type=dataset_type, + data_prefix='data/my_dataset/val', + ann_file='data/my_dataset/meta/val.txt', + classes=classes, + pipeline=test_pipeline + ), + test=dict( + type=dataset_type, + data_prefix='data/my_dataset/test', + ann_file='data/my_dataset/meta/test.txt', + classes=classes, + pipeline=test_pipeline + ) +) +... +``` + +### 创建一个新的数据集类 + +用户可以编写一个继承自 `BasesDataset` 的新数据集类，并重载 `load_annotations(self)` 方法， +类似 [CIFAR10](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/datasets/cifar.py) +和 [ImageNet](https://github.com/open-mmlab/mmclassification/blob/master/mmcls/datasets/imagenet.py)。 + +通常，此方法返回一个包含所有样本的列表，其中的每个样本都是一个字典。字典中包含了必要的数据信息，例如 `img` 和 `gt_label`。 + +假设我们将要实现一个 `Filelist` 数据集，该数据集将使用文件列表进行训练和测试。注释列表的格式如下： + +``` +000001.jpg 0 +000002.jpg 1 +``` + +我们可以在 `mmcls/datasets/filelist.py` 中创建一个新的数据集类以加载数据。 + +```python +import mmcv +import numpy as np + +from .builder import DATASETS +from .base_dataset import BaseDataset + + +@DATASETS.register_module() +class Filelist(BaseDataset): + + def load_annotations(self): + assert isinstance(self.ann_file, str) + + data_infos = [] + with open(self.ann_file) as f: + samples = [x.strip().split(' ') for x in f.readlines()] + for filename, gt_label in samples: + info = {'img_prefix': self.data_prefix} + info['img_info'] = {'filename': filename} + info['gt_label'] = np.array(gt_label, dtype=np.int64) + data_infos.append(info) + return data_infos + +``` + +将新的数据集类加入到 `mmcls/datasets/__init__.py` 中： + +```python +from .base_dataset import BaseDataset +... +from .filelist import Filelist + +__all__ = [ + 'BaseDataset', ... ,'Filelist' +] +``` + +然后在配置文件中，为了使用 `Filelist`，用户可以按以下方式修改配置 + +```python +train = dict( + type='Filelist', + ann_file = 'image_list.txt', + pipeline=train_pipeline +) +``` + +## 使用数据集包装 + +数据集包装是一种可以改变数据集类行为的类，比如将数据集中的样本进行重复，或是将不同类别的数据进行再平衡。 + +### 重复数据集 + +我们使用 `RepeatDataset` 作为一个重复数据集的封装。举个例子，假设原始数据集是 `Dataset_A`，为了重复它，我们需要如下的配置文件： + +```python +data = dict( + train=dict( + type='RepeatDataset', + times=N, + dataset=dict( # 这里是 Dataset_A 的原始配置 + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) + ... +) +``` + +### 类别平衡数据集 + +我们使用 `ClassBalancedDataset` 作为根据类别频率对数据集进行重复采样的封装类。进行重复采样的数据集需要实现函数 `self.get_cat_ids(idx)` 以支持 `ClassBalancedDataset`。 + +举个例子，按照 `oversample_thr=1e-3` 对 `Dataset_A` 进行重复采样，需要如下的配置文件： + +```python +data = dict( + train = dict( + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( # 这里是 Dataset_A 的原始配置 + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) + ... +) +``` + +更加具体的细节，请参考 [API 文档](https://mmclassification.readthedocs.io/zh_CN/latest/api/datasets.html#mmcls.datasets.ClassBalancedDataset)。 diff --git a/docs/zh_CN/tutorials/new_modules.md b/docs/zh_CN/tutorials/new_modules.md new file mode 100644 index 0000000..14ee32c --- /dev/null +++ b/docs/zh_CN/tutorials/new_modules.md @@ -0,0 +1,280 @@ +# 教程 5：如何增加新模块 + +## 开发新组件 + +我们基本上将模型组件分为 3 种类型。 + +- 主干网络：通常是一个特征提取网络，例如 ResNet、MobileNet +- 颈部：用于连接主干网络和头部的组件，例如 GlobalAveragePooling +- 头部：用于执行特定任务的组件，例如分类和回归 + +### 添加新的主干网络 + +这里，我们以 ResNet_CIFAR 为例，展示了如何开发一个新的主干网络组件。 + +ResNet_CIFAR 针对 CIFAR 32x32 的图像输入，将 ResNet 中 `kernel_size=7, stride=2` 的设置替换为 `kernel_size=3, stride=1`，并移除了 stem 层之后的 +`MaxPooling`，以避免传递过小的特征图到残差块中。 + +它继承自 `ResNet` 并只修改了 stem 层。 + +1. 创建一个新文件 `mmcls/models/backbones/resnet_cifar.py`。 + +```python +import torch.nn as nn + +from ..builder import BACKBONES +from .resnet import ResNet + + +@BACKBONES.register_module() +class ResNet_CIFAR(ResNet): + + """ResNet backbone for CIFAR. + + （对这个主干网络的简短描述） + + Args: + depth(int): Network depth, from {18, 34, 50, 101, 152}. + ... + （参数文档） + """ + + def __init__(self, depth, deep_stem=False, **kwargs): + # 调用基类 ResNet 的初始化函数 + super(ResNet_CIFAR, self).__init__(depth, deep_stem=deep_stem **kwargs) + # 其他特殊的初始化流程 + assert not self.deep_stem, 'ResNet_CIFAR do not support deep_stem' + + def _make_stem_layer(self, in_channels, base_channels): + # 重载基类的方法，以实现对网络结构的修改 + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + base_channels, + kernel_size=3, + stride=1, + padding=1, + bias=False) + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, base_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): # 需要返回一个元组 + pass # 此处省略了网络的前向实现 + + def init_weights(self, pretrained=None): + pass # 如果有必要的话，重载基类 ResNet 的参数初始化函数 + + def train(self, mode=True): + pass # 如果有必要的话，重载基类 ResNet 的训练状态函数 +``` + +2. 在 `mmcls/models/backbones/__init__.py` 中导入新模块 + +```python +... +from .resnet_cifar import ResNet_CIFAR + +__all__ = [ + ..., 'ResNet_CIFAR' +] +``` + +3. 在配置文件中使用新的主干网络 + +```python +model = dict( + ... + backbone=dict( + type='ResNet_CIFAR', + depth=18, + other_arg=xxx), + ... +``` + +### 添加新的颈部组件 + +这里我们以 `GlobalAveragePooling` 为例。这是一个非常简单的颈部组件，没有任何参数。 + +要添加新的颈部组件，我们主要需要实现 `forward` 函数，该函数对主干网络的输出进行 +一些操作并将结果传递到头部。 + +1. 创建一个新文件 `mmcls/models/necks/gap.py` + + ```python + import torch.nn as nn + + from ..builder import NECKS + + @NECKS.register_module() + class GlobalAveragePooling(nn.Module): + + def __init__(self): + self.gap = nn.AdaptiveAvgPool2d((1, 1)) + + def forward(self, inputs): + # 简单起见，我们默认输入是一个张量 + outs = self.gap(inputs) + outs = outs.view(inputs.size(0), -1) + return outs + ``` + +2. 在 `mmcls/models/necks/__init__.py` 中导入新模块 + + ```python + ... + from .gap import GlobalAveragePooling + + __all__ = [ + ..., 'GlobalAveragePooling' + ] + ``` + +3. 修改配置文件以使用新的颈部组件 + + ```python + model = dict( + neck=dict(type='GlobalAveragePooling'), + ) + ``` + +### 添加新的头部组件 + +在此，我们以 `LinearClsHead` 为例，说明如何开发新的头部组件。 + +要添加一个新的头部组件，基本上我们需要实现 `forward_train` 函数，它接受来自颈部 +或主干网络的特征图作为输入，并基于真实标签计算。 + +1. 创建一个文件 `mmcls/models/heads/linear_head.py`. + + ```python + from ..builder import HEADS + from .cls_head import ClsHead + + + @HEADS.register_module() + class LinearClsHead(ClsHead): + + def __init__(self, + num_classes, + in_channels, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, )): + super(LinearClsHead, self).__init__(loss=loss, topk=topk) + self.in_channels = in_channels + self.num_classes = num_classes + + if self.num_classes <= 0: + raise ValueError( + f'num_classes={num_classes} must be a positive integer') + + self._init_layers() + + def _init_layers(self): + self.fc = nn.Linear(self.in_channels, self.num_classes) + + def init_weights(self): + normal_init(self.fc, mean=0, std=0.01, bias=0) + + def forward_train(self, x, gt_label): + cls_score = self.fc(x) + losses = self.loss(cls_score, gt_label) + return losses + + ``` + +2. 在 `mmcls/models/heads/__init__.py` 中导入这个模块 + + ```python + ... + from .linear_head import LinearClsHead + + __all__ = [ + ..., 'LinearClsHead' + ] + ``` + +3. 修改配置文件以使用新的头部组件。 + +连同 `GlobalAveragePooling` 颈部组件，完整的模型配置如下： + +```python +model = dict( + type='ImageClassifier', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + style='pytorch'), + neck=dict(type='GlobalAveragePooling'), + head=dict( + type='LinearClsHead', + num_classes=1000, + in_channels=2048, + loss=dict(type='CrossEntropyLoss', loss_weight=1.0), + topk=(1, 5), + )) + +``` + +### 添加新的损失函数 + +要添加新的损失函数，我们主要需要在损失函数模块中 `forward` 函数。另外，利用装饰器 `weighted_loss` 可以方便的实现对每个元素的损失进行加权平均。 + +假设我们要模拟从另一个分类模型生成的概率分布，需要添加 `L1loss` 来实现该目的。 + +1. 创建一个新文件 `mmcls/models/losses/l1_loss.py` + + ```python + import torch + import torch.nn as nn + + from ..builder import LOSSES + from .utils import weighted_loss + + @weighted_loss + def l1_loss(pred, target): + assert pred.size() == target.size() and target.numel() > 0 + loss = torch.abs(pred - target) + return loss + + @LOSSES.register_module() + class L1Loss(nn.Module): + + def __init__(self, reduction='mean', loss_weight=1.0): + super(L1Loss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss = self.loss_weight * l1_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss + ``` + +2. 在文件 `mmcls/models/losses/__init__.py` 中导入这个模块 + + ```python + ... + from .l1_loss import L1Loss, l1_loss + + __all__ = [ + ..., 'L1Loss', 'l1_loss' + ] + ``` + +3. 修改配置文件中的 `loss` 字段以使用新的损失函数 + + ```python + loss=dict(type='L1Loss', loss_weight=1.0)) + ``` diff --git a/docs/zh_CN/tutorials/runtime.md b/docs/zh_CN/tutorials/runtime.md new file mode 100644 index 0000000..0be7999 --- /dev/null +++ b/docs/zh_CN/tutorials/runtime.md @@ -0,0 +1,260 @@ +# 教程 7：如何自定义模型运行参数 + +在本教程中，我们将介绍如何在运行自定义模型时，进行自定义工作流和钩子的方法。 + + + +- [定制工作流](#定制工作流) +- [钩子](#钩子) + - [默认训练钩子](#默认训练钩子) + - [使用内置钩子](#使用内置钩子) + - [自定义钩子](#自定义钩子) +- [常见问题](#常见问题) + + + +## 定制工作流 + +工作流是一个形如 (任务名，周期数) 的列表，用于指定运行顺序和周期。这里“周期数”的单位由执行器的类型来决定。 + +比如在 MMClassification 中，我们默认使用基于**轮次**的执行器（`EpochBasedRunner`），那么“周期数”指的就是对应的任务在一个周期中 +要执行多少个轮次。通常，我们只希望执行训练任务，那么只需要使用以下设置： + +```python +workflow = [('train', 1)] +``` + +有时我们可能希望在训练过程中穿插检查模型在验证集上的一些指标（例如，损失，准确性）。 + +在这种情况下，可以将工作流程设置为： + +```python +[('train', 1), ('val', 1)] +``` + +这样一来，程序会一轮训练一轮测试地反复执行。 + +需要注意的是，默认情况下，我们并不推荐用这种方式来进行模型验证，而是推荐在训练中使用 **`EvalHook`** 进行模型验证。使用上述工作流的方式进行模型验证只是一个替代方案。 + +```{note} +1. 在验证周期时不会更新模型参数。 +2. 配置文件内的关键词 `max_epochs` 控制训练时期数，并且不会影响验证工作流程。 +3. 工作流 `[('train', 1), ('val', 1)]` 和 `[('train', 1)]` 不会改变 `EvalHook` 的行为。 + 因为 `EvalHook` 由 `after_train_epoch` 调用，而验证工作流只会影响 `after_val_epoch` 调用的钩子。 + 因此，`[('train', 1), ('val', 1)]` 和 ``[('train', 1)]`` 的区别在于，runner 在完成每一轮训练后，会计算验证集上的损失。 +``` + +## 钩子 + +钩子机制在 OpenMMLab 开源算法库中应用非常广泛，结合执行器可以实现对训练过程的整个生命周期进行管理，可以通过[相关文章](https://zhuanlan.zhihu.com/p/355272220)进一步理解钩子。 + +钩子只有在构造器中被注册才起作用，目前钩子主要分为两类： + +- 默认训练钩子 + +默认训练钩子由运行器默认注册，一般为一些基础型功能的钩子，已经有确定的优先级，一般不需要修改优先级。 + +- 定制钩子 + +定制钩子通过 `custom_hooks` 注册，一般为一些增强型功能的钩子，需要在配置文件中指定优先级，不指定该钩子的优先级将默被设定为 'NORMAL'。 + +**优先级列表** + +| Level | Value | +| :-------------: | :---: | +| HIGHEST | 0 | +| VERY_HIGH | 10 | +| HIGH | 30 | +| ABOVE_NORMAL | 40 | +| NORMAL(default) | 50 | +| BELOW_NORMAL | 60 | +| LOW | 70 | +| VERY_LOW | 90 | +| LOWEST | 100 | + +优先级确定钩子的执行顺序，每次训练前，日志会打印出各个阶段钩子的执行顺序，方便调试。 + +### 默认训练钩子 + +有一些常见的钩子未通过 `custom_hooks` 注册，但会在运行器（`Runner`）中默认注册，它们是： + +| Hooks | Priority | +| :-------------------: | :---------------: | +| `LrUpdaterHook` | VERY_HIGH (10) | +| `MomentumUpdaterHook` | HIGH (30) | +| `OptimizerHook` | ABOVE_NORMAL (40) | +| `CheckpointHook` | NORMAL (50) | +| `IterTimerHook` | LOW (70) | +| `EvalHook` | LOW (70) | +| `LoggerHook(s)` | VERY_LOW (90) | + +`OptimizerHook`，`MomentumUpdaterHook`和 `LrUpdaterHook` 在 [优化策略](./schedule.md) 部分进行了介绍， +`IterTimerHook` 用于记录所用时间，目前不支持修改; + +下面介绍如何使用去定制 `CheckpointHook`、`LoggerHooks` 以及 `EvalHook`。 + +#### 权重文件钩子（CheckpointHook） + +MMCV 的 runner 使用 `checkpoint_config` 来初始化 [`CheckpointHook`](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/hooks/checkpoint.py#L9)。 + +```python +checkpoint_config = dict(interval=1) +``` + +用户可以设置 “max_keep_ckpts” 来仅保存少量模型权重文件，或者通过 “save_optimizer” 决定是否存储优化器的状态字典。 +更多细节可参考 [这里](https://mmcv.readthedocs.io/zh_CN/latest/api.html#mmcv.runner.CheckpointHook)。 + +#### 日志钩子（LoggerHooks） + +`log_config` 包装了多个记录器钩子，并可以设置间隔。 +目前，MMCV 支持 `TextLoggerHook`、 `WandbLoggerHook`、`MlflowLoggerHook` 和 `TensorboardLoggerHook`。 +更多细节可参考[这里](https://mmcv.readthedocs.io/zh_CN/latest/api.html#mmcv.runner.LoggerHook)。 + +```python +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook') + ]) +``` + +#### 验证钩子（EvalHook） + +配置中的 `evaluation` 字段将用于初始化 [`EvalHook`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/evaluation.py)。 + +`EvalHook` 有一些保留参数，如 `interval`，`save_best` 和 `start` 等。其他的参数，如“metrics”将被传递给 `dataset.evaluate()`。 + +```python +evaluation = dict(interval=1, metric='accuracy', metric_options={'topk': (1, )}) +``` + +我们可以通过参数 `save_best` 保存取得最好验证结果时的模型权重： + +```python +# "auto" 表示自动选择指标来进行模型的比较。也可以指定一个特定的 key 比如 "accuracy_top-1"。 +evaluation = dict(interval=1, save_best=True, metric='accuracy', metric_options={'topk': (1, )}) +``` + +在跑一些大型实验时，可以通过修改参数 `start` 跳过训练靠前轮次时的验证步骤，以节约时间。如下： + +```python +evaluation = dict(interval=1, start=200, metric='accuracy', metric_options={'topk': (1, )}) +``` + +表示在第 200 轮之前，只执行训练流程，不执行验证；从轮次 200 开始，在每一轮训练之后进行验证。 + +```{note} +在 MMClassification 的默认配置文件中，evaluation 字段一般被放在 datasets 基础配置文件中。 +``` + +### 使用内置钩子 + +一些钩子已在 MMCV 和 MMClassification 中实现： + +- [EMAHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/ema.py) +- [SyncBuffersHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/sync_buffer.py) +- [EmptyCacheHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/memory.py) +- [ProfilerHook](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/profiler.py) +- ...... + +可以直接修改配置以使用该钩子，如下格式： + +```python +custom_hooks = [ + dict(type='MMCVHook', a=a_value, b=b_value, priority='NORMAL') +] +``` + +例如使用 `EMAHook`，进行一次 EMA 的间隔是100个迭代： + +```python +custom_hooks = [ + dict(type='EMAHook', interval=100, priority='HIGH') +] +``` + +## 自定义钩子 + +### 创建一个新钩子 + +这里举一个在 MMClassification 中创建一个新钩子，并在训练中使用它的示例： + +```python +from mmcv.runner import HOOKS, Hook + + +@HOOKS.register_module() +class MyHook(Hook): + + def __init__(self, a, b): + pass + + def before_run(self, runner): + pass + + def after_run(self, runner): + pass + + def before_epoch(self, runner): + pass + + def after_epoch(self, runner): + pass + + def before_iter(self, runner): + pass + + def after_iter(self, runner): + pass +``` + +根据钩子的功能，用户需要指定钩子在训练的每个阶段将要执行的操作，比如 `before_run`，`after_run`，`before_epoch`，`after_epoch`，`before_iter` 和 `after_iter`。 + +### 注册新钩子 + +之后，需要导入 `MyHook`。假设该文件在 `mmcls/core/utils/my_hook.py`，有两种办法导入它： + +- 修改 `mmcls/core/utils/__init__.py` 进行导入 + + 新定义的模块应导入到 `mmcls/core/utils/__init__py` 中，以便注册器能找到并添加新模块： + +```python +from .my_hook import MyHook + +__all__ = ['MyHook'] +``` + +- 使用配置文件中的 `custom_imports` 变量手动导入 + +```python +custom_imports = dict(imports=['mmcls.core.utils.my_hook'], allow_failed_imports=False) +``` + +### 修改配置 + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value) +] +``` + +还可通过 `priority` 参数设置钩子优先级，如下所示： + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value, priority='NORMAL') +] +``` + +默认情况下，在注册过程中，钩子的优先级设置为“NORMAL”。 + +## 常见问题 + +### 1. resume_from， load_from，init_cfg.Pretrained 区别 + +- `load_from` ：仅仅加载模型权重，主要用于加载预训练或者训练好的模型； + +- `resume_from` ：不仅导入模型权重，还会导入优化器信息，当前轮次（epoch）信息，主要用于从断点继续训练。 + +- `init_cfg.Pretrained` ：在权重初始化期间加载权重，您可以指定要加载的模块。 这通常在微调模型时使用，请参阅[教程 2：如何微调模型](./finetune.md) diff --git a/docs/zh_CN/tutorials/schedule.md b/docs/zh_CN/tutorials/schedule.md new file mode 100644 index 0000000..931edd0 --- /dev/null +++ b/docs/zh_CN/tutorials/schedule.md @@ -0,0 +1,333 @@ +# 教程 6：如何自定义优化策略 + +在本教程中，我们将介绍如何在运行自定义模型时，进行构造优化器、定制学习率及动量调整策略、梯度裁剪、梯度累计以及用户自定义优化方法等。 + + + +- [构造 PyTorch 内置优化器](#构造-pytorch-内置优化器) +- [定制学习率调整策略](#定制学习率调整策略) + - [学习率衰减曲线](#定制学习率衰减曲线) + - [学习率预热策略](#定制学习率预热策略) +- [定制动量调整策略](#定制动量调整策略) +- [参数化精细配置](#参数化精细配置) +- [梯度裁剪与梯度累计](#梯度裁剪与梯度累计) + - [梯度裁剪](#梯度裁剪) + - [梯度累计](#梯度累计) +- [用户自定义优化方法](#用户自定义优化方法) + - [自定义优化器](#自定义优化器) + - [自定义优化器构造器](#自定义优化器构造器) + + + +## 构造 PyTorch 内置优化器 + +MMClassification 支持 PyTorch 实现的所有优化器，仅需在配置文件中，指定 “optimizer” 字段。 +例如，如果要使用 “SGD”，则修改如下。 + +```python +optimizer = dict(type='SGD', lr=0.0003, weight_decay=0.0001) +``` + +要修改模型的学习率，只需要在优化器的配置中修改 `lr` 即可。 +要配置其他参数，可直接根据 [PyTorch API 文档](https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim) 进行。 + +```{note} +配置文件中的 'type' 不是构造时的参数，而是 PyTorch 内置优化器的类名。 +``` + +例如，如果想使用 `Adam` 并设置参数为 `torch.optim.Adam(params, lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)`， +则需要进行如下修改 + +```python +optimizer = dict(type='Adam', lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False) +``` + +## 定制学习率调整策略 + +### 定制学习率衰减曲线 + +深度学习研究中，广泛应用学习率衰减来提高网络的性能。要使用学习率衰减，可以在配置中设置 `lr_confg` 字段。 + +比如在默认的 ResNet 网络训练中，我们使用阶梯式的学习率衰减策略，配置文件为： + +```python +lr_config = dict(policy='step', step=[100, 150]) +``` + +在训练过程中，程序会周期性地调用 MMCV 中的 [`StepLRHook`](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L153) 来进行学习率更新。 + +此外，我们也支持其他学习率调整方法，如 `CosineAnnealing` 和 `Poly` 等。详情可见 [这里](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py) + +- ConsineAnnealing: + + ```python + lr_config = dict(policy='CosineAnnealing', min_lr_ratio=1e-5) + ``` + +- Poly: + + ```python + lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) + ``` + +### 定制学习率预热策略 + +在训练的早期阶段，网络容易不稳定，而学习率的预热就是为了减少这种不稳定性。通过预热，学习率将会从一个很小的值逐步提高到预定值。 + +在 MMClassification 中，我们同样使用 `lr_config` 配置学习率预热策略，主要的参数有以下几个： + +- `warmup` : 学习率预热曲线类别，必须为 'constant'、 'linear'， 'exp' 或者 `None` 其一， 如果为 `None`, 则不使用学习率预热策略。 +- `warmup_by_epoch` : 是否以轮次（epoch）为单位进行预热。 +- `warmup_iters` : 预热的迭代次数，当 `warmup_by_epoch=True` 时，单位为轮次（epoch）；当 `warmup_by_epoch=False` 时，单位为迭代次数（iter）。 +- `warmup_ratio` : 预测的初始学习率 `lr = lr * warmup_ratio`。 + +例如： + +1. 逐**迭代次数**地**线性**预热 + + ```python + lr_config = dict( + policy='CosineAnnealing', + by_epoch=False, + min_lr_ratio=1e-2, + warmup='linear', + warmup_ratio=1e-3, + warmup_iters=20 * 1252, + warmup_by_epoch=False) + ``` + +2. 逐**轮次**地**指数**预热 + + ```python + lr_config = dict( + policy='CosineAnnealing', + min_lr=0, + warmup='exp', + warmup_iters=5, + warmup_ratio=0.1, + warmup_by_epoch=True) + ``` + +```{tip} +配置完成后，可以使用 MMClassification 提供的 [学习率可视化工具](https://mmclassification.readthedocs.io/zh_CN/latest/tools/visualization.html#id3) 画出对应学习率调整曲线。 +``` + +## 定制动量调整策略 + +MMClassification 支持动量调整器根据学习率修改模型的动量，从而使模型收敛更快。 + +动量调整程序通常与学习率调整器一起使用，例如，以下配置用于加速收敛。 +更多细节可参考 [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327) 和 [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130)。 + +这里是一个用例： + +```python +lr_config = dict( + policy='cyclic', + target_ratio=(10, 1e-4), + cyclic_times=1, + step_ratio_up=0.4, +) +momentum_config = dict( + policy='cyclic', + target_ratio=(0.85 / 0.95, 1), + cyclic_times=1, + step_ratio_up=0.4, +) +``` + +## 参数化精细配置 + +一些模型可能具有一些特定于参数的设置以进行优化，例如 BatchNorm 层不添加权重衰减或者对不同的网络层使用不同的学习率。 +在 MMClassification 中，我们通过 `optimizer` 的 `paramwise_cfg` 参数进行配置，可以参考[MMCV](https://mmcv.readthedocs.io/en/latest/_modules/mmcv/runner/optimizer/default_constructor.html#DefaultOptimizerConstructor)。 + +- 使用指定选项 + + MMClassification 提供了包括 `bias_lr_mult`、 `bias_decay_mult`、 `norm_decay_mult`、 `dwconv_decay_mult`、 `dcn_offset_lr_mult` 和 `bypass_duplicate` 选项，指定相关所有的 `bais`、 `norm`、 `dwconv`、 `dcn` 和 `bypass` 参数。例如令模型中所有的 BN 不进行参数衰减： + + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=dict(norm_decay_mult=0.) + ) + ``` + +- 使用 `custom_keys` 指定参数 + + MMClassification 可通过 `custom_keys` 指定不同的参数使用不同的学习率或者权重衰减，例如对特定的参数不使用权重衰减： + + ```python + paramwise_cfg = dict( + custom_keys={ + 'backbone.cls_token': dict(decay_mult=0.0), + 'backbone.pos_embed': dict(decay_mult=0.0) + }) + + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + paramwise_cfg=paramwise_cfg) + ``` + + 对 backbone 使用更小的学习率与衰减系数： + + ```python + optimizer = dict( + type='SGD', + lr=0.8, + weight_decay=1e-4, + # backbone 的 'lr' and 'weight_decay' 分别为 0.1 * lr 和 0.9 * weight_decay + paramwise_cfg = dict(custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=0.9)})) + ``` + +## 梯度裁剪与梯度累计 + +除了 PyTorch 优化器的基本功能，我们还提供了一些对优化器的增强功能，例如梯度裁剪、梯度累计等，参考 [MMCV](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py)。 + +### 梯度裁剪 + +在训练过程中，损失函数可能接近于一些异常陡峭的区域，从而导致梯度爆炸。而梯度裁剪可以帮助稳定训练过程，更多介绍可以参见[该页面](https://paperswithcode.com/method/gradient-clipping)。 + +目前我们支持在 `optimizer_config` 字段中添加 `grad_clip` 参数来进行梯度裁剪，更详细的参数可参考 [PyTorch 文档](https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html)。 + +用例如下： + +```python +# norm_type: 使用的范数类型，此处使用范数2。 +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +``` + +当使用继承并修改基础配置方式时，如果基础配置中 `grad_clip=None`，需要添加 `_delete_=True`。有关 `_delete_` 可以参考[教程 1：如何编写配置文件](https://mmclassification.readthedocs.io/zh_CN/latest/tutorials/config.html#id16)。案例如下： + +```python +_base_ = [./_base_/schedules/imagenet_bs256_coslr.py] + +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2), _delete_=True, type='OptimizerHook') +# 当 type 为 'OptimizerHook'，可以省略 type；其他情况下，此处必须指明 type='xxxOptimizerHook'。 +``` + +### 梯度累计 + +计算资源缺乏缺乏时，每个训练批次的大小（batch size）只能设置为较小的值，这可能会影响模型的性能。 + +可以使用梯度累计来规避这一问题。 + +用例如下： + +```python +data = dict(samples_per_gpu=64) +optimizer_config = dict(type="GradientCumulativeOptimizerHook", cumulative_iters=4) +``` + +表示训练时，每 4 个 iter 执行一次反向传播。由于此时单张 GPU 上的批次大小为 64，也就等价于单张 GPU 上一次迭代的批次大小为 256，也即： + +```python +data = dict(samples_per_gpu=256) +optimizer_config = dict(type="OptimizerHook") +``` + +```{note} +当在 `optimizer_config` 不指定优化器钩子类型时，默认使用 `OptimizerHook`。 +``` + +## 用户自定义优化方法 + +在学术研究和工业实践中，可能需要使用 MMClassification 未实现的优化方法，可以通过以下方法添加。 + +```{note} +本部分将修改 MMClassification 源码或者向 MMClassification 框架添加代码，初学者可跳过。 +``` + +### 自定义优化器 + +#### 1. 定义一个新的优化器 + +一个自定义的优化器可根据如下规则进行定制 + +假设我们想添加一个名为 `MyOptimzer` 的优化器，其拥有参数 `a`, `b` 和 `c`。 +可以创建一个名为 `mmcls/core/optimizer` 的文件夹，并在目录下的一个文件，如 `mmcls/core/optimizer/my_optimizer.py` 中实现该自定义优化器： + +```python +from mmcv.runner import OPTIMIZERS +from torch.optim import Optimizer + + +@OPTIMIZERS.register_module() +class MyOptimizer(Optimizer): + + def __init__(self, a, b, c): + +``` + +#### 2. 注册优化器 + +要注册上面定义的上述模块，首先需要将此模块导入到主命名空间中。有两种方法可以实现它。 + +- 修改 `mmcls/core/optimizer/__init__.py`，将其导入至 `optimizer` 包；再修改 `mmcls/core/__init__.py` 以导入 `optimizer` 包 + + 创建 `mmcls/core/optimizer/__init__.py` 文件。 + 新定义的模块应导入到 `mmcls/core/optimizer/__init__.py` 中，以便注册器能找到新模块并将其添加： + +```python +# 在 mmcls/core/optimizer/__init__.py 中 +from .my_optimizer import MyOptimizer # MyOptimizer 是我们自定义的优化器的名字 + +__all__ = ['MyOptimizer'] +``` + +```python +# 在 mmcls/core/__init__.py 中 +... +from .optimizer import * # noqa: F401, F403 +``` + +- 在配置中使用 `custom_imports` 手动导入 + +```python +custom_imports = dict(imports=['mmcls.core.optimizer.my_optimizer'], allow_failed_imports=False) +``` + +`mmcls.core.optimizer.my_optimizer` 模块将会在程序开始阶段被导入，`MyOptimizer` 类会随之自动被注册。 +注意，只有包含 `MyOptmizer` 类的包会被导入。`mmcls.core.optimizer.my_optimizer.MyOptimizer` **不会** 被直接导入。 + +#### 3. 在配置文件中指定优化器 + +之后，用户便可在配置文件的 `optimizer` 域中使用 `MyOptimizer`。 +在配置中，优化器由 “optimizer” 字段定义，如下所示： + +```python +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +``` + +要使用自定义的优化器，可以将该字段更改为 + +```python +optimizer = dict(type='MyOptimizer', a=a_value, b=b_value, c=c_value) +``` + +### 自定义优化器构造器 + +某些模型可能具有一些特定于参数的设置以进行优化，例如 BatchNorm 层的权重衰减。 + +虽然我们的 `DefaultOptimizerConstructor` 已经提供了这些强大的功能，但可能仍然无法覆盖需求。 +此时我们可以通过自定义优化器构造函数来进行其他细粒度的参数调整。 + +```python +from mmcv.runner.optimizer import OPTIMIZER_BUILDERS + + +@OPTIMIZER_BUILDERS.register_module() +class MyOptimizerConstructor: + + def __init__(self, optimizer_cfg, paramwise_cfg=None): + pass + + def __call__(self, model): + ... # 在这里实现自己的优化器构造器。 + return my_optimizer +``` + +[这里](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/optimizer/default_constructor.py#L11)是我们默认的优化器构造器的实现，可以作为新优化器构造器实现的模板。 diff --git a/downstream/mmdetection/.circleci/config.yml b/downstream/mmdetection/.circleci/config.yml new file mode 100644 index 0000000..da7cd35 --- /dev/null +++ b/downstream/mmdetection/.circleci/config.yml @@ -0,0 +1,162 @@ +version: 2.1 + +jobs: + lint: + docker: + - image: cimg/python:3.7.4 + steps: + - checkout + - run: + name: Install pre-commit hook + command: | + pip install pre-commit + pre-commit install + - run: + name: Linting + command: pre-commit run --all-files + - run: + name: Check docstring coverage + command: | + pip install interrogate + interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 50 mmdet + + build_cpu: + parameters: + # The python version must match available image tags in + # https://circleci.com/developer/images/image/cimg/python + python: + type: string + default: "3.7.4" + torch: + type: string + torchvision: + type: string + docker: + - image: cimg/python:<< parameters.python >> + resource_class: large + steps: + - checkout + - run: + name: Install Libraries + command: | + sudo apt-get update + sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5 + - run: + name: Configure Python & pip + command: | + pip install --upgrade pip + pip install wheel + - run: + name: Install PyTorch + command: | + python -V + pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html + - when: + condition: + equal: [ "3.9.0", << parameters.python >> ] + steps: + - run: pip install "protobuf <= 3.20.1" && sudo apt-get update && sudo apt-get -y install libprotobuf-dev protobuf-compiler cmake + - run: + name: Install mmdet dependencies + command: | + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch<< parameters.torch >>/index.html + pip install -r requirements/tests.txt -r requirements/optional.txt + pip install albumentations>=0.3.2 --no-binary imgaug,albumentations + pip install git+https://github.com/cocodataset/panopticapi.git + - run: + name: Build and install + command: | + pip install -e . + - run: + name: Run unittests + command: | + coverage run --branch --source mmdet -m pytest tests/ + coverage xml + coverage report -m + + build_cu101: + machine: + image: ubuntu-1604-cuda-10.1:201909-23 + resource_class: gpu.nvidia.small + steps: + - checkout + - run: + name: Install Libraries + command: | + sudo apt-get update + sudo apt-get install -y git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx + - run: + name: Configure Python & pip + command: | + pyenv global 3.7.0 + pip install --upgrade pip + pip install wheel + - run: + name: Install PyTorch + command: | + python -V + pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html + - run: + name: Install mmdet dependencies + # pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.torch_version}}/index.html + command: | + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html + pip install -r requirements/tests.txt -r requirements/optional.txt + pip install pycocotools + pip install albumentations>=0.3.2 --no-binary imgaug,albumentations + pip install git+https://github.com/cocodataset/panopticapi.git + python -c 'import mmcv; print(mmcv.__version__)' + - run: + name: Build and install + command: | + python setup.py check -m -s + TORCH_CUDA_ARCH_LIST=7.0 pip install -e . + - run: + name: Run unittests + command: | + pytest tests/ + +workflows: + unit_tests: + jobs: + - lint + - build_cpu: + name: build_cpu_th1.6 + torch: 1.6.0 + torchvision: 0.7.0 + requires: + - lint + - build_cpu: + name: build_cpu_th1.7 + torch: 1.7.0 + torchvision: 0.8.1 + requires: + - lint + - build_cpu: + name: build_cpu_th1.8_py3.9 + torch: 1.8.0 + torchvision: 0.9.0 + python: "3.9.0" + requires: + - lint + - build_cpu: + name: build_cpu_th1.9_py3.8 + torch: 1.9.0 + torchvision: 0.10.0 + python: "3.8.12" + requires: + - lint + - build_cpu: + name: build_cpu_th1.9_py3.9 + torch: 1.9.0 + torchvision: 0.10.0 + python: "3.9.0" + requires: + - lint + - build_cu101: + requires: + - build_cpu_th1.6 + - build_cpu_th1.7 + - build_cpu_th1.8_py3.9 + - build_cpu_th1.9_py3.8 + - build_cpu_th1.9_py3.9 diff --git a/downstream/mmdetection/.dev_scripts/batch_test_list.py b/downstream/mmdetection/.dev_scripts/batch_test_list.py new file mode 100644 index 0000000..1e74ce2 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/batch_test_list.py @@ -0,0 +1,359 @@ +# Copyright (c) OpenMMLab. All rights reserved. +# yapf: disable +atss = dict( + config='configs/atss/atss_r50_fpn_1x_coco.py', + checkpoint='atss_r50_fpn_1x_coco_20200209-985f7bd0.pth', + eval='bbox', + metric=dict(bbox_mAP=39.4), +) +autoassign = dict( + config='configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py', + checkpoint='auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +carafe = dict( + config='configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=38.6), +) +cascade_rcnn = [ + dict( + config='configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py', + checkpoint='cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth', + eval='bbox', + metric=dict(bbox_mAP=40.3), + ), + dict( + config='configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py', + checkpoint='cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=41.2, segm_mAP=35.9), + ), +] +cascade_rpn = dict( + config='configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py', + checkpoint='crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +centripetalnet = dict( + config='configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py', # noqa + checkpoint='centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=44.7), +) +cornernet = dict( + config='configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py', + checkpoint='cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=41.2), +) +dcn = dict( + config='configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth', + eval='bbox', + metric=dict(bbox_mAP=41.3), +) +deformable_detr = dict( + config='configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py', + checkpoint='deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=44.5), +) +detectors = dict( + config='configs/detectors/detectors_htc_r50_1x_coco.py', + checkpoint='detectors_htc_r50_1x_coco-329b1453.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=49.1, segm_mAP=42.6), +) +detr = dict( + config='configs/detr/detr_r50_8x2_150e_coco.py', + checkpoint='detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth', + eval='bbox', + metric=dict(bbox_mAP=40.1), +) +double_heads = dict( + config='configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth', + eval='bbox', + metric=dict(bbox_mAP=40.0), +) +dynamic_rcnn = dict( + config='configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py', + checkpoint='dynamic_rcnn_r50_fpn_1x-62a3f276.pth', + eval='bbox', + metric=dict(bbox_mAP=38.9), +) +empirical_attention = dict( + config='configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py', # noqa + checkpoint='faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=40.0), +) +faster_rcnn = dict( + config='configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth', + eval='bbox', + metric=dict(bbox_mAP=37.4), +) +fcos = dict( + config='configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py', # noqa + checkpoint='fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=38.7), +) +foveabox = dict( + config='configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py', + checkpoint='fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth', + eval='bbox', + metric=dict(bbox_mAP=37.9), +) +free_anchor = dict( + config='configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py', + checkpoint='retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth', + eval='bbox', + metric=dict(bbox_mAP=38.7), +) +fsaf = dict( + config='configs/fsaf/fsaf_r50_fpn_1x_coco.py', + checkpoint='fsaf_r50_fpn_1x_coco-94ccc51f.pth', + eval='bbox', + metric=dict(bbox_mAP=37.4), +) +gcnet = dict( + config='configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py', # noqa + checkpoint='mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth', # noqa + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=40.4, segm_mAP=36.2), +) +gfl = dict( + config='configs/gfl/gfl_r50_fpn_1x_coco.py', + checkpoint='gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth', + eval='bbox', + metric=dict(bbox_mAP=40.2), +) +gn = dict( + config='configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py', + checkpoint='mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=40.1, segm_mAP=36.4), +) +gn_ws = dict( + config='configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth', + eval='bbox', + metric=dict(bbox_mAP=39.7), +) +grid_rcnn = dict( + config='configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py', + checkpoint='grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +groie = dict( + config='configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=38.3), +) +guided_anchoring = [ + dict( + config='configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py', # noqa + checkpoint='ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth', + eval='bbox', + metric=dict(bbox_mAP=36.9), + ), + dict( + config='configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py', + checkpoint='ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=39.6), + ), +] +hrnet = dict( + config='configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py', + checkpoint='faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth', + eval='bbox', + metric=dict(bbox_mAP=36.9), +) +htc = dict( + config='configs/htc/htc_r50_fpn_1x_coco.py', + checkpoint='htc_r50_fpn_1x_coco_20200317-7332cf16.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=42.3, segm_mAP=37.4), +) +libra_rcnn = dict( + config='configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth', + eval='bbox', + metric=dict(bbox_mAP=38.3), +) +mask_rcnn = dict( + config='configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py', + checkpoint='mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=38.2, segm_mAP=34.7), +) +ms_rcnn = dict( + config='configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py', + checkpoint='ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=38.2, segm_mAP=36.0), +) +nas_fcos = dict( + config='configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py', # noqa + checkpoint='nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=39.4), +) +nas_fpn = dict( + config='configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py', + checkpoint='retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth', + eval='bbox', + metric=dict(bbox_mAP=40.5), +) +paa = dict( + config='configs/paa/paa_r50_fpn_1x_coco.py', + checkpoint='paa_r50_fpn_1x_coco_20200821-936edec3.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +pafpn = dict( + config='configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py', + checkpoint='faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=37.5), +) +pisa = dict( + config='configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth', + eval='bbox', + metric=dict(bbox_mAP=38.4), +) +point_rend = dict( + config='configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py', + checkpoint='point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=38.4, segm_mAP=36.3), +) +regnet = dict( + config='configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py', + checkpoint='mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth', # noqa + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=40.4, segm_mAP=36.7), +) +reppoints = dict( + config='configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py', + checkpoint='reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth', + eval='bbox', + metric=dict(bbox_mAP=37.0), +) +res2net = dict( + config='configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py', + checkpoint='faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth', + eval='bbox', + metric=dict(bbox_mAP=43.0), +) +resnest = dict( + config='configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py', # noqa + checkpoint='faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=42.0), +) +retinanet = dict( + config='configs/retinanet/retinanet_r50_fpn_1x_coco.py', + checkpoint='retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth', + eval='bbox', + metric=dict(bbox_mAP=36.5), +) +rpn = dict( + config='configs/rpn/rpn_r50_fpn_1x_coco.py', + checkpoint='rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth', + eval='proposal_fast', + metric=dict(AR_1000=58.2), +) +sabl = [ + dict( + config='configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py', + checkpoint='sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth', + eval='bbox', + metric=dict(bbox_mAP=37.7), + ), + dict( + config='configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth', + eval='bbox', + metric=dict(bbox_mAP=39.9), + ), +] +scnet = dict( + config='configs/scnet/scnet_r50_fpn_1x_coco.py', + checkpoint='scnet_r50_fpn_1x_coco-c3f09857.pth', + eval='bbox', + metric=dict(bbox_mAP=43.5), +) +sparse_rcnn = dict( + config='configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py', + checkpoint='sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth', + eval='bbox', + metric=dict(bbox_mAP=37.9), +) +ssd = [ + dict( + config='configs/ssd/ssd300_coco.py', + checkpoint='ssd300_coco_20210803_015428-d231a06e.pth', + eval='bbox', + metric=dict(bbox_mAP=25.5), + ), + dict( + config='configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py', + checkpoint='ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth',# noqa + eval='bbox', + metric=dict(bbox_mAP=21.3), + ), +] +tridentnet = dict( + config='configs/tridentnet/tridentnet_r50_caffe_1x_coco.py', + checkpoint='tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth', + eval='bbox', + metric=dict(bbox_mAP=37.6), +) +vfnet = dict( + config='configs/vfnet/vfnet_r50_fpn_1x_coco.py', + checkpoint='vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth', + eval='bbox', + metric=dict(bbox_mAP=41.6), +) +yolact = dict( + config='configs/yolact/yolact_r50_1x8_coco.py', + checkpoint='yolact_r50_1x8_coco_20200908-f38d58df.pth', + eval=['bbox', 'segm'], + metric=dict(bbox_mAP=31.2, segm_mAP=29.0), +) +yolo = dict( + config='configs/yolo/yolov3_d53_320_273e_coco.py', + checkpoint='yolov3_d53_320_273e_coco-421362b6.pth', + eval='bbox', + metric=dict(bbox_mAP=27.9), +) +yolof = dict( + config='configs/yolof/yolof_r50_c5_8x8_1x_coco.py', + checkpoint='yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth', + eval='bbox', + metric=dict(bbox_mAP=37.5), +) +centernet = dict( + config='configs/centernet/centernet_resnet18_dcnv2_140e_coco.py', + checkpoint='centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=29.5), +) +yolox = dict( + config='configs/yolox/yolox_tiny_8x8_300e_coco.py', + checkpoint='yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=31.5), +) +# yapf: enable diff --git a/downstream/mmdetection/.dev_scripts/batch_train_list.txt b/downstream/mmdetection/.dev_scripts/batch_train_list.txt new file mode 100644 index 0000000..a7004d7 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/batch_train_list.txt @@ -0,0 +1,66 @@ +configs/atss/atss_r50_fpn_1x_coco.py +configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py +configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py +configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py +configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py +configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py +configs/detectors/detectors_htc_r50_1x_coco.py +configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py +configs/detr/detr_r50_8x2_150e_coco.py +configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py +configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py +configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py +configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py +configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py +configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py +configs/fsaf/fsaf_r50_fpn_1x_coco.py +configs/gfl/gfl_r50_fpn_1x_coco.py +configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py +configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py +configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py +configs/htc/htc_r50_fpn_1x_coco.py +configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py +configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py +configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py +configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py +configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py +configs/paa/paa_r50_fpn_1x_coco.py +configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py +configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py +configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py +configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py +configs/rpn/rpn_r50_fpn_1x_coco.py +configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py +configs/ssd/ssd300_coco.py +configs/tridentnet/tridentnet_r50_caffe_1x_coco.py +configs/vfnet/vfnet_r50_fpn_1x_coco.py +configs/yolact/yolact_r50_8x8_coco.py +configs/yolo/yolov3_d53_320_273e_coco.py +configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py +configs/scnet/scnet_r50_fpn_1x_coco.py +configs/yolof/yolof_r50_c5_8x8_1x_coco.py +configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py +configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py +configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py +configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py +configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py +configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py +configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py +configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py +configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py +configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py +configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py +configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py +configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py +configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py +configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py +configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py +configs/centernet/centernet_resnet18_dcnv2_140e_coco.py +configs/yolox/yolox_tiny_8x8_300e_coco.py +configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py +configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py diff --git a/downstream/mmdetection/.dev_scripts/benchmark_filter.py b/downstream/mmdetection/.dev_scripts/benchmark_filter.py new file mode 100644 index 0000000..178cd9c --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/benchmark_filter.py @@ -0,0 +1,167 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os +import os.path as osp + + +def parse_args(): + parser = argparse.ArgumentParser(description='Filter configs to train') + parser.add_argument( + '--basic-arch', + action='store_true', + help='to train models in basic arch') + parser.add_argument( + '--datasets', action='store_true', help='to train models in dataset') + parser.add_argument( + '--data-pipeline', + action='store_true', + help='to train models related to data pipeline, e.g. augmentations') + parser.add_argument( + '--nn-module', + action='store_true', + help='to train models related to neural network modules') + parser.add_argument( + '--model-options', + nargs='+', + help='custom options to special model benchmark') + parser.add_argument( + '--out', + type=str, + default='batch_train_list.txt', + help='output path of gathered metrics to be stored') + args = parser.parse_args() + return args + + +basic_arch_root = [ + 'atss', 'autoassign', 'cascade_rcnn', 'cascade_rpn', 'centripetalnet', + 'cornernet', 'detectors', 'deformable_detr', 'detr', 'double_heads', + 'dynamic_rcnn', 'faster_rcnn', 'fcos', 'foveabox', 'fp16', 'free_anchor', + 'fsaf', 'gfl', 'ghm', 'grid_rcnn', 'guided_anchoring', 'htc', 'ld', + 'libra_rcnn', 'mask_rcnn', 'ms_rcnn', 'nas_fcos', 'paa', 'pisa', + 'point_rend', 'reppoints', 'retinanet', 'rpn', 'sabl', 'ssd', 'tridentnet', + 'vfnet', 'yolact', 'yolo', 'sparse_rcnn', 'scnet', 'yolof', 'centernet' +] + +datasets_root = [ + 'wider_face', 'pascal_voc', 'cityscapes', 'lvis', 'deepfashion' +] + +data_pipeline_root = ['albu_example', 'instaboost'] + +nn_module_root = [ + 'carafe', 'dcn', 'empirical_attention', 'gcnet', 'gn', 'gn+ws', 'hrnet', + 'pafpn', 'nas_fpn', 'regnet', 'resnest', 'res2net', 'groie' +] + +benchmark_pool = [ + 'configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py', + 'configs/atss/atss_r50_fpn_1x_coco.py', + 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py', + 'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py', + 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py', + 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py', + 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py', + 'configs/centripetalnet/' + 'centripetalnet_hourglass104_mstest_16x6_210e_coco.py', + 'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py', + 'configs/cornernet/' + 'cornernet_hourglass104_mstest_8x6_210e_coco.py', + 'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py', + 'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py', + 'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py', + 'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py', + 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py', + 'configs/detectors/detectors_htc_r50_1x_coco.py', + 'configs/detr/detr_r50_8x2_150e_coco.py', + 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py', + 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py', + 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py', # noqa + 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py', + 'configs/fcos/fcos_center_r50_caffe_fpn_gn-head_4x4_1x_coco.py', + 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py', + 'configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py', + 'configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py', + 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py', + 'configs/fsaf/fsaf_r50_fpn_1x_coco.py', + 'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py', + 'configs/gfl/gfl_r50_fpn_1x_coco.py', + 'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py', + 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py', + 'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py', + 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py', + 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py', + 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py', + 'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py', + 'configs/htc/htc_r50_fpn_1x_coco.py', + 'configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py', + 'configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py', + 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py', + 'configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py', + 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py', + 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py', + 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py', + 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py', + 'configs/paa/paa_r50_fpn_1x_coco.py', + 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py', + 'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py', + 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py', + 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py', + 'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py', + 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py', + 'configs/resnest/' + 'mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py', + 'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py', + 'configs/rpn/rpn_r50_fpn_1x_coco.py', + 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py', + 'configs/ssd/ssd300_coco.py', + 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py', + 'configs/vfnet/vfnet_r50_fpn_1x_coco.py', + 'configs/yolact/yolact_r50_1x8_coco.py', + 'configs/yolo/yolov3_d53_320_273e_coco.py', + 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py', + 'configs/scnet/scnet_r50_fpn_1x_coco.py', + 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py', +] + + +def main(): + args = parse_args() + + benchmark_type = [] + if args.basic_arch: + benchmark_type += basic_arch_root + if args.datasets: + benchmark_type += datasets_root + if args.data_pipeline: + benchmark_type += data_pipeline_root + if args.nn_module: + benchmark_type += nn_module_root + + special_model = args.model_options + if special_model is not None: + benchmark_type += special_model + + config_dpath = 'configs/' + benchmark_configs = [] + for cfg_root in benchmark_type: + cfg_dir = osp.join(config_dpath, cfg_root) + configs = os.scandir(cfg_dir) + for cfg in configs: + config_path = osp.join(cfg_dir, cfg.name) + if (config_path in benchmark_pool + and config_path not in benchmark_configs): + benchmark_configs.append(config_path) + + print(f'Totally found {len(benchmark_configs)} configs to benchmark') + with open(args.out, 'w') as f: + for config in benchmark_configs: + f.write(config + '\n') + + +if __name__ == '__main__': + main() diff --git a/downstream/mmdetection/.dev_scripts/benchmark_inference_fps.py b/downstream/mmdetection/.dev_scripts/benchmark_inference_fps.py new file mode 100644 index 0000000..81dcd6b --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/benchmark_inference_fps.py @@ -0,0 +1,170 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os +import os.path as osp + +import mmcv +from mmcv import Config, DictAction +from mmcv.runner import init_dist +from terminaltables import GithubFlavoredMarkdownTable + +from tools.analysis_tools.benchmark import repeat_measure_inference_speed + + +def parse_args(): + parser = argparse.ArgumentParser( + description='MMDet benchmark a model of FPS') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint_root', help='Checkpoint file root path') + parser.add_argument( + '--round-num', + type=int, + default=1, + help='round a number to a given precision in decimal digits') + parser.add_argument( + '--repeat-num', + type=int, + default=1, + help='number of repeat times of measurement for averaging the results') + parser.add_argument( + '--out', type=str, help='output path of gathered fps to be stored') + parser.add_argument( + '--max-iter', type=int, default=2000, help='num of max iter') + parser.add_argument( + '--log-interval', type=int, default=50, help='interval of logging') + parser.add_argument( + '--fuse-conv-bn', + action='store_true', + help='Whether to fuse conv and bn, this will slightly increase' + 'the inference speed') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + return args + + +def results2markdown(result_dict): + table_data = [] + is_multiple_results = False + for cfg_name, value in result_dict.items(): + name = cfg_name.replace('configs/', '') + fps = value['fps'] + ms_times_pre_image = value['ms_times_pre_image'] + if isinstance(fps, list): + is_multiple_results = True + mean_fps = value['mean_fps'] + mean_times_pre_image = value['mean_times_pre_image'] + fps_str = ','.join([str(s) for s in fps]) + ms_times_pre_image_str = ','.join( + [str(s) for s in ms_times_pre_image]) + table_data.append([ + name, fps_str, mean_fps, ms_times_pre_image_str, + mean_times_pre_image + ]) + else: + table_data.append([name, fps, ms_times_pre_image]) + + if is_multiple_results: + table_data.insert(0, [ + 'model', 'fps', 'mean_fps', 'times_pre_image(ms)', + 'mean_times_pre_image(ms)' + ]) + + else: + table_data.insert(0, ['model', 'fps', 'times_pre_image(ms)']) + table = GithubFlavoredMarkdownTable(table_data) + print(table.table, flush=True) + + +if __name__ == '__main__': + args = parse_args() + assert args.round_num >= 0 + assert args.repeat_num >= 1 + + config = Config.fromfile(args.config) + + if args.launcher == 'none': + raise NotImplementedError('Only supports distributed mode') + else: + init_dist(args.launcher) + + result_dict = {} + for model_key in config: + model_infos = config[model_key] + if not isinstance(model_infos, list): + model_infos = [model_infos] + for model_info in model_infos: + record_metrics = model_info['metric'] + cfg_path = model_info['config'].strip() + cfg = Config.fromfile(cfg_path) + checkpoint = osp.join(args.checkpoint_root, + model_info['checkpoint'].strip()) + try: + fps = repeat_measure_inference_speed(cfg, checkpoint, + args.max_iter, + args.log_interval, + args.fuse_conv_bn, + args.repeat_num) + if args.repeat_num > 1: + fps_list = [round(fps_, args.round_num) for fps_ in fps] + times_pre_image_list = [ + round(1000 / fps_, args.round_num) for fps_ in fps + ] + mean_fps = round( + sum(fps_list) / len(fps_list), args.round_num) + mean_times_pre_image = round( + sum(times_pre_image_list) / len(times_pre_image_list), + args.round_num) + print( + f'{cfg_path} ' + f'Overall fps: {fps_list}[{mean_fps}] img / s, ' + f'times per image: ' + f'{times_pre_image_list}[{mean_times_pre_image}] ' + f'ms / img', + flush=True) + result_dict[cfg_path] = dict( + fps=fps_list, + mean_fps=mean_fps, + ms_times_pre_image=times_pre_image_list, + mean_times_pre_image=mean_times_pre_image) + else: + print( + f'{cfg_path} fps : {fps:.{args.round_num}f} img / s, ' + f'times per image: {1000 / fps:.{args.round_num}f} ' + f'ms / img', + flush=True) + result_dict[cfg_path] = dict( + fps=round(fps, args.round_num), + ms_times_pre_image=round(1000 / fps, args.round_num)) + except Exception as e: + print(f'{cfg_path} error: {repr(e)}') + if args.repeat_num > 1: + result_dict[cfg_path] = dict( + fps=[0], + mean_fps=0, + ms_times_pre_image=[0], + mean_times_pre_image=0) + else: + result_dict[cfg_path] = dict(fps=0, ms_times_pre_image=0) + + if args.out: + mmcv.mkdir_or_exist(args.out) + mmcv.dump(result_dict, osp.join(args.out, 'batch_inference_fps.json')) + + results2markdown(result_dict) diff --git a/downstream/mmdetection/.dev_scripts/benchmark_test_image.py b/downstream/mmdetection/.dev_scripts/benchmark_test_image.py new file mode 100644 index 0000000..75f7576 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/benchmark_test_image.py @@ -0,0 +1,102 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import logging +import os.path as osp +from argparse import ArgumentParser + +from mmcv import Config + +from mmdet.apis import inference_detector, init_detector, show_result_pyplot +from mmdet.utils import get_root_logger + + +def parse_args(): + parser = ArgumentParser() + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint_root', help='Checkpoint file root path') + parser.add_argument('--img', default='demo/demo.jpg', help='Image file') + parser.add_argument('--aug', action='store_true', help='aug test') + parser.add_argument('--model-name', help='model name to inference') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--wait-time', + type=float, + default=1, + help='the interval of show (s), 0 is block') + parser.add_argument( + '--device', default='cuda:0', help='Device used for inference') + parser.add_argument( + '--score-thr', type=float, default=0.3, help='bbox score threshold') + args = parser.parse_args() + return args + + +def inference_model(config_name, checkpoint, args, logger=None): + cfg = Config.fromfile(config_name) + if args.aug: + if 'flip' in cfg.data.test.pipeline[1]: + cfg.data.test.pipeline[1].flip = True + else: + if logger is not None: + logger.error(f'{config_name}: unable to start aug test') + else: + print(f'{config_name}: unable to start aug test', flush=True) + + model = init_detector(cfg, checkpoint, device=args.device) + # test a single image + result = inference_detector(model, args.img) + + # show the results + if args.show: + show_result_pyplot( + model, + args.img, + result, + score_thr=args.score_thr, + wait_time=args.wait_time) + return result + + +# Sample test whether the inference code is correct +def main(args): + config = Config.fromfile(args.config) + + # test single model + if args.model_name: + if args.model_name in config: + model_infos = config[args.model_name] + if not isinstance(model_infos, list): + model_infos = [model_infos] + model_info = model_infos[0] + config_name = model_info['config'].strip() + print(f'processing: {config_name}', flush=True) + checkpoint = osp.join(args.checkpoint_root, + model_info['checkpoint'].strip()) + # build the model from a config file and a checkpoint file + inference_model(config_name, checkpoint, args) + return + else: + raise RuntimeError('model name input error.') + + # test all model + logger = get_root_logger( + log_file='benchmark_test_image.log', log_level=logging.ERROR) + + for model_key in config: + model_infos = config[model_key] + if not isinstance(model_infos, list): + model_infos = [model_infos] + for model_info in model_infos: + print('processing: ', model_info['config'], flush=True) + config_name = model_info['config'].strip() + checkpoint = osp.join(args.checkpoint_root, + model_info['checkpoint'].strip()) + try: + # build the model from a config file and a checkpoint file + inference_model(config_name, checkpoint, args, logger) + except Exception as e: + logger.error(f'{config_name} " : {repr(e)}') + + +if __name__ == '__main__': + args = parse_args() + main(args) diff --git a/downstream/mmdetection/.dev_scripts/check_links.py b/downstream/mmdetection/.dev_scripts/check_links.py new file mode 100755 index 0000000..b195d2a --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/check_links.py @@ -0,0 +1,157 @@ +# Modified from: +# https://github.com/allenai/allennlp/blob/main/scripts/check_links.py + +import argparse +import logging +import os +import pathlib +import re +import sys +from multiprocessing.dummy import Pool +from typing import NamedTuple, Optional, Tuple + +import requests +from mmcv.utils import get_logger + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Goes through all the inline-links ' + 'in markdown files and reports the breakages') + parser.add_argument( + '--num-threads', + type=int, + default=100, + help='Number of processes to confirm the link') + parser.add_argument('--https-proxy', type=str, help='https proxy') + parser.add_argument( + '--out', + type=str, + default='link_reports.txt', + help='output path of reports') + args = parser.parse_args() + return args + + +OK_STATUS_CODES = ( + 200, + 401, # the resource exists but may require some sort of login. + 403, # ^ same + 405, # HEAD method not allowed. + # the resource exists, but our default 'Accept-' header may not + # match what the server can provide. + 406, +) + + +class MatchTuple(NamedTuple): + source: str + name: str + link: str + + +def check_link( + match_tuple: MatchTuple, + http_session: requests.Session, + logger: logging = None) -> Tuple[MatchTuple, bool, Optional[str]]: + reason: Optional[str] = None + if match_tuple.link.startswith('http'): + result_ok, reason = check_url(match_tuple, http_session) + else: + result_ok = check_path(match_tuple) + if logger is None: + print(f" {'✓' if result_ok else '✗'} {match_tuple.link}") + else: + logger.info(f" {'✓' if result_ok else '✗'} {match_tuple.link}") + return match_tuple, result_ok, reason + + +def check_url(match_tuple: MatchTuple, + http_session: requests.Session) -> Tuple[bool, str]: + """Check if a URL is reachable.""" + try: + result = http_session.head( + match_tuple.link, timeout=5, allow_redirects=True) + return ( + result.ok or result.status_code in OK_STATUS_CODES, + f'status code = {result.status_code}', + ) + except (requests.ConnectionError, requests.Timeout): + return False, 'connection error' + + +def check_path(match_tuple: MatchTuple) -> bool: + """Check if a file in this repository exists.""" + relative_path = match_tuple.link.split('#')[0] + full_path = os.path.join( + os.path.dirname(str(match_tuple.source)), relative_path) + return os.path.exists(full_path) + + +def main(): + args = parse_args() + + # setup logger + logger = get_logger(name='mmdet', log_file=args.out) + + # setup https_proxy + if args.https_proxy: + os.environ['https_proxy'] = args.https_proxy + + # setup http_session + http_session = requests.Session() + for resource_prefix in ('http://', 'https://'): + http_session.mount( + resource_prefix, + requests.adapters.HTTPAdapter( + max_retries=5, + pool_connections=20, + pool_maxsize=args.num_threads), + ) + + logger.info('Finding all markdown files in the current directory...') + + project_root = (pathlib.Path(__file__).parent / '..').resolve() + markdown_files = project_root.glob('**/*.md') + + all_matches = set() + url_regex = re.compile(r'\[([^!][^\]]+)\]\(([^)(]+)\)') + for markdown_file in markdown_files: + with open(markdown_file) as handle: + for line in handle.readlines(): + matches = url_regex.findall(line) + for name, link in matches: + if 'localhost' not in link: + all_matches.add( + MatchTuple( + source=str(markdown_file), + name=name, + link=link)) + + logger.info(f' {len(all_matches)} markdown files found') + logger.info('Checking to make sure we can retrieve each link...') + + with Pool(processes=args.num_threads) as pool: + results = pool.starmap(check_link, [(match, http_session, logger) + for match in list(all_matches)]) + + # collect unreachable results + unreachable_results = [(match_tuple, reason) + for match_tuple, success, reason in results + if not success] + + if unreachable_results: + logger.info('================================================') + logger.info(f'Unreachable links ({len(unreachable_results)}):') + for match_tuple, reason in unreachable_results: + logger.info(' > Source: ' + match_tuple.source) + logger.info(' Name: ' + match_tuple.name) + logger.info(' Link: ' + match_tuple.link) + if reason is not None: + logger.info(' Reason: ' + reason) + sys.exit(1) + logger.info('No Unreachable link found.') + + +if __name__ == '__main__': + main() diff --git a/downstream/mmdetection/.dev_scripts/convert_test_benchmark_script.py b/downstream/mmdetection/.dev_scripts/convert_test_benchmark_script.py new file mode 100644 index 0000000..c31cad4 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/convert_test_benchmark_script.py @@ -0,0 +1,119 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os +import os.path as osp + +from mmcv import Config + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert benchmark model list to script') + parser.add_argument('config', help='test config file path') + parser.add_argument('--port', type=int, default=29666, help='dist port') + parser.add_argument( + '--work-dir', + default='tools/batch_test', + help='the dir to save metric') + parser.add_argument( + '--run', action='store_true', help='run script directly') + parser.add_argument( + '--out', type=str, help='path to save model benchmark script') + + args = parser.parse_args() + return args + + +def process_model_info(model_info, work_dir): + config = model_info['config'].strip() + fname, _ = osp.splitext(osp.basename(config)) + job_name = fname + work_dir = osp.join(work_dir, fname) + checkpoint = model_info['checkpoint'].strip() + if not isinstance(model_info['eval'], list): + evals = [model_info['eval']] + else: + evals = model_info['eval'] + eval = ' '.join(evals) + return dict( + config=config, + job_name=job_name, + work_dir=work_dir, + checkpoint=checkpoint, + eval=eval) + + +def create_test_bash_info(commands, model_test_dict, port, script_name, + partition): + config = model_test_dict['config'] + job_name = model_test_dict['job_name'] + checkpoint = model_test_dict['checkpoint'] + work_dir = model_test_dict['work_dir'] + eval = model_test_dict['eval'] + + echo_info = f' \necho \'{config}\' &' + commands.append(echo_info) + commands.append('\n') + + command_info = f'GPUS=8 GPUS_PER_NODE=8 ' \ + f'CPUS_PER_TASK=2 {script_name} ' + + command_info += f'{partition} ' + command_info += f'{job_name} ' + command_info += f'{config} ' + command_info += f'$CHECKPOINT_DIR/{checkpoint} ' + command_info += f'--work-dir {work_dir} ' + + command_info += f'--eval {eval} ' + command_info += f'--cfg-option dist_params.port={port} ' + command_info += ' &' + + commands.append(command_info) + + +def main(): + args = parse_args() + if args.out: + out_suffix = args.out.split('.')[-1] + assert args.out.endswith('.sh'), \ + f'Expected out file path suffix is .sh, but get .{out_suffix}' + assert args.out or args.run, \ + ('Please specify at least one operation (save/run/ the ' + 'script) with the argument "--out" or "--run"') + + commands = [] + partition_name = 'PARTITION=$1 ' + commands.append(partition_name) + commands.append('\n') + + checkpoint_root = 'CHECKPOINT_DIR=$2 ' + commands.append(checkpoint_root) + commands.append('\n') + + script_name = osp.join('tools', 'slurm_test.sh') + port = args.port + work_dir = args.work_dir + + cfg = Config.fromfile(args.config) + + for model_key in cfg: + model_infos = cfg[model_key] + if not isinstance(model_infos, list): + model_infos = [model_infos] + for model_info in model_infos: + print('processing: ', model_info['config']) + model_test_dict = process_model_info(model_info, work_dir) + create_test_bash_info(commands, model_test_dict, port, script_name, + '$PARTITION') + port += 1 + + command_str = ''.join(commands) + if args.out: + with open(args.out, 'w') as f: + f.write(command_str) + if args.run: + os.system(command_str) + + +if __name__ == '__main__': + main() diff --git a/downstream/mmdetection/.dev_scripts/convert_train_benchmark_script.py b/downstream/mmdetection/.dev_scripts/convert_train_benchmark_script.py new file mode 100644 index 0000000..1ccd8e9 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/convert_train_benchmark_script.py @@ -0,0 +1,99 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import os +import os.path as osp + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert benchmark model json to script') + parser.add_argument( + 'txt_path', type=str, help='txt path output by benchmark_filter') + parser.add_argument( + '--partition', + type=str, + default='openmmlab', + help='slurm partition name') + parser.add_argument( + '--max-keep-ckpts', + type=int, + default=1, + help='The maximum checkpoints to keep') + parser.add_argument( + '--run', action='store_true', help='run script directly') + parser.add_argument( + '--out', type=str, help='path to save model benchmark script') + + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + if args.out: + out_suffix = args.out.split('.')[-1] + assert args.out.endswith('.sh'), \ + f'Expected out file path suffix is .sh, but get .{out_suffix}' + assert args.out or args.run, \ + ('Please specify at least one operation (save/run/ the ' + 'script) with the argument "--out" or "--run"') + + partition = args.partition # cluster name + + root_name = './tools' + train_script_name = osp.join(root_name, 'slurm_train.sh') + # stdout is no output + stdout_cfg = '>/dev/null' + + max_keep_ckpts = args.max_keep_ckpts + + commands = [] + with open(args.txt_path, 'r') as f: + model_cfgs = f.readlines() + for i, cfg in enumerate(model_cfgs): + cfg = cfg.strip() + if len(cfg) == 0: + continue + # print cfg name + echo_info = f'echo \'{cfg}\' &' + commands.append(echo_info) + commands.append('\n') + + fname, _ = osp.splitext(osp.basename(cfg)) + out_fname = osp.join(root_name, 'work_dir', fname) + # default setting + if cfg.find('16x') >= 0: + command_info = f'GPUS=16 GPUS_PER_NODE=8 ' \ + f'CPUS_PER_TASK=2 {train_script_name} ' + elif cfg.find('gn-head_4x4_1x_coco.py') >= 0 or \ + cfg.find('gn-head_4x4_2x_coco.py') >= 0: + command_info = f'GPUS=4 GPUS_PER_NODE=4 ' \ + f'CPUS_PER_TASK=2 {train_script_name} ' + else: + command_info = f'GPUS=8 GPUS_PER_NODE=8 ' \ + f'CPUS_PER_TASK=2 {train_script_name} ' + command_info += f'{partition} ' + command_info += f'{fname} ' + command_info += f'{cfg} ' + command_info += f'{out_fname} ' + if max_keep_ckpts: + command_info += f'--cfg-options ' \ + f'checkpoint_config.max_keep_ckpts=' \ + f'{max_keep_ckpts}' + ' ' + command_info += f'{stdout_cfg} &' + + commands.append(command_info) + + if i < len(model_cfgs): + commands.append('\n') + + command_str = ''.join(commands) + if args.out: + with open(args.out, 'w') as f: + f.write(command_str) + if args.run: + os.system(command_str) + + +if __name__ == '__main__': + main() diff --git a/downstream/mmdetection/.dev_scripts/gather_models.py b/downstream/mmdetection/.dev_scripts/gather_models.py new file mode 100644 index 0000000..42e615c --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/gather_models.py @@ -0,0 +1,340 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import glob +import json +import os.path as osp +import shutil +import subprocess +from collections import OrderedDict + +import mmcv +import torch +import yaml + + +def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds): + + class OrderedDumper(Dumper): + pass + + def _dict_representer(dumper, data): + return dumper.represent_mapping( + yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, data.items()) + + OrderedDumper.add_representer(OrderedDict, _dict_representer) + return yaml.dump(data, stream, OrderedDumper, **kwds) + + +def process_checkpoint(in_file, out_file): + checkpoint = torch.load(in_file, map_location='cpu') + # remove optimizer for smaller file size + if 'optimizer' in checkpoint: + del checkpoint['optimizer'] + + # remove ema state_dict + for key in list(checkpoint['state_dict']): + if key.startswith('ema_'): + checkpoint['state_dict'].pop(key) + + # if it is necessary to remove some sensitive data in checkpoint['meta'], + # add the code here. + if torch.__version__ >= '1.6': + torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False) + else: + torch.save(checkpoint, out_file) + sha = subprocess.check_output(['sha256sum', out_file]).decode() + final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8]) + subprocess.Popen(['mv', out_file, final_file]) + return final_file + + +def is_by_epoch(config): + cfg = mmcv.Config.fromfile('./configs/' + config) + return cfg.runner.type == 'EpochBasedRunner' + + +def get_final_epoch_or_iter(config): + cfg = mmcv.Config.fromfile('./configs/' + config) + if cfg.runner.type == 'EpochBasedRunner': + return cfg.runner.max_epochs + else: + return cfg.runner.max_iters + + +def get_best_epoch_or_iter(exp_dir): + best_epoch_iter_full_path = list( + sorted(glob.glob(osp.join(exp_dir, 'best_*.pth'))))[-1] + best_epoch_or_iter_model_path = best_epoch_iter_full_path.split('/')[-1] + best_epoch_or_iter = best_epoch_or_iter_model_path.\ + split('_')[-1].split('.')[0] + return best_epoch_or_iter_model_path, int(best_epoch_or_iter) + + +def get_real_epoch_or_iter(config): + cfg = mmcv.Config.fromfile('./configs/' + config) + if cfg.runner.type == 'EpochBasedRunner': + epoch = cfg.runner.max_epochs + if cfg.data.train.type == 'RepeatDataset': + epoch *= cfg.data.train.times + return epoch + else: + return cfg.runner.max_iters + + +def get_final_results(log_json_path, + epoch_or_iter, + results_lut, + by_epoch=True): + result_dict = dict() + last_val_line = None + last_train_line = None + last_val_line_idx = -1 + last_train_line_idx = -1 + with open(log_json_path, 'r') as f: + for i, line in enumerate(f.readlines()): + log_line = json.loads(line) + if 'mode' not in log_line.keys(): + continue + + if by_epoch: + if (log_line['mode'] == 'train' + and log_line['epoch'] == epoch_or_iter): + result_dict['memory'] = log_line['memory'] + + if (log_line['mode'] == 'val' + and log_line['epoch'] == epoch_or_iter): + result_dict.update({ + key: log_line[key] + for key in results_lut if key in log_line + }) + return result_dict + else: + if log_line['mode'] == 'train': + last_train_line_idx = i + last_train_line = log_line + + if log_line and log_line['mode'] == 'val': + last_val_line_idx = i + last_val_line = log_line + + # bug: max_iters = 768, last_train_line['iter'] = 750 + assert last_val_line_idx == last_train_line_idx + 1, \ + 'Log file is incomplete' + result_dict['memory'] = last_train_line['memory'] + result_dict.update({ + key: last_val_line[key] + for key in results_lut if key in last_val_line + }) + + return result_dict + + +def get_dataset_name(config): + # If there are more dataset, add here. + name_map = dict( + CityscapesDataset='Cityscapes', + CocoDataset='COCO', + CocoPanopticDataset='COCO', + DeepFashionDataset='Deep Fashion', + LVISV05Dataset='LVIS v0.5', + LVISV1Dataset='LVIS v1', + VOCDataset='Pascal VOC', + WIDERFaceDataset='WIDER Face', + OpenImagesDataset='OpenImagesDataset', + OpenImagesChallengeDataset='OpenImagesChallengeDataset') + cfg = mmcv.Config.fromfile('./configs/' + config) + return name_map[cfg.dataset_type] + + +def convert_model_info_to_pwc(model_infos): + pwc_files = {} + for model in model_infos: + cfg_folder_name = osp.split(model['config'])[-2] + pwc_model_info = OrderedDict() + pwc_model_info['Name'] = osp.split(model['config'])[-1].split('.')[0] + pwc_model_info['In Collection'] = 'Please fill in Collection name' + pwc_model_info['Config'] = osp.join('configs', model['config']) + + # get metadata + memory = round(model['results']['memory'] / 1024, 1) + meta_data = OrderedDict() + meta_data['Training Memory (GB)'] = memory + if 'epochs' in model: + meta_data['Epochs'] = get_real_epoch_or_iter(model['config']) + else: + meta_data['Iterations'] = get_real_epoch_or_iter(model['config']) + pwc_model_info['Metadata'] = meta_data + + # get dataset name + dataset_name = get_dataset_name(model['config']) + + # get results + results = [] + # if there are more metrics, add here. + if 'bbox_mAP' in model['results']: + metric = round(model['results']['bbox_mAP'] * 100, 1) + results.append( + OrderedDict( + Task='Object Detection', + Dataset=dataset_name, + Metrics={'box AP': metric})) + if 'segm_mAP' in model['results']: + metric = round(model['results']['segm_mAP'] * 100, 1) + results.append( + OrderedDict( + Task='Instance Segmentation', + Dataset=dataset_name, + Metrics={'mask AP': metric})) + if 'PQ' in model['results']: + metric = round(model['results']['PQ'], 1) + results.append( + OrderedDict( + Task='Panoptic Segmentation', + Dataset=dataset_name, + Metrics={'PQ': metric})) + pwc_model_info['Results'] = results + + link_string = 'https://download.openmmlab.com/mmdetection/v2.0/' + link_string += '{}/{}'.format(model['config'].rstrip('.py'), + osp.split(model['model_path'])[-1]) + pwc_model_info['Weights'] = link_string + if cfg_folder_name in pwc_files: + pwc_files[cfg_folder_name].append(pwc_model_info) + else: + pwc_files[cfg_folder_name] = [pwc_model_info] + return pwc_files + + +def parse_args(): + parser = argparse.ArgumentParser(description='Gather benchmarked models') + parser.add_argument( + 'root', + type=str, + help='root path of benchmarked models to be gathered') + parser.add_argument( + 'out', type=str, help='output path of gathered models to be stored') + parser.add_argument( + '--best', + action='store_true', + help='whether to gather the best model.') + + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + models_root = args.root + models_out = args.out + mmcv.mkdir_or_exist(models_out) + + # find all models in the root directory to be gathered + raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True)) + + # filter configs that is not trained in the experiments dir + used_configs = [] + for raw_config in raw_configs: + if osp.exists(osp.join(models_root, raw_config)): + used_configs.append(raw_config) + print(f'Find {len(used_configs)} models to be gathered') + + # find final_ckpt and log file for trained each config + # and parse the best performance + model_infos = [] + for used_config in used_configs: + exp_dir = osp.join(models_root, used_config) + by_epoch = is_by_epoch(used_config) + # check whether the exps is finished + if args.best is True: + final_model, final_epoch_or_iter = get_best_epoch_or_iter(exp_dir) + else: + final_epoch_or_iter = get_final_epoch_or_iter(used_config) + final_model = '{}_{}.pth'.format('epoch' if by_epoch else 'iter', + final_epoch_or_iter) + + model_path = osp.join(exp_dir, final_model) + # skip if the model is still training + if not osp.exists(model_path): + continue + + # get the latest logs + log_json_path = list( + sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1] + log_txt_path = list(sorted(glob.glob(osp.join(exp_dir, '*.log'))))[-1] + cfg = mmcv.Config.fromfile('./configs/' + used_config) + results_lut = cfg.evaluation.metric + if not isinstance(results_lut, list): + results_lut = [results_lut] + # case when using VOC, the evaluation key is only 'mAP' + # when using Panoptic Dataset, the evaluation key is 'PQ'. + for i, key in enumerate(results_lut): + if 'mAP' not in key and 'PQ' not in key: + results_lut[i] = key + '_mAP' + model_performance = get_final_results(log_json_path, + final_epoch_or_iter, results_lut, + by_epoch) + + if model_performance is None: + continue + + model_time = osp.split(log_txt_path)[-1].split('.')[0] + model_info = dict( + config=used_config, + results=model_performance, + model_time=model_time, + final_model=final_model, + log_json_path=osp.split(log_json_path)[-1]) + model_info['epochs' if by_epoch else 'iterations'] =\ + final_epoch_or_iter + model_infos.append(model_info) + + # publish model for each checkpoint + publish_model_infos = [] + for model in model_infos: + model_publish_dir = osp.join(models_out, model['config'].rstrip('.py')) + mmcv.mkdir_or_exist(model_publish_dir) + + model_name = osp.split(model['config'])[-1].split('.')[0] + + model_name += '_' + model['model_time'] + publish_model_path = osp.join(model_publish_dir, model_name) + trained_model_path = osp.join(models_root, model['config'], + model['final_model']) + + # convert model + final_model_path = process_checkpoint(trained_model_path, + publish_model_path) + + # copy log + shutil.copy( + osp.join(models_root, model['config'], model['log_json_path']), + osp.join(model_publish_dir, f'{model_name}.log.json')) + shutil.copy( + osp.join(models_root, model['config'], + model['log_json_path'].rstrip('.json')), + osp.join(model_publish_dir, f'{model_name}.log')) + + # copy config to guarantee reproducibility + config_path = model['config'] + config_path = osp.join( + 'configs', + config_path) if 'configs' not in config_path else config_path + target_config_path = osp.split(config_path)[-1] + shutil.copy(config_path, osp.join(model_publish_dir, + target_config_path)) + + model['model_path'] = final_model_path + publish_model_infos.append(model) + + models = dict(models=publish_model_infos) + print(f'Totally gathered {len(publish_model_infos)} models') + mmcv.dump(models, osp.join(models_out, 'model_info.json')) + + pwc_files = convert_model_info_to_pwc(publish_model_infos) + for name in pwc_files: + with open(osp.join(models_out, name + '_metafile.yml'), 'w') as f: + ordered_yaml_dump(pwc_files[name], f, encoding='utf-8') + + +if __name__ == '__main__': + main() diff --git a/downstream/mmdetection/.dev_scripts/gather_test_benchmark_metric.py b/downstream/mmdetection/.dev_scripts/gather_test_benchmark_metric.py new file mode 100644 index 0000000..07c6bf4 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/gather_test_benchmark_metric.py @@ -0,0 +1,96 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import glob +import os.path as osp + +import mmcv +from mmcv import Config + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Gather benchmarked models metric') + parser.add_argument('config', help='test config file path') + parser.add_argument( + 'root', + type=str, + help='root path of benchmarked models to be gathered') + parser.add_argument( + '--out', type=str, help='output path of gathered metrics to be stored') + parser.add_argument( + '--not-show', action='store_true', help='not show metrics') + parser.add_argument( + '--show-all', action='store_true', help='show all model metrics') + + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + + root_path = args.root + metrics_out = args.out + result_dict = {} + + cfg = Config.fromfile(args.config) + + for model_key in cfg: + model_infos = cfg[model_key] + if not isinstance(model_infos, list): + model_infos = [model_infos] + for model_info in model_infos: + record_metrics = model_info['metric'] + config = model_info['config'].strip() + fname, _ = osp.splitext(osp.basename(config)) + metric_json_dir = osp.join(root_path, fname) + if osp.exists(metric_json_dir): + json_list = glob.glob(osp.join(metric_json_dir, '*.json')) + if len(json_list) > 0: + log_json_path = list(sorted(json_list))[-1] + + metric = mmcv.load(log_json_path) + if config in metric.get('config', {}): + + new_metrics = dict() + for record_metric_key in record_metrics: + record_metric_key_bk = record_metric_key + old_metric = record_metrics[record_metric_key] + if record_metric_key == 'AR_1000': + record_metric_key = 'AR@1000' + if record_metric_key not in metric['metric']: + raise KeyError( + 'record_metric_key not exist, please ' + 'check your config') + new_metric = round( + metric['metric'][record_metric_key] * 100, 1) + new_metrics[record_metric_key_bk] = new_metric + + if args.show_all: + result_dict[config] = dict( + before=record_metrics, after=new_metrics) + else: + for record_metric_key in record_metrics: + old_metric = record_metrics[record_metric_key] + new_metric = new_metrics[record_metric_key] + if old_metric != new_metric: + result_dict[config] = dict( + before=record_metrics, + after=new_metrics) + break + else: + print(f'{config} not included in: {log_json_path}') + else: + print(f'{config} not exist file: {metric_json_dir}') + else: + print(f'{config} not exist dir: {metric_json_dir}') + + if metrics_out: + mmcv.mkdir_or_exist(metrics_out) + mmcv.dump(result_dict, + osp.join(metrics_out, 'batch_test_metric_info.json')) + if not args.not_show: + print('===================================') + for config_name, metrics in result_dict.items(): + print(config_name, metrics) + print('===================================') diff --git a/downstream/mmdetection/.dev_scripts/gather_train_benchmark_metric.py b/downstream/mmdetection/.dev_scripts/gather_train_benchmark_metric.py new file mode 100644 index 0000000..f9c6c80 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/gather_train_benchmark_metric.py @@ -0,0 +1,150 @@ +# Copyright (c) OpenMMLab. All rights reserved. +import argparse +import glob +import os.path as osp + +import mmcv +from gather_models import get_final_results + +try: + import xlrd +except ImportError: + xlrd = None +try: + import xlutils + from xlutils.copy import copy +except ImportError: + xlutils = None + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Gather benchmarked models metric') + parser.add_argument( + 'root', + type=str, + help='root path of benchmarked models to be gathered') + parser.add_argument( + 'txt_path', type=str, help='txt path output by benchmark_filter') + parser.add_argument( + '--out', type=str, help='output path of gathered metrics to be stored') + parser.add_argument( + '--not-show', action='store_true', help='not show metrics') + parser.add_argument( + '--excel', type=str, help='input path of excel to be recorded') + parser.add_argument( + '--ncol', type=int, help='Number of column to be modified or appended') + + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + + if args.excel: + assert args.ncol, 'Please specify "--excel" and "--ncol" ' \ + 'at the same time' + if xlrd is None: + raise RuntimeError( + 'xlrd is not installed,' + 'Please use “pip install xlrd==1.2.0” to install') + if xlutils is None: + raise RuntimeError( + 'xlutils is not installed,' + 'Please use “pip install xlutils==2.0.0” to install') + readbook = xlrd.open_workbook(args.excel) + sheet = readbook.sheet_by_name('Sheet1') + sheet_info = {} + total_nrows = sheet.nrows + for i in range(3, sheet.nrows): + sheet_info[sheet.row_values(i)[0]] = i + xlrw = copy(readbook) + table = xlrw.get_sheet(0) + + root_path = args.root + metrics_out = args.out + + result_dict = {} + with open(args.txt_path, 'r') as f: + model_cfgs = f.readlines() + for i, config in enumerate(model_cfgs): + config = config.strip() + if len(config) == 0: + continue + + config_name = osp.split(config)[-1] + config_name = osp.splitext(config_name)[0] + result_path = osp.join(root_path, config_name) + if osp.exists(result_path): + # 1 read config + cfg = mmcv.Config.fromfile(config) + total_epochs = cfg.runner.max_epochs + final_results = cfg.evaluation.metric + if not isinstance(final_results, list): + final_results = [final_results] + final_results_out = [] + for key in final_results: + if 'proposal_fast' in key: + final_results_out.append('AR@1000') # RPN + elif 'mAP' not in key: + final_results_out.append(key + '_mAP') + + # 2 determine whether total_epochs ckpt exists + ckpt_path = f'epoch_{total_epochs}.pth' + if osp.exists(osp.join(result_path, ckpt_path)): + log_json_path = list( + sorted(glob.glob(osp.join(result_path, + '*.log.json'))))[-1] + + # 3 read metric + model_performance = get_final_results( + log_json_path, total_epochs, final_results_out) + if model_performance is None: + print(f'log file error: {log_json_path}') + continue + for performance in model_performance: + if performance in ['AR@1000', 'bbox_mAP', 'segm_mAP']: + metric = round( + model_performance[performance] * 100, 1) + model_performance[performance] = metric + result_dict[config] = model_performance + + # update and append excel content + if args.excel: + if 'AR@1000' in model_performance: + metrics = f'{model_performance["AR@1000"]}' \ + f'(AR@1000)' + elif 'segm_mAP' in model_performance: + metrics = f'{model_performance["bbox_mAP"]}/' \ + f'{model_performance["segm_mAP"]}' + else: + metrics = f'{model_performance["bbox_mAP"]}' + + row_num = sheet_info.get(config, None) + if row_num: + table.write(row_num, args.ncol, metrics) + else: + table.write(total_nrows, 0, config) + table.write(total_nrows, args.ncol, metrics) + total_nrows += 1 + + else: + print(f'{config} not exist: {ckpt_path}') + else: + print(f'not exist: {config}') + + # 4 save or print results + if metrics_out: + mmcv.mkdir_or_exist(metrics_out) + mmcv.dump(result_dict, + osp.join(metrics_out, 'model_metric_info.json')) + if not args.not_show: + print('===================================') + for config_name, metrics in result_dict.items(): + print(config_name, metrics) + print('===================================') + if args.excel: + filename, sufflx = osp.splitext(args.excel) + xlrw.save(f'{filename}_o{sufflx}') + print(f'>>> Output {filename}_o{sufflx}') diff --git a/downstream/mmdetection/.dev_scripts/linter.sh b/downstream/mmdetection/.dev_scripts/linter.sh new file mode 100644 index 0000000..b0fe0ac --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/linter.sh @@ -0,0 +1,3 @@ +yapf -r -i mmdet/ configs/ tests/ tools/ +isort -rc mmdet/ configs/ tests/ tools/ +flake8 . diff --git a/downstream/mmdetection/.dev_scripts/test_benchmark.sh b/downstream/mmdetection/.dev_scripts/test_benchmark.sh new file mode 100644 index 0000000..cb79950 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/test_benchmark.sh @@ -0,0 +1,119 @@ +PARTITION=$1 +CHECKPOINT_DIR=$2 + +echo 'configs/atss/atss_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py $CHECKPOINT_DIR/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth --work-dir tools/batch_test/atss_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29666 & +echo 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION autoassign_r50_fpn_8x2_1x_coco configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py $CHECKPOINT_DIR/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth --work-dir tools/batch_test/autoassign_r50_fpn_8x2_1x_coco --eval bbox --cfg-option dist_params.port=29667 & +echo 'configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_carafe_1x_coco configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_carafe_1x_coco --eval bbox --cfg-option dist_params.port=29668 & +echo 'configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION cascade_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth --work-dir tools/batch_test/cascade_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29669 & +echo 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION cascade_mask_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth --work-dir tools/batch_test/cascade_mask_rcnn_r50_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29670 & +echo 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION crpn_faster_rcnn_r50_caffe_fpn_1x_coco configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth --work-dir tools/batch_test/crpn_faster_rcnn_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29671 & +echo 'configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION centripetalnet_hourglass104_mstest_16x6_210e_coco configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py $CHECKPOINT_DIR/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth --work-dir tools/batch_test/centripetalnet_hourglass104_mstest_16x6_210e_coco --eval bbox --cfg-option dist_params.port=29672 & +echo 'configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION cornernet_hourglass104_mstest_8x6_210e_coco configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py $CHECKPOINT_DIR/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth --work-dir tools/batch_test/cornernet_hourglass104_mstest_8x6_210e_coco --eval bbox --cfg-option dist_params.port=29673 & +echo 'configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco --eval bbox --cfg-option dist_params.port=29674 & +echo 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION deformable_detr_r50_16x2_50e_coco configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py $CHECKPOINT_DIR/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth --work-dir tools/batch_test/deformable_detr_r50_16x2_50e_coco --eval bbox --cfg-option dist_params.port=29675 & +echo 'configs/detectors/detectors_htc_r50_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION detectors_htc_r50_1x_coco configs/detectors/detectors_htc_r50_1x_coco.py $CHECKPOINT_DIR/detectors_htc_r50_1x_coco-329b1453.pth --work-dir tools/batch_test/detectors_htc_r50_1x_coco --eval bbox segm --cfg-option dist_params.port=29676 & +echo 'configs/detr/detr_r50_8x2_150e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION detr_r50_8x2_150e_coco configs/detr/detr_r50_8x2_150e_coco.py $CHECKPOINT_DIR/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth --work-dir tools/batch_test/detr_r50_8x2_150e_coco --eval bbox --cfg-option dist_params.port=29677 & +echo 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION dh_faster_rcnn_r50_fpn_1x_coco configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth --work-dir tools/batch_test/dh_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29678 & +echo 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION dynamic_rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dynamic_rcnn_r50_fpn_1x-62a3f276.pth --work-dir tools/batch_test/dynamic_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29679 & +echo 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_attention_1111_1x_coco configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_attention_1111_1x_coco --eval bbox --cfg-option dist_params.port=29680 & +echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29681 & +echo 'configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py $CHECKPOINT_DIR/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth --work-dir tools/batch_test/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco --eval bbox --cfg-option dist_params.port=29682 & +echo 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fovea_align_r50_fpn_gn-head_4x4_2x_coco configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py $CHECKPOINT_DIR/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth --work-dir tools/batch_test/fovea_align_r50_fpn_gn-head_4x4_2x_coco --eval bbox --cfg-option dist_params.port=29683 & +echo 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION retinanet_free_anchor_r50_fpn_1x_coco configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth --work-dir tools/batch_test/retinanet_free_anchor_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29684 & +echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py $CHECKPOINT_DIR/fsaf_r50_fpn_1x_coco-94ccc51f.pth --work-dir tools/batch_test/fsaf_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29685 & +echo 'configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco --eval bbox segm --cfg-option dist_params.port=29686 & +echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py $CHECKPOINT_DIR/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth --work-dir tools/batch_test/gfl_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29687 & +echo 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_gn-all_2x_coco --eval bbox segm --cfg-option dist_params.port=29688 & +echo 'configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_gn_ws-all_1x_coco configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_gn_ws-all_1x_coco --eval bbox --cfg-option dist_params.port=29689 & +echo 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION grid_rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py $CHECKPOINT_DIR/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth --work-dir tools/batch_test/grid_rcnn_r50_fpn_gn-head_2x_coco --eval bbox --cfg-option dist_params.port=29690 & +echo 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_groie_1x_coco configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_groie_1x_coco --eval bbox --cfg-option dist_params.port=29691 & +echo 'configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ga_retinanet_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth --work-dir tools/batch_test/ga_retinanet_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29692 & +echo 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ga_faster_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth --work-dir tools/batch_test/ga_faster_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29693 & +echo 'configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_hrnetv2p_w18_1x_coco configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth --work-dir tools/batch_test/faster_rcnn_hrnetv2p_w18_1x_coco --eval bbox --cfg-option dist_params.port=29694 & +echo 'configs/htc/htc_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py $CHECKPOINT_DIR/htc_r50_fpn_1x_coco_20200317-7332cf16.pth --work-dir tools/batch_test/htc_r50_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29695 & +echo 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION libra_faster_rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth --work-dir tools/batch_test/libra_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29696 & +echo 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_r50_fpn_1x_coco configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29697 & +echo 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ms_rcnn_r50_caffe_fpn_1x_coco configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth --work-dir tools/batch_test/ms_rcnn_r50_caffe_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29698 & +echo 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py $CHECKPOINT_DIR/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth --work-dir tools/batch_test/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco --eval bbox --cfg-option dist_params.port=29699 & +echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION retinanet_r50_nasfpn_crop640_50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py $CHECKPOINT_DIR/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth --work-dir tools/batch_test/retinanet_r50_nasfpn_crop640_50e_coco --eval bbox --cfg-option dist_params.port=29700 & +echo 'configs/paa/paa_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py $CHECKPOINT_DIR/paa_r50_fpn_1x_coco_20200821-936edec3.pth --work-dir tools/batch_test/paa_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29701 & +echo 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_pafpn_1x_coco configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth --work-dir tools/batch_test/faster_rcnn_r50_pafpn_1x_coco --eval bbox --cfg-option dist_params.port=29702 & +echo 'configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pisa_faster_rcnn_r50_fpn_1x_coco configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth --work-dir tools/batch_test/pisa_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29703 & +echo 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION point_rend_r50_caffe_fpn_mstrain_1x_coco configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py $CHECKPOINT_DIR/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth --work-dir tools/batch_test/point_rend_r50_caffe_fpn_mstrain_1x_coco --eval bbox segm --cfg-option dist_params.port=29704 & +echo 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_regnetx-3.2GF_fpn_1x_coco configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth --work-dir tools/batch_test/mask_rcnn_regnetx-3.2GF_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29705 & +echo 'configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION reppoints_moment_r50_fpn_1x_coco configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py $CHECKPOINT_DIR/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth --work-dir tools/batch_test/reppoints_moment_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29706 & +echo 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r2_101_fpn_2x_coco configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py $CHECKPOINT_DIR/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth --work-dir tools/batch_test/faster_rcnn_r2_101_fpn_2x_coco --eval bbox --cfg-option dist_params.port=29707 & +echo 'configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth --work-dir tools/batch_test/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco --eval bbox --cfg-option dist_params.port=29708 & +echo 'configs/retinanet/retinanet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION retinanet_r50_fpn_1x_coco configs/retinanet/retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth --work-dir tools/batch_test/retinanet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29709 & +echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth --work-dir tools/batch_test/rpn_r50_fpn_1x_coco --eval proposal_fast --cfg-option dist_params.port=29710 & +echo 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION sabl_retinanet_r50_fpn_1x_coco configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth --work-dir tools/batch_test/sabl_retinanet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29711 & +echo 'configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION sabl_faster_rcnn_r50_fpn_1x_coco configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth --work-dir tools/batch_test/sabl_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29712 & +echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/scnet_r50_fpn_1x_coco-c3f09857.pth --work-dir tools/batch_test/scnet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29713 & +echo 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION sparse_rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth --work-dir tools/batch_test/sparse_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29714 & +echo 'configs/ssd/ssd300_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ssd300_coco configs/ssd/ssd300_coco.py $CHECKPOINT_DIR/ssd300_coco_20210803_015428-d231a06e.pth --work-dir tools/batch_test/ssd300_coco --eval bbox --cfg-option dist_params.port=29715 & +echo 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION tridentnet_r50_caffe_1x_coco configs/tridentnet/tridentnet_r50_caffe_1x_coco.py $CHECKPOINT_DIR/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth --work-dir tools/batch_test/tridentnet_r50_caffe_1x_coco --eval bbox --cfg-option dist_params.port=29716 & +echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth --work-dir tools/batch_test/vfnet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29717 & +echo 'configs/yolact/yolact_r50_1x8_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolact_r50_1x8_coco configs/yolact/yolact_r50_1x8_coco.py $CHECKPOINT_DIR/yolact_r50_1x8_coco_20200908-f38d58df.pth --work-dir tools/batch_test/yolact_r50_1x8_coco --eval bbox segm --cfg-option dist_params.port=29718 & +echo 'configs/yolo/yolov3_d53_320_273e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolov3_d53_320_273e_coco configs/yolo/yolov3_d53_320_273e_coco.py $CHECKPOINT_DIR/yolov3_d53_320_273e_coco-421362b6.pth --work-dir tools/batch_test/yolov3_d53_320_273e_coco --eval bbox --cfg-option dist_params.port=29719 & +echo 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolof_r50_c5_8x8_1x_coco configs/yolof/yolof_r50_c5_8x8_1x_coco.py $CHECKPOINT_DIR/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth --work-dir tools/batch_test/yolof_r50_c5_8x8_1x_coco --eval bbox --cfg-option dist_params.port=29720 & +echo 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION centernet_resnet18_dcnv2_140e_coco configs/centernet/centernet_resnet18_dcnv2_140e_coco.py $CHECKPOINT_DIR/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth --work-dir tools/batch_test/centernet_resnet18_dcnv2_140e_coco --eval bbox --cfg-option dist_params.port=29721 & +echo 'configs/yolox/yolox_tiny_8x8_300e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolox_tiny_8x8_300e_coco configs/yolox/yolox_tiny_8x8_300e_coco.py $CHECKPOINT_DIR/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth --work-dir tools/batch_test/yolox_tiny_8x8_300e_coco --eval bbox --cfg-option dist_params.port=29722 & +echo 'configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ssdlite_mobilenetv2_scratch_600e_coco configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py $CHECKPOINT_DIR/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth --work-dir tools/batch_test/ssdlite_mobilenetv2_scratch_600e_coco --eval bbox --cfg-option dist_params.port=29723 & diff --git a/downstream/mmdetection/.dev_scripts/test_init_backbone.py b/downstream/mmdetection/.dev_scripts/test_init_backbone.py new file mode 100644 index 0000000..862f4af --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/test_init_backbone.py @@ -0,0 +1,181 @@ +# Copyright (c) OpenMMLab. All rights reserved. +"""Check out backbone whether successfully load pretrained checkpoint.""" +import copy +import os +from os.path import dirname, exists, join + +import pytest +from mmcv import Config, ProgressBar +from mmcv.runner import _load_checkpoint + +from mmdet.models import build_detector + + +def _get_config_directory(): + """Find the predefined detector config directory.""" + try: + # Assume we are running in the source mmdetection repo + repo_dpath = dirname(dirname(__file__)) + except NameError: + # For IPython development when this __file__ is not defined + import mmdet + repo_dpath = dirname(dirname(mmdet.__file__)) + config_dpath = join(repo_dpath, 'configs') + if not exists(config_dpath): + raise Exception('Cannot find config path') + return config_dpath + + +def _get_config_module(fname): + """Load a configuration as a python module.""" + from mmcv import Config + config_dpath = _get_config_directory() + config_fpath = join(config_dpath, fname) + config_mod = Config.fromfile(config_fpath) + return config_mod + + +def _get_detector_cfg(fname): + """Grab configs necessary to create a detector. + + These are deep copied to allow for safe modification of parameters without + influencing other tests. + """ + config = _get_config_module(fname) + model = copy.deepcopy(config.model) + return model + + +def _traversed_config_file(): + """We traversed all potential config files under the `config` file. If you + need to print details or debug code, you can use this function. + + If the `backbone.init_cfg` is None (do not use `Pretrained` init way), you + need add the folder name in `ignores_folder` (if the config files in this + folder all set backbone.init_cfg is None) or add config name in + `ignores_file` (if the config file set backbone.init_cfg is None) + """ + config_path = _get_config_directory() + check_cfg_names = [] + + # `base`, `legacy_1.x` and `common` ignored by default. + ignores_folder = ['_base_', 'legacy_1.x', 'common'] + # 'ld' need load teacher model, if want to check 'ld', + # please check teacher_config path first. + ignores_folder += ['ld'] + # `selfsup_pretrain` need convert model, if want to check this model, + # need to convert the model first. + ignores_folder += ['selfsup_pretrain'] + + # the `init_cfg` in 'centripetalnet', 'cornernet', 'cityscapes', + # 'scratch' is None. + # the `init_cfg` in ssdlite(`ssdlite_mobilenetv2_scratch_600e_coco.py`) + # is None + # Please confirm `bockbone.init_cfg` is None first. + ignores_folder += ['centripetalnet', 'cornernet', 'cityscapes', 'scratch'] + ignores_file = ['ssdlite_mobilenetv2_scratch_600e_coco.py'] + + for config_file_name in os.listdir(config_path): + if config_file_name not in ignores_folder: + config_file = join(config_path, config_file_name) + if os.path.isdir(config_file): + for config_sub_file in os.listdir(config_file): + if config_sub_file.endswith('py') and \ + config_sub_file not in ignores_file: + name = join(config_file, config_sub_file) + check_cfg_names.append(name) + return check_cfg_names + + +def _check_backbone(config, print_cfg=True): + """Check out backbone whether successfully load pretrained model, by using + `backbone.init_cfg`. + + First, using `mmcv._load_checkpoint` to load the checkpoint without + loading models. + Then, using `build_detector` to build models, and using + `model.init_weights()` to initialize the parameters. + Finally, assert weights and bias of each layer loaded from pretrained + checkpoint are equal to the weights and bias of original checkpoint. + For the convenience of comparison, we sum up weights and bias of + each loaded layer separately. + + Args: + config (str): Config file path. + print_cfg (bool): Whether print logger and return the result. + + Returns: + results (str or None): If backbone successfully load pretrained + checkpoint, return None; else, return config file path. + """ + if print_cfg: + print('-' * 15 + 'loading ', config) + cfg = Config.fromfile(config) + init_cfg = None + try: + init_cfg = cfg.model.backbone.init_cfg + init_flag = True + except AttributeError: + init_flag = False + if init_cfg is None or init_cfg.get('type') != 'Pretrained': + init_flag = False + if init_flag: + checkpoint = _load_checkpoint(init_cfg.checkpoint) + if 'state_dict' in checkpoint: + state_dict = checkpoint['state_dict'] + else: + state_dict = checkpoint + + model = build_detector( + cfg.model, + train_cfg=cfg.get('train_cfg'), + test_cfg=cfg.get('test_cfg')) + model.init_weights() + + checkpoint_layers = state_dict.keys() + for name, value in model.backbone.state_dict().items(): + if name in checkpoint_layers: + assert value.equal(state_dict[name]) + + if print_cfg: + print('-' * 10 + 'Successfully load checkpoint' + '-' * 10 + + '\n', ) + return None + else: + if print_cfg: + print(config + '\n' + '-' * 10 + + 'config file do not have init_cfg' + '-' * 10 + '\n') + return config + + +@pytest.mark.parametrize('config', _traversed_config_file()) +def test_load_pretrained(config): + """Check out backbone whether successfully load pretrained model by using + `backbone.init_cfg`. + + Details please refer to `_check_backbone` + """ + _check_backbone(config, print_cfg=False) + + +def _test_load_pretrained(): + """We traversed all potential config files under the `config` file. If you + need to print details or debug code, you can use this function. + + Returns: + check_cfg_names (list[str]): Config files that backbone initialized + from pretrained checkpoint might be problematic. Need to recheck + the config file. The output including the config files that the + backbone.init_cfg is None + """ + check_cfg_names = _traversed_config_file() + need_check_cfg = [] + + prog_bar = ProgressBar(len(check_cfg_names)) + for config in check_cfg_names: + init_cfg_name = _check_backbone(config) + if init_cfg_name is not None: + need_check_cfg.append(init_cfg_name) + prog_bar.update() + print('These config files need to be checked again') + print(need_check_cfg) diff --git a/downstream/mmdetection/.dev_scripts/train_benchmark.sh b/downstream/mmdetection/.dev_scripts/train_benchmark.sh new file mode 100644 index 0000000..dc30be9 --- /dev/null +++ b/downstream/mmdetection/.dev_scripts/train_benchmark.sh @@ -0,0 +1,134 @@ +echo 'configs/atss/atss_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py ./tools/work_dir/atss_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab autoassign_r50_fpn_8x2_1x_coco configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py ./tools/work_dir/autoassign_r50_fpn_8x2_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab cascade_mask_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/cascade_mask_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab crpn_faster_rcnn_r50_caffe_fpn_1x_coco configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/crpn_faster_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab centernet_resnet18_dcnv2_140e_coco configs/centernet/centernet_resnet18_dcnv2_140e_coco.py ./tools/work_dir/centernet_resnet18_dcnv2_140e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py' & +GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab centripetalnet_hourglass104_mstest_16x6_210e_coco configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py ./tools/work_dir/centripetalnet_hourglass104_mstest_16x6_210e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab cornernet_hourglass104_mstest_8x6_210e_coco configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py ./tools/work_dir/cornernet_hourglass104_mstest_8x6_210e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/detectors/detectors_htc_r50_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab detectors_htc_r50_1x_coco configs/detectors/detectors_htc_r50_1x_coco.py ./tools/work_dir/detectors_htc_r50_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py' & +GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab deformable_detr_r50_16x2_50e_coco configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py ./tools/work_dir/deformable_detr_r50_16x2_50e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/detr/detr_r50_8x2_150e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab detr_r50_8x2_150e_coco configs/detr/detr_r50_8x2_150e_coco.py ./tools/work_dir/detr_r50_8x2_150e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab dh_faster_rcnn_r50_fpn_1x_coco configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/dh_faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab dynamic_rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/dynamic_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_dc5_mstrain_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_fpn_mstrain_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_ohem_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_ohem_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py' & +GPUS=4 GPUS_PER_NODE=4 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab fovea_align_r50_fpn_gn-head_4x4_2x_coco configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py ./tools/work_dir/fovea_align_r50_fpn_gn-head_4x4_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_fp16_1x_coco configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_fp16_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_fpn_fp16_1x_coco configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py ./tools/work_dir/retinanet_r50_fpn_fp16_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_free_anchor_r50_fpn_1x_coco configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py ./tools/work_dir/retinanet_free_anchor_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py ./tools/work_dir/fsaf_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py ./tools/work_dir/gfl_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_ghm_r50_fpn_1x_coco configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py ./tools/work_dir/retinanet_ghm_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab grid_rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py ./tools/work_dir/grid_rcnn_r50_fpn_gn-head_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ga_faster_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py ./tools/work_dir/ga_faster_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/htc/htc_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py ./tools/work_dir/htc_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ld_r18_gflv1_r101_fpn_coco_1x configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py ./tools/work_dir/ld_r18_gflv1_r101_fpn_coco_1x --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab libra_faster_rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/libra_faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py ./tools/work_dir/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ms_rcnn_r50_caffe_fpn_1x_coco configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/ms_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py' & +GPUS=4 GPUS_PER_NODE=4 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py ./tools/work_dir/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/paa/paa_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py ./tools/work_dir/paa_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab pisa_mask_rcnn_r50_fpn_1x_coco configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/pisa_mask_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab point_rend_r50_caffe_fpn_mstrain_1x_coco configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py ./tools/work_dir/point_rend_r50_caffe_fpn_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab reppoints_moment_r50_fpn_gn-neck+head_1x_coco configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py ./tools/work_dir/reppoints_moment_r50_fpn_gn-neck+head_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_caffe_fpn_1x_coco configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py ./tools/work_dir/retinanet_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py ./tools/work_dir/rpn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab sabl_retinanet_r50_fpn_1x_coco configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py ./tools/work_dir/sabl_retinanet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ssd/ssd300_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ssd300_coco configs/ssd/ssd300_coco.py ./tools/work_dir/ssd300_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab tridentnet_r50_caffe_1x_coco configs/tridentnet/tridentnet_r50_caffe_1x_coco.py ./tools/work_dir/tridentnet_r50_caffe_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py ./tools/work_dir/vfnet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/yolact/yolact_r50_8x8_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolact_r50_8x8_coco configs/yolact/yolact_r50_8x8_coco.py ./tools/work_dir/yolact_r50_8x8_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/yolo/yolov3_d53_320_273e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolov3_d53_320_273e_coco configs/yolo/yolov3_d53_320_273e_coco.py ./tools/work_dir/yolov3_d53_320_273e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab sparse_rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/sparse_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py ./tools/work_dir/scnet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolof_r50_c5_8x8_1x_coco configs/yolof/yolof_r50_c5_8x8_1x_coco.py ./tools/work_dir/yolof_r50_c5_8x8_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_carafe_1x_coco configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_carafe_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_mdpool_1x_coco configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_mdpool_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_dpool_1x_coco configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_dpool_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_gn-all_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_gn_ws-all_2x_coco configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_gn_ws-all_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_hrnetv2p_w18_1x_coco configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py ./tools/work_dir/mask_rcnn_hrnetv2p_w18_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_pafpn_1x_coco configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_pafpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_nasfpn_crop640_50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py ./tools/work_dir/retinanet_r50_nasfpn_crop640_50e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_regnetx-3.2GF_fpn_1x_coco configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py ./tools/work_dir/mask_rcnn_regnetx-3.2GF_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py ./tools/work_dir/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r2_101_fpn_2x_coco configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py ./tools/work_dir/faster_rcnn_r2_101_fpn_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_groie_1x_coco configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_groie_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_1x_cityscapes configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py ./tools/work_dir/mask_rcnn_r50_fpn_1x_cityscapes --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab panoptic_fpn_r50_fpn_1x_coco configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py ./tools/work_dir/panoptic_fpn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/yolox/yolox_tiny_8x8_300e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolox_tiny_8x8_300e_coco configs/yolox/yolox_tiny_8x8_300e_coco.py ./tools/work_dir/yolox_tiny_8x8_300e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ssdlite_mobilenetv2_scratch_600e_coco configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py ./tools/work_dir/ssdlite_mobilenetv2_scratch_600e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & diff --git a/downstream/mmdetection/.github/CODE_OF_CONDUCT.md b/downstream/mmdetection/.github/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..92afad1 --- /dev/null +++ b/downstream/mmdetection/.github/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +- Using welcoming and inclusive language +- Being respectful of differing viewpoints and experiences +- Gracefully accepting constructive criticism +- Focusing on what is best for the community +- Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +- The use of sexualized language or imagery and unwelcome sexual attention or + advances +- Trolling, insulting/derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or electronic + address, without explicit permission +- Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at chenkaidev@gmail.com. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq + +[homepage]: https://www.contributor-covenant.org diff --git a/downstream/mmdetection/.github/CONTRIBUTING.md b/downstream/mmdetection/.github/CONTRIBUTING.md new file mode 100644 index 0000000..c669626 --- /dev/null +++ b/downstream/mmdetection/.github/CONTRIBUTING.md @@ -0,0 +1 @@ +We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline. diff --git a/downstream/mmdetection/.github/ISSUE_TEMPLATE/config.yml b/downstream/mmdetection/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000..56bbd88 --- /dev/null +++ b/downstream/mmdetection/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,9 @@ +blank_issues_enabled: false + +contact_links: + - name: Common Issues + url: https://mmdetection.readthedocs.io/en/latest/faq.html + about: Check if your issue already has solutions + - name: MMDetection Documentation + url: https://mmdetection.readthedocs.io/en/latest/ + about: Check if your question is answered in docs diff --git a/downstream/mmdetection/.github/ISSUE_TEMPLATE/error-report.md b/downstream/mmdetection/.github/ISSUE_TEMPLATE/error-report.md new file mode 100644 index 0000000..9dbd3ff --- /dev/null +++ b/downstream/mmdetection/.github/ISSUE_TEMPLATE/error-report.md @@ -0,0 +1,46 @@ +--- +name: Error report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' +--- + +Thanks for your error report and we appreciate it a lot. + +**Checklist** + +1. I have searched related issues but cannot get the expected help. +2. I have read the [FAQ documentation](https://mmdetection.readthedocs.io/en/latest/faq.html) but cannot get the expected help. +3. The bug has not been fixed in the latest version. + +**Describe the bug** +A clear and concise description of what the bug is. + +**Reproduction** + +1. What command or script did you run? + +```none +A placeholder for the command. +``` + +2. Did you make any modifications on the code or config? Did you understand what you have modified? +3. What dataset did you use? + +**Environment** + +1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here. +2. You may add addition that may be helpful for locating the problem, such as + - How you installed PyTorch \[e.g., pip, conda, source\] + - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) + +**Error traceback** +If applicable, paste the error trackback here. + +```none +A placeholder for trackback. +``` + +**Bug fix** +If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! diff --git a/downstream/mmdetection/.github/ISSUE_TEMPLATE/feature_request.md b/downstream/mmdetection/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..7bf92e8 --- /dev/null +++ b/downstream/mmdetection/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,21 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' +--- + +**Describe the feature** + +**Motivation** +A clear and concise description of the motivation of the feature. +Ex1. It is inconvenient when \[....\]. +Ex2. There is a recent paper \[....\], which is very helpful for \[....\]. + +**Related resources** +If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful. + +**Additional context** +Add any other context or screenshots about the feature request here. +If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated. diff --git a/downstream/mmdetection/.github/ISSUE_TEMPLATE/general_questions.md b/downstream/mmdetection/.github/ISSUE_TEMPLATE/general_questions.md new file mode 100644 index 0000000..f02dd63 --- /dev/null +++ b/downstream/mmdetection/.github/ISSUE_TEMPLATE/general_questions.md @@ -0,0 +1,7 @@ +--- +name: General questions +about: Ask general questions to get help +title: '' +labels: '' +assignees: '' +--- diff --git a/downstream/mmdetection/.github/ISSUE_TEMPLATE/reimplementation_questions.md b/downstream/mmdetection/.github/ISSUE_TEMPLATE/reimplementation_questions.md new file mode 100644 index 0000000..83607ac --- /dev/null +++ b/downstream/mmdetection/.github/ISSUE_TEMPLATE/reimplementation_questions.md @@ -0,0 +1,67 @@ +--- +name: Reimplementation Questions +about: Ask about questions during model reimplementation +title: '' +labels: reimplementation +assignees: '' +--- + +**Notice** + +There are several common situations in the reimplementation issues as below + +1. Reimplement a model in the model zoo using the provided configs +2. Reimplement a model in the model zoo on other dataset (e.g., custom datasets) +3. Reimplement a custom model but all the components are implemented in MMDetection +4. Reimplement a custom model with new modules implemented by yourself + +There are several things to do for different cases as below. + +- For case 1 & 3, please follow the steps in the following sections thus we could help to quick identify the issue. +- For case 2 & 4, please understand that we are not able to do much help here because we usually do not know the full code and the users should be responsible to the code they write. +- One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemented code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you. + +**Checklist** + +1. I have searched related issues but cannot get the expected help. +2. The issue has not been fixed in the latest version. + +**Describe the issue** + +A clear and concise description of what the problem you meet and what have you done. + +**Reproduction** + +1. What command or script did you run? + +```none +A placeholder for the command. +``` + +2. What config dir you run? + +```none +A placeholder for the config. +``` + +3. Did you make any modifications on the code or config? Did you understand what you have modified? +4. What dataset did you use? + +**Environment** + +1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here. +2. You may add addition that may be helpful for locating the problem, such as + 1. How you installed PyTorch \[e.g., pip, conda, source\] + 2. Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) + +**Results** + +If applicable, paste the related results here, e.g., what you expect and what you get. + +```none +A placeholder for results comparison +``` + +**Issue fix** + +If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! diff --git a/downstream/mmdetection/.github/pull_request_template.md b/downstream/mmdetection/.github/pull_request_template.md new file mode 100644 index 0000000..8f8e289 --- /dev/null +++ b/downstream/mmdetection/.github/pull_request_template.md @@ -0,0 +1,25 @@ +Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. + +## Motivation + +Please describe the motivation of this PR and the goal you want to achieve through this PR. + +## Modification + +Please briefly describe what modification is made in this PR. + +## BC-breaking (Optional) + +Does the modification introduce changes that break the backward-compatibility of the downstream repos? +If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. + +## Use cases (Optional) + +If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. + +## Checklist + +1. Pre-commit or other linting tools are used to fix the potential lint issues. +2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. +3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMCls. +4. The documentation has been modified accordingly, like docstring or example tutorials. diff --git a/downstream/mmdetection/.github/workflows/build.yml b/downstream/mmdetection/.github/workflows/build.yml new file mode 100644 index 0000000..918ce92 --- /dev/null +++ b/downstream/mmdetection/.github/workflows/build.yml @@ -0,0 +1,288 @@ +name: build + +on: + push: + paths-ignore: + - ".dev_scripts/**" + - ".github/**.md" + - "demo/**" + - "docker/**" + - "tools/**" + - "README.md" + - "README_zh-CN.md" + + pull_request: + paths-ignore: + - ".dev_scripts/**" + - ".github/**.md" + - "demo/**" + - "docker/**" + - "docs/**" + - "docs_zh-CN/**" + - "tools/**" + - "README.md" + - "README_zh-CN.md" + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + build_cpu: + runs-on: ubuntu-18.04 + strategy: + matrix: + python-version: [3.7] + torch: [1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0, 1.10.1] + include: + - torch: 1.5.1 + torchvision: 0.6.1 + mmcv: 1.5 + - torch: 1.6.0 + torchvision: 0.7.0 + mmcv: 1.6 + - torch: 1.7.0 + torchvision: 0.8.1 + mmcv: 1.7 + - torch: 1.8.0 + torchvision: 0.9.0 + mmcv: 1.8 + - torch: 1.9.0 + torchvision: 0.10.0 + mmcv: 1.9 + - torch: 1.10.1 + torchvision: 0.11.2 + mmcv: "1.10" + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install Pillow + run: pip install Pillow==6.2.2 + if: ${{matrix.torchvision == '0.4.2'}} + - name: Install PyTorch + run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html + - name: Install MMCV + run: | + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.mmcv}}/index.html + python -c 'import mmcv; print(mmcv.__version__)' + - name: Install unittest dependencies + run: | + pip install -r requirements/tests.txt -r requirements/optional.txt + pip install albumentations>=0.3.2 --no-binary imgaug,albumentations + pip install git+https://github.com/cocodataset/panopticapi.git + - name: Build and install + run: rm -rf .eggs && pip install -e . + - name: Run unittests and generate coverage report + run: | + coverage run --branch --source mmdet -m pytest tests/ + coverage xml + coverage report -m + + build_cuda101: + runs-on: ubuntu-18.04 + container: + image: pytorch/pytorch:1.6.0-cuda10.1-cudnn7-devel + + strategy: + matrix: + python-version: [3.7] + torch: [1.5.1+cu101, 1.6.0+cu101, 1.7.0+cu101, 1.8.0+cu101] + include: + - torch: 1.5.1+cu101 + torch_version: torch1.5.1 + torchvision: 0.6.1+cu101 + mmcv: 1.5 + - torch: 1.6.0+cu101 + torch_version: torch1.6.0 + torchvision: 0.7.0+cu101 + mmcv: 1.6 + - torch: 1.7.0+cu101 + torch_version: torch1.7.0 + torchvision: 0.8.1+cu101 + mmcv: 1.7 + - torch: 1.8.0+cu101 + torch_version: torch1.8.0 + torchvision: 0.9.0+cu101 + mmcv: 1.8 + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Fetch GPG keys + run: | + apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub + apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub + - name: Install system dependencies + run: | + apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 python${{matrix.python-version}}-dev + apt-get clean + rm -rf /var/lib/apt/lists/* + - name: Install Pillow + run: python -m pip install Pillow==6.2.2 + if: ${{matrix.torchvision < 0.5}} + - name: Install PyTorch + run: python -m pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html + - name: Install dependencies for compiling onnx when python=3.9 + run: python -m pip install "protobuf <= 3.20.1" && apt-get install libprotobuf-dev protobuf-compiler + if: ${{matrix.python-version == '3.9'}} + - name: Install mmdet dependencies + run: | + python -V + python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.mmcv}}/index.html + python -m pip install pycocotools + python -m pip install -r requirements/tests.txt -r requirements/optional.txt + python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations + python -m pip install git+https://github.com/cocodataset/panopticapi.git + python -c 'import mmcv; print(mmcv.__version__)' + - name: Build and install + run: | + rm -rf .eggs + python setup.py check -m -s + TORCH_CUDA_ARCH_LIST=7.0 pip install . + - name: Run unittests and generate coverage report + run: | + coverage run --branch --source mmdet -m pytest tests/ + coverage xml + coverage report -m + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v1.0.10 + with: + file: ./coverage.xml + flags: unittests + env_vars: OS,PYTHON + name: codecov-umbrella + fail_ci_if_error: false + + build_cuda102: + runs-on: ubuntu-18.04 + container: + image: pytorch/pytorch:1.9.0-cuda10.2-cudnn7-devel + + strategy: + matrix: + python-version: [3.6, 3.7, 3.8, 3.9] + torch: [1.9.0+cu102, 1.10.1+cu102] + include: + - torch: 1.9.0+cu102 + torch_version: torch1.9.0 + torchvision: 0.10.0+cu102 + mmcv: 1.9 + - torch: 1.10.1+cu102 + torch_version: torch1.10.1 + torchvision: 0.11.2+cu102 + mmcv: "1.10" + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Fetch GPG keys + run: | + apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub + apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub + # Add ppa source repo for python3.9. + - name: Add python3.9 source + run: | + apt-get update && apt-get install -y software-properties-common + add-apt-repository -y ppa:deadsnakes/ppa + if: ${{matrix.python-version == '3.9'}} + # Install python-dev for some packages which require libpython3.Xm. + # Github's setup-python cannot install python3.9-dev, so we have to use apt install. + # Set DEBIAN_FRONTEND=noninteractive to avoid some interactions. + - name: Install python-dev + run: apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends python${{matrix.python-version}}-dev + - name: Install system dependencies + run: | + apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 + apt-get clean + rm -rf /var/lib/apt/lists/* + - name: Install Pillow + run: python -m pip install Pillow==6.2.2 + if: ${{matrix.torchvision < 0.5}} + - name: Install PyTorch + run: python -m pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html + - name: Install dependencies for compiling onnx when python=3.9 + run: python -m pip install "protobuf <= 3.20.1" && apt-get update && apt-get -y install libprotobuf-dev protobuf-compiler cmake + if: ${{matrix.python-version == '3.9'}} + - name: Install mmdet dependencies + run: | + python -V + python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch${{matrix.mmcv}}/index.html + python -m pip install pycocotools + python -m pip install -r requirements/tests.txt -r requirements/optional.txt + python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations + python -m pip install git+https://github.com/cocodataset/panopticapi.git + python -c 'import mmcv; print(mmcv.__version__)' + - name: Build and install + run: | + rm -rf .eggs + python setup.py check -m -s + TORCH_CUDA_ARCH_LIST=7.0 pip install . + - name: Run unittests and generate coverage report + run: | + coverage run --branch --source mmdet -m pytest tests/ + coverage xml + coverage report -m + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v2 + with: + files: ./coverage.xml + flags: unittests + env_vars: OS,PYTHON + name: codecov-umbrella + fail_ci_if_error: false + + build_windows: + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [windows-2022] + python: [3.8] + platform: [cpu, cu111] + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python }} + - name: Upgrade pip + run: python -m pip install pip --upgrade --user + - name: Install PyTorch + # As a complement to Linux CI, we test on PyTorch LTS version + run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html + - name: Install MMCV + run: pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full + - name: Install unittest dependencies + run: | + python -V + python -m pip install pycocotools + python -m pip install -r requirements/tests.txt -r requirements/optional.txt + python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations + python -m pip install git+https://github.com/cocodataset/panopticapi.git + python -c 'import mmcv; print(mmcv.__version__)' + - name: Show pip list + run: pip list + - name: Build and install + run: pip install -e . + - name: Run unittests + run: coverage run --branch --source mmdet -m pytest tests + - name: Generate coverage report + run: | + coverage xml + coverage report -m + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v2 + with: + file: ./coverage.xml + flags: unittests + env_vars: OS,PYTHON + name: codecov-umbrella + fail_ci_if_error: false diff --git a/downstream/mmdetection/.github/workflows/build_pat.yml b/downstream/mmdetection/.github/workflows/build_pat.yml new file mode 100644 index 0000000..438e27e --- /dev/null +++ b/downstream/mmdetection/.github/workflows/build_pat.yml @@ -0,0 +1,28 @@ +name: build_pat + +on: push + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + build_parrots: + runs-on: ubuntu-latest + container: + image: ghcr.io/zhouzaida/parrots-mmcv:1.3.4 + credentials: + username: zhouzaida + password: ${{ secrets.CR_PAT }} + + steps: + - uses: actions/checkout@v2 + - name: Install mmdet dependencies + run: | + git clone https://github.com/open-mmlab/mmcv.git && cd mmcv + MMCV_WITH_OPS=1 python setup.py install + cd .. && rm -rf mmcv + python -c 'import mmcv; print(mmcv.__version__)' + pip install -r requirements.txt + - name: Build and install + run: rm -rf .eggs && pip install -e . diff --git a/downstream/mmdetection/.github/workflows/deploy.yml b/downstream/mmdetection/.github/workflows/deploy.yml new file mode 100644 index 0000000..f575061 --- /dev/null +++ b/downstream/mmdetection/.github/workflows/deploy.yml @@ -0,0 +1,28 @@ +name: deploy + +on: push + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + build-n-publish: + runs-on: ubuntu-latest + if: startsWith(github.event.ref, 'refs/tags') + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: 3.7 + - name: Install torch + run: pip install torch + - name: Install wheel + run: pip install wheel + - name: Build MMDetection + run: python setup.py sdist bdist_wheel + - name: Publish distribution to PyPI + run: | + pip install twine + twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }} diff --git a/downstream/mmdetection/.github/workflows/lint.yml b/downstream/mmdetection/.github/workflows/lint.yml new file mode 100644 index 0000000..91565fe --- /dev/null +++ b/downstream/mmdetection/.github/workflows/lint.yml @@ -0,0 +1,27 @@ +name: lint + +on: [push, pull_request] + +concurrency: + group: ${{ github.workflow }}-${{ github.ref }} + cancel-in-progress: true + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: 3.7 + - name: Install pre-commit hook + run: | + pip install pre-commit + pre-commit install + - name: Linting + run: pre-commit run --all-files + - name: Check docstring coverage + run: | + pip install interrogate + interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmdet diff --git a/downstream/mmdetection/.gitignore b/downstream/mmdetection/.gitignore new file mode 100644 index 0000000..892731d --- /dev/null +++ b/downstream/mmdetection/.gitignore @@ -0,0 +1,124 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/en/_build/ +docs/zh_cn/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + +data/ +data +.vscode +.idea +.DS_Store + +# custom +*.pkl +*.pkl.json +*.log.json +docs/modelzoo_statistics.md +mmdet/.mim +work_dirs/ + +# Pytorch +*.pth +*.py~ +*.sh~ diff --git a/downstream/mmdetection/.owners.yml b/downstream/mmdetection/.owners.yml new file mode 100644 index 0000000..97296aa --- /dev/null +++ b/downstream/mmdetection/.owners.yml @@ -0,0 +1,14 @@ +assign: + strategy: + # random + daily-shift-based + scedule: + '*/1 * * * *' + assignees: + - Czm369 + - hhaAndroid + - jbwang1997 + - RangiLyu + - BIGWangYuDong + - chhluo + - ZwwWayne diff --git a/downstream/mmdetection/.pre-commit-config.yaml b/downstream/mmdetection/.pre-commit-config.yaml new file mode 100644 index 0000000..82dd58c --- /dev/null +++ b/downstream/mmdetection/.pre-commit-config.yaml @@ -0,0 +1,50 @@ +repos: + - repo: https://github.com/PyCQA/flake8 + rev: 3.8.3 + hooks: + - id: flake8 + - repo: https://github.com/PyCQA/isort + rev: 5.10.1 + hooks: + - id: isort + - repo: https://github.com/pre-commit/mirrors-yapf + rev: v0.30.0 + hooks: + - id: yapf + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.1.0 + hooks: + - id: trailing-whitespace + - id: check-yaml + - id: end-of-file-fixer + - id: requirements-txt-fixer + - id: double-quote-string-fixer + - id: check-merge-conflict + - id: fix-encoding-pragma + args: ["--remove"] + - id: mixed-line-ending + args: ["--fix=lf"] + - repo: https://github.com/codespell-project/codespell + rev: v2.1.0 + hooks: + - id: codespell + - repo: https://github.com/executablebooks/mdformat + rev: 0.7.14 + hooks: + - id: mdformat + args: ["--number"] + additional_dependencies: + - mdformat-gfm + - mdformat_frontmatter + - linkify-it-py + - repo: https://github.com/myint/docformatter + rev: v1.3.1 + hooks: + - id: docformatter + args: ["--in-place", "--wrap-descriptions", "79"] + - repo: https://github.com/open-mmlab/pre-commit-hooks + rev: v0.2.0 # Use the ref you want to point at + hooks: + - id: check-algo-readme + - id: check-copyright + args: ["mmdet"] # replace the dir_to_check with your expected directory to check diff --git a/downstream/mmdetection/.readthedocs.yml b/downstream/mmdetection/.readthedocs.yml new file mode 100644 index 0000000..6cfbf5d --- /dev/null +++ b/downstream/mmdetection/.readthedocs.yml @@ -0,0 +1,9 @@ +version: 2 + +formats: all + +python: + version: 3.7 + install: + - requirements: requirements/docs.txt + - requirements: requirements/readthedocs.txt diff --git a/downstream/mmdetection/CITATION.cff b/downstream/mmdetection/CITATION.cff new file mode 100644 index 0000000..aac9313 --- /dev/null +++ b/downstream/mmdetection/CITATION.cff @@ -0,0 +1,8 @@ +cff-version: 1.2.0 +message: "If you use this software, please cite it as below." +authors: + - name: "MMDetection Contributors" +title: "OpenMMLab Detection Toolbox and Benchmark" +date-released: 2018-08-22 +url: "https://github.com/open-mmlab/mmdetection" +license: Apache-2.0 diff --git a/downstream/mmdetection/LICENSE b/downstream/mmdetection/LICENSE new file mode 100644 index 0000000..1bfc23e --- /dev/null +++ b/downstream/mmdetection/LICENSE @@ -0,0 +1,203 @@ +Copyright 2018-2023 OpenMMLab. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2018-2023 OpenMMLab. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/downstream/mmdetection/MANIFEST.in b/downstream/mmdetection/MANIFEST.in new file mode 100644 index 0000000..6300b22 --- /dev/null +++ b/downstream/mmdetection/MANIFEST.in @@ -0,0 +1,6 @@ +include requirements/*.txt +include mmdet/VERSION +include mmdet/.mim/model-index.yml +include mmdet/.mim/demo/*/* +recursive-include mmdet/.mim/configs *.py *.yml +recursive-include mmdet/.mim/tools *.sh *.py diff --git a/downstream/mmdetection/README.md b/downstream/mmdetection/README.md new file mode 100644 index 0000000..522cfb8 --- /dev/null +++ b/downstream/mmdetection/README.md @@ -0,0 +1,357 @@ +
+ +
 
+
+ OpenMMLab website + + + HOT + + +      + OpenMMLab platform + + + TRY IT OUT + + +
+
 
+ +[![PyPI](https://img.shields.io/pypi/v/mmdet)](https://pypi.org/project/mmdet) +[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection.readthedocs.io/en/latest/) +[![badge](https://github.com/open-mmlab/mmdetection/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection/actions) +[![codecov](https://codecov.io/gh/open-mmlab/mmdetection/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection) +[![license](https://img.shields.io/github/license/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/blob/master/LICENSE) +[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues) +[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection.svg)](https://github.com/open-mmlab/mmdetection/issues) + +[📘Documentation](https://mmdetection.readthedocs.io/en/stable/) | +[🛠️Installation](https://mmdetection.readthedocs.io/en/stable/get_started.html) | +[👀Model Zoo](https://mmdetection.readthedocs.io/en/stable/model_zoo.html) | +[🆕Update News](https://mmdetection.readthedocs.io/en/stable/changelog.html) | +[🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) | +[🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose) + +
+ +
+ +English | [简体中文](README_zh-CN.md) + +
+ +## Introduction + +MMDetection is an open source object detection toolbox based on PyTorch. It is +a part of the [OpenMMLab](https://openmmlab.com/) project. + +The master branch works with **PyTorch 1.5+**. + + + +
+Major features + +- **Modular Design** + + We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. + +- **Support of multiple frameworks out of box** + + The toolbox directly supports popular and contemporary detection frameworks, *e.g.* Faster RCNN, Mask RCNN, RetinaNet, etc. + +- **High efficiency** + + All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). + +- **State of the art** + + The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. + +
+ +Apart from MMDetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox. + +## What's New + +**2.25.0** was released in 1/6/2022: + +- Support dedicated `MMDetWandbHook` hook +- Support [ConvNeXt](configs/convnext), [DDOD](configs/ddod), [SOLOv2](configs/solov2) +- Support [Mask2Former](configs/mask2former) for instance segmentation +- Rename [config files of Mask2Former](configs/mask2former) + +Please refer to [changelog.md](docs/en/changelog.md) for details and release history. + +For compatibility changes between different versions of MMDetection, please refer to [compatibility.md](docs/en/compatibility.md). + +## Installation + +Please refer to [Installation](docs/en/get_started.md/#Installation) for installation instructions. + +## Getting Started + +Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMDetection. We provide [colab tutorial](demo/MMDet_Tutorial.ipynb) and [instance segmentation colab tutorial](demo/MMDet_InstanceSeg_Tutorial.ipynb), and other tutorials for: + +- [with existing dataset](docs/en/1_exist_data_model.md) +- [with new dataset](docs/en/2_new_data_model.md) +- [with existing dataset_new_model](docs/en/3_exist_data_new_model.md) +- [learn about configs](docs/en/tutorials/config.md) +- [customize_datasets](docs/en/tutorials/customize_dataset.md) +- [customize data pipelines](docs/en/tutorials/data_pipeline.md) +- [customize_models](docs/en/tutorials/customize_models.md) +- [customize runtime settings](docs/en/tutorials/customize_runtime.md) +- [customize_losses](docs/en/tutorials/customize_losses.md) +- [finetuning models](docs/en/tutorials/finetune.md) +- [export a model to ONNX](docs/en/tutorials/pytorch2onnx.md) +- [export ONNX to TRT](docs/en/tutorials/onnx2tensorrt.md) +- [weight initialization](docs/en/tutorials/init_cfg.md) +- [how to xxx](docs/en/tutorials/how_to.md) + +## Overview of Benchmark and Model Zoo + +Results and models are available in the [model zoo](docs/en/model_zoo.md). + +
+ Architectures +
+ + + + + + + + + + + + + + + + + +
+ Object Detection + + Instance Segmentation + + Panoptic Segmentation + + Other +
+ + + + + + + +
  • Contrastive Learning
    • + + +
  • Distillation
    • + + +
    + +
    + Components +
    + + + + + + + + + + + + + + + + + +
    + Backbones + + Necks + + Loss + + Common +
    + + + + + + + +
    + +Some other methods are also supported in [projects using MMDetection](./docs/en/projects.md). + +## FAQ + +Please refer to [FAQ](docs/en/faq.md) for frequently asked questions. + +## Contributing + +We appreciate all contributions to improve MMDetection. Ongoing projects can be found in out [GitHub Projects](https://github.com/open-mmlab/mmdetection/projects). Welcome community users to participate in these projects. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. + +## Acknowledgement + +MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. +We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors. + +## Citation + +If you use this toolbox or benchmark in your research, please cite this project. + +``` +@article{mmdetection, + title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark}, + author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and + Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and + Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and + Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and + Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong + and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua}, + journal= {arXiv preprint arXiv:1906.07155}, + year={2019} +} +``` + +## License + +This project is released under the [Apache 2.0 license](LICENSE). + +## Projects in OpenMMLab + +- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. +- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages. +- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. +- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. +- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. +- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark. +- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. +- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox. +- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. +- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark. +- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark. +- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark. +- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark. +- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. +- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. +- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark. +- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. +- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox. +- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework. diff --git a/downstream/mmdetection/configs/_base_/datasets/cityscapes_detection.py b/downstream/mmdetection/configs/_base_/datasets/cityscapes_detection.py new file mode 100644 index 0000000..e341b59 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/cityscapes_detection.py @@ -0,0 +1,56 @@ +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=1, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=8, + dataset=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_train.json', + img_prefix=data_root + 'leftImg8bit/train/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_val.json', + img_prefix=data_root + 'leftImg8bit/val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_test.json', + img_prefix=data_root + 'leftImg8bit/test/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') diff --git a/downstream/mmdetection/configs/_base_/datasets/cityscapes_instance.py b/downstream/mmdetection/configs/_base_/datasets/cityscapes_instance.py new file mode 100644 index 0000000..4e3c34e --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/cityscapes_instance.py @@ -0,0 +1,56 @@ +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=1, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=8, + dataset=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_train.json', + img_prefix=data_root + 'leftImg8bit/train/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_val.json', + img_prefix=data_root + 'leftImg8bit/val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_test.json', + img_prefix=data_root + 'leftImg8bit/test/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/downstream/mmdetection/configs/_base_/datasets/coco_detection.py b/downstream/mmdetection/configs/_base_/datasets/coco_detection.py new file mode 100644 index 0000000..149f590 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/coco_detection.py @@ -0,0 +1,49 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') diff --git a/downstream/mmdetection/configs/_base_/datasets/coco_instance.py b/downstream/mmdetection/configs/_base_/datasets/coco_instance.py new file mode 100644 index 0000000..9901a85 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/coco_instance.py @@ -0,0 +1,49 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/downstream/mmdetection/configs/_base_/datasets/coco_instance_semantic.py b/downstream/mmdetection/configs/_base_/datasets/coco_instance_semantic.py new file mode 100644 index 0000000..6c8bf07 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/coco_instance_semantic.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + seg_prefix=data_root + 'stuffthingmaps/train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/downstream/mmdetection/configs/_base_/datasets/coco_panoptic.py b/downstream/mmdetection/configs/_base_/datasets/coco_panoptic.py new file mode 100644 index 0000000..dbade7c --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/coco_panoptic.py @@ -0,0 +1,59 @@ +# dataset settings +dataset_type = 'CocoPanopticDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadPanopticAnnotations', + with_bbox=True, + with_mask=True, + with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 4), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_train2017.json', + img_prefix=data_root + 'train2017/', + seg_prefix=data_root + 'annotations/panoptic_train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_val2017.json', + img_prefix=data_root + 'val2017/', + seg_prefix=data_root + 'annotations/panoptic_val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_val2017.json', + img_prefix=data_root + 'val2017/', + seg_prefix=data_root + 'annotations/panoptic_val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['PQ']) diff --git a/downstream/mmdetection/configs/_base_/datasets/deepfashion.py b/downstream/mmdetection/configs/_base_/datasets/deepfashion.py new file mode 100644 index 0000000..308b4b2 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/deepfashion.py @@ -0,0 +1,53 @@ +# dataset settings +dataset_type = 'DeepFashionDataset' +data_root = 'data/DeepFashion/In-shop/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(750, 1101), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(750, 1101), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + imgs_per_gpu=2, + workers_per_gpu=1, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', + img_prefix=data_root + 'Img/', + pipeline=train_pipeline, + data_root=data_root), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', + img_prefix=data_root + 'Img/', + pipeline=test_pipeline, + data_root=data_root), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/DeepFashion_segmentation_gallery.json', + img_prefix=data_root + 'Img/', + pipeline=test_pipeline, + data_root=data_root)) +evaluation = dict(interval=5, metric=['bbox', 'segm']) diff --git a/downstream/mmdetection/configs/_base_/datasets/lvis_v0.5_instance.py b/downstream/mmdetection/configs/_base_/datasets/lvis_v0.5_instance.py new file mode 100644 index 0000000..207e005 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/lvis_v0.5_instance.py @@ -0,0 +1,24 @@ +# dataset settings +_base_ = 'coco_instance.py' +dataset_type = 'LVISV05Dataset' +data_root = 'data/lvis_v0.5/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_train.json', + img_prefix=data_root + 'train2017/')), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_val.json', + img_prefix=data_root + 'val2017/'), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_val.json', + img_prefix=data_root + 'val2017/')) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/downstream/mmdetection/configs/_base_/datasets/lvis_v1_instance.py b/downstream/mmdetection/configs/_base_/datasets/lvis_v1_instance.py new file mode 100644 index 0000000..be791ed --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/lvis_v1_instance.py @@ -0,0 +1,24 @@ +# dataset settings +_base_ = 'coco_instance.py' +dataset_type = 'LVISV1Dataset' +data_root = 'data/lvis_v1/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_train.json', + img_prefix=data_root)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/downstream/mmdetection/configs/_base_/datasets/openimages_detection.py b/downstream/mmdetection/configs/_base_/datasets/openimages_detection.py new file mode 100644 index 0000000..a65d306 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/openimages_detection.py @@ -0,0 +1,65 @@ +# dataset settings +dataset_type = 'OpenImagesDataset' +data_root = 'data/OpenImages/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, denorm_bbox=True), + dict(type='Resize', img_scale=(1024, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1024, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ], + ), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=0, # workers_per_gpu > 0 may occur out of memory + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/oidv6-train-annotations-bbox.csv', + img_prefix=data_root + 'OpenImages/train/', + label_file=data_root + 'annotations/class-descriptions-boxable.csv', + hierarchy_file=data_root + + 'annotations/bbox_labels_600_hierarchy.json', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/validation-annotations-bbox.csv', + img_prefix=data_root + 'OpenImages/validation/', + label_file=data_root + 'annotations/class-descriptions-boxable.csv', + hierarchy_file=data_root + + 'annotations/bbox_labels_600_hierarchy.json', + meta_file=data_root + 'annotations/validation-image-metas.pkl', + image_level_ann_file=data_root + + 'annotations/validation-annotations-human-imagelabels-boxable.csv', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/validation-annotations-bbox.csv', + img_prefix=data_root + 'OpenImages/validation/', + label_file=data_root + 'annotations/class-descriptions-boxable.csv', + hierarchy_file=data_root + + 'annotations/bbox_labels_600_hierarchy.json', + meta_file=data_root + 'annotations/validation-image-metas.pkl', + image_level_ann_file=data_root + + 'annotations/validation-annotations-human-imagelabels-boxable.csv', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='mAP') diff --git a/downstream/mmdetection/configs/_base_/datasets/voc0712.py b/downstream/mmdetection/configs/_base_/datasets/voc0712.py new file mode 100644 index 0000000..ae09acd --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/voc0712.py @@ -0,0 +1,55 @@ +# dataset settings +dataset_type = 'VOCDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1000, 600), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=[ + data_root + 'VOC2007/ImageSets/Main/trainval.txt', + data_root + 'VOC2012/ImageSets/Main/trainval.txt' + ], + img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'], + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', + img_prefix=data_root + 'VOC2007/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', + img_prefix=data_root + 'VOC2007/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='mAP') diff --git a/downstream/mmdetection/configs/_base_/datasets/wider_face.py b/downstream/mmdetection/configs/_base_/datasets/wider_face.py new file mode 100644 index 0000000..d1d649b --- /dev/null +++ b/downstream/mmdetection/configs/_base_/datasets/wider_face.py @@ -0,0 +1,63 @@ +# dataset settings +dataset_type = 'WIDERFaceDataset' +data_root = 'data/WIDERFace/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=60, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=2, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'train.txt', + img_prefix=data_root + 'WIDER_train/', + min_size=17, + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'val.txt', + img_prefix=data_root + 'WIDER_val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'val.txt', + img_prefix=data_root + 'WIDER_val/', + pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/_base_/default_runtime.py b/downstream/mmdetection/configs/_base_/default_runtime.py new file mode 100644 index 0000000..5b0b145 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/default_runtime.py @@ -0,0 +1,27 @@ +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +custom_hooks = [dict(type='NumClassCheckHook')] + +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] + +# disable opencv multithreading to avoid system being overloaded +opencv_num_threads = 0 +# set multi-process start method as `fork` to speed up the training +mp_start_method = 'fork' + +# Default setting for scaling LR automatically +# - `enable` means enable scaling LR automatically +# or not by default. +# - `base_batch_size` = (8 GPUs) x (2 samples per GPU). +auto_scale_lr = dict(enable=False, base_batch_size=16) diff --git a/downstream/mmdetection/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py b/downstream/mmdetection/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py new file mode 100644 index 0000000..2902cca --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py @@ -0,0 +1,196 @@ +# model settings +model = dict( + type='CascadeRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='CascadeRoIHead', + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/downstream/mmdetection/configs/_base_/models/cascade_rcnn_r50_fpn.py b/downstream/mmdetection/configs/_base_/models/cascade_rcnn_r50_fpn.py new file mode 100644 index 0000000..42f74ae --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/cascade_rcnn_r50_fpn.py @@ -0,0 +1,179 @@ +# model settings +model = dict( + type='CascadeRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='CascadeRoIHead', + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ]), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/downstream/mmdetection/configs/_base_/models/fast_rcnn_r50_fpn.py b/downstream/mmdetection/configs/_base_/models/fast_rcnn_r50_fpn.py new file mode 100644 index 0000000..9982fe0 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/fast_rcnn_r50_fpn.py @@ -0,0 +1,62 @@ +# model settings +model = dict( + type='FastRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_caffe_c4.py b/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_caffe_c4.py new file mode 100644 index 0000000..dbf965a --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_caffe_c4.py @@ -0,0 +1,117 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='FasterRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + shared_head=dict( + type='ResLayer', + depth=50, + stage=3, + stride=2, + dilation=1, + style='caffe', + norm_cfg=norm_cfg, + norm_eval=True, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=1024, + featmap_strides=[16]), + bbox_head=dict( + type='BBoxHead', + with_avg_pool=True, + roi_feat_size=7, + in_channels=2048, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=6000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py b/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py new file mode 100644 index 0000000..a377a6f --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py @@ -0,0 +1,105 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='FasterRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + strides=(1, 2, 2, 1), + dilations=(1, 1, 1, 2), + out_indices=(3, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + rpn_head=dict( + type='RPNHead', + in_channels=2048, + feat_channels=2048, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=2048, + featmap_strides=[16]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=2048, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms=dict(type='nms', iou_threshold=0.7), + nms_pre=6000, + max_per_img=1000, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_fpn.py b/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_fpn.py new file mode 100644 index 0000000..1ef8e7b --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/faster_rcnn_r50_fpn.py @@ -0,0 +1,108 @@ +# model settings +model = dict( + type='FasterRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100) + # soft-nms is also supported for rcnn testing + # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) + )) diff --git a/downstream/mmdetection/configs/_base_/models/mask_rcnn_r50_caffe_c4.py b/downstream/mmdetection/configs/_base_/models/mask_rcnn_r50_caffe_c4.py new file mode 100644 index 0000000..122202e --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/mask_rcnn_r50_caffe_c4.py @@ -0,0 +1,125 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='MaskRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + shared_head=dict( + type='ResLayer', + depth=50, + stage=3, + stride=2, + dilation=1, + style='caffe', + norm_cfg=norm_cfg, + norm_eval=True), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=1024, + featmap_strides=[16]), + bbox_head=dict( + type='BBoxHead', + with_avg_pool=True, + roi_feat_size=7, + in_channels=2048, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=None, + mask_head=dict( + type='FCNMaskHead', + num_convs=0, + in_channels=2048, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=14, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=6000, + nms=dict(type='nms', iou_threshold=0.7), + max_per_img=1000, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/downstream/mmdetection/configs/_base_/models/mask_rcnn_r50_fpn.py b/downstream/mmdetection/configs/_base_/models/mask_rcnn_r50_fpn.py new file mode 100644 index 0000000..d903e55 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/mask_rcnn_r50_fpn.py @@ -0,0 +1,120 @@ +# model settings +model = dict( + type='MaskRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/downstream/mmdetection/configs/_base_/models/retinanet_r50_fpn.py b/downstream/mmdetection/configs/_base_/models/retinanet_r50_fpn.py new file mode 100644 index 0000000..56e43fa --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/retinanet_r50_fpn.py @@ -0,0 +1,60 @@ +# model settings +model = dict( + type='RetinaNet', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_input', + num_outs=5), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) diff --git a/downstream/mmdetection/configs/_base_/models/rpn_r50_caffe_c4.py b/downstream/mmdetection/configs/_base_/models/rpn_r50_caffe_c4.py new file mode 100644 index 0000000..8b32ca9 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/rpn_r50_caffe_c4.py @@ -0,0 +1,58 @@ +# model settings +model = dict( + type='RPN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + neck=None, + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/downstream/mmdetection/configs/_base_/models/rpn_r50_fpn.py b/downstream/mmdetection/configs/_base_/models/rpn_r50_fpn.py new file mode 100644 index 0000000..edaf4d4 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/rpn_r50_fpn.py @@ -0,0 +1,58 @@ +# model settings +model = dict( + type='RPN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/downstream/mmdetection/configs/_base_/models/ssd300.py b/downstream/mmdetection/configs/_base_/models/ssd300.py new file mode 100644 index 0000000..f17df01 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/models/ssd300.py @@ -0,0 +1,56 @@ +# model settings +input_size = 300 +model = dict( + type='SingleStageDetector', + backbone=dict( + type='SSDVGG', + depth=16, + with_last_pool=False, + ceil_mode=True, + out_indices=(3, 4), + out_feature_indices=(22, 34), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')), + neck=dict( + type='SSDNeck', + in_channels=(512, 1024), + out_channels=(512, 1024, 512, 256, 256, 256), + level_strides=(2, 2, 1, 1), + level_paddings=(1, 1, 0, 0), + l2_norm_scale=20), + bbox_head=dict( + type='SSDHead', + in_channels=(512, 1024, 512, 256, 256, 256), + num_classes=80, + anchor_generator=dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=input_size, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2])), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False), + test_cfg=dict( + nms_pre=1000, + nms=dict(type='nms', iou_threshold=0.45), + min_bbox_size=0, + score_thr=0.02, + max_per_img=200)) +cudnn_benchmark = True diff --git a/downstream/mmdetection/configs/_base_/schedules/schedule_1x.py b/downstream/mmdetection/configs/_base_/schedules/schedule_1x.py new file mode 100644 index 0000000..13b3783 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/schedules/schedule_1x.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/_base_/schedules/schedule_20e.py b/downstream/mmdetection/configs/_base_/schedules/schedule_20e.py new file mode 100644 index 0000000..00e8590 --- /dev/null +++ b/downstream/mmdetection/configs/_base_/schedules/schedule_20e.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/_base_/schedules/schedule_2x.py b/downstream/mmdetection/configs/_base_/schedules/schedule_2x.py new file mode 100644 index 0000000..69dc9ee --- /dev/null +++ b/downstream/mmdetection/configs/_base_/schedules/schedule_2x.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/albu_example/README.md b/downstream/mmdetection/configs/albu_example/README.md new file mode 100644 index 0000000..9a180f0 --- /dev/null +++ b/downstream/mmdetection/configs/albu_example/README.md @@ -0,0 +1,31 @@ +# Albu Example + +> [Albumentations: fast and flexible image augmentations](https://arxiv.org/abs/1809.06839) + + + +## Abstract + +Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve output labels. In computer vision domain, image augmentations have become a common implicit regularization technique to combat overfitting in deep convolutional neural networks and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations and combinations of flipping, rotating, scaling, and cropping. Moreover, the image processing speed varies in existing tools for image augmentation. We present Albumentations, a fast and flexible library for image augmentations with many various image transform operations available, that is also an easy-to-use wrapper around other augmentation libraries. We provide examples of image augmentations for different computer vision tasks and show that Albumentations is faster than other commonly used image augmentation tools on the most of commonly used image transformations. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | 1x | 4.4 | 16.6 | 38.0 | 34.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) | + +## Citation + +```latex +@article{2018arXiv180906839B, + author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin}, + title = "{Albumentations: fast and flexible image augmentations}", + journal = {ArXiv e-prints}, + eprint = {1809.06839}, + year = 2018 +} +``` diff --git a/downstream/mmdetection/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py b/downstream/mmdetection/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py new file mode 100644 index 0000000..b3f879a --- /dev/null +++ b/downstream/mmdetection/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py @@ -0,0 +1,73 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +albu_train_transforms = [ + dict( + type='ShiftScaleRotate', + shift_limit=0.0625, + scale_limit=0.0, + rotate_limit=0, + interpolation=1, + p=0.5), + dict( + type='RandomBrightnessContrast', + brightness_limit=[0.1, 0.3], + contrast_limit=[0.1, 0.3], + p=0.2), + dict( + type='OneOf', + transforms=[ + dict( + type='RGBShift', + r_shift_limit=10, + g_shift_limit=10, + b_shift_limit=10, + p=1.0), + dict( + type='HueSaturationValue', + hue_shift_limit=20, + sat_shift_limit=30, + val_shift_limit=20, + p=1.0) + ], + p=0.1), + dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2), + dict(type='ChannelShuffle', p=0.1), + dict( + type='OneOf', + transforms=[ + dict(type='Blur', blur_limit=3, p=1.0), + dict(type='MedianBlur', blur_limit=3, p=1.0) + ], + p=0.1), +] +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='Pad', size_divisor=32), + dict( + type='Albu', + transforms=albu_train_transforms, + bbox_params=dict( + type='BboxParams', + format='pascal_voc', + label_fields=['gt_labels'], + min_visibility=0.0, + filter_lost_elements=True), + keymap={ + 'img': 'image', + 'gt_masks': 'masks', + 'gt_bboxes': 'bboxes' + }, + update_pad_shape=False, + skip_img_without_anno=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'pad_shape', 'scale_factor')) +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/downstream/mmdetection/configs/atss/README.md b/downstream/mmdetection/configs/atss/README.md new file mode 100644 index 0000000..055ed05 --- /dev/null +++ b/downstream/mmdetection/configs/atss/README.md @@ -0,0 +1,31 @@ +# ATSS + +> [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://arxiv.org/abs/1912.02424) + + + +## Abstract + +Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to 50.7% AP without introducing any overhead. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) | +| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) | + +## Citation + +```latex +@article{zhang2019bridging, + title = {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection}, + author = {Zhang, Shifeng and Chi, Cheng and Yao, Yongqiang and Lei, Zhen and Li, Stan Z.}, + journal = {arXiv preprint arXiv:1912.02424}, + year = {2019} +} +``` diff --git a/downstream/mmdetection/configs/atss/atss_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/atss/atss_r101_fpn_1x_coco.py new file mode 100644 index 0000000..5225d2a --- /dev/null +++ b/downstream/mmdetection/configs/atss/atss_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './atss_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/atss/atss_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/atss/atss_r50_fpn_1x_coco.py new file mode 100644 index 0000000..42ff4c5 --- /dev/null +++ b/downstream/mmdetection/configs/atss/atss_r50_fpn_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='ATSS', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='ATSSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/atss/metafile.yml b/downstream/mmdetection/configs/atss/metafile.yml new file mode 100644 index 0000000..f4c567e --- /dev/null +++ b/downstream/mmdetection/configs/atss/metafile.yml @@ -0,0 +1,60 @@ +Collections: + - Name: ATSS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - ATSS + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1912.02424 + Title: 'Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection' + README: configs/atss/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/atss.py#L6 + Version: v2.0.0 + +Models: + - Name: atss_r50_fpn_1x_coco + In Collection: ATSS + Config: configs/atss/atss_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + inference time (ms/im): + - value: 50.76 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth + + - Name: atss_r101_fpn_1x_coco + In Collection: ATSS + Config: configs/atss/atss_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (ms/im): + - value: 81.3 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth diff --git a/downstream/mmdetection/configs/autoassign/README.md b/downstream/mmdetection/configs/autoassign/README.md new file mode 100644 index 0000000..1297206 --- /dev/null +++ b/downstream/mmdetection/configs/autoassign/README.md @@ -0,0 +1,35 @@ +# AutoAssign + +> [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://arxiv.org/abs/2007.03496) + + + +## Abstract + +Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledge and achieves appearance-aware through a fully differentiable weighting mechanism. During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions. To adapt to object appearances, Confidence Weighting is proposed to adjust the specific assign strategy of each instance. The two weighting modules are then combined to generate positive and negative weights to adjust each location's confidence. Extensive experiments on the MS COCO show that our method steadily surpasses other best sampling strategies by large margins with various backbones. Moreover, our best model achieves 52.1% AP, outperforming all existing one-stage detectors. Besides, experiments on other datasets, e.g., PASCAL VOC, Objects365, and WiderFace, demonstrate the broad applicability of AutoAssign. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download | +| :------: | :---: | :-----: | :------: | :----: | :------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | caffe | 1x | 4.08 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.log.json) | + +**Note**: + +1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation. +2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch. + +## Citation + +```latex +@article{zhu2020autoassign, + title={AutoAssign: Differentiable Label Assignment for Dense Object Detection}, + author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian}, + journal={arXiv preprint arXiv:2007.03496}, + year={2020} +} +``` diff --git a/downstream/mmdetection/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py b/downstream/mmdetection/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py new file mode 100644 index 0000000..db548dc --- /dev/null +++ b/downstream/mmdetection/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py @@ -0,0 +1,85 @@ +# We follow the original implementation which +# adopts the Caffe pre-trained backbone. +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='AutoAssign', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + relu_before_extra_convs=True, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')), + bbox_head=dict( + type='AutoAssignHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + loss_bbox=dict(type='GIoULoss', loss_weight=5.0)), + train_cfg=None, + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(lr=0.01, paramwise_cfg=dict(norm_decay_mult=0.)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 1000, + step=[8, 11]) +total_epochs = 12 diff --git a/downstream/mmdetection/configs/autoassign/metafile.yml b/downstream/mmdetection/configs/autoassign/metafile.yml new file mode 100644 index 0000000..f1e9051 --- /dev/null +++ b/downstream/mmdetection/configs/autoassign/metafile.yml @@ -0,0 +1,33 @@ +Collections: + - Name: AutoAssign + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - AutoAssign + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/2007.03496 + Title: 'AutoAssign: Differentiable Label Assignment for Dense Object Detection' + README: configs/autoassign/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/autoassign.py#L6 + Version: v2.12.0 + +Models: + - Name: autoassign_r50_fpn_8x2_1x_coco + In Collection: AutoAssign + Config: configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py + Metadata: + Training Memory (GB): 4.08 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth diff --git a/downstream/mmdetection/configs/carafe/README.md b/downstream/mmdetection/configs/carafe/README.md new file mode 100644 index 0000000..803abe0 --- /dev/null +++ b/downstream/mmdetection/configs/carafe/README.md @@ -0,0 +1,42 @@ +# CARAFE + +> [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188) + + + +## Abstract + +Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit sub-pixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute. CARAFE introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting. CARAFE shows consistent and substantial gains across all the tasks (1.2%, 1.3%, 1.8%, 1.1db respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. It has great potential to serve as a strong building block for future research. + +
    + +
    + +## Results and Models + +The results on COCO 2017 val is shown in the below table. + +| Method | Backbone | Style | Lr schd | Test Proposal Num | Inf time (fps) | Box AP | Mask AP | Config | Download | +| :--------------------: | :------: | :-----: | :-----: | :---------------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 16.5 | 38.6 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_20200504_175733.log.json) | +| - | - | - | - | 2000 | | | | | | +| Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 14.0 | 39.3 | 35.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_20200503_135957.log.json) | +| - | - | - | - | 2000 | | | | | | + +## Implementation + +The CUDA implementation of CARAFE can be find at https://github.com/myownskyW7/CARAFE. + +## Citation + +We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188). + +```latex +@inproceedings{Wang_2019_ICCV, + title = {CARAFE: Content-Aware ReAssembly of FEatures}, + author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua}, + booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2019} +} +``` diff --git a/downstream/mmdetection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py b/downstream/mmdetection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py new file mode 100644 index 0000000..dedac3f --- /dev/null +++ b/downstream/mmdetection/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py @@ -0,0 +1,50 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + neck=dict( + type='FPN_CARAFE', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py b/downstream/mmdetection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py new file mode 100644 index 0000000..668c023 --- /dev/null +++ b/downstream/mmdetection/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py @@ -0,0 +1,60 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + neck=dict( + type='FPN_CARAFE', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64)), + roi_head=dict( + mask_head=dict( + upsample_cfg=dict( + type='carafe', + scale_factor=2, + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64)))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/carafe/metafile.yml b/downstream/mmdetection/configs/carafe/metafile.yml new file mode 100644 index 0000000..b58a3f6 --- /dev/null +++ b/downstream/mmdetection/configs/carafe/metafile.yml @@ -0,0 +1,55 @@ +Collections: + - Name: CARAFE + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RPN + - FPN_CARAFE + - ResNet + - RoIPool + Paper: + URL: https://arxiv.org/abs/1905.02188 + Title: 'CARAFE: Content-Aware ReAssembly of FEatures' + README: configs/carafe/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/necks/fpn_carafe.py#L11 + Version: v2.12.0 + +Models: + - Name: faster_rcnn_r50_fpn_carafe_1x_coco + In Collection: CARAFE + Config: configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py + Metadata: + Training Memory (GB): 4.26 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth + + - Name: mask_rcnn_r50_fpn_carafe_1x_coco + In Collection: CARAFE + Config: configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py + Metadata: + Training Memory (GB): 4.31 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth diff --git a/downstream/mmdetection/configs/cascade_rcnn/README.md b/downstream/mmdetection/configs/cascade_rcnn/README.md new file mode 100644 index 0000000..5a9e817 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/README.md @@ -0,0 +1,79 @@ +# Cascade R-CNN + +> [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://arxiv.org/abs/1906.09756) + + + +## Abstract + +In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN. + +
    + +
    + +## Results and Models + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | caffe | 1x | 4.2 | | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_20200504_174853.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_20200504_175131.log.json) | +| R-101-FPN | caffe | 1x | 6.2 | | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_20200504_175649.log.json) | +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_20200504_231812.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 10.9 | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 7.6 | | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 10.7 | | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | caffe | 1x | 5.9 | | 41.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_20200504_174659.log.json) | +| R-50-FPN | pytorch | 1x | 6.0 | 11.2 | 41.2 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.9 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_20200504_174711.log.json) | +| R-101-FPN | caffe | 1x | 7.8 | | 43.2 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_20200504_174813.log.json) | +| R-101-FPN | pytorch | 1x | 7.9 | 9.8 | 42.9 | 37.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 43.4 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 8.6 | 44.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 9.2 | - | 45.0 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 6.7 | 45.3 | 39.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033.log.json) | + +**Notes:** + +- The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs. + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training for Cascade Mask R-CNN. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | caffe | 3x | 5.7 | | 44.0 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651.log.json) | +| R-50-FPN | pytorch | 3x | 5.9 | | 44.3 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719.log.json) | +| R-101-FPN | caffe | 3x | 7.7 | | 45.4 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620.log.json) | +| R-101-FPN | pytorch | 3x | 7.8 | | 45.5 | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236.log.json) | +| X-101-32x4d-FPN | pytorch | 3x | 9.0 | | 46.3 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234.log.json) | +| X-101-32x8d-FPN | pytorch | 3x | 12.1 | | 46.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640.log.json) | +| X-101-64x4d-FPN | pytorch | 3x | 12.0 | | 46.6 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311.log.json) | + +## Citation + +```latex +@article{Cai_2019, + title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation}, + ISSN={1939-3539}, + url={http://dx.doi.org/10.1109/tpami.2019.2956516}, + DOI={10.1109/tpami.2019.2956516}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + publisher={Institute of Electrical and Electronics Engineers (IEEE)}, + author={Cai, Zhaowei and Vasconcelos, Nuno}, + year={2019}, + pages={1–1} +} +``` diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..5ee6231 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..1df87fc --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,7 @@ +_base_ = './cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..f59c155 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py new file mode 100644 index 0000000..45ab7ed --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py @@ -0,0 +1,6 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..1b20f16 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,6 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..12d37ef --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,41 @@ +_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py'] + +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..9fb817e --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,49 @@ +_base_ = ['./cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'] +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..49ab539 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py new file mode 100644 index 0000000..1296dc4 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py' +] diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..ed0c6d1 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = [ + '../common/mstrain_3x_coco_instance.py', + '../_base_/models/cascade_mask_rcnn_r50_fpn.py' +] diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..06cbbe7 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py new file mode 100644 index 0000000..4e35236 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..7d37d17 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..eeec1aa --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py @@ -0,0 +1,60 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) + +# ResNeXt-101-32x8d model trained with Caffe2 at FB, +# so the mean and std need to be changed. +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..7dbef5f --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 0000000..579b1ac --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..ed6cf4b --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..1e90f4b --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..5c07776 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py new file mode 100644 index 0000000..b1719c2 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py @@ -0,0 +1,6 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..696bcfb --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,42 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..87e21fb --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py new file mode 100644 index 0000000..6f886e1 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..5ac02c1 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py new file mode 100644 index 0000000..486e45e --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..78229f0 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,15 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + type='CascadeRCNN', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 0000000..58812de --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,15 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + type='CascadeRCNN', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/cascade_rcnn/metafile.yml b/downstream/mmdetection/configs/cascade_rcnn/metafile.yml new file mode 100644 index 0000000..1007f2e --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rcnn/metafile.yml @@ -0,0 +1,525 @@ +Collections: + - Name: Cascade R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Cascade R-CNN + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: http://dx.doi.org/10.1109/tpami.2019.2956516 + Title: 'Cascade R-CNN: Delving into High Quality Object Detection' + README: configs/cascade_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/cascade_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: cascade_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth + + - Name: cascade_rcnn_r50_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth + + - Name: cascade_rcnn_r50_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth + + - Name: cascade_rcnn_r101_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth + + - Name: cascade_rcnn_r101_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth + + - Name: cascade_rcnn_r101_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth + + - Name: cascade_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 91.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth + + - Name: cascade_rcnn_x101_32x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.6 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth + + - Name: cascade_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth + + - Name: cascade_rcnn_x101_64x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth + + - Name: cascade_mask_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth + + - Name: cascade_mask_rcnn_r50_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 89.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth + + - Name: cascade_mask_rcnn_r50_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 89.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth + + - Name: cascade_mask_rcnn_r101_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth + + - Name: cascade_mask_rcnn_r101_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (ms/im): + - value: 102.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth + + - Name: cascade_mask_rcnn_r101_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (ms/im): + - value: 102.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 12.2 + inference time (ms/im): + - value: 149.25 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 12.2 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth + + - Name: cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.7 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth + + - Name: cascade_mask_rcnn_r50_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth + + - Name: cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 7.7 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth + + - Name: cascade_mask_rcnn_r101_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 9.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth + + - Name: cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 12.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 12.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth diff --git a/downstream/mmdetection/configs/cascade_rpn/README.md b/downstream/mmdetection/configs/cascade_rpn/README.md new file mode 100644 index 0000000..fb2b482 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rpn/README.md @@ -0,0 +1,41 @@ +# Cascade RPN + +> [Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution](https://arxiv.org/abs/1909.06720) + + + +## Abstract + +This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by systematically addressing the limitation of the conventional RPN that heuristically defines the anchors and aligns the features to the anchors. First, instead of using multiple anchors with predefined scales and aspect ratios, Cascade RPN relies on a single anchor per location and performs multi-stage refinement. Each stage is progressively more stringent in defining positive samples by starting out with an anchor-free metric followed by anchor-based metrics in the ensuing stages. Second, to attain alignment between the features and the anchors throughout the stages, adaptive convolution is proposed that takes the anchors in addition to the image features as its input and learns the sampled features guided by the anchors. A simple implementation of a two-stage Cascade RPN achieves AR 13.4 points higher than that of the conventional RPN, surpassing any existing region proposal methods. When adopting to Fast R-CNN and Faster R-CNN, Cascade RPN can improve the detection mAP by 3.1 and 3.5 points, respectively. + +
    + +
    + +## Results and Models + +### Region proposal performance + +| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 | Config | Download | +| :----: | :------: | :---: | :------: | :-----------------: | :------------: | :-----: | :---------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------: | +| CRPN | R-50-FPN | caffe | - | - | - | 72.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_r50_caffe_fpn_1x_coco/cascade_rpn_r50_caffe_fpn_1x_coco-7aa93cef.pth) | + +### Detection performance + +| Method | Proposal | Backbone | Style | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Config | Download | +| :----------: | :---------: | :------: | :---: | :------: | :------: | :-----------------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Fast R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco/crpn_fast_rcnn_r50_caffe_fpn_1x_coco-cb486e66.pth) | +| Faster R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth) | + +## Citation + +We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720). + +```latex +@inproceedings{vu2019cascade, + title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution}, + author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D}, + booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..29f5d07 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,77 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + roi_head=dict( + bbox_head=dict( + bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict( + pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65), + sampler=dict(num=256))), + test_cfg=dict(rcnn=dict(score_thr=1e-3))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..bad86e6 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,92 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' +rpn_weight = 0.7 +model = dict( + rpn_head=dict( + _delete_=True, + type='CascadeRPNHead', + num_stages=2, + stages=[ + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=True, + sampling=False, + with_cls=False, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.1, 0.1, 0.5, 0.5)), + loss_bbox=dict( + type='IoULoss', linear=True, + loss_weight=10.0 * rpn_weight)), + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + adapt_cfg=dict(type='offset'), + bridged_feature=False, + sampling=True, + with_cls=True, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.05, 0.05, 0.1, 0.1)), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0 * rpn_weight), + loss_bbox=dict( + type='IoULoss', linear=True, + loss_weight=10.0 * rpn_weight)) + ]), + roi_head=dict( + bbox_head=dict( + bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=[ + dict( + assigner=dict( + type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), + allowed_border=-1, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False) + ], + rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)), + rcnn=dict( + assigner=dict( + pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)), + rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..5562e69 --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,77 @@ +_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='CascadeRPNHead', + num_stages=2, + stages=[ + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=True, + sampling=False, + with_cls=False, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.1, 0.1, 0.5, 0.5)), + loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)), + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + adapt_cfg=dict(type='offset'), + bridged_feature=False, + sampling=True, + with_cls=True, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.05, 0.05, 0.1, 0.1)), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)) + ]), + train_cfg=dict(rpn=[ + dict( + assigner=dict( + type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), + allowed_border=-1, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.3, + ignore_iof_thr=-1, + iou_calculator=dict(type='BboxOverlaps2D')), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.8), + min_bbox_size=0))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/cascade_rpn/metafile.yml b/downstream/mmdetection/configs/cascade_rpn/metafile.yml new file mode 100644 index 0000000..335b2bc --- /dev/null +++ b/downstream/mmdetection/configs/cascade_rpn/metafile.yml @@ -0,0 +1,44 @@ +Collections: + - Name: Cascade RPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Cascade RPN + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1909.06720 + Title: 'Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution' + README: configs/cascade_rpn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.8.0/mmdet/models/dense_heads/cascade_rpn_head.py#L538 + Version: v2.8.0 + +Models: + - Name: crpn_fast_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade RPN + Config: configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco/crpn_fast_rcnn_r50_caffe_fpn_1x_coco-cb486e66.pth + + - Name: crpn_faster_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade RPN + Config: configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth diff --git a/downstream/mmdetection/configs/centernet/README.md b/downstream/mmdetection/configs/centernet/README.md new file mode 100644 index 0000000..0f951a0 --- /dev/null +++ b/downstream/mmdetection/configs/centernet/README.md @@ -0,0 +1,40 @@ +# CenterNet + +> [Objects as Points](https://arxiv.org/abs/1904.07850) + + + +## Abstract + +Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time. + +
    + +
    + +## Results and Models + +| Backbone | DCN | Mem (GB) | Box AP | Flip box AP | Config | Download | +| :-------: | :-: | :------: | :----: | :---------: | :---------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ResNet-18 | N | 3.45 | 25.9 | 27.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630.log.json) | +| ResNet-18 | Y | 3.47 | 29.5 | 30.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131.log.json) | + +Note: + +- Flip box AP setting is single-scale and `flip=True`. +- Due to complex data enhancement, we find that the performance is unstable and may fluctuate by about 0.4 mAP. mAP 29.4 ~ 29.8 is acceptable in ResNet-18-DCNv2. +- Compared to the source code, we refer to [CenterNet-Better](https://github.com/FateScript/CenterNet-better), and make the following changes + - fix wrong image mean and variance in image normalization to be compatible with the pre-trained backbone. + - Use SGD rather than ADAM optimizer and add warmup and grad clip. + - Use DistributedDataParallel as other models in MMDetection rather than using DataParallel. + +## Citation + +```latex +@article{zhou2019objects, + title={Objects as Points}, + author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp}, + booktitle={arXiv preprint arXiv:1904.07850}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/centernet/centernet_resnet18_140e_coco.py b/downstream/mmdetection/configs/centernet/centernet_resnet18_140e_coco.py new file mode 100644 index 0000000..52c86a5 --- /dev/null +++ b/downstream/mmdetection/configs/centernet/centernet_resnet18_140e_coco.py @@ -0,0 +1,3 @@ +_base_ = './centernet_resnet18_dcnv2_140e_coco.py' + +model = dict(neck=dict(use_dcn=False)) diff --git a/downstream/mmdetection/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py b/downstream/mmdetection/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py new file mode 100644 index 0000000..b8a0bb1 --- /dev/null +++ b/downstream/mmdetection/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py @@ -0,0 +1,127 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='CenterNet', + backbone=dict( + type='ResNet', + depth=18, + norm_eval=False, + norm_cfg=dict(type='BN'), + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), + neck=dict( + type='CTResNetNeck', + in_channel=512, + num_deconv_filters=(256, 128, 64), + num_deconv_kernels=(4, 4, 4), + use_dcn=True), + bbox_head=dict( + type='CenterNetHead', + num_classes=80, + in_channel=64, + feat_channel=64, + loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0), + loss_wh=dict(type='L1Loss', loss_weight=0.1), + loss_offset=dict(type='L1Loss', loss_weight=1.0)), + train_cfg=None, + test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100)) + +# We fixed the incorrect img_norm_cfg problem in the source code. +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True, color_type='color'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(512, 512), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + mean=[0, 0, 0], + std=[1, 1, 1], + to_rgb=True, + test_pad_mode=None), + dict(type='Resize', img_scale=(512, 512), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict( + type='RandomCenterCropPad', + ratios=None, + border=None, + mean=[0, 0, 0], + std=[1, 1, 1], + to_rgb=True, + test_mode=True, + test_pad_mode=['logical_or', 31], + test_pad_add_pix=1), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + meta_keys=('filename', 'ori_filename', 'ori_shape', + 'img_shape', 'pad_shape', 'scale_factor', 'flip', + 'flip_direction', 'img_norm_cfg', 'border'), + keys=['img']) + ]) +] + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=16, + workers_per_gpu=4, + train=dict( + _delete_=True, + type='RepeatDataset', + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +# Based on the default settings of modern detectors, the SGD effect is better +# than the Adam in the source code, so we use SGD default settings and +# if you use adam+lr5e-4, the map is 29.1. +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) + +# learning policy +# Based on the default settings of modern detectors, we added warmup settings. +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 1000, + step=[18, 24]) # the real step is [18*5, 24*5] +runner = dict(max_epochs=28) # the real epoch is 28*5=140 + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (16 samples per GPU) +auto_scale_lr = dict(base_batch_size=128) diff --git a/downstream/mmdetection/configs/centernet/metafile.yml b/downstream/mmdetection/configs/centernet/metafile.yml new file mode 100644 index 0000000..e86e57b --- /dev/null +++ b/downstream/mmdetection/configs/centernet/metafile.yml @@ -0,0 +1,46 @@ +Collections: + - Name: CenterNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x TITANXP GPUs + Architecture: + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.07850 + Title: 'Objects as Points' + README: configs/centernet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.13.0/mmdet/models/detectors/centernet.py#L10 + Version: v2.13.0 + +Models: + - Name: centernet_resnet18_dcnv2_140e_coco + In Collection: CenterNet + Config: configs/centernet/centernet_resnet18_dcnv2_140e_coco.py + Metadata: + Batch Size: 128 + Training Memory (GB): 3.47 + Epochs: 140 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 29.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth + + - Name: centernet_resnet18_140e_coco + In Collection: CenterNet + Config: configs/centernet/centernet_resnet18_140e_coco.py + Metadata: + Batch Size: 128 + Training Memory (GB): 3.45 + Epochs: 140 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 25.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth diff --git a/downstream/mmdetection/configs/centripetalnet/README.md b/downstream/mmdetection/configs/centripetalnet/README.md new file mode 100644 index 0000000..b01b00a --- /dev/null +++ b/downstream/mmdetection/configs/centripetalnet/README.md @@ -0,0 +1,36 @@ +# CentripetalNet + +> [CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection](https://arxiv.org/abs/2003.09119) + + + +## Abstract + +Keypoint-based detectors have achieved pretty-well performance. However, incorrect keypoint matching is still widespread and greatly affects the performance of the detector. In this paper, we propose CentripetalNet which uses centripetal shift to pair corner keypoints from the same instance. CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned. Combining position information, our approach matches corner points more accurately than the conventional embedding approaches do. Corner pooling extracts information inside the bounding boxes onto the border. To make this information more aware at the corners, we design a cross-star deformable convolution network to conduct feature adaption. Furthermore, we explore instance segmentation on anchor-free detectors by equipping our CentripetalNet with a mask prediction module. On MS-COCO test-dev, our CentripetalNet not only outperforms all existing anchor-free detectors with an AP of 48.0% but also achieves comparable performance to the state-of-the-art instance segmentation approaches with a 40.2% MaskAP. + +
    + +
    + +## Results and Models + +| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :--------------: | :--------------------------------------------------------------: | :---------------: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HourglassNet-104 | [16 x 6](./centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | 190/210 | 16.7 | 3.7 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804.log.json) | + +Note: + +- TTA setting is single-scale and `flip=True`. +- The model we released is the best checkpoint rather than the latest checkpoint (box AP 44.8 vs 44.6 in our experiment). + +## Citation + +```latex +@InProceedings{Dong_2020_CVPR, +author = {Dong, Zhiwei and Li, Guoxuan and Liao, Yue and Wang, Fei and Ren, Pengju and Qian, Chen}, +title = {CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection}, +booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, +month = {June}, +year = {2020} +} +``` diff --git a/downstream/mmdetection/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py b/downstream/mmdetection/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py new file mode 100644 index 0000000..5281c5b --- /dev/null +++ b/downstream/mmdetection/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py @@ -0,0 +1,110 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CentripetalHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=0, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1), + loss_guiding_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=0.05), + loss_centripetal_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=6, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[190]) +runner = dict(type='EpochBasedRunner', max_epochs=210) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (16 GPUs) x (6 samples per GPU) +auto_scale_lr = dict(base_batch_size=96) diff --git a/downstream/mmdetection/configs/centripetalnet/metafile.yml b/downstream/mmdetection/configs/centripetalnet/metafile.yml new file mode 100644 index 0000000..61aed3e --- /dev/null +++ b/downstream/mmdetection/configs/centripetalnet/metafile.yml @@ -0,0 +1,39 @@ +Collections: + - Name: CentripetalNet + Metadata: + Training Data: COCO + Training Techniques: + - Adam + Training Resources: 16x V100 GPUs + Architecture: + - Corner Pooling + - Stacked Hourglass Network + Paper: + URL: https://arxiv.org/abs/2003.09119 + Title: 'CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection' + README: configs/centripetalnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/cornernet.py#L9 + Version: v2.5.0 + +Models: + - Name: centripetalnet_hourglass104_mstest_16x6_210e_coco + In Collection: CentripetalNet + Config: configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py + Metadata: + Batch Size: 96 + Training Memory (GB): 16.7 + inference time (ms/im): + - value: 270.27 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth diff --git a/downstream/mmdetection/configs/cityscapes/README.md b/downstream/mmdetection/configs/cityscapes/README.md new file mode 100644 index 0000000..c52a79f --- /dev/null +++ b/downstream/mmdetection/configs/cityscapes/README.md @@ -0,0 +1,46 @@ +# Cityscapes + +> [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://arxiv.org/abs/1604.01685) + + + +## Abstract + +Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. +To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark. + +
    + +
    + +## Common settings + +- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate. +- All models were trained on `cityscapes_train`, and tested on `cityscapes_val`. +- 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870) +- COCO pre-trained weights are used to initialize. +- A conversion [script](../../tools/dataset_converters/cityscapes.py) is provided to convert Cityscapes into COCO format. Please refer to [install.md](../../docs/1_exist_data_model.md#prepare-datasets) for details. +- `CityscapesDataset` implemented three evaluation methods. `bbox` and `segm` are standard COCO bbox/mask AP. `cityscapes` is the cityscapes dataset official evaluation, which may be slightly higher than COCO. + +### Faster R-CNN + +| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 800-1024 | 5.2 | - | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502-829424c0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502_114915.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 800-1024 | 5.3 | - | 40.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733-d2858245.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733.log.json) | + +## Citation + +```latex +@inproceedings{Cordts2016Cityscapes, + title={The Cityscapes Dataset for Semantic Urban Scene Understanding}, + author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt}, + booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2016} +} +``` diff --git a/downstream/mmdetection/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py b/downstream/mmdetection/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py new file mode 100644 index 0000000..ca636bd --- /dev/null +++ b/downstream/mmdetection/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py @@ -0,0 +1,44 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_detection.py', + '../_base_/default_runtime.py' +] +model = dict( + backbone=dict(init_cfg=None), + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))) +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + # [7] yields higher performance than [6] + step=[7]) +runner = dict( + type='EpochBasedRunner', max_epochs=8) # actual epoch = 8 * 8 = 64 +log_config = dict(interval=100) +# For better, more stable performance initialize from COCO +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' # noqa + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (1 samples per GPU) +auto_scale_lr = dict(base_batch_size=8) diff --git a/downstream/mmdetection/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py b/downstream/mmdetection/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py new file mode 100644 index 0000000..83ea058 --- /dev/null +++ b/downstream/mmdetection/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py @@ -0,0 +1,51 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' +] +model = dict( + backbone=dict(init_cfg=None), + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=8, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))) +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + # [7] yields higher performance than [6] + step=[7]) +runner = dict( + type='EpochBasedRunner', max_epochs=8) # actual epoch = 8 * 8 = 64 +log_config = dict(interval=100) +# For better, more stable performance initialize from COCO +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth' # noqa + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (1 samples per GPU) +auto_scale_lr = dict(base_batch_size=8) diff --git a/downstream/mmdetection/configs/common/lsj_100e_coco_instance.py b/downstream/mmdetection/configs/common/lsj_100e_coco_instance.py new file mode 100644 index 0000000..cacf23d --- /dev/null +++ b/downstream/mmdetection/configs/common/lsj_100e_coco_instance.py @@ -0,0 +1,90 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +image_size = (1024, 1024) + +file_client_args = dict(backend='disk') +# comment out the code below to use different file client +# file_client_args = dict( +# backend='petrel', +# path_mapping=dict({ +# './data/': 's3://openmmlab/datasets/detection/', +# 'data/': 's3://openmmlab/datasets/detection/' +# })) + +train_pipeline = [ + dict(type='LoadImageFromFile', file_client_args=file_client_args), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=image_size, + ratio_range=(0.1, 2.0), + multiscale_mode='range', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=image_size, + recompute_bbox=True, + allow_negative_crop=True), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=image_size), # padding to image_size leads 0.5+ mAP + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', file_client_args=file_client_args), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=4, # simply change this from 2 to 16 for 50e - 400e training. + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=5, metric=['bbox', 'segm']) + +# optimizer assumes bs=64 +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004) +optimizer_config = dict(grad_clip=None) + +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.067, + step=[22, 24]) +runner = dict(type='EpochBasedRunner', max_epochs=25) diff --git a/downstream/mmdetection/configs/common/mstrain-poly_3x_coco_instance.py b/downstream/mmdetection/configs/common/mstrain-poly_3x_coco_instance.py new file mode 100644 index 0000000..c22ed94 --- /dev/null +++ b/downstream/mmdetection/configs/common/mstrain-poly_3x_coco_instance.py @@ -0,0 +1,80 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['bbox', 'segm']) + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/common/mstrain_3x_coco.py b/downstream/mmdetection/configs/common/mstrain_3x_coco.py new file mode 100644 index 0000000..80ec8b8 --- /dev/null +++ b/downstream/mmdetection/configs/common/mstrain_3x_coco.py @@ -0,0 +1,76 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/common/mstrain_3x_coco_instance.py b/downstream/mmdetection/configs/common/mstrain_3x_coco_instance.py new file mode 100644 index 0000000..50f39be --- /dev/null +++ b/downstream/mmdetection/configs/common/mstrain_3x_coco_instance.py @@ -0,0 +1,76 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['bbox', 'segm']) + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/common/ssj_270k_coco_instance.py b/downstream/mmdetection/configs/common/ssj_270k_coco_instance.py new file mode 100644 index 0000000..851098f --- /dev/null +++ b/downstream/mmdetection/configs/common/ssj_270k_coco_instance.py @@ -0,0 +1,91 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +image_size = (1024, 1024) + +file_client_args = dict(backend='disk') + +# Standard Scale Jittering (SSJ) resizes and crops an image +# with a resize range of 0.8 to 1.25 of the original image size. +train_pipeline = [ + dict(type='LoadImageFromFile', file_client_args=file_client_args), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=image_size, + ratio_range=(0.8, 1.25), + multiscale_mode='range', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=image_size, + recompute_bbox=True, + allow_negative_crop=True), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=image_size), # padding to image_size leads 0.5+ mAP + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', file_client_args=file_client_args), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) + +evaluation = dict(interval=6000, metric=['bbox', 'segm']) + +# optimizer assumes batch_size = (32 GPUs) x (2 samples per GPU) +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004) +optimizer_config = dict(grad_clip=None) + +# lr steps at [0.9, 0.95, 0.975] of the maximum iterations +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.001, + step=[243000, 256500, 263250]) +checkpoint_config = dict(interval=6000) +# The model is trained by 270k iterations with batch_size 64, +# which is roughly equivalent to 144 epochs. +runner = dict(type='IterBasedRunner', max_iters=270000) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (32 GPUs) x (2 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/common/ssj_scp_270k_coco_instance.py b/downstream/mmdetection/configs/common/ssj_scp_270k_coco_instance.py new file mode 100644 index 0000000..540839f --- /dev/null +++ b/downstream/mmdetection/configs/common/ssj_scp_270k_coco_instance.py @@ -0,0 +1,97 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +image_size = (1024, 1024) + +file_client_args = dict(backend='disk') + +# Standard Scale Jittering (SSJ) resizes and crops an image +# with a resize range of 0.8 to 1.25 of the original image size. +load_pipeline = [ + dict(type='LoadImageFromFile', file_client_args=file_client_args), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=image_size, + ratio_range=(0.8, 1.25), + multiscale_mode='range', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=image_size, + recompute_bbox=True, + allow_negative_crop=True), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Pad', size=image_size), +] +train_pipeline = [ + dict(type='CopyPaste', max_num_pasted=100), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', file_client_args=file_client_args), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='MultiImageMixDataset', + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=load_pipeline), + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) + +evaluation = dict(interval=6000, metric=['bbox', 'segm']) + +# optimizer assumes batch_size = (32 GPUs) x (2 samples per GPU) +optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004) +optimizer_config = dict(grad_clip=None) + +# lr steps at [0.9, 0.95, 0.975] of the maximum iterations +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.001, + step=[243000, 256500, 263250]) +checkpoint_config = dict(interval=6000) +# The model is trained by 270k iterations with batch_size 64, +# which is roughly equivalent to 144 epochs. +runner = dict(type='IterBasedRunner', max_iters=270000) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (32 GPUs) x (2 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/convnext/README.md b/downstream/mmdetection/configs/convnext/README.md new file mode 100644 index 0000000..edf72e8 --- /dev/null +++ b/downstream/mmdetection/configs/convnext/README.md @@ -0,0 +1,40 @@ +# ConvNeXt + +> [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) + +## Abstract + +The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets. + +
    + +
    + +## Results and models + +| Method | Backbone | Pretrain | Lr schd | Multi-scale crop | FP16 | Mem (GB) | box AP | mask AP | Config | Download | +| :----------------: | :--------: | :---------: | :-----: | :--------------: | :--: | :------: | :----: | :-----: | :-------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Mask R-CNN | ConvNeXt-T | ImageNet-1K | 3x | yes | yes | 7.3 | 46.2 | 41.7 | [config](./mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953.log.json) | +| Cascade Mask R-CNN | ConvNeXt-T | ImageNet-1K | 3x | yes | yes | 9.0 | 50.3 | 43.6 | [config](./cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200-8f07c40b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200.log.json) | +| Cascade Mask R-CNN | ConvNeXt-S | ImageNet-1K | 3x | yes | yes | 12.3 | 51.8 | 44.8 | [config](./cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004.log.json) | + +**Note**: + +- ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks. + +```shell +pip install mmcls>=0.22.0 +``` + +- The performance is unstable. `Cascade Mask R-CNN` may fluctuate about 0.2 mAP. + +## Citation + +```bibtex +@article{liu2022convnet, + title={A ConvNet for the 2020s}, + author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining}, + journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2022} +} +``` diff --git a/downstream/mmdetection/configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py b/downstream/mmdetection/configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py new file mode 100644 index 0000000..0ccc31d --- /dev/null +++ b/downstream/mmdetection/configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py @@ -0,0 +1,32 @@ +_base_ = './cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa + +# please install mmcls>=0.22.0 +# import mmcls.models to trigger register_module in mmcls +custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth' # noqa + +model = dict( + backbone=dict( + _delete_=True, + type='mmcls.ConvNeXt', + arch='small', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.6, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.'))) + +optimizer = dict( + _delete_=True, + constructor='LearningRateDecayOptimizerConstructor', + type='AdamW', + lr=0.0002, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.7, + 'decay_type': 'layer_wise', + 'num_layers': 12 + }) diff --git a/downstream/mmdetection/configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py b/downstream/mmdetection/configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py new file mode 100644 index 0000000..93304c0 --- /dev/null +++ b/downstream/mmdetection/configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py @@ -0,0 +1,149 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +# please install mmcls>=0.22.0 +# import mmcls.models to trigger register_module in mmcls +custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa + +model = dict( + backbone=dict( + _delete_=True, + type='mmcls.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + neck=dict(in_channels=[96, 192, 384, 768]), + roi_head=dict(bbox_head=[ + dict( + type='ConvFCBBoxHead', + num_shared_convs=4, + num_shared_fcs=1, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + reg_decoded_bbox=True, + norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), + dict( + type='ConvFCBBoxHead', + num_shared_convs=4, + num_shared_fcs=1, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=False, + reg_decoded_bbox=True, + norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=10.0)), + dict( + type='ConvFCBBoxHead', + num_shared_convs=4, + num_shared_fcs=1, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=False, + reg_decoded_bbox=True, + norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=10.0)) + ])) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline), persistent_workers=True) + +optimizer = dict( + _delete_=True, + constructor='LearningRateDecayOptimizerConstructor', + type='AdamW', + lr=0.0002, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.7, + 'decay_type': 'layer_wise', + 'num_layers': 6 + }) + +lr_config = dict(warmup_iters=1000, step=[27, 33]) +runner = dict(max_epochs=36) + +# you need to set mode='dynamic' if you are using pytorch<=1.5.0 +fp16 = dict(loss_scale=dict(init_scale=512)) diff --git a/downstream/mmdetection/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py b/downstream/mmdetection/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py new file mode 100644 index 0000000..e8a283f --- /dev/null +++ b/downstream/mmdetection/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py @@ -0,0 +1,90 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +# please install mmcls>=0.22.0 +# import mmcls.models to trigger register_module in mmcls +custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False) +checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth' # noqa + +model = dict( + backbone=dict( + _delete_=True, + type='mmcls.ConvNeXt', + arch='tiny', + out_indices=[0, 1, 2, 3], + drop_path_rate=0.4, + layer_scale_init_value=1.0, + gap_before_final_norm=False, + init_cfg=dict( + type='Pretrained', checkpoint=checkpoint_file, + prefix='backbone.')), + neck=dict(in_channels=[96, 192, 384, 768])) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline), persistent_workers=True) + +optimizer = dict( + _delete_=True, + constructor='LearningRateDecayOptimizerConstructor', + type='AdamW', + lr=0.0001, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg={ + 'decay_rate': 0.95, + 'decay_type': 'layer_wise', + 'num_layers': 6 + }) + +lr_config = dict(warmup_iters=1000, step=[27, 33]) +runner = dict(max_epochs=36) + +# you need to set mode='dynamic' if you are using pytorch<=1.5.0 +fp16 = dict(loss_scale=dict(init_scale=512)) diff --git a/downstream/mmdetection/configs/convnext/metafile.yml b/downstream/mmdetection/configs/convnext/metafile.yml new file mode 100644 index 0000000..20425bf --- /dev/null +++ b/downstream/mmdetection/configs/convnext/metafile.yml @@ -0,0 +1,93 @@ +Models: + - Name: mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco + In Collection: Mask R-CNN + Config: configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco + Metadata: + Training Memory (GB): 7.3 + Epochs: 36 + Training Data: COCO + Training Techniques: + - AdamW + - Mixed Precision Training + Training Resources: 8x A100 GPUs + Architecture: + - ConvNeXt + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth + Paper: + URL: https://arxiv.org/abs/2201.03545 + Title: 'A ConvNet for the 2020s' + README: configs/convnext/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465 + Version: v2.16.0 + + - Name: cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco + In Collection: Cascade Mask R-CNN + Config: configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py + Metadata: + Training Memory (GB): 9.0 + Epochs: 36 + Training Data: COCO + Training Techniques: + - AdamW + - Mixed Precision Training + Training Resources: 8x A100 GPUs + Architecture: + - ConvNeXt + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 43.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200-8f07c40b.pth + Paper: + URL: https://arxiv.org/abs/2201.03545 + Title: 'A ConvNet for the 2020s' + README: configs/convnext/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465 + Version: v2.25.0 + + - Name: cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco + In Collection: Cascade Mask R-CNN + Config: configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py + Metadata: + Training Memory (GB): 12.3 + Epochs: 36 + Training Data: COCO + Training Techniques: + - AdamW + - Mixed Precision Training + Training Resources: 8x A100 GPUs + Architecture: + - ConvNeXt + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 51.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 44.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth + Paper: + URL: https://arxiv.org/abs/2201.03545 + Title: 'A ConvNet for the 2020s' + README: configs/convnext/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465 + Version: v2.25.0 diff --git a/downstream/mmdetection/configs/cornernet/README.md b/downstream/mmdetection/configs/cornernet/README.md new file mode 100644 index 0000000..d0b9e98 --- /dev/null +++ b/downstream/mmdetection/configs/cornernet/README.md @@ -0,0 +1,43 @@ +# CornerNet + +> [Cornernet: Detecting objects as paired keypoints](https://arxiv.org/abs/1808.01244) + + + +## Abstract + +We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that CornerNet achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors. + +
    + +
    + +## Results and Models + +| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :--------------: | :---------------------------------------------------------: | :---------------: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) | +| HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) | +| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) | + +Note: + +- TTA setting is single-scale and `flip=True`. +- Experiments with `images_per_gpu=6` are conducted on Tesla V100-SXM2-32GB, `images_per_gpu=3` are conducted on GeForce GTX 1080 Ti. +- Here are the descriptions of each experiment setting: + - 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper. + - 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train. + - 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train. + +## Citation + +```latex +@inproceedings{law2018cornernet, + title={Cornernet: Detecting objects as paired keypoints}, + author={Law, Hei and Deng, Jia}, + booktitle={15th European Conference on Computer Vision, ECCV 2018}, + pages={765--781}, + year={2018}, + organization={Springer Verlag} +} +``` diff --git a/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py b/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py new file mode 100644 index 0000000..6cb05a7 --- /dev/null +++ b/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py @@ -0,0 +1,110 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=5, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (10 GPUs) x (5 samples per GPU) +auto_scale_lr = dict(base_batch_size=50) diff --git a/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py b/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py new file mode 100644 index 0000000..f539cdb --- /dev/null +++ b/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py @@ -0,0 +1,110 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=3, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (32 GPUs) x (3 samples per GPU) +auto_scale_lr = dict(base_batch_size=96) diff --git a/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py b/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py new file mode 100644 index 0000000..9b115d7 --- /dev/null +++ b/downstream/mmdetection/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py @@ -0,0 +1,110 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=6, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (6 samples per GPU) +auto_scale_lr = dict(base_batch_size=48) diff --git a/downstream/mmdetection/configs/cornernet/metafile.yml b/downstream/mmdetection/configs/cornernet/metafile.yml new file mode 100644 index 0000000..c2f6143 --- /dev/null +++ b/downstream/mmdetection/configs/cornernet/metafile.yml @@ -0,0 +1,83 @@ +Collections: + - Name: CornerNet + Metadata: + Training Data: COCO + Training Techniques: + - Adam + Training Resources: 8x V100 GPUs + Architecture: + - Corner Pooling + - Stacked Hourglass Network + Paper: + URL: https://arxiv.org/abs/1808.01244 + Title: 'CornerNet: Detecting Objects as Paired Keypoints' + README: configs/cornernet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.3.0/mmdet/models/detectors/cornernet.py#L9 + Version: v2.3.0 + +Models: + - Name: cornernet_hourglass104_mstest_10x5_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py + Metadata: + Training Resources: 10x V100 GPUs + Batch Size: 50 + Training Memory (GB): 13.9 + inference time (ms/im): + - value: 238.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth + + - Name: cornernet_hourglass104_mstest_8x6_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py + Metadata: + Batch Size: 48 + Training Memory (GB): 15.9 + inference time (ms/im): + - value: 238.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth + + - Name: cornernet_hourglass104_mstest_32x3_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py + Metadata: + Training Resources: 32x V100 GPUs + Batch Size: 96 + Training Memory (GB): 9.5 + inference time (ms/im): + - value: 256.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth diff --git a/downstream/mmdetection/configs/dcn/README.md b/downstream/mmdetection/configs/dcn/README.md new file mode 100644 index 0000000..745b01c --- /dev/null +++ b/downstream/mmdetection/configs/dcn/README.md @@ -0,0 +1,48 @@ +# DCN + +> [Deformable Convolutional Networks](https://arxiv.org/abs/1703.06211) + + + +## Abstract + +Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation. + +
    + +
    + +## Results and Models + +| Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :----------: | :-----: | :----------: | :---: | :-----: | :------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 4.0 | 17.8 | 41.3 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_212941.log.json) | +| R-50-FPN | Faster | pytorch | - | dpool | 1x | 5.0 | 17.2 | 38.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307_203250.log.json) | +| R-101-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 12.5 | 42.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_230019.log.json) | +| X-101-32x4d-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 7.3 | 10.0 | 44.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203_001325.log.json) | +| R-50-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 15.4 | 41.8 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203_061339.log.json) | +| R-101-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 6.5 | 11.7 | 43.5 | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216_191601.log.json) | +| R-50-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 14.6 | 43.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_220843.log.json) | +| R-101-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 6.4 | 11.0 | 45.0 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_224829.log.json) | +| R-50-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 10.0 | 44.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202_010309.log.json) | +| R-101-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 8.0 | 8.6 | 45.8 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204_134006.log.json) | +| X-101-32x4d-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 9.2 | | 47.3 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-20200606_183737.log.json) | +| R-50-FPN (FP16) | Mask | pytorch | dconv(c3-c5) | - | 1x | 3.0 | | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247.log.json) | + +**Notes:** + +- `dconv` denotes deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` denotes deformable roi pooling. +- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster. +- (\*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance. +- **Memory, Train/Inf time is outdated.** + +## Citation + +```latex +@inproceedings{dai2017deformable, + title={Deformable Convolutional Networks}, + author={Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen}, + booktitle={Proceedings of the IEEE international conference on computer vision}, + year={2017} +} +``` diff --git a/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..081b998 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..3b3683a --- /dev/null +++ b/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..daaa472 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..a01df33 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..aa664bd --- /dev/null +++ b/downstream/mmdetection/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..f5fee7e --- /dev/null +++ b/downstream/mmdetection/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..8787088 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py b/downstream/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py new file mode 100644 index 0000000..1b695f0 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + _delete_=True, + type='DeformRoIPoolPack', + output_size=7, + output_channels=256), + out_channels=256, + featmap_strides=[4, 8, 16, 32]))) diff --git a/downstream/mmdetection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..e3bea19 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..cb34002 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..ababe58 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..ee5cca7 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) + +fp16 = dict(loss_scale=512.) diff --git a/downstream/mmdetection/configs/dcn/metafile.yml b/downstream/mmdetection/configs/dcn/metafile.yml new file mode 100644 index 0000000..36f3887 --- /dev/null +++ b/downstream/mmdetection/configs/dcn/metafile.yml @@ -0,0 +1,272 @@ +Collections: + - Name: Deformable Convolutional Networks + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Deformable Convolution + Paper: + URL: https://arxiv.org/abs/1703.06211 + Title: "Deformable Convolutional Networks" + README: configs/dcn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/dcn/deform_conv.py#L15 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 56.18 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth + + - Name: faster_rcnn_r50_fpn_dpool_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + inference time (ms/im): + - value: 58.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth + + - Name: faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 80 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth + + - Name: faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.3 + inference time (ms/im): + - value: 100 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth + + - Name: mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (ms/im): + - value: 64.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth + + - Name: mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py + Metadata: + Training Techniques: + - SGD with Momentum + - Weight Decay + - Mixed Precision Training + Training Memory (GB): 3.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth + + - Name: mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth + + - Name: cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (ms/im): + - value: 68.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth + + - Name: cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth + + - Name: cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 100 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth + + - Name: cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth diff --git a/downstream/mmdetection/configs/dcnv2/README.md b/downstream/mmdetection/configs/dcnv2/README.md new file mode 100644 index 0000000..d230f20 --- /dev/null +++ b/downstream/mmdetection/configs/dcnv2/README.md @@ -0,0 +1,37 @@ +# DCNv2 + +> [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/abs/1811.11168) + + + +## Abstract + +The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training. The modeling power is enhanced through a more comprehensive integration of deformable convolution within the network, and by introducing a modulation mechanism that expands the scope of deformation modeling. To effectively harness this enriched modeling capability, we guide network training via a proposed feature mimicking scheme that helps the network to learn features that reflect the object focus and classification power of RCNN features. With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading results on the COCO benchmark for object detection and instance segmentation. + +## Results and Models + +| Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :---------------: | :----: | :-----: | :-----------: | :----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.1 | 17.6 | 41.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130_222144.log.json) | +| \*R-50-FPN (dg=4) | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.2 | 17.4 | 41.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130_222058.log.json) | +| R-50-FPN | Faster | pytorch | - | mdpool | 1x | 5.8 | 16.6 | 38.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307_203304.log.json) | +| R-50-FPN | Mask | pytorch | mdconv(c3-c5) | - | 1x | 4.5 | 15.1 | 41.5 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203_063443.log.json) | +| R-50-FPN (FP16) | Mask | pytorch | mdconv(c3-c5) | - | 1x | 3.1 | | 42.0 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434.log.json) | + +**Notes:** + +- `mdconv` denotes modulated deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `mdpool` denotes modulated deformable roi pooling. +- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster. +- (\*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance. +- **Memory, Train/Inf time is outdated.** + +## Citation + +```latex +@article{zhu2018deformable, + title={Deformable ConvNets v2: More Deformable, Better Results}, + author={Zhu, Xizhou and Hu, Han and Lin, Stephen and Dai, Jifeng}, + journal={arXiv preprint arXiv:1811.11168}, + year={2018} +} +``` diff --git a/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..d1bcf3c --- /dev/null +++ b/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py b/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py new file mode 100644 index 0000000..d0ab89c --- /dev/null +++ b/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py b/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py new file mode 100644 index 0000000..ad7b034 --- /dev/null +++ b/downstream/mmdetection/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + _delete_=True, + type='ModulatedDeformRoIPoolPack', + output_size=7, + output_channels=256), + out_channels=256, + featmap_strides=[4, 8, 16, 32]))) diff --git a/downstream/mmdetection/configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..7e21454 --- /dev/null +++ b/downstream/mmdetection/configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) + +fp16 = dict(loss_scale=512.) diff --git a/downstream/mmdetection/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..5ca2a67 --- /dev/null +++ b/downstream/mmdetection/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/dcnv2/metafile.yml b/downstream/mmdetection/configs/dcnv2/metafile.yml new file mode 100644 index 0000000..9049421 --- /dev/null +++ b/downstream/mmdetection/configs/dcnv2/metafile.yml @@ -0,0 +1,123 @@ +Collections: + - Name: Deformable Convolutional Networks v2 + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Deformable Convolution + Paper: + URL: https://arxiv.org/abs/1811.11168 + Title: "Deformable ConvNets v2: More Deformable, Better Results" + README: configs/dcnv2/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/dcn/deform_conv.py#L15 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks v2 + Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.1 + inference time (ms/im): + - value: 56.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth + + - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco + In Collection: Deformable Convolutional Networks v2 + Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 57.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth + + - Name: faster_rcnn_r50_fpn_mdpool_1x_coco + In Collection: Deformable Convolutional Networks v2 + Config: configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py + Metadata: + Training Memory (GB): 5.8 + inference time (ms/im): + - value: 60.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth + + - Name: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks v2 + Config: configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (ms/im): + - value: 66.23 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth + + - Name: mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks v2 + Config: configs/dcn/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 3.1 + Training Techniques: + - SGD with Momentum + - Weight Decay + - Mixed Precision Training + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth diff --git a/downstream/mmdetection/configs/ddod/README.md b/downstream/mmdetection/configs/ddod/README.md new file mode 100644 index 0000000..9ab1f48 --- /dev/null +++ b/downstream/mmdetection/configs/ddod/README.md @@ -0,0 +1,31 @@ +# DDOD + +> [Disentangle Your Dense Object Detector](https://arxiv.org/pdf/2107.02963.pdf) + + + +## Abstract + +Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the current training pipeline for dense detectors is compromised to lots of conjunctions that may not hold. In this paper, we investigate three such important conjunctions: 1) only samples assigned as positive in classification head are used to train the regression head; 2) classification and regression share the same input feature and computational fields defined by the parallel head architecture; and 3) samples distributed in different feature pyramid layers are treated equally when computing the loss. We first carry out a series of pilot experiments to show disentangling such conjunctions can lead to persistent performance improvement. Then, based on these findings, we propose Disentangled Dense Object Detector(DDOD), in which simple and effective disentanglement mechanisms are designed and integrated into the current state-of-the-art dense object detectors. Extensive experiments on MS COCO benchmark show that our approach can lead to 2.0 mAP, 2.4 mAP and 2.2 mAP absolute improvements on RetinaNet, FCOS, and ATSS baselines with negligible extra overhead. Notably, our best model reaches 55.0 mAP on the COCO test-dev set and 93.5 AP on the hard subset of WIDER FACE, achieving new state-of-the-art performance on these two competitive benchmarks. Code is available at https://github.com/zehuichen123/DDOD. + +
    + +
    + +## Results and Models + +| Model | Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download | +| :-------: | :------: | :-----: | :-----: | :------: | :----: | :--------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| DDOD-ATSS | R-50 | pytorch | 1x | 3.4 | 41.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ddod/ddod_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737.log.json) | + +## Citation + +```latex +@inproceedings{chen2021disentangle, +title={Disentangle Your Dense Object Detector}, +author={Chen, Zehui and Yang, Chenhongyi and Li, Qiaofei and Zhao, Feng and Zha, Zheng-Jun and Wu, Feng}, +booktitle={Proceedings of the 29th ACM International Conference on Multimedia}, +pages={4939--4948}, +year={2021} +} +``` diff --git a/downstream/mmdetection/configs/ddod/ddod_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/ddod/ddod_r50_fpn_1x_coco.py new file mode 100644 index 0000000..02dd2fe --- /dev/null +++ b/downstream/mmdetection/configs/ddod/ddod_r50_fpn_1x_coco.py @@ -0,0 +1,67 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='DDOD', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='DDODHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + loss_iou=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + train_cfg=dict( + # assigner is mean cls_assigner + assigner=dict(type='ATSSAssigner', topk=9, alpha=0.8), + reg_assigner=dict(type='ATSSAssigner', topk=9, alpha=0.5), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +# This `persistent_workers` is only valid when PyTorch>=1.7.0 +data = dict(persistent_workers=True) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/ddod/metafile.yml b/downstream/mmdetection/configs/ddod/metafile.yml new file mode 100644 index 0000000..c223950 --- /dev/null +++ b/downstream/mmdetection/configs/ddod/metafile.yml @@ -0,0 +1,33 @@ +Collections: + - Name: DDOD + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - DDOD + - FPN + - ResNet + Paper: + URL: https://arxiv.org/pdf/2107.02963.pdf + Title: 'Disentangle Your Dense Object Detector' + README: configs/ddod/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.25.0/mmdet/models/detectors/ddod.py#L6 + Version: v2.25.0 + +Models: + - Name: ddod_r50_fpn_1x_coco + In Collection: DDOD + Config: configs/ddod/ddod_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.4 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth diff --git a/downstream/mmdetection/configs/deepfashion/README.md b/downstream/mmdetection/configs/deepfashion/README.md new file mode 100644 index 0000000..45daec0 --- /dev/null +++ b/downstream/mmdetection/configs/deepfashion/README.md @@ -0,0 +1,70 @@ +# DeepFashion + +> [DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations](https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.html) + + + +## Abstract + +Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion. + +
    + +
    + +## Introduction + +[MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module +based on the dataset +[DeepFashion-Inshop](https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E?usp=sharing). +Its annotation follows COCO style. +To use it, you need to first download the data. Note that we only use "img_highres" in this task. +The file tree should be like this: + +```sh +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── DeepFashion +│ │ ├── In-shop +│ │ ├── Anno +│ │ │   ├── segmentation +│ │ │   | ├── DeepFashion_segmentation_train.json +│ │ │   | ├── DeepFashion_segmentation_query.json +│ │ │   | ├── DeepFashion_segmentation_gallery.json +│ │ │   ├── list_bbox_inshop.txt +│ │ │   ├── list_description_inshop.json +│ │ │   ├── list_item_inshop.txt +│ │ │   └── list_landmarks_inshop.txt +│ │ ├── Eval +│ │ │ └── list_eval_partition.txt +│ │ ├── Img +│ │ │ ├── img +│ │ │ │ ├──XXX.jpg +│ │ │ ├── img_highres +│ │ │ └── ├──XXX.jpg + +``` + +After that you can train the Mask RCNN r50 on DeepFashion-In-shop dataset by launching training with the `mask_rcnn_r50_fpn_1x.py` config +or creating your own config file. + +## Results and Models + +| Backbone | Model type | Dataset | bbox detection Average Precision | segmentation Average Precision | Config | Download (Google) | +| :------: | :--------: | :-----------------: | :------------------------------: | :----------------------------: | :----------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ResNet50 | Mask RCNN | DeepFashion-In-shop | 0.599 | 0.584 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion/mask_rcnn_r50_fpn_15e_deepfashion_20200329_192752.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion/20200329_192752.log.json) | + +## Citation + +```latex +@inproceedings{liuLQWTcvpr16DeepFashion, + author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou}, + title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2016} +} +``` diff --git a/downstream/mmdetection/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py b/downstream/mmdetection/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py new file mode 100644 index 0000000..c4e8638 --- /dev/null +++ b/downstream/mmdetection/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py', + '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15))) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=15) diff --git a/downstream/mmdetection/configs/deformable_detr/README.md b/downstream/mmdetection/configs/deformable_detr/README.md new file mode 100644 index 0000000..378e1f2 --- /dev/null +++ b/downstream/mmdetection/configs/deformable_detr/README.md @@ -0,0 +1,41 @@ +# Deformable DETR + +> [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) + + + +## Abstract + +DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach. + +
    + +
    + +## Results and Models + +| Backbone | Model | Lr schd | box AP | Config | Download | +| :------: | :---------------------------------: | :-----: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | Deformable DETR | 50e | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.log.json) | +| R-50 | + iterative bounding box refinement | 50e | 46.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.log.json) | +| R-50 | ++ two-stage Deformable DETR | 50e | 46.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.log.json) | + +# NOTE + +1. All models are trained with batch size 32. +2. The performance is unstable. `Deformable DETR` and `iterative bounding box refinement` may fluctuate about 0.3 mAP. `two-stage Deformable DETR` may fluctuate about 0.2 mAP. + +## Citation + +We provide the config files for Deformable DETR: [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159). + +```latex +@inproceedings{ +zhu2021deformable, +title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, +author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai}, +booktitle={International Conference on Learning Representations}, +year={2021}, +url={https://openreview.net/forum?id=gZ9hCDWe6ke} +} +``` diff --git a/downstream/mmdetection/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py b/downstream/mmdetection/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py new file mode 100644 index 0000000..c64d09f --- /dev/null +++ b/downstream/mmdetection/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py @@ -0,0 +1,177 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +model = dict( + type='DeformableDETR', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='ChannelMapper', + in_channels=[512, 1024, 2048], + kernel_size=1, + out_channels=256, + act_cfg=None, + norm_cfg=dict(type='GN', num_groups=32), + num_outs=4), + bbox_head=dict( + type='DeformableDETRHead', + num_query=300, + num_classes=80, + in_channels=2048, + sync_cls_avg_factor=True, + as_two_stage=False, + transformer=dict( + type='DeformableDetrTransformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=dict( + type='MultiScaleDeformableAttention', embed_dims=256), + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DeformableDetrTransformerDecoder', + num_layers=6, + return_intermediate=True, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + dict( + type='MultiScaleDeformableAttention', + embed_dims=256) + ], + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')))), + positional_encoding=dict( + type='SinePositionalEncoding', + num_feats=128, + normalize=True, + offset=-0.5), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different +# from the default setting in mmdet. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[ + [ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + # The radio of all image in train dataset < 7 + # follow the original impl + img_scale=[(400, 4200), (500, 4200), (600, 4200)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +# test_pipeline, NOTE the Pad's size_divisor is different from the default +# setting (size_divisor=32). While there is little effect on the performance +# whether we use the default setting or use size_divisor=1. +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(filter_empty_gt=False, pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='AdamW', + lr=2e-4, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1), + 'sampling_offsets': dict(lr_mult=0.1), + 'reference_points': dict(lr_mult=0.1) + })) +optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[40]) +runner = dict(type='EpochBasedRunner', max_epochs=50) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (16 GPUs) x (2 samples per GPU) +auto_scale_lr = dict(base_batch_size=32) diff --git a/downstream/mmdetection/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py b/downstream/mmdetection/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py new file mode 100644 index 0000000..01f13df --- /dev/null +++ b/downstream/mmdetection/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(with_box_refine=True)) diff --git a/downstream/mmdetection/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py b/downstream/mmdetection/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py new file mode 100644 index 0000000..2aa840d --- /dev/null +++ b/downstream/mmdetection/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(as_two_stage=True)) diff --git a/downstream/mmdetection/configs/deformable_detr/metafile.yml b/downstream/mmdetection/configs/deformable_detr/metafile.yml new file mode 100644 index 0000000..873292d --- /dev/null +++ b/downstream/mmdetection/configs/deformable_detr/metafile.yml @@ -0,0 +1,56 @@ +Collections: + - Name: Deformable DETR + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Multi Scale Train + - Gradient Clip + Training Resources: 8x V100 GPUs + Architecture: + - ResNet + - Transformer + Paper: + URL: https://openreview.net/forum?id=gZ9hCDWe6ke + Title: 'Deformable DETR: Deformable Transformers for End-to-End Object Detection' + README: configs/deformable_detr/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/deformable_detr.py#L6 + Version: v2.12.0 + +Models: + - Name: deformable_detr_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth + + - Name: deformable_detr_refine_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth + + - Name: deformable_detr_twostage_refine_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth diff --git a/downstream/mmdetection/configs/detectors/README.md b/downstream/mmdetection/configs/detectors/README.md new file mode 100644 index 0000000..baa245f --- /dev/null +++ b/downstream/mmdetection/configs/detectors/README.md @@ -0,0 +1,69 @@ +# DetectoRS + +> [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/abs/2006.02334) + + + +## Abstract + +Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation. + +
    + +
    + +## Introduction + +DetectoRS requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +DetectoRS includes two major components: + +- Recursive Feature Pyramid (RFP). +- Switchable Atrous Convolution (SAC). + +They can be used independently. +Combining them together results in DetectoRS. +The results on COCO 2017 val are shown in the below table. + +| Method | Detector | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------: | :-----------------: | :-----: | :------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| RFP | Cascade + ResNet-50 | 1x | 7.5 | - | 44.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json) | +| SAC | Cascade + ResNet-50 | 1x | 5.6 | - | 45.0 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json) | +| DetectoRS | Cascade + ResNet-50 | 1x | 9.9 | - | 47.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200706_001203.log.json) | +| RFP | HTC + ResNet-50 | 1x | 11.2 | - | 46.6 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json) | +| SAC | HTC + ResNet-50 | 1x | 9.3 | - | 46.4 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json) | +| DetectoRS | HTC + ResNet-50 | 1x | 13.6 | - | 49.1 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json) | +| DetectoRS | HTC + ResNet-101 | 20e | 19.6 | | 50.5 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638.log.json) | + +*Note*: This is a re-implementation based on MMDetection-V2. +The original implementation is based on MMDetection-V1. + +## Citation + +We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf). + +```latex +@article{qiao2020detectors, + title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution}, + author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan}, + journal={arXiv preprint arXiv:2006.02334}, + year={2020} +} +``` diff --git a/downstream/mmdetection/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py b/downstream/mmdetection/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py new file mode 100644 index 0000000..4430d8a --- /dev/null +++ b/downstream/mmdetection/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/downstream/mmdetection/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py b/downstream/mmdetection/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py new file mode 100644 index 0000000..ccd9319 --- /dev/null +++ b/downstream/mmdetection/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py b/downstream/mmdetection/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py new file mode 100644 index 0000000..f760404 --- /dev/null +++ b/downstream/mmdetection/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py @@ -0,0 +1,32 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/downstream/mmdetection/configs/detectors/detectors_htc_r101_20e_coco.py b/downstream/mmdetection/configs/detectors/detectors_htc_r101_20e_coco.py new file mode 100644 index 0000000..93d7d2b --- /dev/null +++ b/downstream/mmdetection/configs/detectors/detectors_htc_r101_20e_coco.py @@ -0,0 +1,28 @@ +_base_ = '../htc/htc_r101_fpn_20e_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet101', + style='pytorch'))) diff --git a/downstream/mmdetection/configs/detectors/detectors_htc_r50_1x_coco.py b/downstream/mmdetection/configs/detectors/detectors_htc_r50_1x_coco.py new file mode 100644 index 0000000..0d2fc4f --- /dev/null +++ b/downstream/mmdetection/configs/detectors/detectors_htc_r50_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/downstream/mmdetection/configs/detectors/htc_r50_rfp_1x_coco.py b/downstream/mmdetection/configs/detectors/htc_r50_rfp_1x_coco.py new file mode 100644 index 0000000..496104e --- /dev/null +++ b/downstream/mmdetection/configs/detectors/htc_r50_rfp_1x_coco.py @@ -0,0 +1,24 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/downstream/mmdetection/configs/detectors/htc_r50_sac_1x_coco.py b/downstream/mmdetection/configs/detectors/htc_r50_sac_1x_coco.py new file mode 100644 index 0000000..72d4db9 --- /dev/null +++ b/downstream/mmdetection/configs/detectors/htc_r50_sac_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/detectors/metafile.yml b/downstream/mmdetection/configs/detectors/metafile.yml new file mode 100644 index 0000000..4bed569 --- /dev/null +++ b/downstream/mmdetection/configs/detectors/metafile.yml @@ -0,0 +1,114 @@ +Collections: + - Name: DetectoRS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - ASPP + - FPN + - RFP + - RPN + - ResNet + - RoIAlign + - SAC + Paper: + URL: https://arxiv.org/abs/2006.02334 + Title: 'DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution' + README: configs/detectors/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/backbones/detectors_resnet.py#L205 + Version: v2.2.0 + +Models: + - Name: cascade_rcnn_r50_rfp_1x_coco + In Collection: DetectoRS + Config: configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py + Metadata: + Training Memory (GB): 7.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth + + - Name: cascade_rcnn_r50_sac_1x_coco + In Collection: DetectoRS + Config: configs/detectors/cascade_rcnn_r50_sac_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth + + - Name: detectors_cascade_rcnn_r50_1x_coco + In Collection: DetectoRS + Config: configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py + Metadata: + Training Memory (GB): 9.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth + + - Name: htc_r50_rfp_1x_coco + In Collection: DetectoRS + Config: configs/detectors/htc_r50_rfp_1x_coco.py + Metadata: + Training Memory (GB): 11.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth + + - Name: htc_r50_sac_1x_coco + In Collection: DetectoRS + Config: configs/detectors/htc_r50_sac_1x_coco.py + Metadata: + Training Memory (GB): 9.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth + + - Name: detectors_htc_r50_1x_coco + In Collection: DetectoRS + Config: configs/detectors/detectors_htc_r50_1x_coco.py + Metadata: + Training Memory (GB): 13.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 49.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth diff --git a/downstream/mmdetection/configs/detr/README.md b/downstream/mmdetection/configs/detr/README.md new file mode 100644 index 0000000..9f2485d --- /dev/null +++ b/downstream/mmdetection/configs/detr/README.md @@ -0,0 +1,37 @@ +# DETR + +> [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) + + + +## Abstract + +We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines. + +
    + +
    + +## Results and Models + +| Backbone | Model | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :---: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | DETR | 150e | 7.9 | | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835.log.json) | + +## Citation + +We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872). + +```latex +@inproceedings{detr, + author = {Nicolas Carion and + Francisco Massa and + Gabriel Synnaeve and + Nicolas Usunier and + Alexander Kirillov and + Sergey Zagoruyko}, + title = {End-to-End Object Detection with Transformers}, + booktitle = {ECCV}, + year = {2020} +} +``` diff --git a/downstream/mmdetection/configs/detr/detr_r50_8x2_150e_coco.py b/downstream/mmdetection/configs/detr/detr_r50_8x2_150e_coco.py new file mode 100644 index 0000000..892447d --- /dev/null +++ b/downstream/mmdetection/configs/detr/detr_r50_8x2_150e_coco.py @@ -0,0 +1,150 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +model = dict( + type='DETR', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + bbox_head=dict( + type='DETRHead', + num_classes=80, + in_channels=2048, + transformer=dict( + type='Transformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')), + )), + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + loss_cls=dict( + type='CrossEntropyLoss', + bg_cls_weight=0.1, + use_sigmoid=False, + loss_weight=1.0, + class_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='ClassificationCost', weight=1.), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different +# from the default setting in mmdet. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +# test_pipeline, NOTE the Pad's size_divisor is different from the default +# setting (size_divisor=32). While there is little effect on the performance +# whether we use the default setting or use size_divisor=1. +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='AdamW', + lr=0.0001, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) +optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[100]) +runner = dict(type='EpochBasedRunner', max_epochs=150) diff --git a/downstream/mmdetection/configs/detr/metafile.yml b/downstream/mmdetection/configs/detr/metafile.yml new file mode 100644 index 0000000..45622cf --- /dev/null +++ b/downstream/mmdetection/configs/detr/metafile.yml @@ -0,0 +1,33 @@ +Collections: + - Name: DETR + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Multi Scale Train + - Gradient Clip + Training Resources: 8x V100 GPUs + Architecture: + - ResNet + - Transformer + Paper: + URL: https://arxiv.org/abs/2005.12872 + Title: 'End-to-End Object Detection with Transformers' + README: configs/detr/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/detectors/detr.py#L7 + Version: v2.7.0 + +Models: + - Name: detr_r50_8x2_150e_coco + In Collection: DETR + Config: configs/detr/detr_r50_8x2_150e_coco.py + Metadata: + Training Memory (GB): 7.9 + Epochs: 150 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth diff --git a/downstream/mmdetection/configs/double_heads/README.md b/downstream/mmdetection/configs/double_heads/README.md new file mode 100644 index 0000000..4a149b5 --- /dev/null +++ b/downstream/mmdetection/configs/double_heads/README.md @@ -0,0 +1,32 @@ +# Double Heads + +> [Rethinking Classification and Localization for Object Detection](https://arxiv.org/abs/1904.06493) + + + +## Abstract + +Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 6.8 | 9.5 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130_220238.log.json) | + +## Citation + +```latex +@article{wu2019rethinking, + title={Rethinking Classification and Localization for Object Detection}, + author={Yue Wu and Yinpeng Chen and Lu Yuan and Zicheng Liu and Lijuan Wang and Hongzhi Li and Yun Fu}, + year={2019}, + eprint={1904.06493}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/downstream/mmdetection/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..9b8118b --- /dev/null +++ b/downstream/mmdetection/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,23 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DoubleHeadRoIHead', + reg_roi_scale_factor=1.3, + bbox_head=dict( + _delete_=True, + type='DoubleConvFCBBoxHead', + num_convs=4, + num_fcs=2, + in_channels=256, + conv_out_channels=1024, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0)))) diff --git a/downstream/mmdetection/configs/double_heads/metafile.yml b/downstream/mmdetection/configs/double_heads/metafile.yml new file mode 100644 index 0000000..6fe9b7a --- /dev/null +++ b/downstream/mmdetection/configs/double_heads/metafile.yml @@ -0,0 +1,41 @@ +Collections: + - Name: Rethinking Classification and Localization for Object Detection + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/pdf/1904.06493 + Title: 'Rethinking Classification and Localization for Object Detection' + README: configs/double_heads/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/roi_heads/double_roi_head.py#L6 + Version: v2.0.0 + +Models: + - Name: dh_faster_rcnn_r50_fpn_1x_coco + In Collection: Rethinking Classification and Localization for Object Detection + Config: configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.8 + inference time (ms/im): + - value: 105.26 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth diff --git a/downstream/mmdetection/configs/dyhead/README.md b/downstream/mmdetection/configs/dyhead/README.md new file mode 100644 index 0000000..8e6aed3 --- /dev/null +++ b/downstream/mmdetection/configs/dyhead/README.md @@ -0,0 +1,52 @@ +# DyHead + +> [Dynamic Head: Unifying Object Detection Heads with Attentions](https://arxiv.org/abs/2106.08322) + + + +## Abstract + +The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the performance in various object detection heads but failed to present a unified view. In this paper, we present a novel dynamic head framework to unify object detection heads with attentions. By coherently combining multiple self-attention mechanisms between feature levels for scale-awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness, the proposed approach significantly improves the representation ability of object detection heads without any computational overhead. Further experiments demonstrate that the effectiveness and efficiency of the proposed dynamic head on the COCO benchmark. With a standard ResNeXt-101-DCN backbone, we largely improve the performance over popular object detectors and achieve a new state-of-the-art at 54.0 AP. Furthermore, with latest transformer backbone and extra data, we can push current best COCO result to a new record at 60.6 AP. + +
    + +
    + +## Results and Models + +| Method | Backbone | Style | Setting | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :----: | :------: | :-----: | :----------: | :-----: | :------: | :------------: | :----: | :----------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ATSS | R-50 | caffe | reproduction | 1x | 5.4 | 13.2 | 42.5 | [config](./atss_r50_caffe_fpn_dyhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939-162888e6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939.log.json) | +| ATSS | R-50 | pytorch | simple | 1x | 4.9 | 13.7 | 43.3 | [config](./atss_r50_fpn_dyhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314.log.json) | + +- We trained the above models with 4 GPUs and 4 `samples_per_gpu`. +- The `reproduction` setting aims to reproduce the official implementation based on Detectron2. +- The `simple` setting serves as a minimum example to use DyHead in MMDetection. Specifically, + - it adds `DyHead` to `neck` after `FPN` + - it sets `stacked_convs=0` to `bbox_head` +- The `simple` setting achieves higher AP than the original implementation. + We have not conduct ablation study between the two settings. + `dict(type='Pad', size_divisor=128)` may further improve AP by prefer spatial alignment across pyramid levels, although large padding reduces efficiency. + +We also trained the model with Swin-L backbone. Results are as below. + +| Method | Backbone | Style | Setting | Lr schd | mstrain | box AP | Config | Download | +| :----: | :------: | :---: | :----------: | :-----: | :------: | :----: | :----------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| ATSS | Swin-L | caffe | reproduction | 2x | 480~1200 | 56.2 | [config](./atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco_20220509_100315-bc5b6516.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco_20220509_100315.log.json) | + +## Relation to Other Methods + +- DyHead can be regarded as an improved [SEPC](https://arxiv.org/abs/2005.03101) with [DyReLU modules](https://arxiv.org/abs/2003.10027) and simplified [SE blocks](https://arxiv.org/abs/1709.01507). +- Xiyang Dai et al., the author team of DyHead, adopt it for [Dynamic DETR](https://openaccess.thecvf.com/content/ICCV2021/html/Dai_Dynamic_DETR_End-to-End_Object_Detection_With_Dynamic_Attention_ICCV_2021_paper.html). + The description of Dynamic Encoder in Sec. 3.2 will help you understand DyHead. + +## Citation + +```latex +@inproceedings{DyHead_CVPR2021, + author = {Dai, Xiyang and Chen, Yinpeng and Xiao, Bin and Chen, Dongdong and Liu, Mengchen and Yuan, Lu and Zhang, Lei}, + title = {Dynamic Head: Unifying Object Detection Heads With Attentions}, + booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2021} +} +``` diff --git a/downstream/mmdetection/configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py b/downstream/mmdetection/configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py new file mode 100644 index 0000000..223b653 --- /dev/null +++ b/downstream/mmdetection/configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py @@ -0,0 +1,112 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='ATSS', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + dict( + type='DyHead', + in_channels=256, + out_channels=256, + num_blocks=6, + # disable zero_init_offset to follow official implementation + zero_init_offset=False) + ], + bbox_head=dict( + type='ATSSHead', + num_classes=80, + in_channels=256, + pred_kernel_size=1, # follow DyHead official implementation + stacked_convs=0, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128], + center_offset=0.5), # follow DyHead official implementation + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) + +# use caffe img_norm, size_divisor=128, pillow resize +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=(1333, 800), + keep_ratio=True, + backend='pillow'), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=128), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True, backend='pillow'), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=128), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py b/downstream/mmdetection/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py new file mode 100644 index 0000000..8c5109d --- /dev/null +++ b/downstream/mmdetection/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='ATSS', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + dict(type='DyHead', in_channels=256, out_channels=256, num_blocks=6) + ], + bbox_head=dict( + type='ATSSHead', + num_classes=80, + in_channels=256, + stacked_convs=0, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py b/downstream/mmdetection/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py new file mode 100644 index 0000000..dc9c328 --- /dev/null +++ b/downstream/mmdetection/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py @@ -0,0 +1,164 @@ +_base_ = '../_base_/default_runtime.py' + +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa +model = dict( + type='ATSS', + backbone=dict( + type='SwinTransformer', + pretrain_img_size=384, + embed_dims=192, + depths=[2, 2, 18, 2], + num_heads=[6, 12, 24, 48], + window_size=12, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.2, + patch_norm=True, + out_indices=(1, 2, 3), + # Please only add indices that would be used + # in FPN, otherwise some parameter will not be used + with_cp=False, + convert_weights=True, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + neck=[ + dict( + type='FPN', + in_channels=[384, 768, 1536], + out_channels=256, + start_level=0, + add_extra_convs='on_output', + num_outs=5), + dict( + type='DyHead', + in_channels=256, + out_channels=256, + num_blocks=6, + # disable zero_init_offset to follow official implementation + zero_init_offset=False) + ], + bbox_head=dict( + type='ATSSHead', + num_classes=80, + in_channels=256, + pred_kernel_size=1, # follow DyHead official implementation + stacked_convs=0, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128], + center_offset=0.5), # follow DyHead official implementation + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(2000, 480), (2000, 1200)], + multiscale_mode='range', + keep_ratio=True, + backend='pillow'), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=128), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2000, 1200), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True, backend='pillow'), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=128), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=2, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') + +# optimizer +optimizer_config = dict(grad_clip=None) +optimizer = dict( + type='AdamW', + lr=0.00005, + betas=(0.9, 0.999), + weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'absolute_pos_embed': dict(decay_mult=0.), + 'relative_position_bias_table': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.) + })) + +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/dyhead/metafile.yml b/downstream/mmdetection/configs/dyhead/metafile.yml new file mode 100644 index 0000000..3fb7370 --- /dev/null +++ b/downstream/mmdetection/configs/dyhead/metafile.yml @@ -0,0 +1,76 @@ +Collections: + - Name: DyHead + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 4x T4 GPUs + Architecture: + - ATSS + - DyHead + - FPN + - ResNet + - Deformable Convolution + - Pyramid Convolution + Paper: + URL: https://arxiv.org/abs/2106.08322 + Title: 'Dynamic Head: Unifying Object Detection Heads with Attentions' + README: configs/dyhead/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/mmdet/models/necks/dyhead.py#L130 + Version: v2.22.0 + +Models: + - Name: atss_r50_caffe_fpn_dyhead_1x_coco + In Collection: DyHead + Config: configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py + Metadata: + Training Memory (GB): 5.4 + inference time (ms/im): + - value: 75.7 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939-162888e6.pth + + - Name: atss_r50_fpn_dyhead_1x_coco + In Collection: DyHead + Config: configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py + Metadata: + Training Memory (GB): 4.9 + inference time (ms/im): + - value: 73.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth + + - Name: atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco + In Collection: DyHead + Config: configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py + Metadata: + Training Memory (GB): 58.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 56.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco_20220509_100315-bc5b6516.pth diff --git a/downstream/mmdetection/configs/dynamic_rcnn/README.md b/downstream/mmdetection/configs/dynamic_rcnn/README.md new file mode 100644 index 0000000..0045df7 --- /dev/null +++ b/downstream/mmdetection/configs/dynamic_rcnn/README.md @@ -0,0 +1,30 @@ +# Dynamic R-CNN + +> [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://arxiv.org/abs/2004.06002) + + + +## Abstract + +Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from crystal. In this work, we first point out the inconsistency problem between the fixed network settings and the dynamic training procedure, which greatly affects the performance. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors. Consequently, we propose Dynamic R-CNN to adjust the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of SmoothL1 Loss) automatically based on the statistics of proposals during training. This dynamic design makes better use of the training samples and pushes the detector to fit more high quality samples. Specifically, our method improves upon ResNet-50-FPN baseline with 1.9% AP and 5.5% AP90 on the MS COCO dataset with no extra overhead. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :-----: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | 1x | 3.8 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) | + +## Citation + +```latex +@article{DynamicRCNN, + author = {Hongkai Zhang and Hong Chang and Bingpeng Ma and Naiyan Wang and Xilin Chen}, + title = {Dynamic {R-CNN}: Towards High Quality Object Detection via Dynamic Training}, + journal = {arXiv preprint arXiv:2004.06002}, + year = {2020} +} +``` diff --git a/downstream/mmdetection/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..f2deb99 --- /dev/null +++ b/downstream/mmdetection/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DynamicRoIHead', + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict(nms=dict(iou_threshold=0.85)), + rcnn=dict( + dynamic_rcnn=dict( + iou_topk=75, + beta_topk=10, + update_iter_interval=100, + initial_iou=0.4, + initial_beta=1.0))), + test_cfg=dict(rpn=dict(nms=dict(iou_threshold=0.85)))) diff --git a/downstream/mmdetection/configs/dynamic_rcnn/metafile.yml b/downstream/mmdetection/configs/dynamic_rcnn/metafile.yml new file mode 100644 index 0000000..fec43db --- /dev/null +++ b/downstream/mmdetection/configs/dynamic_rcnn/metafile.yml @@ -0,0 +1,35 @@ +Collections: + - Name: Dynamic R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Dynamic R-CNN + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/pdf/2004.06002 + Title: 'Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training' + README: configs/dynamic_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/roi_heads/dynamic_roi_head.py#L11 + Version: v2.2.0 + +Models: + - Name: dynamic_rcnn_r50_fpn_1x_coco + In Collection: Dynamic R-CNN + Config: configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth diff --git a/downstream/mmdetection/configs/efficientnet/README.md b/downstream/mmdetection/configs/efficientnet/README.md new file mode 100644 index 0000000..99b0572 --- /dev/null +++ b/downstream/mmdetection/configs/efficientnet/README.md @@ -0,0 +1,30 @@ +# EfficientNet + +> [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946v5) + + + +## Introduction + +Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. + +To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters. + +## Results and Models + +### RetinaNet + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Efficientnet-b3 | pytorch | 1x | - | - | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806-615a0dda.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806.log.json) | + +## Citation + +```latex +@article{tan2019efficientnet, + title={Efficientnet: Rethinking model scaling for convolutional neural networks}, + author={Tan, Mingxing and Le, Quoc V}, + journal={arXiv preprint arXiv:1905.11946}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/efficientnet/metafile.yml b/downstream/mmdetection/configs/efficientnet/metafile.yml new file mode 100644 index 0000000..de40b95 --- /dev/null +++ b/downstream/mmdetection/configs/efficientnet/metafile.yml @@ -0,0 +1,19 @@ +Models: + - Name: retinanet_effb3_fpn_crop896_8x4_1x_coco + In Collection: RetinaNet + Config: configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806-615a0dda.pth + Paper: + URL: https://arxiv.org/abs/1905.11946v5 + Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks' + README: configs/efficientnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.23.0/mmdet/models/backbones/efficientnet.py#L159 + Version: v2.23.0 diff --git a/downstream/mmdetection/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py b/downstream/mmdetection/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py new file mode 100644 index 0000000..c90bc16 --- /dev/null +++ b/downstream/mmdetection/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py @@ -0,0 +1,94 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] + +cudnn_benchmark = True +norm_cfg = dict(type='BN', requires_grad=True) +checkpoint = 'https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth' # noqa +model = dict( + backbone=dict( + _delete_=True, + type='EfficientNet', + arch='b3', + drop_path_rate=0.2, + out_indices=(3, 4, 5), + frozen_stages=0, + norm_cfg=dict( + type='SyncBN', requires_grad=True, eps=1e-3, momentum=0.01), + norm_eval=False, + init_cfg=dict( + type='Pretrained', prefix='backbone', checkpoint=checkpoint)), + neck=dict( + in_channels=[48, 136, 384], + start_level=0, + out_channels=256, + relu_before_extra_convs=True, + no_norm_on_lateral=True, + norm_cfg=norm_cfg), + bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), + # training and testing settings + train_cfg=dict(assigner=dict(neg_iou_thr=0.5))) + +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +img_size = (896, 896) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=img_size, + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=img_size), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=img_size), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=img_size, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=img_size), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=4, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer_config = dict(grad_clip=None) +optimizer = dict( + type='SGD', + lr=0.04, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[8, 11]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=12) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (4 samples per GPU) +auto_scale_lr = dict(base_batch_size=32) diff --git a/downstream/mmdetection/configs/empirical_attention/README.md b/downstream/mmdetection/configs/empirical_attention/README.md new file mode 100644 index 0000000..fc2620a --- /dev/null +++ b/downstream/mmdetection/configs/empirical_attention/README.md @@ -0,0 +1,33 @@ +# Empirical Attention + +> [An Empirical Study of Spatial Attention Mechanisms in Deep Networks](https://arxiv.org/abs/1904.05873) + + + +## Abstract + +Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance. Toward a better general understanding of attention mechanisms, we present an empirical study that ablates various spatial attention elements within a generalized attention formulation, encompassing the dominant Transformer attention as well as the prevalent deformable convolution and dynamic convolution modules. Conducted on a variety of applications, the study yields significant findings about spatial attention in deep networks, some of which run counter to conventional understanding. For example, we find that the query and key content comparison in Transformer attention is negligible for self-attention, but vital for encoder-decoder attention. A proper combination of deformable convolution with key content only saliency achieves the best accuracy-efficiency tradeoff in self-attention. Our results suggest that there exists much room for improvement in the design of attention mechanisms. + +
    + +
    + +## Results and Models + +| Backbone | Attention Component | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :-----------------: | :-: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | 1111 | N | 1x | 8.0 | 13.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json) | +| R-50 | 0010 | N | 1x | 4.2 | 18.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json) | +| R-50 | 1111 | Y | 1x | 8.0 | 12.7 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) | +| R-50 | 0010 | Y | 1x | 4.2 | 17.1 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) | + +## Citation + +```latex +@article{zhu2019empirical, + title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks}, + author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng}, + journal={arXiv preprint arXiv:1904.05873}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py new file mode 100644 index 0000000..a544e3a --- /dev/null +++ b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ])) diff --git a/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py new file mode 100644 index 0000000..bbefd27 --- /dev/null +++ b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ], + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py new file mode 100644 index 0000000..13a4645 --- /dev/null +++ b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='1111', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ])) diff --git a/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py new file mode 100644 index 0000000..b1f26c0 --- /dev/null +++ b/downstream/mmdetection/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='1111', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ], + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/downstream/mmdetection/configs/empirical_attention/metafile.yml b/downstream/mmdetection/configs/empirical_attention/metafile.yml new file mode 100644 index 0000000..923bcb2 --- /dev/null +++ b/downstream/mmdetection/configs/empirical_attention/metafile.yml @@ -0,0 +1,103 @@ +Collections: + - Name: Empirical Attention + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Deformable Convolution + - FPN + - RPN + - ResNet + - RoIAlign + - Spatial Attention + Paper: + URL: https://arxiv.org/pdf/1904.05873 + Title: 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks' + README: configs/empirical_attention/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/generalized_attention.py#L10 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_attention_1111_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (ms/im): + - value: 72.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth + + - Name: faster_rcnn_r50_fpn_attention_0010_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 54.35 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth + + - Name: faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (ms/im): + - value: 78.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth + + - Name: faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 58.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth diff --git a/downstream/mmdetection/configs/fast_rcnn/README.md b/downstream/mmdetection/configs/fast_rcnn/README.md new file mode 100644 index 0000000..767f76c --- /dev/null +++ b/downstream/mmdetection/configs/fast_rcnn/README.md @@ -0,0 +1,73 @@ +# Fast R-CNN + +> [Fast R-CNN](https://arxiv.org/abs/1504.08083) + + + +## Abstract + +This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate. + +
    + +
    + +## Introduction + +Before training the Fast R-CNN, users should first train an [RPN](../rpn/README.md), and use the RPN to extract the region proposals. + +- Firstly, extract the region proposals of the val set by this command as below: + +```bash +./tools/dist_test.sh \ + configs/rpn_r50_fpn_1x_coco.py \ + checkpoints/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth \ + 8 \ + --out proposals/rpn_r50_fpn_1x_val2017.pkl +``` + +- Then, change the `ann_file` and `img_prefix` of `data.test` in the RPN config to train set as below: + +```python +data = dict( + test=dict( + ann_file='data/coco/annotations/instances_train2017.json', + img_prefix='data/coco/train2017/')) +``` + +- Extract the region proposals of the train set by this command as below: + +```bash +./tools/dist_test.sh \ + configs/rpn_r50_fpn_1x_coco.py \ + checkpoints/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth \ + 8 \ + --out proposals/rpn_r50_fpn_1x_train2017.pkl +``` + +- Modify the path of `proposal_file` in Fast R-CNN config as below: + +```python +data = dict( + train=dict( + proposal_file='proposals/rpn_r50_fpn_1x_train2017.pkl'), + val=dict( + proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl'), + test=dict( + proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl')) +``` + +Finally, users can start training the Fast R-CNN. + +## Results and Models + +## Citation + +```latex +@inproceedings{girshick2015fast, + title={Fast r-cnn}, + author={Girshick, Ross}, + booktitle={Proceedings of the IEEE international conference on computer vision}, + year={2015} +} +``` diff --git a/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..3ab8e98 --- /dev/null +++ b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..83852b2 --- /dev/null +++ b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 0000000..c220885 --- /dev/null +++ b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,6 @@ +_base_ = './fast_rcnn_r50_fpn_2x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..f1b29ef --- /dev/null +++ b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,48 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + norm_cfg=dict(type='BN', requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=2000), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..d2f080e --- /dev/null +++ b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/models/fast_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=2000), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 0000000..228e856 --- /dev/null +++ b/downstream/mmdetection/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/faster_rcnn/README.md b/downstream/mmdetection/configs/faster_rcnn/README.md new file mode 100644 index 0000000..865d375 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/README.md @@ -0,0 +1,88 @@ +# Faster R-CNN + +> [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497) + + + +## Abstract + +State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-C4 | caffe | 1x | - | - | 35.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152-3f885b85.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152.log.json) | +| R-50-DC5 | caffe | 1x | - | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909.log.json) | +| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | +| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN (FP16) | pytorch | 1x | 3.4 | 28.8 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | +| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | + +## Different regression loss + +We trained with R-50-FPN pytorch style backbone for 1x schedule. + +| Backbone | Loss type | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :------------: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | IoULoss | | | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | +| R-50-FPN | GIoULoss | | | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | +| R-50-FPN | BoundedIoULoss | | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :----------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [R-50-C4](./faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py) | caffe | 1x | - | | 35.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527.log.json) | +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | caffe | 1x | - | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json) | +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | caffe | 3x | - | | 38.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | caffe | 2x | 3.7 | | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 3.7 | | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054.log.json) | +| [R-50-FPN](./faster_rcnn_r50_fpn_mstrain_3x_coco.py) | pytorch | 3x | 3.9 | | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [R-101-FPN](./faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 5.6 | | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742.log.json) | +| [R-101-FPN](./faster_rcnn_r101_fpn_mstrain_3x_coco.py) | pytorch | 3x | 5.8 | | 41.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [X-101-32x4d-FPN](./faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 7.0 | | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151.log.json) | +| [X-101-32x8d-FPN](./faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 10.1 | | 42.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954.log.json) | +| [X-101-64x4d-FPN](./faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 10.0 | | 43.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528.log.json) | + +We further finetune some pre-trained models on the COCO subsets, which only contain only a few of the 80 categories. + +| Backbone | Style | Class name | Pre-traind model | Mem (GB) | box AP | Config | Download | +| ----------------------------------------------------------------------------- | ----- | ------------------ | ------------------------------------------------------------------- | -------- | ------ | --------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py) | caffe | person | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7 | 55.8 | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929-d022e227.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | caffe | person-bicycle-car | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7 | 44.1 | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117-6eda6d92.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117.log.json) | + +## Torchvision New Receipe (TNR) + +Torchvision released its high-precision ResNet models. The training details can be found on the [Pytorch website](https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/). Here, we have done grid searches on learning rate and weight decay and found the optimal hyper-parameter on the detection task. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [R-50-TNR](./faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py) | pytorch | 1x | - | | 40.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147-efedfda4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147.log.json) | + +## Citation + +```latex +@article{Ren_2017, + title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks}, + journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, + publisher={Institute of Electrical and Electronics Engineers (IEEE)}, + author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian}, + year={2017}, + month={Jun}, +} +``` diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..c6f078c --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..6a13fe9 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,49 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict( + backbone=dict( + depth=101, + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..1de53a6 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 0000000..0d41599 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..0b498bb --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,7 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py new file mode 100644 index 0000000..b071962 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py new file mode 100644 index 0000000..f4d83e6 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py @@ -0,0 +1,38 @@ +_base_ = './faster_rcnn_r50_caffe_c4_1x_coco.py' +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py new file mode 100644 index 0000000..ee2010c --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_dc5.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py new file mode 100644 index 0000000..14eaef2 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py @@ -0,0 +1,42 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_dc5.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py new file mode 100644 index 0000000..403747f --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..56c01bd --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,41 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py new file mode 100644 index 0000000..b5aea6a --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py @@ -0,0 +1,15 @@ +_base_ = 'faster_rcnn_r50_caffe_fpn_1x_coco.py' + +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[60000, 80000]) + +# Runner type +runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000) + +checkpoint_config = dict(interval=10000) +evaluation = dict(interval=10000, metric='bbox') diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py new file mode 100644 index 0000000..4f1f376 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py @@ -0,0 +1,9 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +model = dict(roi_head=dict(bbox_head=dict(num_classes=3))) +classes = ('person', 'bicycle', 'car') +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) + +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py new file mode 100644 index 0000000..b5dfb4f --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py @@ -0,0 +1,9 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +model = dict(roi_head=dict(bbox_head=dict(num_classes=1))) +classes = ('person', ) +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) + +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 0000000..f807a19 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,46 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..df58973 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..9eeaace --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,47 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py new file mode 100644 index 0000000..74dca24 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py @@ -0,0 +1,15 @@ +_base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' + +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[60000, 80000]) + +# Runner type +runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000) + +checkpoint_config = dict(interval=10000) +evaluation = dict(interval=10000, metric='bbox') diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..009bd93 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 0000000..e77a7fa --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py new file mode 100644 index 0000000..648081f --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0)))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py new file mode 100644 index 0000000..886d566 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='CIoULoss', loss_weight=12.0)))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py new file mode 100644 index 0000000..acd4040 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py new file mode 100644 index 0000000..5556c49 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='GIoULoss', loss_weight=10.0)))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py new file mode 100644 index 0000000..ddf663e --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='IoULoss', loss_weight=10.0)))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..faf8f92 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py new file mode 100644 index 0000000..f897e7c --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler')))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py new file mode 100644 index 0000000..759ae3a --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + test_cfg=dict( + rcnn=dict( + score_thr=0.05, + nms=dict(type='soft_nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py new file mode 100644 index 0000000..ecbfb92 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth' +model = dict( + backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=checkpoint))) + +# `lr` and `weight_decay` have been searched to be optimal. +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.0001, + weight_decay=0.1, + paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..3808c9f --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 0000000..e93f5d8 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..f55985d --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..a5d5aeb --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py @@ -0,0 +1,62 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) + +# ResNeXt-101-32x8d model trained with Caffe2 at FB, +# so the mean and std need to be changed. +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..8bf2b65 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..7ea9b2d --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..80397f4 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/faster_rcnn/metafile.yml b/downstream/mmdetection/configs/faster_rcnn/metafile.yml new file mode 100644 index 0000000..91d6751 --- /dev/null +++ b/downstream/mmdetection/configs/faster_rcnn/metafile.yml @@ -0,0 +1,451 @@ +Collections: + - Name: Faster R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + - RoIPool + Paper: + URL: https://arxiv.org/abs/1506.01497 + Title: "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" + README: configs/faster_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/faster_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_caffe_c4_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152-3f885b85.pth + + - Name: faster_rcnn_r50_caffe_c4_mstrain_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth + + - Name: faster_rcnn_r50_caffe_dc5_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth + + - Name: faster_rcnn_r50_caffe_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth + + - Name: faster_rcnn_r50_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 46.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth + + - Name: faster_rcnn_r50_fpn_fp16_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 3.4 + Training Techniques: + - SGD with Momentum + - Weight Decay + - Mixed Precision Training + inference time (ms/im): + - value: 34.72 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth + + - Name: faster_rcnn_r50_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 46.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth + + - Name: faster_rcnn_r101_caffe_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth + + - Name: faster_rcnn_r101_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 64.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth + + - Name: faster_rcnn_r101_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 64.1 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth + + - Name: faster_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (ms/im): + - value: 72.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth + + - Name: faster_rcnn_x101_32x4d_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (ms/im): + - value: 72.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth + + - Name: faster_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 106.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth + + - Name: faster_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 106.38 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth + + - Name: faster_rcnn_r50_fpn_iou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth + + - Name: faster_rcnn_r50_fpn_giou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth + + - Name: faster_rcnn_r50_fpn_bounded_iou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth + + - Name: faster_rcnn_r50_caffe_dc5_mstrain_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth + + - Name: faster_rcnn_r50_caffe_dc5_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth + + - Name: faster_rcnn_r50_caffe_fpn_mstrain_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth + + - Name: faster_rcnn_r50_caffe_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.7 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth + + - Name: faster_rcnn_r50_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth + + - Name: faster_rcnn_r101_caffe_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth + + - Name: faster_rcnn_r101_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth + + - Name: faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 7.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth + + - Name: faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 10.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth + + - Name: faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 10.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth + + - Name: faster_rcnn_r50_fpn_tnr-pretrain_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 46.73 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147-efedfda4.pth diff --git a/downstream/mmdetection/configs/fcos/README.md b/downstream/mmdetection/configs/fcos/README.md new file mode 100644 index 0000000..76be365 --- /dev/null +++ b/downstream/mmdetection/configs/fcos/README.md @@ -0,0 +1,45 @@ +# FCOS + +> [FCOS: Fully Convolutional One-Stage Object Detection](https://arxiv.org/abs/1904.01355) + + + +## Abstract + +We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks. + +
    + +
    + +## Results and Models + +| Backbone | Style | GN | MS train | Tricks | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :---: | :-: | :------: | :----: | :-: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | caffe | Y | N | N | N | 1x | 3.6 | 22.7 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/20201227_180009.log.json) | +| R-50 | caffe | Y | N | Y | N | 1x | 3.7 | - | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/20210105_135818.log.json) | +| R-50 | caffe | Y | N | Y | Y | 1x | 3.8 | - | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/20210105_224556.log.json) | +| R-101 | caffe | Y | N | N | N | 1x | 5.5 | 17.3 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/20210103_155046.log.json) | + +| Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :-----: | :-: | :------: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | caffe | Y | Y | 2x | 2.6 | 22.9 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20201227_161900.log.json) | +| R-101 | caffe | Y | Y | 2x | 5.5 | 17.3 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20210103_155046.log.json) | +| X-101 | pytorch | Y | Y | 2x | 10.0 | 9.7 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/20210114_133041.log.json) | + +**Notes:** + +- The X-101 backbone is X-101-64x4d. +- Tricks means setting `norm_on_bbox`, `centerness_on_reg`, `center_sampling` as `True`. +- DCN means using `DCNv2` in both backbone and head. + +## Citation + +```latex +@article{tian2019fcos, + title={FCOS: Fully Convolutional One-Stage Object Detection}, + author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, + journal={arXiv preprint arXiv:1904.01355}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py b/downstream/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..2699bdb --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,54 @@ +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +model = dict( + backbone=dict( + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + bbox_head=dict( + norm_on_bbox=True, + centerness_on_reg=True, + dcn_on_last_conv=False, + center_sampling=True, + conv_bias=True, + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) + +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer_config = dict(_delete_=True, grad_clip=None) + +lr_config = dict(warmup='linear') diff --git a/downstream/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py b/downstream/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py new file mode 100644 index 0000000..cf93c91 --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py @@ -0,0 +1,56 @@ +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + bbox_head=dict( + norm_on_bbox=True, + centerness_on_reg=True, + dcn_on_last_conv=True, + center_sampling=True, + conv_bias=True, + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) + +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer_config = dict(_delete_=True, grad_clip=None) + +lr_config = dict(warmup='linear') diff --git a/downstream/mmdetection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py b/downstream/mmdetection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..9f502e7 --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5)) diff --git a/downstream/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py b/downstream/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..45bea48 --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/downstream/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 0000000..f4d36f1 --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,47 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet101_caffe'))) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py b/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..955787b --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,106 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='FCOS', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet50_caffe')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', # use P5 + num_outs=5, + relu_before_extra_convs=True), + bbox_head=dict( + type='FCOSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='constant', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..2816b16 --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,4 @@ +# TODO: Remove this config after benchmarking all related configs +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +data = dict(samples_per_gpu=4, workers_per_gpu=4) diff --git a/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 0000000..497d03f --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py b/downstream/mmdetection/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 0000000..e70e465 --- /dev/null +++ b/downstream/mmdetection/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,60 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/fcos/metafile.yml b/downstream/mmdetection/configs/fcos/metafile.yml new file mode 100644 index 0000000..ae922eb --- /dev/null +++ b/downstream/mmdetection/configs/fcos/metafile.yml @@ -0,0 +1,146 @@ +Collections: + - Name: FCOS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - Group Normalization + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.01355 + Title: 'FCOS: Fully Convolutional One-Stage Object Detection' + README: configs/fcos/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fcos.py#L6 + Version: v2.0.0 + +Models: + - Name: fcos_r50_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 3.6 + inference time (ms/im): + - value: 44.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth + + - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth + + - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth + + - Name: fcos_r101_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (ms/im): + - value: 57.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth + + - Name: fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 2.6 + inference time (ms/im): + - value: 43.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth + + - Name: fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (ms/im): + - value: 57.8 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth + + - Name: fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 10.0 + inference time (ms/im): + - value: 103.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth diff --git a/downstream/mmdetection/configs/foveabox/README.md b/downstream/mmdetection/configs/foveabox/README.md new file mode 100644 index 0000000..7fcd094 --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/README.md @@ -0,0 +1,53 @@ +# FoveaBox + +> [FoveaBox: Beyond Anchor-based Object Detector](https://arxiv.org/abs/1904.03797) + + + +## Abstract + +We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. + +
    + +
    + +## Introduction + +FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797): +Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. + +## Results and Models + +### Results on R50/101-FPN + +| Backbone | Style | align | ms-train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :-----: | :---: | :------: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | N | N | 1x | 5.6 | 24.1 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json) | +| R-50 | pytorch | N | N | 2x | 5.6 | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json) | +| R-50 | pytorch | Y | N | 2x | 8.1 | 19.4 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json) | +| R-50 | pytorch | Y | Y | 2x | 8.1 | 18.3 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json) | +| R-101 | pytorch | N | N | 1x | 9.2 | 17.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json) | +| R-101 | pytorch | N | N | 2x | 11.7 | - | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json) | +| R-101 | pytorch | Y | N | 2x | 11.7 | 14.7 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json) | +| R-101 | pytorch | Y | Y | 2x | 11.7 | 14.7 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) | + +\[1\] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \ +\[2\] *Align means utilizing deformable convolution to align the cls branch.* \ +\[3\] *All results are obtained with a single model and without any test time data augmentation.*\ +\[4\] *We use 4 GPUs for training.* + +Any pull requests or issues are welcome. + +## Citation + +Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows. + +```latex +@article{kong2019foveabox, + title={FoveaBox: Beyond Anchor-based Object Detector}, + author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Shi, Jianbo}, + journal={arXiv preprint arXiv:1904.03797}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..c5d1784 --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py @@ -0,0 +1,12 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..cc5affe --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,29 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..e7265bc --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..8fc39be --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,25 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py new file mode 100644 index 0000000..9201af1 --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py new file mode 100644 index 0000000..1ef5243 --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py @@ -0,0 +1,6 @@ +_base_ = './fovea_r50_fpn_4x4_2x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py new file mode 100644 index 0000000..7e986eb --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='FOVEA', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + num_outs=5, + add_extra_convs='on_input'), + bbox_head=dict( + type='FoveaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + base_edge_list=[16, 32, 64, 128, 256], + scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), + sigma=0.4, + with_deform=False, + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=1.50, + alpha=0.4, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + # training and testing settings + train_cfg=dict(), + test_cfg=dict( + nms_pre=1000, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) +data = dict(samples_per_gpu=4, workers_per_gpu=4) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py b/downstream/mmdetection/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py new file mode 100644 index 0000000..68ce4d2 --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/foveabox/metafile.yml b/downstream/mmdetection/configs/foveabox/metafile.yml new file mode 100644 index 0000000..fe9a283 --- /dev/null +++ b/downstream/mmdetection/configs/foveabox/metafile.yml @@ -0,0 +1,172 @@ +Collections: + - Name: FoveaBox + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 4x V100 GPUs + Architecture: + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.03797 + Title: 'FoveaBox: Beyond Anchor-based Object Detector' + README: configs/foveabox/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fovea.py#L6 + Version: v2.0.0 + +Models: + - Name: fovea_r50_fpn_4x4_1x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (ms/im): + - value: 41.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth + + - Name: fovea_r50_fpn_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (ms/im): + - value: 41.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth + + - Name: fovea_align_r50_fpn_gn-head_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (ms/im): + - value: 51.55 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth + + - Name: fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (ms/im): + - value: 54.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth + + - Name: fovea_r101_fpn_4x4_1x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 57.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth + + - Name: fovea_r101_fpn_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth + + - Name: fovea_align_r101_fpn_gn-head_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + inference time (ms/im): + - value: 68.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth + + - Name: fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + inference time (ms/im): + - value: 68.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth diff --git a/downstream/mmdetection/configs/fpg/README.md b/downstream/mmdetection/configs/fpg/README.md new file mode 100644 index 0000000..c9bb1fe --- /dev/null +++ b/downstream/mmdetection/configs/fpg/README.md @@ -0,0 +1,43 @@ +# FPG + +> [Feature Pyramid Grids](https://arxiv.org/abs/2004.03580) + + + +## Abstract + +Feature pyramid networks have been widely adopted in the object detection literature to improve feature representations for better handling of variations in scale. In this paper, we present Feature Pyramid Grids (FPG), a deep multi-pathway feature pyramid, that represents the feature scale-space as a regular grid of parallel bottom-up pathways which are fused by multi-directional lateral connections. FPG can improve single-pathway feature pyramid networks by significantly increasing its performance at similar computation cost, highlighting importance of deep pyramid representations. In addition to its general and uniform structure, over complicated structures that have been found with neural architecture search, it also compares favorably against such approaches without relying on search. We hope that FPG with its uniform and effective nature can serve as a strong component for future work in object recognition. + +
    + +
    + +## Results and Models + +We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. +All backbones are Resnet-50 in pytorch style. + +| Method | Neck | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :----------: | :--------: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Faster R-CNN | FPG | 50e | 20.0 | - | 42.3 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856-74109f42.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856.log.json) | +| Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857-9376aa9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857.log.json) | +| Faster R-CNN | FPN | 50e | 20.0 | - | 38.9 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpn_crop640_50e_coco/faster_rcnn_r50_fpn_crop640_50e_coco_20220311_011857-be7c9f42.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpn_crop640_50e_coco/faster_rcnn_r50_fpn_crop640_50e_coco_20220311_011857.log.json) | +| Mask R-CNN | FPG | 50e | 23.2 | - | 43.0 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857.log.json) | +| Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859-043c9b4e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859.log.json) | +| Mask R-CNN | FPN | 50e | 23.2 | - | 49.6 | 35.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpn_crop640_50e_coco/mask_rcnn_r50_fpn_crop640_50e_coco_20220311_011855-a756664a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpn_crop640_50e_coco/mask_rcnn_r50_fpn_crop640_50e_coco_20220311_011855.log.json) | +| RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809-b0bcf5f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809.log.json) | +| RetinaNet | FPG-chn128 | 50e | 19.9 | - | 39.9 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829-ee99a686.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829.log.json) | + +**Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in +Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision. + +## Citation + +```latex +@article{chen2020feature, + title={Feature pyramid grids}, + author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph}, + journal={arXiv preprint arXiv:2004.03580}, + year={2020} +} +``` diff --git a/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 0000000..4535034 --- /dev/null +++ b/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,9 @@ +_base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict(out_channels=128, inter_channels=128), + rpn_head=dict(in_channels=128), + roi_head=dict( + bbox_roi_extractor=dict(out_channels=128), + bbox_head=dict(in_channels=128))) diff --git a/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py new file mode 100644 index 0000000..3ab2a2c --- /dev/null +++ b/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,48 @@ +_base_ = 'faster_rcnn_r50_fpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) diff --git a/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py new file mode 100644 index 0000000..e4ec940 --- /dev/null +++ b/downstream/mmdetection/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,73 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + backbone=dict(norm_cfg=norm_cfg, norm_eval=False), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict(bbox_head=dict(norm_cfg=norm_cfg))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(max_epochs=50) +evaluation = dict(interval=2) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 0000000..baa4a5a --- /dev/null +++ b/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,10 @@ +_base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py' + +model = dict( + neck=dict(out_channels=128, inter_channels=128), + rpn_head=dict(in_channels=128), + roi_head=dict( + bbox_roi_extractor=dict(out_channels=128), + bbox_head=dict(in_channels=128), + mask_roi_extractor=dict(out_channels=128), + mask_head=dict(in_channels=128))) diff --git a/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py new file mode 100644 index 0000000..3c9ea27 --- /dev/null +++ b/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,48 @@ +_base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) diff --git a/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py new file mode 100644 index 0000000..c6bcc24 --- /dev/null +++ b/downstream/mmdetection/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + backbone=dict(norm_cfg=norm_cfg, norm_eval=False), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + norm_cfg=norm_cfg, + num_outs=5), + roi_head=dict( + bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(max_epochs=50) +evaluation = dict(interval=2) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/fpg/metafile.yml b/downstream/mmdetection/configs/fpg/metafile.yml new file mode 100644 index 0000000..6b0a6a7 --- /dev/null +++ b/downstream/mmdetection/configs/fpg/metafile.yml @@ -0,0 +1,104 @@ +Collections: + - Name: Feature Pyramid Grids + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Feature Pyramid Grids + Paper: + URL: https://arxiv.org/abs/2004.03580 + Title: 'Feature Pyramid Grids' + README: configs/fpg/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.10.0/mmdet/models/necks/fpg.py#L101 + Version: v2.10.0 + +Models: + - Name: faster_rcnn_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 20.0 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856-74109f42.pth + + - Name: faster_rcnn_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 11.9 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857-9376aa9d.pth + + - Name: mask_rcnn_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 23.2 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth + + - Name: mask_rcnn_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 15.3 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859-043c9b4e.pth + + - Name: retinanet_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 20.8 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809-b0bcf5f4.pth + + - Name: retinanet_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 19.9 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829-ee99a686.pth diff --git a/downstream/mmdetection/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 0000000..9a6cf7e --- /dev/null +++ b/downstream/mmdetection/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,5 @@ +_base_ = 'retinanet_r50_fpg_crop640_50e_coco.py' + +model = dict( + neck=dict(out_channels=128, inter_channels=128), + bbox_head=dict(in_channels=128)) diff --git a/downstream/mmdetection/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py b/downstream/mmdetection/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py new file mode 100644 index 0000000..504ed5e --- /dev/null +++ b/downstream/mmdetection/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,53 @@ +_base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + _delete_=True, + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + add_extra_convs=True, + start_level=1, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) + +evaluation = dict(interval=2) diff --git a/downstream/mmdetection/configs/free_anchor/README.md b/downstream/mmdetection/configs/free_anchor/README.md new file mode 100644 index 0000000..d24c340 --- /dev/null +++ b/downstream/mmdetection/configs/free_anchor/README.md @@ -0,0 +1,37 @@ +# FreeAnchor + +> [FreeAnchor: Learning to Match Anchors for Visual Object Detection](https://arxiv.org/abs/1909.02466) + + + +## Abstract + +Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | 1x | 4.9 | 18.4 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json) | +| R-101 | pytorch | 1x | 6.8 | 14.9 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json) | +| X-101-32x4d | pytorch | 1x | 8.1 | 11.1 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) | + +**Notes:** + +- We use 8 GPUs with 2 images/GPU. +- For more settings and models, please refer to the [official repo](https://github.com/zhangxiaosong18/FreeAnchor). + +## Citation + +```latex +@inproceedings{zhang2019freeanchor, + title = {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection}, + author = {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang}, + booktitle = {Neural Information Processing Systems}, + year = {2019} +} +``` diff --git a/downstream/mmdetection/configs/free_anchor/metafile.yml b/downstream/mmdetection/configs/free_anchor/metafile.yml new file mode 100644 index 0000000..170fb5c --- /dev/null +++ b/downstream/mmdetection/configs/free_anchor/metafile.yml @@ -0,0 +1,79 @@ +Collections: + - Name: FreeAnchor + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FreeAnchor + - ResNet + Paper: + URL: https://arxiv.org/abs/1909.02466 + Title: 'FreeAnchor: Learning to Match Anchors for Visual Object Detection' + README: configs/free_anchor/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/dense_heads/free_anchor_retina_head.py#L10 + Version: v2.0.0 + +Models: + - Name: retinanet_free_anchor_r50_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.9 + inference time (ms/im): + - value: 54.35 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth + + - Name: retinanet_free_anchor_r101_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.8 + inference time (ms/im): + - value: 67.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth + + - Name: retinanet_free_anchor_x101_32x4d_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (ms/im): + - value: 90.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth diff --git a/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py new file mode 100644 index 0000000..f4aea53 --- /dev/null +++ b/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py new file mode 100644 index 0000000..28f983c --- /dev/null +++ b/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py @@ -0,0 +1,22 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='FreeAnchorRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..65f8a9e --- /dev/null +++ b/downstream/mmdetection/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/fsaf/README.md b/downstream/mmdetection/configs/fsaf/README.md new file mode 100644 index 0000000..4392a6e --- /dev/null +++ b/downstream/mmdetection/configs/fsaf/README.md @@ -0,0 +1,57 @@ +# FSAF + +> [Feature Selective Anchor-Free Module for Single-Shot Object Detection](https://arxiv.org/abs/1903.00621) + + + +## Abstract + +We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) heuristic-guided feature selection; 2) overlap-based anchor sampling. The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches. Specifically, an anchor-free branch is attached to each level of the feature pyramid, allowing box encoding and decoding in the anchor-free manner at an arbitrary level. During training, we dynamically assign each instance to the most suitable feature level. At the time of inference, the FSAF module can work jointly with anchor-based branches by outputting predictions in parallel. We instantiate this concept with simple implementations of anchor-free branches and online feature selection strategy. Experimental results on the COCO detection track show that our FSAF module performs better than anchor-based counterparts while being faster. When working jointly with anchor-based branches, the FSAF module robustly improves the baseline RetinaNet by a large margin under various settings, while introducing nearly free inference overhead. And the resulting best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing single-shot detectors on COCO. + +
    + +
    + +## Introduction + +FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)). +Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level. +And this is how we implemented it. +Only the anchor-free branch is released for its better compatibility with the current framework and less computational budget. + +In the original paper, feature maps within the central 0.2-0.5 area of a gt box are tagged as ignored. However, +it is empirically found that a hard threshold (0.2-0.2) gives a further gain on the performance. (see the table below) + +## Results and Models + +### Results on R50/R101/X101-FPN + +| Backbone | ignore range | ms-train | Lr schd | Train Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Config | Download | +| :------: | :----------: | :------: | :-----: | :------------: | :-----------------: | :------------: | :---------: | :---------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) | +| R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json) | +| R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json) | +| X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json) | + +**Notes:** + +- *1x means the model is trained for 12 epochs.* +- *AP values in the brackets represent those reported in the original paper.* +- *All results are obtained with a single model and single-scale test.* +- *X-101 backbone represents ResNext-101-64x4d.* +- *All pretrained backbones use pytorch style.* +- *All models are trained on 8 Titan-XP gpus and tested on a single gpu.* + +## Citation + +BibTeX reference is as follows. + +```latex +@inproceedings{zhu2019feature, + title={Feature Selective Anchor-Free Module for Single-Shot Object Detection}, + author={Zhu, Chenchen and He, Yihui and Savvides, Marios}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + pages={840--849}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/fsaf/fsaf_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/fsaf/fsaf_r101_fpn_1x_coco.py new file mode 100644 index 0000000..12b49fe --- /dev/null +++ b/downstream/mmdetection/configs/fsaf/fsaf_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './fsaf_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/fsaf/fsaf_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/fsaf/fsaf_r50_fpn_1x_coco.py new file mode 100644 index 0000000..67f3ec1 --- /dev/null +++ b/downstream/mmdetection/configs/fsaf/fsaf_r50_fpn_1x_coco.py @@ -0,0 +1,48 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +# model settings +model = dict( + type='FSAF', + bbox_head=dict( + type='FSAFHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + reg_decoded_bbox=True, + # Only anchor-free branch is implemented. The anchor generator only + # generates 1 anchor at each feature point, as a substitute of the + # grid of features. + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=1, + scales_per_octave=1, + ratios=[1.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(_delete_=True, type='TBLRBBoxCoder', normalizer=4.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0, + reduction='none'), + loss_bbox=dict( + _delete_=True, + type='IoULoss', + eps=1e-6, + loss_weight=1.0, + reduction='none')), + # training and testing settings + train_cfg=dict( + assigner=dict( + _delete_=True, + type='CenterRegionAssigner', + pos_scale=0.2, + neg_scale=0.2, + min_pos_iof=0.01), + allowed_border=-1, + pos_weight=-1, + debug=False)) +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=10, norm_type=2)) diff --git a/downstream/mmdetection/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..89c0c63 --- /dev/null +++ b/downstream/mmdetection/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './fsaf_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/fsaf/metafile.yml b/downstream/mmdetection/configs/fsaf/metafile.yml new file mode 100644 index 0000000..5434e9a --- /dev/null +++ b/downstream/mmdetection/configs/fsaf/metafile.yml @@ -0,0 +1,80 @@ +Collections: + - Name: FSAF + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x Titan-XP GPUs + Architecture: + - FPN + - FSAF + - ResNet + Paper: + URL: https://arxiv.org/abs/1903.00621 + Title: 'Feature Selective Anchor-Free Module for Single-Shot Object Detection' + README: configs/fsaf/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/detectors/fsaf.py#L6 + Version: v2.1.0 + +Models: + - Name: fsaf_r50_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.15 + inference time (ms/im): + - value: 76.92 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth + + - Name: fsaf_r101_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.08 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.3 (37.9) + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth + + - Name: fsaf_x101_64x4d_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.38 + inference time (ms/im): + - value: 178.57 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 (41.0) + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth diff --git a/downstream/mmdetection/configs/gcnet/README.md b/downstream/mmdetection/configs/gcnet/README.md new file mode 100644 index 0000000..403e086 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/README.md @@ -0,0 +1,69 @@ +# GCNet + +> [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://arxiv.org/abs/1904.11492) + + + +## Abstract + +The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks. + +
    + +
    + +## Introduction + +By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/). + +We provide config files to reproduce the results in the paper for +["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection. + +**GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks. + +## Results and Models + +The results on COCO 2017val are shown in the below table. + +| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------: | :---: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | | 39.7 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.0 | 39.9 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 11.4 | 41.3 | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.6 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json) | + +| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | Mask | - | 1x | 4.4 | 16.6 | 38.4 | 34.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | 15.5 | 40.4 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.1 | 40.7 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-101-FPN | Mask | - | 1x | 6.4 | 13.3 | 40.5 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 12.0 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.8 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| X-101-FPN | Mask | - | 1x | 7.6 | 11.3 | 42.4 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.8 | 9.8 | 43.5 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 9.0 | 9.7 | 43.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json) | +| X-101-FPN | Cascade Mask | - | 1x | 9.2 | 8.4 | 44.7 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | 10.3 | 7.7 | 46.2 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | 10.6 | | 46.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json) | +| X-101-FPN | DCN Cascade Mask | - | 1x | | | 47.5 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019.log.json) | +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | | | 48.0 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648.log.json) | +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | | | 47.9 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851.log.json) | + +**Notes:** + +- The `SyncBN` is added in the backbone for all models in **Table 2**. +- `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone. +- `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone. +- `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively. + +## Citation + +```latex +@article{cao2019GCNet, + title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond}, + author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han}, + journal={arXiv preprint arXiv:1904.11492}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..5118895 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..413499d --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..50689aa --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..1367231 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..50883ff --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..31fdd07 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..ad6ad47 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..29f9167 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..6e1c5d0 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..781dba7 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..32972de --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..d299b69 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..5ac908e --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..0308a56 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..e04780c --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..980f819 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 0000000..f0c96e5 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..7fb8e82 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 0000000..b1ddbee --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/downstream/mmdetection/configs/gcnet/metafile.yml b/downstream/mmdetection/configs/gcnet/metafile.yml new file mode 100644 index 0000000..1281122 --- /dev/null +++ b/downstream/mmdetection/configs/gcnet/metafile.yml @@ -0,0 +1,440 @@ +Collections: + - Name: GCNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Global Context Block + - FPN + - RPN + - ResNet + - ResNeXt + Paper: + URL: https://arxiv.org/abs/1904.11492 + Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond' + README: configs/gcnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/context_block.py#L13 + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth + + - Name: mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (ms/im): + - value: 66.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth + + - Name: mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 87.72 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth + + - Name: mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + inference time (ms/im): + - value: 86.21 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 60.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + inference time (ms/im): + - value: 64.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (ms/im): + - value: 66.23 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 75.19 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 83.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + inference time (ms/im): + - value: 84.75 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 8.8 + inference time (ms/im): + - value: 102.04 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (ms/im): + - value: 103.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (ms/im): + - value: 119.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 129.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 10.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth diff --git a/downstream/mmdetection/configs/gfl/README.md b/downstream/mmdetection/configs/gfl/README.md new file mode 100644 index 0000000..703936b --- /dev/null +++ b/downstream/mmdetection/configs/gfl/README.md @@ -0,0 +1,42 @@ +# GFL + +> [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) + + + +## Abstract + +One-stage detector basically formulates object detection as dense classification and localization. The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization, where the predicted quality facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Two problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference and (2) the inflexible Dirac delta distribution for localization when there is ambiguity and uncertainty in complex scenes. To address the problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFL) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. On COCO test-dev, GFL achieves 45.0% AP using ResNet-101 backbone, surpassing state-of-the-art SAPD (43.5%) and ATSS (43.6%) with higher or comparable inference speed, under the same backbone and training settings. Notably, our best model can achieve a single-model single-scale AP of 48.2%, at 10 FPS on a single 2080Ti GPU. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Multi-scale Training | Inf time (fps) | box AP | Config | Download | +| :---------------: | :-----: | :-----: | :------------------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | 1x | No | 19.5 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json) | +| R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json) | +| R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json) | +| R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) | + +\[1\] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \ +\[2\] *All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..* \ +\[3\] *`dcnv2` denotes deformable convolutional networks v2.* \ +\[4\] *FPS is tested with a single GeForce RTX 2080Ti GPU, using a batch size of 1.* + +## Citation + +We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) + +```latex +@article{li2020generalized, + title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection}, + author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian}, + journal={arXiv preprint arXiv:2006.04388}, + year={2020} +} +``` diff --git a/downstream/mmdetection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py b/downstream/mmdetection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..b72c2b6 --- /dev/null +++ b/downstream/mmdetection/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,15 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py b/downstream/mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..e33b5c0 --- /dev/null +++ b/downstream/mmdetection/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/gfl/gfl_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/gfl/gfl_r50_fpn_1x_coco.py new file mode 100644 index 0000000..cfd4b02 --- /dev/null +++ b/downstream/mmdetection/configs/gfl/gfl_r50_fpn_1x_coco.py @@ -0,0 +1,57 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='GFL', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='GFLHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), + reg_max=16, + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py b/downstream/mmdetection/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..b8be601 --- /dev/null +++ b/downstream/mmdetection/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py @@ -0,0 +1,22 @@ +_base_ = './gfl_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +# multi-scale training +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/downstream/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py b/downstream/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py new file mode 100644 index 0000000..2539807 --- /dev/null +++ b/downstream/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py @@ -0,0 +1,18 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + type='GFL', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, False, True, True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py b/downstream/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py new file mode 100644 index 0000000..effda19 --- /dev/null +++ b/downstream/mmdetection/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py @@ -0,0 +1,16 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + type='GFL', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/gfl/metafile.yml b/downstream/mmdetection/configs/gfl/metafile.yml new file mode 100644 index 0000000..8f049c6 --- /dev/null +++ b/downstream/mmdetection/configs/gfl/metafile.yml @@ -0,0 +1,134 @@ +Collections: + - Name: Generalized Focal Loss + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Generalized Focal Loss + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/2006.04388 + Title: 'Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection' + README: configs/gfl/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/gfl.py#L6 + Version: v2.2.0 + +Models: + - Name: gfl_r50_fpn_1x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r50_fpn_1x_coco.py + Metadata: + inference time (ms/im): + - value: 51.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth + + - Name: gfl_r50_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 51.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth + + - Name: gfl_r101_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 68.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth + + - Name: gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth + + - Name: gfl_x101_32x4d_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 82.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth + + - Name: gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py + Metadata: + inference time (ms/im): + - value: 93.46 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth diff --git a/downstream/mmdetection/configs/ghm/README.md b/downstream/mmdetection/configs/ghm/README.md new file mode 100644 index 0000000..cf9fb73 --- /dev/null +++ b/downstream/mmdetection/configs/ghm/README.md @@ -0,0 +1,33 @@ +# GHM + +> [Gradient Harmonized Single-stage Detector](https://arxiv.org/abs/1811.05181) + + + +## Abstract + +Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i.e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples. In this work, we first point out that the essential effect of the two disharmonies can be summarized in term of the gradient. Further, we propose a novel gradient harmonizing mechanism (GHM) to be a hedging for the disharmonies. The philosophy behind GHM can be easily embedded into both classification loss function like cross-entropy (CE) and regression loss function like smooth-L1 (SL1) loss. To this end, two novel loss functions called GHM-C and GHM-R are designed to balancing the gradient flow for anchor classification and bounding box refinement, respectively. Ablation study on MS COCO demonstrates that without laborious hyper-parameter tuning, both GHM-C and GHM-R can bring substantial improvement for single-stage detector. Without any whistles and bells, our model achieves 41.6 mAP on COCO test-dev set which surpasses the state-of-the-art method, Focal Loss (FL) + SL1, by 0.8. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 4.0 | 3.3 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 4.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 5.1 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 5.2 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) | + +## Citation + +```latex +@inproceedings{li2019gradient, + title={Gradient Harmonized Single-stage Detector}, + author={Li, Buyu and Liu, Yu and Wang, Xiaogang}, + booktitle={AAAI Conference on Artificial Intelligence}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/ghm/metafile.yml b/downstream/mmdetection/configs/ghm/metafile.yml new file mode 100644 index 0000000..b4f488c --- /dev/null +++ b/downstream/mmdetection/configs/ghm/metafile.yml @@ -0,0 +1,101 @@ +Collections: + - Name: GHM + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - GHM-C + - GHM-R + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1811.05181 + Title: 'Gradient Harmonized Single-stage Detector' + README: configs/ghm/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/losses/ghm_loss.py#L21 + Version: v2.0.0 + +Models: + - Name: retinanet_ghm_r50_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 303.03 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth + + - Name: retinanet_ghm_r101_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 227.27 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth + + - Name: retinanet_ghm_x101_32x4d_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (ms/im): + - value: 196.08 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth + + - Name: retinanet_ghm_x101_64x4d_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 192.31 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth diff --git a/downstream/mmdetection/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py new file mode 100644 index 0000000..aaf6fc2 --- /dev/null +++ b/downstream/mmdetection/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py new file mode 100644 index 0000000..61b9751 --- /dev/null +++ b/downstream/mmdetection/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py @@ -0,0 +1,19 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + loss_cls=dict( + _delete_=True, + type='GHMC', + bins=30, + momentum=0.75, + use_sigmoid=True, + loss_weight=1.0), + loss_bbox=dict( + _delete_=True, + type='GHMR', + mu=0.02, + bins=10, + momentum=0.7, + loss_weight=10.0))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..cd2e4cc --- /dev/null +++ b/downstream/mmdetection/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..b6107d8 --- /dev/null +++ b/downstream/mmdetection/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/gn+ws/README.md b/downstream/mmdetection/configs/gn+ws/README.md new file mode 100644 index 0000000..184bed3 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/README.md @@ -0,0 +1,54 @@ +# GN + WS + +> [Weight Standardization](https://arxiv.org/abs/1903.10520) + + + +## Abstract + +Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typically has only 1-2 images for training, which is inevitable for many computer vision tasks, e.g., object detection and semantic segmentation, constrained by memory consumption. To address this issue, we propose Weight Standardization (WS) and Batch-Channel Normalization (BCN) to bring two success factors of BN into micro-batch training: 1) the smoothing effects on the loss landscape and 2) the ability to avoid harmful elimination singularities along the training trajectory. WS standardizes the weights in convolutional layers to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients; BCN combines batch and channel normalizations and leverages estimated statistics of the activations in convolutional layers to keep networks away from elimination singularities. We validate WS and BCN on comprehensive computer vision tasks, including image classification, object detection, instance segmentation, video recognition and semantic segmentation. All experimental results consistently show that WS and BCN improve micro-batch training significantly. Moreover, using WS and BCN with micro-batch training is even able to match or outperform the performances of BN with large-batch training. + +
    + +
    + +## Results and Models + +Faster R-CNN + +| Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | GN+WS | 1x | 5.9 | 11.7 | 39.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json) | +| R-101-FPN | pytorch | GN+WS | 1x | 8.9 | 9.0 | 41.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 1x | 7.0 | 10.3 | 40.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 1x | 10.8 | 7.6 | 42.1 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) | + +Mask R-CNN + +| Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----------: | :-------: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | GN+WS | 2x | 7.3 | 10.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json) | +| R-101-FPN | pytorch | GN+WS | 2x | 10.3 | 8.6 | 42.0 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 2x | 8.4 | 9.3 | 41.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 2x | 12.2 | 7.1 | 42.1 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json) | +| R-50-FPN | pytorch | GN+WS | 20-23-24e | 7.3 | - | 41.1 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json) | +| R-101-FPN | pytorch | GN+WS | 20-23-24e | 10.3 | - | 43.1 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 8.4 | - | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 12.2 | - | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) | + +Note: + +- GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN. +- In the paper, a 20-23-24e lr schedule is used instead of 2x. +- The X-50-GN and X-101-GN pretrained models are also shared by the authors. + +## Citation + +```latex +@article{weightstandardization, + author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille}, + title = {Weight Standardization}, + journal = {arXiv preprint arXiv:1903.10520}, + year = {2019}, +} +``` diff --git a/downstream/mmdetection/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py b/downstream/mmdetection/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..cd2cb2b --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) diff --git a/downstream/mmdetection/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py b/downstream/mmdetection/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..1b326b8 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')), + neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg))) diff --git a/downstream/mmdetection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py b/downstream/mmdetection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..f64ae89 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,18 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws'))) diff --git a/downstream/mmdetection/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py b/downstream/mmdetection/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 0000000..246851b --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,18 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + type='ResNeXt', + depth=50, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws'))) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..a790d93 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..a9fa6a2 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws'))) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..5516808 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..63be60f --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,20 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')), + neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg), + mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..cfa14c9 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..6498b03 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,19 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# model settings +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws'))) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 0000000..79ce0ad --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 0000000..7fac317 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,19 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# model settings +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + type='ResNeXt', + depth=50, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws'))) diff --git a/downstream/mmdetection/configs/gn+ws/metafile.yml b/downstream/mmdetection/configs/gn+ws/metafile.yml new file mode 100644 index 0000000..bc89359 --- /dev/null +++ b/downstream/mmdetection/configs/gn+ws/metafile.yml @@ -0,0 +1,263 @@ +Collections: + - Name: Weight Standardization + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Group Normalization + - Weight Standardization + Paper: + URL: https://arxiv.org/abs/1903.10520 + Title: 'Weight Standardization' + README: configs/gn+ws/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 5.9 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth + + - Name: faster_rcnn_r101_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 8.9 + inference time (ms/im): + - value: 111.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth + + - Name: faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 97.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth + + - Name: faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (ms/im): + - value: 131.58 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth + + - Name: mask_rcnn_r50_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 7.3 + inference time (ms/im): + - value: 95.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth + + - Name: mask_rcnn_r101_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (ms/im): + - value: 116.28 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth + + - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 8.4 + inference time (ms/im): + - value: 107.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth + + - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 12.2 + inference time (ms/im): + - value: 140.85 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth + + - Name: mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 7.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth + + - Name: mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 10.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth + + - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 8.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 12.2 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth diff --git a/downstream/mmdetection/configs/gn/README.md b/downstream/mmdetection/configs/gn/README.md new file mode 100644 index 0000000..9bb2888 --- /dev/null +++ b/downstream/mmdetection/configs/gn/README.md @@ -0,0 +1,41 @@ +# GN + +> [Group Normalization](https://arxiv.org/abs/1803.08494) + + + +## Abstract + +Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code in modern libraries. + +
    + +
    + +## Results and Models + +| Backbone | model | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-----------: | :--------: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN (d) | Mask R-CNN | 2x | 7.1 | 11.0 | 40.2 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json) | +| R-50-FPN (d) | Mask R-CNN | 3x | 7.1 | - | 40.5 | 36.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json) | +| R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 9.0 | 41.9 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json) | +| R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json) | +| R-50-FPN (c) | Mask R-CNN | 2x | 7.1 | 10.9 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) | +| R-50-FPN (c) | Mask R-CNN | 3x | 7.1 | - | 40.1 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) | + +**Notes:** + +- (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). +- The `3x` schedule is epoch \[28, 34, 36\]. +- **Memory, Train/Inf time is outdated.** + +## Citation + +```latex +@inproceedings{wu2018group, + title={Group Normalization}, + author={Wu, Yuxin and He, Kaiming}, + booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, + year={2018} +} +``` diff --git a/downstream/mmdetection/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py b/downstream/mmdetection/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py new file mode 100644 index 0000000..a505ba0 --- /dev/null +++ b/downstream/mmdetection/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py @@ -0,0 +1,7 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet101_gn'))) diff --git a/downstream/mmdetection/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py b/downstream/mmdetection/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py new file mode 100644 index 0000000..12a9d17 --- /dev/null +++ b/downstream/mmdetection/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py new file mode 100644 index 0000000..1de7d98 --- /dev/null +++ b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py @@ -0,0 +1,49 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet50_gn')), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py new file mode 100644 index 0000000..f917719 --- /dev/null +++ b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py new file mode 100644 index 0000000..2f430fd --- /dev/null +++ b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + backbone=dict( + norm_cfg=norm_cfg, + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py new file mode 100644 index 0000000..66834f0 --- /dev/null +++ b/downstream/mmdetection/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/gn/metafile.yml b/downstream/mmdetection/configs/gn/metafile.yml new file mode 100644 index 0000000..4a1ecae --- /dev/null +++ b/downstream/mmdetection/configs/gn/metafile.yml @@ -0,0 +1,162 @@ +Collections: + - Name: Group Normalization + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Group Normalization + Paper: + URL: https://arxiv.org/abs/1803.08494 + Title: 'Group Normalization' + README: configs/gn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_fpn_gn-all_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth + + - Name: mask_rcnn_r50_fpn_gn-all_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth + + - Name: mask_rcnn_r101_fpn_gn-all_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (ms/im): + - value: 111.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth + + - Name: mask_rcnn_r101_fpn_gn-all_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (ms/im): + - value: 111.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth + + - Name: mask_rcnn_r50_fpn_gn-all_contrib_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 91.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth + + - Name: mask_rcnn_r50_fpn_gn-all_contrib_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 91.74 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_1x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_1x.py new file mode 100644 index 0000000..af90b16 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_1x.py @@ -0,0 +1,173 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] +checkpoint_url = '' +embed_dims = 216 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L1', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) +work_dir = 'work_dirs/gpvit_l1_maskrcnn_1x' diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_3x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_3x.py new file mode 100644 index 0000000..92f8716 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l1_maskrcnn_3x.py @@ -0,0 +1,207 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 216 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L1', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l1_maskrcnn_3x' diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_1x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_1x.py new file mode 100644 index 0000000..c22423f --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_1x.py @@ -0,0 +1,173 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] +checkpoint_url = '' +embed_dims = 348 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L2', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) +work_dir = 'work_dirs/gpvit_l2_maskrcnn_1x' diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_3x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_3x.py new file mode 100644 index 0000000..57eccd5 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l2_maskrcnn_3x.py @@ -0,0 +1,207 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 348 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L2', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l2_maskrcnn_3x' diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_1x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_1x.py new file mode 100644 index 0000000..7b9f3c1 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_1x.py @@ -0,0 +1,173 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] +checkpoint_url = '' +embed_dims = 432 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L3', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) +work_dir = 'work_dirs/gpvit_l3_maskrcnn_1x' diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_3x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_3x.py new file mode 100644 index 0000000..a68474b --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l3_maskrcnn_3x.py @@ -0,0 +1,207 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 432 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L3', + drop_path_rate=0.2, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l3_maskrcnn_3x' diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_1x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_1x.py new file mode 100644 index 0000000..d5e1e98 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_1x.py @@ -0,0 +1,173 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] +checkpoint_url = '' +embed_dims = 624 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L4', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) +work_dir = 'work_dirs/gpvit_l4_maskrcnn_1x' diff --git a/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_3x.py b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_3x.py new file mode 100644 index 0000000..ab193e5 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/mask_rcnn/gpvit_l4_maskrcnn_3x.py @@ -0,0 +1,207 @@ +_base_ = [ + '../../_base_/datasets/coco_instance.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 624 +model = dict( + type='MaskRCNN', + backbone=dict( + type='GPViTAdapter', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L4', + drop_path_rate=0.2, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims, embed_dims], + out_channels=256, + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + norm_cfg=dict(type='SyncBN', requires_grad=True), + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + # norm_cfg=dict(type='SyncBN', requires_grad=True), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) +optimizer_config = dict(grad_clip=None) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l4_maskrcnn_3x' diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_1x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_1x.py new file mode 100644 index 0000000..687b097 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_1x.py @@ -0,0 +1,115 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 216 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L1', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l1_retinanet_1x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_3x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_3x.py new file mode 100644 index 0000000..c373335 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l1_retinanet_3x.py @@ -0,0 +1,149 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 216 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L1', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +find_unused_parameters = True +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l1_retinanet_3x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_1x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_1x.py new file mode 100644 index 0000000..6833bec --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_1x.py @@ -0,0 +1,115 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 348 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L2', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l2_retinanet_1x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_3x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_3x.py new file mode 100644 index 0000000..d42864a --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l2_retinanet_3x.py @@ -0,0 +1,149 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 348 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L2', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +find_unused_parameters = True +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l2_retinanet_3x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_1x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_1x.py new file mode 100644 index 0000000..ea033c6 --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_1x.py @@ -0,0 +1,115 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 432 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L3', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l3_retinanet_1x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_3x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_3x.py new file mode 100644 index 0000000..f0885ef --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l3_retinanet_3x.py @@ -0,0 +1,149 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 432 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L3', + drop_path_rate=0.2, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +find_unused_parameters = True +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l3_retinanet_3x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_1x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_1x.py new file mode 100644 index 0000000..5b1592e --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_1x.py @@ -0,0 +1,115 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_1x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 624 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L4', + drop_path_rate=0.1, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l4_retinanet_1x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_3x.py b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_3x.py new file mode 100644 index 0000000..d92440f --- /dev/null +++ b/downstream/mmdetection/configs/gpvit/retinanet/gpvit_l4_retinanet_3x.py @@ -0,0 +1,149 @@ +_base_ = [ + '../../_base_/datasets/coco_detection.py', + '../../_base_/schedules/schedule_3x.py', + '../../_base_/default_runtime.py' +] + +checkpoint_url = '' +embed_dims = 624 +model = dict( + type='RetinaNet', + backbone=dict( + type='GPViTAdapterSingleStage', + conv_inplane=64, + n_points=4, + deform_num_heads=6, + cffn_ratio=0.25, + deform_ratio=1.0, + interaction_indexes=[[0, 2], [3, 5], [6, 8], [9, 11]], + arch='L4', + drop_path_rate=0.2, + out_indices=(11,), + final_norm=False, + init_cfg=dict(type='Pretrained', checkpoint=checkpoint_url, prefix="backbone."), + convert_syncbn=True), + neck=dict( + type='FPN', + in_channels=[embed_dims, embed_dims, embed_dims], + out_channels=256, + add_extra_convs='on_output', + num_outs=5, + norm_cfg=dict(type='SyncBN', requires_grad=True)), + bbox_head=dict( + type='RetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + +# optimizer +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# augmentation strategy originates from DETR / Sparse RCNN +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='AutoAugment', + policies=[ + [ + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict(type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict(type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='RandomCrop', + crop_type='absolute_range', + crop_size=(1024, 1024), + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline)) + +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0002, weight_decay=0.05, + paramwise_cfg=dict( + custom_keys={ + 'level_embed': dict(decay_mult=0.), + 'pos_embed': dict(decay_mult=0.), + 'norm': dict(decay_mult=0.), + 'bias': dict(decay_mult=0.), + '.absolute_pos_embed': dict(decay_mult=0.0), + '.relative_position_bias_table': dict(decay_mult=0.0), + '.pos_embed': dict(decay_mult=0.0), + '.group_token': dict(decay_mult=0.0), + '.dw_norm': dict(decay_mult=0.0) + })) + +find_unused_parameters = True +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +fp16 = dict(loss_scale=dict(init_scale=512)) +checkpoint_config = dict( + interval=1, + max_keep_ckpts=3, + save_last=True, +) + +work_dir = 'work_dirs/gpvit_l4_retinanet_3x' \ No newline at end of file diff --git a/downstream/mmdetection/configs/grid_rcnn/README.md b/downstream/mmdetection/configs/grid_rcnn/README.md new file mode 100644 index 0000000..e844021 --- /dev/null +++ b/downstream/mmdetection/configs/grid_rcnn/README.md @@ -0,0 +1,47 @@ +# Grid R-CNN + +> [Grid R-CNN](https://arxiv.org/abs/1811.12030) + + + +## Abstract + +This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture. + +Grid R-CNN is a well-performed objection detection framework. It transforms the traditional box offset regression problem into a grid point estimation problem. With the guidance of the grid points, it can obtain high-quality localization results. However, the speed of Grid R-CNN is not so satisfactory. In this technical report we present Grid R-CNN Plus, a better and faster version of Grid R-CNN. We have made several updates that significantly speed up the framework and simultaneously improve the accuracy. On COCO dataset, the Res50-FPN based Grid R-CNN Plus detector achieves an mAP of 40.4%, outperforming the baseline on the same model by 3.0 points with similar inference time. + +
    + +
    + +## Results and Models + +| Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | 2x | 5.1 | 15.0 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130_221140.log.json) | +| R-101 | 2x | 7.0 | 12.6 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309_164224.log.json) | +| X-101-32x4d | 2x | 8.3 | 10.8 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130_215413.log.json) | +| X-101-64x4d | 2x | 11.3 | 7.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204_080641.log.json) | + +**Notes:** + +- All models are trained with 8 GPUs instead of 32 GPUs in the original paper. +- The warming up lasts for 1 epoch and `2x` here indicates 25 epochs. + +## Citation + +```latex +@inproceedings{lu2019grid, + title={Grid r-cnn}, + author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} + +@article{lu2019grid, + title={Grid R-CNN Plus: Faster and Better}, + author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, + journal={arXiv preprint arXiv:1906.05688}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..1bb5889 --- /dev/null +++ b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py @@ -0,0 +1,7 @@ +_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py new file mode 100644 index 0000000..4aa00ec --- /dev/null +++ b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = ['grid_rcnn_r50_fpn_gn-head_2x_coco.py'] +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +checkpoint_config = dict(interval=1) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..df63cd5 --- /dev/null +++ b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py @@ -0,0 +1,131 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='GridRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='GridRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + with_reg=False, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False), + grid_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + grid_head=dict( + type='GridHead', + grid_points=9, + num_convs=8, + in_channels=256, + point_feat_channels=64, + norm_cfg=dict(type='GN', num_groups=36), + loss_grid=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=15))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_radius=1, + pos_weight=-1, + max_num_grid=192, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.03, + nms=dict(type='nms', iou_threshold=0.3), + max_per_img=100))) +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=3665, + warmup_ratio=1.0 / 80, + step=[17, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=25) diff --git a/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..3bc8516 --- /dev/null +++ b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py @@ -0,0 +1,24 @@ +_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=3665, + warmup_ratio=1.0 / 80, + step=[17, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=25) diff --git a/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py new file mode 100644 index 0000000..c78f8f6 --- /dev/null +++ b/downstream/mmdetection/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/grid_rcnn/metafile.yml b/downstream/mmdetection/configs/grid_rcnn/metafile.yml new file mode 100644 index 0000000..d1aa851 --- /dev/null +++ b/downstream/mmdetection/configs/grid_rcnn/metafile.yml @@ -0,0 +1,101 @@ +Collections: + - Name: Grid R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RPN + - Dilated Convolution + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1906.05688 + Title: 'Grid R-CNN' + README: configs/grid_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/grid_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: grid_rcnn_r50_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (ms/im): + - value: 66.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth + + - Name: grid_rcnn_r101_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 79.37 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth + + - Name: grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 8.3 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth + + - Name: grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 11.3 + inference time (ms/im): + - value: 129.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth diff --git a/downstream/mmdetection/configs/groie/README.md b/downstream/mmdetection/configs/groie/README.md new file mode 100644 index 0000000..4a1eba0 --- /dev/null +++ b/downstream/mmdetection/configs/groie/README.md @@ -0,0 +1,72 @@ +# GRoIE + +> [A novel Region of Interest Extraction Layer for Instance Segmentation](https://arxiv.org/abs/2004.13665) + + + +## Abstract + +Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extracting a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone. +This paper is motivated by the need to overcome the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance. +A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought about by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP improvement on bounding box detection and 1.7% AP improvement on instance segmentation. + +
    + +
    + +## Introduction + +By Leonardo Rossi, Akbar Karimi and Andrea Prati from +[IMPLab](http://implab.ce.unipr.it/). + +We provide configs to reproduce the results in the paper for +"*A novel Region of Interest Extraction Layer for Instance Segmentation*" +on COCO object detection. + +This paper is motivated by the need to overcome to the limitations of existing +RoI extractors which select only one (the best) layer from FPN. + +Our intuition is that all the layers of FPN retain useful information. + +Therefore, the proposed layer (called Generic RoI Extractor - **GRoIE**) +introduces non-local building blocks and attention mechanisms to boost the +performance. + +## Results and Models + +The results on COCO 2017 minival (5k images) are shown in the below table. + +### Application of GRoIE to different architectures + +| Backbone | Method | Lr schd | box AP | mask AP | Config | Download | +| :-------: | :-------------: | :-----: | :----: | :-----: | :---------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | Faster Original | 1x | 37.4 | | [config](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | + GRoIE | 1x | 38.3 | | [config](./faster_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | Grid R-CNN | 1x | 39.1 | | [config](./grid_rcnn_r50_fpn_gn-head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059-64f00ee8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059.log.json) | +| R-50-FPN | + GRoIE | 1x | | | [config](./grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py) | | +| R-50-FPN | Mask R-CNN | 1x | 38.2 | 34.7 | [config](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN | + GRoIE | 1x | 39.0 | 36.0 | [config](./mask_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | GC-Net | 1x | 40.7 | 36.5 | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-50-FPN | + GRoIE | 1x | 41.0 | 37.8 | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | +| R-101-FPN | GC-Net | 1x | 42.2 | 37.8 | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| R-101-FPN | + GRoIE | 1x | 42.6 | 38.7 | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) | + +## Citation + +If you use this work or benchmark in your research, please cite this project. + +```latex +@inproceedings{rossi2021novel, + title={A novel region of interest extraction layer for instance segmentation}, + author={Rossi, Leonardo and Karimi, Akbar and Prati, Andrea}, + booktitle={2020 25th International Conference on Pattern Recognition (ICPR)}, + pages={2203--2209}, + year={2021}, + organization={IEEE} +} +``` + +## Contact + +The implementation of GRoIE is currently maintained by +[Leonardo Rossi](https://github.com/hachreak/). diff --git a/downstream/mmdetection/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py b/downstream/mmdetection/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py new file mode 100644 index 0000000..0fc528b --- /dev/null +++ b/downstream/mmdetection/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py @@ -0,0 +1,25 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/downstream/mmdetection/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py b/downstream/mmdetection/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py new file mode 100644 index 0000000..8e4b4ab --- /dev/null +++ b/downstream/mmdetection/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + grid_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/downstream/mmdetection/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/downstream/mmdetection/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py new file mode 100644 index 0000000..8b83722 --- /dev/null +++ b/downstream/mmdetection/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/downstream/mmdetection/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py b/downstream/mmdetection/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py new file mode 100644 index 0000000..81dfb48 --- /dev/null +++ b/downstream/mmdetection/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/downstream/mmdetection/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/downstream/mmdetection/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py new file mode 100644 index 0000000..852c5ca --- /dev/null +++ b/downstream/mmdetection/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/downstream/mmdetection/configs/groie/metafile.yml b/downstream/mmdetection/configs/groie/metafile.yml new file mode 100644 index 0000000..269cb39 --- /dev/null +++ b/downstream/mmdetection/configs/groie/metafile.yml @@ -0,0 +1,93 @@ +Collections: + - Name: GRoIE + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Generic RoI Extractor + - FPN + - RPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/2004.13665 + Title: 'A novel Region of Interest Extraction Layer for Instance Segmentation' + README: configs/groie/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/roi_heads/roi_extractors/groie.py#L15 + Version: v2.1.0 + +Models: + - Name: faster_rcnn_r50_fpn_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth + + - Name: grid_rcnn_r50_fpn_gn-head_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + + - Name: mask_rcnn_r50_fpn_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth diff --git a/downstream/mmdetection/configs/guided_anchoring/README.md b/downstream/mmdetection/configs/guided_anchoring/README.md new file mode 100644 index 0000000..563e43f --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/README.md @@ -0,0 +1,59 @@ +# Guided Anchoring + +> [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278) + + + +## Abstract + +Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios. In this paper, we revisit this foundational stage. Our study shows that it can be done much more effectively and efficiently. Specifically, we present an alternative scheme, named Guided Anchoring, which leverages semantic features to guide the anchoring. The proposed method jointly predicts the locations where the center of objects of interest are likely to exist as well as the scales and aspect ratios at different locations. On top of predicted anchor shapes, we mitigate the feature inconsistency with a feature adaption module. We also study the use of high-quality proposals to improve detection performance. The anchoring scheme can be seamlessly integrated into proposal methods and detectors. With Guided Anchoring, we achieve 9.1% higher recall on MS COCO with 90% fewer anchors than the RPN baseline. We also adopt Guided Anchoring in Fast R-CNN, Faster R-CNN and RetinaNet, respectively improving the detection mAP by 2.2%, 2.7% and 1.2%. + +
    + +
    + +## Results and Models + +The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val). + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 | Config | Download | +| :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :-----------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| GA-RPN | R-50-FPN | caffe | 1x | 5.3 | 15.8 | 68.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json) | +| GA-RPN | R-101-FPN | caffe | 1x | 7.3 | 13.0 | 69.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) | +| GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 10.0 | 70.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) | +| GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 7.1 | 7.5 | 71.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) | + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.5 | | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json) | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.5 | | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) | +| GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 9.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json) | +| GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.8 | 7.3 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json) | +| GA-RetinaNet | R-50-FPN | caffe | 1x | 3.5 | 16.8 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json) | +| GA-RetinaNet | R-101-FPN | caffe | 1x | 5.5 | 12.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json) | +| GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.9 | 10.6 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json) | +| GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 7.7 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json) | + +- In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring. + +- Performance on COCO test-dev benchmark are shown as follows. + +| Method | Backbone | Style | Lr schd | Aug Train | Score thr | AP | AP_50 | AP_75 | AP_small | AP_medium | AP_large | Download | +| :------------: | :-------: | :---: | :-----: | :-------: | :-------: | :-: | :---: | :---: | :------: | :-------: | :------: | :------: | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | F | 0.05 | | | | | | | | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | F | 0.001 | | | | | | | | +| GA-RetinaNet | R-101-FPN | caffe | 1x | F | 0.05 | | | | | | | | +| GA-RetinaNet | R-101-FPN | caffe | 2x | T | 0.05 | | | | | | | | + +## Citation + +We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278). + +```latex +@inproceedings{wang2019region, + title={Region Proposal by Guided Anchoring}, + author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..8fc203c --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(num=256))), + test_cfg=dict(rcnn=dict(score_thr=1e-3))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..a40e7c6 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './ga_faster_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..b0add92 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5), + rpn_proposal=dict(nms_post=1000, max_per_img=300), + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py new file mode 100644 index 0000000..e3d8238 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5), + rpn_proposal=dict(nms_post=1000, max_per_img=300), + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..f1dda94 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ga_faster_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..fb9e2af --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ga_faster_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..1b1cccd --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py new file mode 100644 index 0000000..260895b --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py @@ -0,0 +1,169 @@ +_base_ = '../_base_/default_runtime.py' + +# model settings +model = dict( + type='RetinaNet', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5), + bbox_head=dict( + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0))) +# training and testing settings +train_cfg = dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + center_ratio=0.2, + ignore_ratio=0.5, + debug=False) +test_cfg = dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 960)], + keep_ratio=True, + multiscale_mode='range'), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[16, 22]) +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..3351201 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0), + center_ratio=0.2, + ignore_ratio=0.5)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..7694723 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0), + center_ratio=0.2, + ignore_ratio=0.5)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..c5eb34f --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ga_retinanet_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..5c69a6f --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ga_retinanet_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..039703e --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py' +# model settings +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..7830894 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5)), + test_cfg=dict(rpn=dict(nms_post=1000))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..27ab3e7 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = '../rpn/rpn_r50_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5)), + test_cfg=dict(rpn=dict(nms_post=1000))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..cccc985 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ga_rpn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..4e134d2 --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ga_rpn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/guided_anchoring/metafile.yml b/downstream/mmdetection/configs/guided_anchoring/metafile.yml new file mode 100644 index 0000000..3019d4a --- /dev/null +++ b/downstream/mmdetection/configs/guided_anchoring/metafile.yml @@ -0,0 +1,246 @@ +Collections: + - Name: Guided Anchoring + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - Guided Anchoring + - ResNet + Paper: + URL: https://arxiv.org/abs/1901.03278 + Title: 'Region Proposal by Guided Anchoring' + README: configs/guided_anchoring/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/dense_heads/ga_retina_head.py#L10 + Version: v2.0.0 + +Models: + - Name: ga_rpn_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.3 + inference time (ms/im): + - value: 63.29 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 68.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth + + - Name: ga_rpn_r101_caffe_fpn_1x_coco.py + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py.py + Metadata: + Training Memory (GB): 7.3 + inference time (ms/im): + - value: 76.92 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 69.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth + + - Name: ga_rpn_x101_32x4d_fpn_1x_coco.py + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py.py + Metadata: + Training Memory (GB): 8.5 + inference time (ms/im): + - value: 100 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 70.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth + + - Name: ga_rpn_x101_64x4d_fpn_1x_coco.py.py + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py.py.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 133.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Region Proposal + Dataset: COCO + Metrics: + AR@1000: 70.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth + + - Name: ga_faster_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth + + - Name: ga_faster_r101_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth + + - Name: ga_faster_x101_32x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.7 + inference time (ms/im): + - value: 103.09 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth + + - Name: ga_faster_x101_64x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 11.8 + inference time (ms/im): + - value: 136.99 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth + + - Name: ga_retinanet_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.5 + inference time (ms/im): + - value: 59.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth + + - Name: ga_retinanet_r101_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth + + - Name: ga_retinanet_x101_32x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.9 + inference time (ms/im): + - value: 94.34 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth + + - Name: ga_retinanet_x101_64x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (ms/im): + - value: 129.87 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth diff --git a/downstream/mmdetection/configs/hrnet/README.md b/downstream/mmdetection/configs/hrnet/README.md new file mode 100644 index 0000000..e340c78 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/README.md @@ -0,0 +1,101 @@ +# HRNet + +> [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1902.09212) + + + +## Abstract + +This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset. + +High-resolution representation learning plays an essential role in many vision problems, e.g., pose estimation and semantic segmentation. The high-resolution network (HRNet), recently developed for human pose estimation, maintains high-resolution representations through the whole process by connecting high-to-low resolution convolutions in parallel and produces strong high-resolution representations by repeatedly conducting fusions across parallel convolutions. +In this paper, we conduct a further study on high-resolution representations by introducing a simple yet effective modification and apply it to a wide range of vision tasks. We augment the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions rather than only the representation from the high-resolution convolution as done in HRNet. This simple modification leads to stronger representations, evidenced by superior results. We show top results in semantic segmentation on Cityscapes, LIP, and PASCAL Context, and facial landmark detection on AFLW, COFW, 300W, and WFLW. In addition, we build a multi-level representation from the high-resolution representation and apply it to the Faster R-CNN object detection framework and the extended frameworks. The proposed approach achieves superior results to existing single-model networks on COCO object detection. + +
    + +
    + +## Results and Models + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HRNetV2p-W18 | pytorch | 1x | 6.6 | 13.4 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130_211246.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 6.6 | - | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.0 | 12.4 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130_204442.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.0 | - | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.4 | 10.5 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210_125315.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.4 | - | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HRNetV2p-W18 | pytorch | 1x | 7.0 | 11.7 | 37.7 | 34.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205_232523.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 7.0 | - | 39.8 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212_134222.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.4 | 11.3 | 41.2 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207_055017.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.4 | - | 42.5 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213_150518.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.9 | | 42.1 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.9 | | 42.8 | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732.log.json) | + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HRNetV2p-W18 | pytorch | 20e | 7.0 | 11.0 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210_105632.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 9.4 | 11.0 | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208_160511.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 10.8 | | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HRNetV2p-W18 | pytorch | 20e | 8.5 | 8.5 | 41.6 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json) | +| HRNetV2p-W32 | pytorch | 20e | | 8.3 | 44.3 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 12.5 | | 45.1 | 39.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922.log.json) | + +### Hybrid Task Cascade (HTC) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HRNetV2p-W18 | pytorch | 20e | 10.8 | 4.7 | 42.8 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210_182735.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 13.1 | 4.9 | 45.4 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207_193153.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 14.6 | | 46.4 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411.log.json) | + +### FCOS + +| Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :----------: | :-----: | :-: | :------: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| HRNetV2p-W18 | pytorch | Y | N | 1x | 13.0 | 12.9 | 35.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710.log.json) | +| HRNetV2p-W18 | pytorch | Y | N | 2x | 13.0 | - | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110.log.json) | +| HRNetV2p-W32 | pytorch | Y | N | 1x | 17.5 | 12.9 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730.log.json) | +| HRNetV2p-W32 | pytorch | Y | N | 2x | 17.5 | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133.log.json) | +| HRNetV2p-W18 | pytorch | Y | Y | 2x | 13.0 | 12.9 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651.log.json) | +| HRNetV2p-W32 | pytorch | Y | Y | 2x | 17.5 | 12.4 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846.log.json) | +| HRNetV2p-W48 | pytorch | Y | Y | 2x | 20.3 | 10.8 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752.log.json) | + +**Note:** + +- The `28e` schedule in HTC indicates decreasing the lr at 24 and 27 epochs, with a total of 28 epochs. +- HRNetV2 ImageNet pretrained models are in [HRNets for Image Classification](https://github.com/HRNet/HRNet-Image-Classification). + +## Citation + +```latex +@inproceedings{SunXLW19, + title={Deep High-Resolution Representation Learning for Human Pose Estimation}, + author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, + booktitle={CVPR}, + year={2019} +} + +@article{SunZJCXLMWLW19, + title={High-Resolution Representations for Labeling Pixels and Regions}, + author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao + and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang}, + journal = {CoRR}, + volume = {abs/1904.04514}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py new file mode 100644 index 0000000..839cf3e --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,11 @@ +_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py b/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py new file mode 100644 index 0000000..9942602 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,40 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py b/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py new file mode 100644 index 0000000..10d5e83 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,12 @@ +_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py b/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py new file mode 100644 index 0000000..ebd5e20 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,11 @@ +_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py b/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py new file mode 100644 index 0000000..e7f89a9 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,40 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py b/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py new file mode 100644 index 0000000..265e8d6 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,12 @@ +_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py new file mode 100644 index 0000000..1df2c3d --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +# model settings +model = dict( + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py new file mode 100644 index 0000000..a4b987a --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py new file mode 100644 index 0000000..be05809 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py new file mode 100644 index 0000000..63c8717 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py new file mode 100644 index 0000000..886a7c9 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +model = dict( + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py new file mode 100644 index 0000000..585cc2c --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..fd662bd --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +model = dict( + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..3497595 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..37bfdae --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' +model = dict( + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..10617f2 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py @@ -0,0 +1,70 @@ +_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py' +model = dict( + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256, + stride=2, + num_outs=5)) +img_norm_cfg = dict( + mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py new file mode 100644 index 0000000..7b38130 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..482f887 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +img_norm_cfg = dict( + mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 0000000..0ae9dbe --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,11 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' +model = dict( + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py new file mode 100644 index 0000000..3c2eb1d --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,10 @@ +_base_ = './htc_hrnetv2p_w32_20e_coco.py' +model = dict( + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py new file mode 100644 index 0000000..545cb83 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,37 @@ +_base_ = '../htc/htc_r50_fpn_20e_coco.py' +model = dict( + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py new file mode 100644 index 0000000..94bff1b --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,11 @@ +_base_ = './htc_hrnetv2p_w32_20e_coco.py' +model = dict( + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py new file mode 100644 index 0000000..7067e8b --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py @@ -0,0 +1,4 @@ +_base_ = './htc_hrnetv2p_w40_20e_coco.py' +# learning policy +lr_config = dict(step=[24, 27]) +runner = dict(type='EpochBasedRunner', max_epochs=28) diff --git a/downstream/mmdetection/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py b/downstream/mmdetection/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py new file mode 100644 index 0000000..815f285 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py @@ -0,0 +1,4 @@ +_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py' +# learning policy +lr_config = dict(step=[24, 27]) +runner = dict(type='EpochBasedRunner', max_epochs=28) diff --git a/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py new file mode 100644 index 0000000..cb12200 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' +model = dict( + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py new file mode 100644 index 0000000..ca62682 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py new file mode 100644 index 0000000..d5f0eb5 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py new file mode 100644 index 0000000..63d5d13 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py new file mode 100644 index 0000000..5a76f4b --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' +model = dict( + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320))), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py new file mode 100644 index 0000000..3a2a510 --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/hrnet/metafile.yml b/downstream/mmdetection/configs/hrnet/metafile.yml new file mode 100644 index 0000000..ac36efa --- /dev/null +++ b/downstream/mmdetection/configs/hrnet/metafile.yml @@ -0,0 +1,971 @@ +Models: + - Name: faster_rcnn_hrnetv2p_w18_1x_coco + In Collection: Faster R-CNN + Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py + Metadata: + Training Memory (GB): 6.6 + inference time (ms/im): + - value: 74.63 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: faster_rcnn_hrnetv2p_w18_2x_coco + In Collection: Faster R-CNN + Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py + Metadata: + Training Memory (GB): 6.6 + inference time (ms/im): + - value: 74.63 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: faster_rcnn_hrnetv2p_w32_1x_coco + In Collection: Faster R-CNN + Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (ms/im): + - value: 80.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: faster_rcnn_hrnetv2p_w32_2x_coco + In Collection: Faster R-CNN + Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (ms/im): + - value: 80.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: faster_rcnn_hrnetv2p_w40_1x_coco + In Collection: Faster R-CNN + Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py + Metadata: + Training Memory (GB): 10.4 + inference time (ms/im): + - value: 95.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: faster_rcnn_hrnetv2p_w40_2x_coco + In Collection: Faster R-CNN + Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py + Metadata: + Training Memory (GB): 10.4 + inference time (ms/im): + - value: 95.24 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: mask_rcnn_hrnetv2p_w18_1x_coco + In Collection: Mask R-CNN + Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: mask_rcnn_hrnetv2p_w18_2x_coco + In Collection: Mask R-CNN + Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 85.47 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: mask_rcnn_hrnetv2p_w32_1x_coco + In Collection: Mask R-CNN + Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: mask_rcnn_hrnetv2p_w32_2x_coco + In Collection: Mask R-CNN + Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: mask_rcnn_hrnetv2p_w40_1x_coco + In Collection: Mask R-CNN + Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py + Metadata: + Training Memory (GB): 10.9 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: mask_rcnn_hrnetv2p_w40_2x_coco + In Collection: Mask R-CNN + Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py + Metadata: + Training Memory (GB): 10.9 + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: cascade_rcnn_hrnetv2p_w18_20e_coco + In Collection: Cascade R-CNN + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: cascade_rcnn_hrnetv2p_w32_20e_coco + In Collection: Cascade R-CNN + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: cascade_rcnn_hrnetv2p_w40_20e_coco + In Collection: Cascade R-CNN + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 10.8 + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: cascade_mask_rcnn_hrnetv2p_w18_20e_coco + In Collection: Cascade R-CNN + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 8.5 + inference time (ms/im): + - value: 117.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: cascade_mask_rcnn_hrnetv2p_w32_20e_coco + In Collection: Cascade R-CNN + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py + Metadata: + inference time (ms/im): + - value: 120.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: cascade_mask_rcnn_hrnetv2p_w40_20e_coco + In Collection: Cascade R-CNN + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 12.5 + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: htc_hrnetv2p_w18_20e_coco + In Collection: HTC + Config: configs/hrnet/htc_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (ms/im): + - value: 212.77 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: htc_hrnetv2p_w32_20e_coco + In Collection: HTC + Config: configs/hrnet/htc_hrnetv2p_w32_20e_coco.py + Metadata: + Training Memory (GB): 13.1 + inference time (ms/im): + - value: 204.08 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: htc_hrnetv2p_w40_20e_coco + In Collection: HTC + Config: configs/hrnet/htc_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 14.6 + Epochs: 20 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: fcos_hrnetv2p_w18_gn-head_4x4_1x_coco + In Collection: FCOS + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 13.0 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: fcos_hrnetv2p_w18_gn-head_4x4_2x_coco + In Collection: FCOS + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 13.0 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: fcos_hrnetv2p_w32_gn-head_4x4_1x_coco + In Collection: FCOS + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 17.5 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: fcos_hrnetv2p_w32_gn-head_4x4_2x_coco + In Collection: FCOS + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 17.5 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FCOS + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 13.0 + inference time (ms/im): + - value: 77.52 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FCOS + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 17.5 + inference time (ms/im): + - value: 80.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 + + - Name: fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FCOS + Config: configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Resources: 4x V100 GPUs + Batch Size: 16 + Training Memory (GB): 20.3 + inference time (ms/im): + - value: 92.59 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Architecture: + - HRNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth + Paper: + URL: https://arxiv.org/abs/1904.04514 + Title: 'Deep High-Resolution Representation Learning for Visual Recognition' + README: configs/hrnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195 + Version: v2.0.0 diff --git a/downstream/mmdetection/configs/htc/README.md b/downstream/mmdetection/configs/htc/README.md new file mode 100644 index 0000000..03a89aa --- /dev/null +++ b/downstream/mmdetection/configs/htc/README.md @@ -0,0 +1,67 @@ +# HTC + +> [Hybrid Task Cascade for Instance Segmentation](ttps://arxiv.org/abs/1901.07518) + + + +## Abstract + +Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4 and 1.5 improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task. + +
    + +
    + +## Introduction + +HTC requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 8.2 | 5.8 | 42.3 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317_070435.log.json) | +| R-50-FPN | pytorch | 20e | 8.2 | - | 43.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319_070313.log.json) | +| R-101-FPN | pytorch | 20e | 10.2 | 5.5 | 44.8 | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317_153107.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 11.4 | 5.0 | 46.1 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318_034519.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 14.5 | 4.4 | 47.0 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318_081711.log.json) | + +- In the HTC paper and COCO 2018 Challenge, `score_thr` is set to 0.001 for both baselines and HTC. +- We use 8 GPUs with 2 images/GPU for R-50 and R-101 models, and 16 GPUs with 1 image/GPU for X-101 models. + If you would like to train X-101 HTC with 8 GPUs, you need to change the lr from 0.02 to 0.01. + +We also provide a powerful HTC with DCN and multi-scale training model. No testing augmentation is used. + +| Backbone | Style | DCN | training scales | Lr schd | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :---: | :-------------: | :-----: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| X-101-64x4d-FPN | pytorch | c3-c5 | 400~1400 | 20e | 50.4 | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312_203410.log.json) | + +## Citation + +We provide config files to reproduce the results in the CVPR 2019 paper for [Hybrid Task Cascade](https://arxiv.org/abs/1901.07518). + +```latex +@inproceedings{chen2019hybrid, + title={Hybrid task cascade for instance segmentation}, + author={Chen, Kai and Pang, Jiangmiao and Wang, Jiaqi and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Shi, Jianping and Ouyang, Wanli and Chen Change Loy and Dahua Lin}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/htc/htc_r101_fpn_20e_coco.py b/downstream/mmdetection/configs/htc/htc_r101_fpn_20e_coco.py new file mode 100644 index 0000000..b42297b --- /dev/null +++ b/downstream/mmdetection/configs/htc/htc_r101_fpn_20e_coco.py @@ -0,0 +1,9 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/htc/htc_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/htc/htc_r50_fpn_1x_coco.py new file mode 100644 index 0000000..1e8e18a --- /dev/null +++ b/downstream/mmdetection/configs/htc/htc_r50_fpn_1x_coco.py @@ -0,0 +1,56 @@ +_base_ = './htc_without_semantic_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + semantic_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[8]), + semantic_head=dict( + type='FusedSemanticHead', + num_ins=5, + fusion_level=1, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=183, + loss_seg=dict( + type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2)))) +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict( + seg_prefix=data_root + 'stuffthingmaps/train2017/', + pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/htc/htc_r50_fpn_20e_coco.py b/downstream/mmdetection/configs/htc/htc_r50_fpn_20e_coco.py new file mode 100644 index 0000000..7d2e011 --- /dev/null +++ b/downstream/mmdetection/configs/htc/htc_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py new file mode 100644 index 0000000..565104f --- /dev/null +++ b/downstream/mmdetection/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py @@ -0,0 +1,236 @@ +_base_ = [ + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='HybridTaskCascade', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='HybridTaskCascadeRoIHead', + interleaved=True, + mask_info_flow=True, + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=[ + dict( + type='HTCMaskHead', + with_conv_res=False, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)) + ]), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.001, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py b/downstream/mmdetection/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py new file mode 100644 index 0000000..0c834f2 --- /dev/null +++ b/downstream/mmdetection/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py @@ -0,0 +1,19 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) +data = dict(samples_per_gpu=1, workers_per_gpu=1) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py b/downstream/mmdetection/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py new file mode 100644 index 0000000..8b0d962 --- /dev/null +++ b/downstream/mmdetection/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py @@ -0,0 +1,19 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) +data = dict(samples_per_gpu=1, workers_per_gpu=1) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py b/downstream/mmdetection/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py new file mode 100644 index 0000000..c8d8703 --- /dev/null +++ b/downstream/mmdetection/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py @@ -0,0 +1,43 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict( + type='Resize', + img_scale=[(1600, 400), (1600, 1400)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +data = dict( + samples_per_gpu=1, workers_per_gpu=1, train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/downstream/mmdetection/configs/htc/metafile.yml b/downstream/mmdetection/configs/htc/metafile.yml new file mode 100644 index 0000000..acd038c --- /dev/null +++ b/downstream/mmdetection/configs/htc/metafile.yml @@ -0,0 +1,165 @@ +Collections: + - Name: HTC + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - HTC + - RPN + - ResNet + - ResNeXt + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1901.07518 + Title: 'Hybrid Task Cascade for Instance Segmentation' + README: configs/htc/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/htc.py#L6 + Version: v2.0.0 + +Models: + - Name: htc_r50_fpn_1x_coco + In Collection: HTC + Config: configs/htc/htc_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.2 + inference time (ms/im): + - value: 172.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth + + - Name: htc_r50_fpn_20e_coco + In Collection: HTC + Config: configs/htc/htc_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 8.2 + inference time (ms/im): + - value: 172.41 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth + + - Name: htc_r101_fpn_20e_coco + In Collection: HTC + Config: configs/htc/htc_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 10.2 + inference time (ms/im): + - value: 181.82 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth + + - Name: htc_x101_32x4d_fpn_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py + Metadata: + Training Resources: 16x V100 GPUs + Batch Size: 16 + Training Memory (GB): 11.4 + inference time (ms/im): + - value: 200 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth + + - Name: htc_x101_64x4d_fpn_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py + Metadata: + Training Resources: 16x V100 GPUs + Batch Size: 16 + Training Memory (GB): 14.5 + inference time (ms/im): + - value: 227.27 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth + + - Name: htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py + Metadata: + Training Resources: 16x V100 GPUs + Batch Size: 16 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth diff --git a/downstream/mmdetection/configs/instaboost/README.md b/downstream/mmdetection/configs/instaboost/README.md new file mode 100644 index 0000000..82ed334 --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/README.md @@ -0,0 +1,58 @@ +# Instaboost + +> [Instaboost: Boosting instance segmentation via probability map guided copy-pasting](https://arxiv.org/abs/1908.07801) + + + +## Abstract + +Instance segmentation requires a large number of training samples to achieve satisfactory performance and benefits from proper data augmentation. To enlarge the training set and increase the diversity, previous methods have investigated using data annotation from other domain (e.g. bbox, point) in a weakly supervised mechanism. In this paper, we present a simple, efficient and effective method to augment the training set using the existing instance mask annotations. Exploiting the pixel redundancy of the background, we are able to improve the performance of Mask R-CNN for 1.7 mAP on COCO dataset and 3.3 mAP on Pascal VOC dataset by simply introducing random jittering to objects. Furthermore, we propose a location probability map based approach to explore the feasible locations that objects can be placed based on local appearance similarity. With the guidance of such map, we boost the performance of R101-Mask R-CNN on instance segmentation from 35.7 mAP to 37.9 mAP without modifying the backbone or network structure. Our method is simple to implement and does not increase the computational complexity. It can be integrated into the training pipeline of any instance segmentation model without affecting the training and inference efficiency. + +
    + +
    + +## Introduction + +Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801). + +## Usage + +### Requirements + +You need to install `instaboostfast` before using it. + +```shell +pip install instaboostfast +``` + +The code and more details can be found [here](https://github.com/GothicAi/Instaboost). + +### Integration with MMDetection + +InstaBoost have been already integrated in the data pipeline, thus all you need is to add or change **InstaBoost** configurations after **LoadImageFromFile**. We have provided examples like [this](mask_rcnn_r50_fpn_instaboost_4x#L121). You can refer to [`InstaBoostConfig`](https://github.com/GothicAi/InstaBoost-pypi#instaboostconfig) for more details. + +## Results and Models + +- All models were trained on `coco_2017_train` and tested on `coco_2017_val` for convenience of evaluation and comparison. In the paper, the results are obtained from `test-dev`. +- To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework. +- For results and models in MMDetection V1.x, please refer to [Instaboost](https://github.com/GothicAi/Instaboost). + +| Network | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-----------: | :-------------: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Mask R-CNN | R-50-FPN | 4x | 4.4 | 17.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json) | +| Mask R-CNN | R-101-FPN | 4x | 6.4 | | 42.5 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json) | +| Mask R-CNN | X-101-64x4d-FPN | 4x | 10.7 | | 44.7 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) | +| Cascade R-CNN | R-101-FPN | 4x | 6.0 | 12.0 | 43.7 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) | + +## Citation + +```latex +@inproceedings{fang2019instaboost, + title={Instaboost: Boosting instance segmentation via probability map guided copy-pasting}, + author={Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu}, + booktitle={Proceedings of the IEEE International Conference on Computer Vision}, + pages={682--691}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py b/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..9d0515d --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -0,0 +1,7 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py b/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..a89a81f --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='InstaBoost', + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[32, 44]) +runner = dict(type='EpochBasedRunner', max_epochs=48) diff --git a/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..d67b799 --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py b/downstream/mmdetection/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..ebbb43e --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py b/downstream/mmdetection/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..55ca62b --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='InstaBoost', + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[32, 44]) +runner = dict(type='EpochBasedRunner', max_epochs=48) diff --git a/downstream/mmdetection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/downstream/mmdetection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py new file mode 100644 index 0000000..2010f44 --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/instaboost/metafile.yml b/downstream/mmdetection/configs/instaboost/metafile.yml new file mode 100644 index 0000000..325283d --- /dev/null +++ b/downstream/mmdetection/configs/instaboost/metafile.yml @@ -0,0 +1,99 @@ +Collections: + - Name: InstaBoost + Metadata: + Training Data: COCO + Training Techniques: + - InstaBoost + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Paper: + URL: https://arxiv.org/abs/1908.07801 + Title: 'Instaboost: Boosting instance segmentation via probability map guided copy-pasting' + README: configs/instaboost/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/datasets/pipelines/instaboost.py#L7 + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 57.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth + + - Name: mask_rcnn_r101_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth + + - Name: mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth + + - Name: cascade_mask_rcnn_r50_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (ms/im): + - value: 83.33 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth diff --git a/downstream/mmdetection/configs/lad/README.md b/downstream/mmdetection/configs/lad/README.md new file mode 100644 index 0000000..6cad7d9 --- /dev/null +++ b/downstream/mmdetection/configs/lad/README.md @@ -0,0 +1,44 @@ +# LAD + +> [Improving Object Detection by Label Assignment Distillation](https://arxiv.org/abs/2108.10520) + + + +## Abstract + +Label assignment in object detection aims to assign targets, foreground or background, to sampled regions in an image. Unlike labeling for image classification, this problem is not well defined due to the object's bounding box. In this paper, we investigate the problem from a perspective of distillation, hence we call Label Assignment Distillation (LAD). Our initial motivation is very simple, we use a teacher network to generate labels for the student. This can be achieved in two ways: either using the teacher's prediction as the direct targets (soft label), or through the hard labels dynamically assigned by the teacher (LAD). Our experiments reveal that: (i) LAD is more effective than soft-label, but they are complementary. (ii) Using LAD, a smaller teacher can also improve a larger student significantly, while soft-label can't. We then introduce Co-learning LAD, in which two networks simultaneously learn from scratch and the role of teacher and student are dynamically interchanged. Using PAA-ResNet50 as a teacher, our LAD techniques can improve detectors PAA-ResNet101 and PAA-ResNeXt101 to 46AP and 47.5AP on the COCO test-dev set. With a stronger teacher PAA-SwinB, we improve the students PAA-ResNet50 to 43.7AP by only 1x schedule training and standard setting, and PAA-ResNet101 to 47.9AP, significantly surpassing the current methods. + +
    + +
    + +## Results and Models + +We provide config files to reproduce the object detection results in the +WACV 2022 paper for Improving Object Detection by Label Assignment +Distillation. + +### PAA with LAD + +| Teacher | Student | Training schedule | AP (val) | Config | +| :-----: | :-----: | :---------------: | :------: | :---------------------------------------------------: | +| -- | R-50 | 1x | 40.4 | | +| -- | R-101 | 1x | 42.6 | | +| R-101 | R-50 | 1x | 41.6 | [config](configs/lad/lad_r50_paa_r101_fpn_coco_1x.py) | +| R-50 | R-101 | 1x | 43.2 | [config](configs/lad/lad_r101_paa_r50_fpn_coco_1x.py) | + +## Note + +- Meaning of Config name: lad_r50(student model)\_paa(based on paa)\_r101(teacher model)\_fpn(neck)\_coco(dataset)\_1x(12 epoch).py +- Results may fluctuate by about 0.2 mAP. + +## Citation + +```latex +@inproceedings{nguyen2021improving, + title={Improving Object Detection by Label Assignment Distillation}, + author={Chuong H. Nguyen and Thuy C. Nguyen and Tuan N. Tang and Nam L. H. Phan}, + booktitle = {WACV}, + year={2022} +} +``` diff --git a/downstream/mmdetection/configs/lad/lad_r101_paa_r50_fpn_coco_1x.py b/downstream/mmdetection/configs/lad/lad_r101_paa_r50_fpn_coco_1x.py new file mode 100644 index 0000000..4877d95 --- /dev/null +++ b/downstream/mmdetection/configs/lad/lad_r101_paa_r50_fpn_coco_1x.py @@ -0,0 +1,126 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa +model = dict( + type='LAD', + # student + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='LADHead', + reg_decoded_bbox=True, + score_voting=True, + topk=9, + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), + # teacher + teacher_ckpt=teacher_ckpt, + teacher_backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + teacher_neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + teacher_bbox_head=dict( + type='LADHead', + reg_decoded_bbox=True, + score_voting=True, + topk=9, + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.1, + neg_iou_thr=0.1, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + score_voting=True, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +data = dict(samples_per_gpu=8, workers_per_gpu=4) +optimizer = dict(lr=0.01) +fp16 = dict(loss_scale=512.) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/lad/lad_r50_paa_r101_fpn_coco_1x.py b/downstream/mmdetection/configs/lad/lad_r50_paa_r101_fpn_coco_1x.py new file mode 100644 index 0000000..29bbe69 --- /dev/null +++ b/downstream/mmdetection/configs/lad/lad_r50_paa_r101_fpn_coco_1x.py @@ -0,0 +1,125 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' # noqa +model = dict( + type='LAD', + # student + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='LADHead', + reg_decoded_bbox=True, + score_voting=True, + topk=9, + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), + # teacher + teacher_ckpt=teacher_ckpt, + teacher_backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + teacher_neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + teacher_bbox_head=dict( + type='LADHead', + reg_decoded_bbox=True, + score_voting=True, + topk=9, + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.1, + neg_iou_thr=0.1, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + score_voting=True, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +data = dict(samples_per_gpu=8, workers_per_gpu=4) +optimizer = dict(lr=0.01) +fp16 = dict(loss_scale=512.) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/lad/metafile.yml b/downstream/mmdetection/configs/lad/metafile.yml new file mode 100644 index 0000000..5076f28 --- /dev/null +++ b/downstream/mmdetection/configs/lad/metafile.yml @@ -0,0 +1,42 @@ +Collections: + - Name: Label Assignment Distillation + Metadata: + Training Data: COCO + Training Techniques: + - Label Assignment Distillation + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/2108.10520 + Title: 'Improving Object Detection by Label Assignment Distillation' + README: configs/lad/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.19.0/mmdet/models/detectors/lad.py#L10 + Version: v2.19.0 + +Models: + - Name: lad_r50_paa_r101_fpn_coco_1x + In Collection: Label Assignment Distillation + Config: configs/lad/lad_r50_paa_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + + - Name: lad_r101_paa_r50_fpn_coco_1x + In Collection: Label Assignment Distillation + Config: configs/lad/lad_r101_paa_r50_fpn_coco_1x.py + Metadata: + Teacher: R-50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.2 diff --git a/downstream/mmdetection/configs/ld/README.md b/downstream/mmdetection/configs/ld/README.md new file mode 100644 index 0000000..6556542 --- /dev/null +++ b/downstream/mmdetection/configs/ld/README.md @@ -0,0 +1,43 @@ +# LD + +> [Localization Distillation for Dense Object Detection](https://arxiv.org/abs/2102.12252) + + + +## Abstract + +Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logits due to its inefficiency in distilling localization information. In this paper, by reformulating the knowledge distillation process on localization, we present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Moreover, we also heuristically introduce the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and localization knowledge distillation is more important and efficient than semantic knowledge for distilling object detectors. Our distillation scheme is simple as well as effective and can be easily applied to different dense object detectors. Experiments show that our LD can boost the AP score of GFocal-ResNet-50 with a single-scale 1× training schedule from 40.1 to 42.1 on the COCO benchmark without any sacrifice on the inference speed. + +
    + +
    + +## Results and Models + +### GFocalV1 with LD + +| Teacher | Student | Training schedule | Mini-batch size | AP (val) | AP50 (val) | AP75 (val) | Config | +| :-------: | :-----: | :---------------: | :-------------: | :------: | :--------: | :--------: | :-------------------------------------------------------------------------------------------------------------: | +| -- | R-18 | 1x | 6 | 35.8 | 53.1 | 38.2 | | +| R-101 | R-18 | 1x | 6 | 36.5 | 52.9 | 39.3 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py) | +| -- | R-34 | 1x | 6 | 38.9 | 56.6 | 42.2 | | +| R-101 | R-34 | 1x | 6 | 39.8 | 56.6 | 43.1 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py) | +| -- | R-50 | 1x | 6 | 40.1 | 58.2 | 43.1 | | +| R-101 | R-50 | 1x | 6 | 41.1 | 58.7 | 44.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py) | +| -- | R-101 | 2x | 6 | 44.6 | 62.9 | 48.4 | | +| R-101-DCN | R-101 | 2x | 6 | 45.4 | 63.1 | 49.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py) | + +## Note + +- Meaning of Config name: ld_r18(student model)\_gflv1(based on gflv1)\_r101(teacher model)\_fpn(neck)\_coco(dataset)\_1x(12 epoch).py + +## Citation + +```latex +@Inproceedings{zheng2022LD, + title={Localization Distillation for Dense Object Detection}, + author= {Zheng, Zhaohui and Ye, Rongguang and Wang, Ping and Ren, Dongwei and Zuo, Wangmeng and Hou, Qibin and Cheng, Mingming}, + booktitle={CVPR}, + year={2022} +} +``` diff --git a/downstream/mmdetection/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py b/downstream/mmdetection/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py new file mode 100644 index 0000000..1cbdb4c --- /dev/null +++ b/downstream/mmdetection/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py @@ -0,0 +1,44 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa +model = dict( + teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) + +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +# multi-scale training +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/downstream/mmdetection/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py b/downstream/mmdetection/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..18dce81 --- /dev/null +++ b/downstream/mmdetection/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,62 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa +model = dict( + type='KnowledgeDistillationSingleStageDetector', + teacher_config='configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='LDHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), + loss_ld=dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10), + reg_max=16, + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py b/downstream/mmdetection/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..3b6996d --- /dev/null +++ b/downstream/mmdetection/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + backbone=dict( + type='ResNet', + depth=34, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet34')), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/downstream/mmdetection/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py b/downstream/mmdetection/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 0000000..2b18785 --- /dev/null +++ b/downstream/mmdetection/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/downstream/mmdetection/configs/ld/metafile.yml b/downstream/mmdetection/configs/ld/metafile.yml new file mode 100644 index 0000000..d555a6d --- /dev/null +++ b/downstream/mmdetection/configs/ld/metafile.yml @@ -0,0 +1,72 @@ +Collections: + - Name: Localization Distillation + Metadata: + Training Data: COCO + Training Techniques: + - Localization Distillation + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/2102.12252 + Title: 'Localization Distillation for Dense Object Detection' + README: configs/ld/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.11.0/mmdet/models/dense_heads/ld_head.py#L11 + Version: v2.11.0 + +Models: + - Name: ld_r18_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + box AP@0.5: 52.9 + box AP@0.75: 39.3 + + - Name: ld_r34_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + box AP@0.5: 56.6 + box AP@0.75: 43.1 + + - Name: ld_r50_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + box AP@0.5: 58.7 + box AP@0.75: 44.9 + + - Name: ld_r101_gflv1_r101dcn_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py + Metadata: + Teacher: R-101-DCN + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + box AP@0.5: 63.1 + box AP@0.75: 49.5 diff --git a/downstream/mmdetection/configs/legacy_1.x/README.md b/downstream/mmdetection/configs/legacy_1.x/README.md new file mode 100644 index 0000000..c48477f --- /dev/null +++ b/downstream/mmdetection/configs/legacy_1.x/README.md @@ -0,0 +1,54 @@ +# Legacy Configs in MMDetection V1.x + + + +Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos. + +To help users convert their models from V1.x to MMDetection V2.0, we provide v1.x configs to inference the converted v1.x models. +Due to the BC-breaking changes in MMDetection V2.0 from MMDetection V1.x, running inference with the same model weights in these two version will produce different results. The difference will cause within 1% AP absolute difference as can be found in the following table. + +## Usage + +To upgrade the model version, the users need to do the following steps. + +### 1. Convert model weights + +There are three main difference in the model weights between V1.x and V2.0 codebases. + +1. Since the class order in all the detector's classification branch is reordered, all the legacy model weights need to go through the conversion process. +2. The regression and segmentation head no longer contain the background channel. Weights in these background channels should be removed to fix in the current codebase. +3. For two-stage detectors, their wegihts need to be upgraded since MMDetection V2.0 refactors all the two-stage detectors with `RoIHead`. + +The users can do the same modification as mentioned above for the self-implemented +detectors. We provide a scripts `tools/model_converters/upgrade_model_version.py` to convert the model weights in the V1.x model zoo. + +```bash +python tools/model_converters/upgrade_model_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH} --num-classes ${NUM_CLASSES} + +``` + +- OLD_MODEL_PATH: the path to load the model weights in 1.x version. +- NEW_MODEL_PATH: the path to save the converted model weights in 2.0 version. +- NUM_CLASSES: number of classes of the original model weights. Usually it is 81 for COCO dataset, 21 for VOC dataset. + The number of classes in V2.0 models should be equal to that in V1.x models - 1. + +### 2. Use configs with legacy settings + +After converting the model weights, checkout to the v1.2 release to find the corresponding config file that uses the legacy settings. +The V1.x models usually need these three legacy modules: `LegacyAnchorGenerator`, `LegacyDeltaXYWHBBoxCoder`, and `RoIAlign(align=False)`. +For models using ResNet Caffe backbones, they also need to change the pretrain name and the corresponding `img_norm_cfg`. +An example is in [`retinanet_r50_caffe_fpn_1x_coco_v1.py`](retinanet_r50_caffe_fpn_1x_coco_v1.py) +Then use the config to test the model weights. For most models, the obtained results should be close to that in V1.x. +We provide configs of some common structures in this directory. + +## Performance + +The performance change after converting the models in this directory are listed as the following. + +| Method | Style | Lr schd | V1.x box AP | V1.x mask AP | V2.0 box AP | V2.0 mask AP | Config | Download | +| :-------------------------: | :-----: | :-----: | :---------: | :----------: | :---------: | :----------: | :------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------: | +| Mask R-CNN R-50-FPN | pytorch | 1x | 37.3 | 34.2 | 36.8 | 33.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth) | +| RetinaNet R-50-FPN | caffe | 1x | 35.8 | - | 35.4 | - | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_caffe_1x_coco_v1.py) | | +| RetinaNet R-50-FPN | pytorch | 1x | 35.6 | - | 35.2 | - | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth) | +| Cascade Mask R-CNN R-50-FPN | pytorch | 1x | 41.2 | 35.7 | 40.8 | 35.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) | +| SSD300-VGG16 | caffe | 120e | 25.7 | - | 25.4 | - | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/ssd300_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth) | diff --git a/downstream/mmdetection/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py b/downstream/mmdetection/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..fc9d004 --- /dev/null +++ b/downstream/mmdetection/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='CascadeRCNN', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5), + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0])), + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2])), + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1])), + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067])), + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)))) +dist_params = dict(backend='nccl', port=29515) diff --git a/downstream/mmdetection/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py b/downstream/mmdetection/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..8c573be --- /dev/null +++ b/downstream/mmdetection/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,38 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='FasterRCNN', + backbone=dict( + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + rpn_head=dict( + type='RPNHead', + anchor_generator=dict( + type='LegacyAnchorGenerator', + center_offset=0.5, + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn_proposal=dict(max_per_img=2000), + rcnn=dict(assigner=dict(match_low_quality=True)))) diff --git a/downstream/mmdetection/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py b/downstream/mmdetection/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..04581bb --- /dev/null +++ b/downstream/mmdetection/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,34 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + rpn_head=dict( + anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)), + bbox_head=dict( + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + + # model training and testing settings + train_cfg=dict( + rpn_proposal=dict(max_per_img=2000), + rcnn=dict(assigner=dict(match_low_quality=True)))) diff --git a/downstream/mmdetection/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py b/downstream/mmdetection/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py new file mode 100644 index 0000000..a63d248 --- /dev/null +++ b/downstream/mmdetection/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py @@ -0,0 +1,41 @@ +_base_ = './retinanet_r50_fpn_1x_coco_v1.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron/resnet50_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py b/downstream/mmdetection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py new file mode 100644 index 0000000..6198b97 --- /dev/null +++ b/downstream/mmdetection/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py @@ -0,0 +1,17 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + bbox_head=dict( + type='RetinaHead', + anchor_generator=dict( + type='LegacyAnchorGenerator', + center_offset=0.5, + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0))) diff --git a/downstream/mmdetection/configs/legacy_1.x/ssd300_coco_v1.py b/downstream/mmdetection/configs/legacy_1.x/ssd300_coco_v1.py new file mode 100644 index 0000000..65ccc1e --- /dev/null +++ b/downstream/mmdetection/configs/legacy_1.x/ssd300_coco_v1.py @@ -0,0 +1,84 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# model settings +input_size = 300 +model = dict( + bbox_head=dict( + type='SSDHead', + anchor_generator=dict( + type='LegacySSDAnchorGenerator', + scale_major=False, + input_size=input_size, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]))) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + _delete_=True, + type='RepeatDataset', + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict(_delete_=True) +dist_params = dict(backend='nccl', port=29555) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/libra_rcnn/README.md b/downstream/mmdetection/configs/libra_rcnn/README.md new file mode 100644 index 0000000..87a128a --- /dev/null +++ b/downstream/mmdetection/configs/libra_rcnn/README.md @@ -0,0 +1,53 @@ +# Libra R-CNN + +> [Libra R-CNN: Towards Balanced Learning for Object Detection](https://arxiv.org/abs/1904.02701) + + + +## Abstract + +Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple but effective framework towards balanced learning for object detection. It integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level. Benefitted from the overall balanced design, Libra R-CNN significantly improves the detection performance. Without bells and whistles, it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN Faster R-CNN and RetinaNet respectively on MSCOCO. + +Instance recognition is rapidly advanced along with the developments of various deep convolutional neural networks. Compared to the architectures of networks, the training process, which is also crucial to the success of detectors, has received relatively less attention. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple yet effective framework towards balanced learning for instance recognition. It integrates IoU-balanced sampling, balanced feature pyramid, and objective re-weighting, respectively for reducing the imbalance at sample, feature, and objective level. Extensive experiments conducted on MS COCO, LVIS and Pascal VOC datasets prove the effectiveness of the overall balanced design. + +
    + +
    + +## Results and Models + +The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) + +| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :----------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| Fast R-CNN | R-50-FPN | pytorch | 1x | | | | | | +| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203_001405.log.json) | +| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315_231625.log.json) | +| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205_112757.log.json) | + +## Citation + +We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf). + +The extended version of [Libra R-CNN](https://arxiv.org/pdf/2108.10175.pdf) is accpeted by IJCV. + +```latex +@inproceedings{pang2019libra, + title={Libra R-CNN: Towards Balanced Learning for Object Detection}, + author={Pang, Jiangmiao and Chen, Kai and Shi, Jianping and Feng, Huajun and Ouyang, Wanli and Dahua Lin}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} + +@article{pang2021towards, + title={Towards Balanced Learning for Instance Recognition}, + author={Pang, Jiangmiao and Chen, Kai and Li, Qi and Xu, Zhihai and Feng, Huajun and Shi, Jianping and Ouyang, Wanli and Lin, Dahua}, + journal={International Journal of Computer Vision}, + volume={129}, + number={5}, + pages={1376--1393}, + year={2021}, + publisher={Springer} +} +``` diff --git a/downstream/mmdetection/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..efbedc8 --- /dev/null +++ b/downstream/mmdetection/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,50 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + dict( + type='BFP', + in_channels=256, + num_levels=5, + refine_level=2, + refine_type='non_local') + ], + roi_head=dict( + bbox_head=dict( + loss_bbox=dict( + _delete_=True, + type='BalancedL1Loss', + alpha=0.5, + gamma=1.5, + beta=1.0, + loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + sampler=dict( + _delete_=True, + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3))))) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +data = dict( + train=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_train2017.pkl'), + val=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_val2017.pkl'), + test=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_val2017.pkl')) diff --git a/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..e899706 --- /dev/null +++ b/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..89a0d7b --- /dev/null +++ b/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,41 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + dict( + type='BFP', + in_channels=256, + num_levels=5, + refine_level=2, + refine_type='non_local') + ], + roi_head=dict( + bbox_head=dict( + loss_bbox=dict( + _delete_=True, + type='BalancedL1Loss', + alpha=0.5, + gamma=1.5, + beta=1.0, + loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict(sampler=dict(neg_pos_ub=5), allowed_border=-1), + rcnn=dict( + sampler=dict( + _delete_=True, + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3))))) diff --git a/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..06740a7 --- /dev/null +++ b/downstream/mmdetection/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..be27420 --- /dev/null +++ b/downstream/mmdetection/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,26 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +# model settings +model = dict( + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_input', + num_outs=5), + dict( + type='BFP', + in_channels=256, + num_levels=5, + refine_level=1, + refine_type='non_local') + ], + bbox_head=dict( + loss_bbox=dict( + _delete_=True, + type='BalancedL1Loss', + alpha=0.5, + gamma=1.5, + beta=0.11, + loss_weight=1.0))) diff --git a/downstream/mmdetection/configs/libra_rcnn/metafile.yml b/downstream/mmdetection/configs/libra_rcnn/metafile.yml new file mode 100644 index 0000000..8c32795 --- /dev/null +++ b/downstream/mmdetection/configs/libra_rcnn/metafile.yml @@ -0,0 +1,99 @@ +Collections: + - Name: Libra R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - IoU-Balanced Sampling + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Balanced Feature Pyramid + Paper: + URL: https://arxiv.org/abs/1904.02701 + Title: 'Libra R-CNN: Towards Balanced Learning for Object Detection' + README: configs/libra_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/bfp.py#L10 + Version: v2.0.0 + +Models: + - Name: libra_faster_rcnn_r50_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + inference time (ms/im): + - value: 52.63 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth + + - Name: libra_faster_rcnn_r101_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + inference time (ms/im): + - value: 69.44 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth + + - Name: libra_faster_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (ms/im): + - value: 117.65 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth + + - Name: libra_retinanet_r50_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (ms/im): + - value: 56.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth diff --git a/downstream/mmdetection/configs/lvis/README.md b/downstream/mmdetection/configs/lvis/README.md new file mode 100644 index 0000000..0c2760e --- /dev/null +++ b/downstream/mmdetection/configs/lvis/README.md @@ -0,0 +1,56 @@ +# LVIS + +> [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://arxiv.org/abs/1908.03195) + + + +## Abstract + +Progress on object detection is enabled by datasets that focus the research community's attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced \`el-vis'): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect ~2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge. + +
    + +
    + +## Common Setting + +- Please follow [install guide](../../docs/get_started.md#install-mmdetection) to install open-mmlab forked cocoapi first. + +- Run following scripts to install our forked lvis-api. + + ```shell + pip install git+https://github.com/lvis-dataset/lvis-api.git + ``` + +- All experiments use oversample strategy [here](../../docs/tutorials/customize_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`. + +- The size of LVIS v0.5 is half of COCO, so schedule `2x` in LVIS is roughly the same iterations as `1x` in COCO. + +## Results and models of LVIS v0.5 + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 2x | - | - | 26.1 | 25.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis-dbd06831.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_20200531_160435.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 27.1 | 27.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis-54582ee2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_20200601_134748.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 26.7 | 26.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis-3cf55ea2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_20200531_221749.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 26.4 | 26.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis-1c99a5ad.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_20200601_194651.log.json) | + +## Results and models of LVIS v1 + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 9.1 | - | 22.5 | 21.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-aa78ac3d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_061305.log.json) | +| R-101-FPN | pytorch | 1x | 10.8 | - | 24.6 | 23.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-ec55ce32.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_070959.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 11.8 | - | 26.7 | 25.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-ebbc5c81.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_071317.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 14.6 | - | 27.2 | 25.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-43d9edfe.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200830_060206.log.json) | + +## Citation + +```latex +@inproceedings{gupta2019lvis, + title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation}, + author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross}, + booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/downstream/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..0f017f5 --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/downstream/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..637f4a6 --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/downstream/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..92ddb52 --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v1_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/downstream/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..d53c5dc --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v0.5_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..a6115c1 --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..96b6252 --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 0000000..0f95a73 --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 0000000..986acda --- /dev/null +++ b/downstream/mmdetection/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/mask2former/README.md b/downstream/mmdetection/configs/mask2former/README.md new file mode 100644 index 0000000..ebce50d --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/README.md @@ -0,0 +1,73 @@ +# Mask2Former + +> [Masked-attention Mask Transformer for Universal Image Segmentation](http://arxiv.org/abs/2112.01527) + + + +## Abstract + +Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K). + +
    + +
    + +## Introduction + +Mask2Former requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +| | | ├── instances_train2017.json +| | | ├── instances_val2017.json +│ │ │ ├── panoptic_train2017.json +│ │ │ ├── panoptic_train2017 +│ │ │ ├── panoptic_val2017.json +│ │ │ ├── panoptic_val2017 +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +``` + +## Results and Models + +### Panoptic segmentation + +| Backbone | style | Pretrain | Lr schd | Mem (GB) | Inf time (fps) | PQ | box mAP | mask mAP | Config | Download | +| :------: | :-----: | :----------: | :-----: | :------: | :------------: | :--: | :-----: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | ImageNet-1K | 50e | 13.9 | - | 51.9 | 44.8 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516.log.json) | +| R-101 | pytorch | ImageNet-1K | 50e | 16.1 | - | 52.4 | 45.3 | 42.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104-c54e64c9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104.log.json) | +| Swin-T | - | ImageNet-1K | 50e | 15.9 | - | 53.4 | 46.3 | 43.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553-fc567107.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553.log.json) | +| Swin-S | - | ImageNet-1K | 50e | 19.1 | - | 54.5 | 47.8 | 44.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200-c7b94355.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200.log.json) | +| Swin-B | - | ImageNet-1K | 50e | 26.0 | - | 55.1 | 48.2 | 44.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244-c149a9e9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244.log.json) | +| Swin-B | - | ImageNet-21K | 50e | 25.8 | - | 56.3 | 50.0 | 46.3 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021-3bb8b482.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021.log.json) | +| Swin-L | - | ImageNet-21K | 100e | 21.1 | - | 57.6 | 52.2 | 48.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949-d4919c44.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949.log.json) | + +### Instance segmentation + +| Backbone | style | Pretrain | Lr schd | Mem (GB) | Inf time (fps) | box mAP | mask mAP | Config | Download | +| -------- | ------- | ----------- | ------- | -------- | -------------- | ------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| R-50 | pytorch | ImageNet-1K | 50e | 13.7 | - | 45.7 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028.log.json) | +| R-101 | pytorch | ImageNet-1K | 50e | 15.5 | - | 46.7 | 44.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250-c50b6fa6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250.log.json) | +| Swin-T | - | ImageNet-1K | 50e | 15.3 | - | 47.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649-4a943037.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649.log.json) | +| Swin-S | - | ImageNet-1K | 50e | 18.8 | - | 49.3 | 46.1 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756-743b7d99.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756.log.json) | + +Note: We have trained the instance segmentation models many times (see more details in [PR 7571](https://github.com/open-mmlab/mmdetection/pull/7571)). The results of the trained models are relatively stable (+- 0.2), and have a certain gap (about 0.2 AP) in comparison with the results in the [paper](http://arxiv.org/abs/2112.01527). However, the performance of the model trained with the official code is unstable and may also be slightly lower than the reported results as mentioned in the [issue](https://github.com/facebookresearch/Mask2Former/issues/46). + +## Citation + +```latex +@article{cheng2021mask2former, + title={Masked-attention Mask Transformer for Universal Image Segmentation}, + author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar}, + journal={arXiv}, + year={2021} +} +``` diff --git a/downstream/mmdetection/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py b/downstream/mmdetection/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py new file mode 100644 index 0000000..33fdde6 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py @@ -0,0 +1,7 @@ +_base_ = './mask2former_r50_lsj_8x2_50e_coco-panoptic.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py b/downstream/mmdetection/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py new file mode 100644 index 0000000..5543fb0 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py @@ -0,0 +1,7 @@ +_base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py'] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py b/downstream/mmdetection/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py new file mode 100644 index 0000000..2c23625 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py @@ -0,0 +1,253 @@ +_base_ = [ + '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py' +] +num_things_classes = 80 +num_stuff_classes = 53 +num_classes = num_things_classes + num_stuff_classes +model = dict( + type='Mask2Former', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + panoptic_head=dict( + type='Mask2FormerHead', + in_channels=[256, 512, 1024, 2048], # pass to pixel_decoder inside + strides=[4, 8, 16, 32], + feat_channels=256, + out_channels=256, + num_things_classes=num_things_classes, + num_stuff_classes=num_stuff_classes, + num_queries=100, + num_transformer_feat_level=3, + pixel_decoder=dict( + type='MSDeformAttnPixelDecoder', + num_outs=3, + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=dict( + type='MultiScaleDeformableAttention', + embed_dims=256, + num_heads=8, + num_levels=3, + num_points=4, + im2col_step=64, + dropout=0.0, + batch_first=False, + norm_cfg=None, + init_cfg=None), + ffn_cfgs=dict( + type='FFN', + embed_dims=256, + feedforward_channels=1024, + num_fcs=2, + ffn_drop=0.0, + act_cfg=dict(type='ReLU', inplace=True)), + operation_order=('self_attn', 'norm', 'ffn', 'norm')), + init_cfg=None), + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + init_cfg=None), + enforce_decoder_input_project=False, + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + transformer_decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=9, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + attn_drop=0.0, + proj_drop=0.0, + dropout_layer=None, + batch_first=False), + ffn_cfgs=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.0, + dropout_layer=None, + add_identity=True), + feedforward_channels=2048, + operation_order=('cross_attn', 'norm', 'self_attn', 'norm', + 'ffn', 'norm')), + init_cfg=None), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=2.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + reduction='mean', + loss_weight=5.0), + loss_dice=dict( + type='DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=5.0)), + panoptic_fusion_head=dict( + type='MaskFormerFusionHead', + num_things_classes=num_things_classes, + num_stuff_classes=num_stuff_classes, + loss_panoptic=None, + init_cfg=None), + train_cfg=dict( + num_points=12544, + oversample_ratio=3.0, + importance_sample_ratio=0.75, + assigner=dict( + type='MaskHungarianAssigner', + cls_cost=dict(type='ClassificationCost', weight=2.0), + mask_cost=dict( + type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True), + dice_cost=dict( + type='DiceCost', weight=5.0, pred_act=True, eps=1.0)), + sampler=dict(type='MaskPseudoSampler')), + test_cfg=dict( + panoptic_on=True, + # For now, the dataset does not support + # evaluating semantic segmentation metric. + semantic_on=False, + instance_on=True, + # max_per_image is for instance segmentation. + max_per_image=100, + iou_thr=0.8, + # In Mask2Former's panoptic postprocessing, + # it will filter mask area where score is less than 0.5 . + filter_low_score=True), + init_cfg=None) + +# dataset settings +image_size = (1024, 1024) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='LoadPanopticAnnotations', + with_bbox=True, + with_mask=True, + with_seg=True), + dict(type='RandomFlip', flip_ratio=0.5), + # large scale jittering + dict( + type='Resize', + img_scale=image_size, + ratio_range=(0.1, 2.0), + multiscale_mode='range', + keep_ratio=True), + dict( + type='RandomCrop', + crop_size=image_size, + crop_type='absolute', + recompute_bbox=True, + allow_negative_crop=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=image_size), + dict(type='DefaultFormatBundle', img_to_float=True), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data_root = 'data/coco/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict( + pipeline=test_pipeline, + ins_ann_file=data_root + 'annotations/instances_val2017.json', + ), + test=dict( + pipeline=test_pipeline, + ins_ann_file=data_root + 'annotations/instances_val2017.json', + )) + +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +# optimizer +optimizer = dict( + type='AdamW', + lr=0.0001, + weight_decay=0.05, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi, + }, + norm_decay_mult=0.0)) +optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2)) + +# learning policy +lr_config = dict( + policy='step', + gamma=0.1, + by_epoch=False, + step=[327778, 355092], + warmup='linear', + warmup_by_epoch=False, + warmup_ratio=1.0, # no warmup + warmup_iters=10) + +max_iters = 368750 +runner = dict(type='IterBasedRunner', max_iters=max_iters) + +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook', by_epoch=False), + dict(type='TensorboardLoggerHook', by_epoch=False) + ]) +interval = 5000 +workflow = [('train', interval)] +checkpoint_config = dict( + by_epoch=False, interval=interval, save_last=True, max_keep_ckpts=3) + +# Before 365001th iteration, we do evaluation every 5000 iterations. +# After 365000th iteration, we do evaluation every 368750 iterations, +# which means that we do evaluation at the end of training. +dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)] +evaluation = dict( + interval=interval, + dynamic_intervals=dynamic_intervals, + metric=['PQ', 'bbox', 'segm']) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py b/downstream/mmdetection/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py new file mode 100644 index 0000000..eca6135 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py @@ -0,0 +1,79 @@ +_base_ = ['./mask2former_r50_lsj_8x2_50e_coco-panoptic.py'] +num_things_classes = 80 +num_stuff_classes = 0 +num_classes = num_things_classes + num_stuff_classes +model = dict( + panoptic_head=dict( + num_things_classes=num_things_classes, + num_stuff_classes=num_stuff_classes, + loss_cls=dict(class_weight=[1.0] * num_classes + [0.1])), + panoptic_fusion_head=dict( + num_things_classes=num_things_classes, + num_stuff_classes=num_stuff_classes), + test_cfg=dict(panoptic_on=False)) + +# dataset settings +image_size = (1024, 1024) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +pad_cfg = dict(img=(128, 128, 128), masks=0, seg=255) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + # large scale jittering + dict( + type='Resize', + img_scale=image_size, + ratio_range=(0.1, 2.0), + multiscale_mode='range', + keep_ratio=True), + dict( + type='RandomCrop', + crop_size=image_size, + crop_type='absolute', + recompute_bbox=True, + allow_negative_crop=True), + dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-5, 1e-5), by_mask=True), + dict(type='Pad', size=image_size, pad_val=pad_cfg), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle', img_to_float=True), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Pad', size_divisor=32, pad_val=pad_cfg), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +data = dict( + _delete_=True, + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py b/downstream/mmdetection/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py new file mode 100644 index 0000000..f13f5e1 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py @@ -0,0 +1,5 @@ +_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py'] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa + +model = dict( + backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained))) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py b/downstream/mmdetection/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py new file mode 100644 index 0000000..33a805c --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py @@ -0,0 +1,42 @@ +_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py'] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + pretrain_img_size=384, + embed_dims=128, + depths=depths, + num_heads=[4, 8, 16, 32], + window_size=12, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + panoptic_head=dict(in_channels=[128, 256, 512, 1024])) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optimizer = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py b/downstream/mmdetection/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py new file mode 100644 index 0000000..91a180d --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py @@ -0,0 +1,26 @@ +_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py'] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa + +model = dict( + backbone=dict( + embed_dims=192, + num_heads=[6, 12, 24, 48], + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + panoptic_head=dict(num_queries=200, in_channels=[192, 384, 768, 1536])) + +data = dict(samples_per_gpu=1, workers_per_gpu=1) + +lr_config = dict(step=[655556, 710184]) + +max_iters = 737500 +runner = dict(type='IterBasedRunner', max_iters=max_iters) + +# Before 735001th iteration, we do evaluation every 5000 iterations. +# After 735000th iteration, we do evaluation every 737500 iterations, +# which means that we do evaluation at the end of training.' +interval = 5000 +dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)] +evaluation = dict( + interval=interval, + dynamic_intervals=dynamic_intervals, + metric=['PQ', 'bbox', 'segm']) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py b/downstream/mmdetection/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py new file mode 100644 index 0000000..b2b621c --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py @@ -0,0 +1,37 @@ +_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py'] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optimizer = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py b/downstream/mmdetection/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py new file mode 100644 index 0000000..7b1b05a --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py @@ -0,0 +1,37 @@ +_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py'] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa + +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + depths=depths, init_cfg=dict(type='Pretrained', + checkpoint=pretrained))) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optimizer = dict( + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py b/downstream/mmdetection/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py new file mode 100644 index 0000000..04b2f10 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py @@ -0,0 +1,62 @@ +_base_ = ['./mask2former_r50_lsj_8x2_50e_coco-panoptic.py'] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa + +depths = [2, 2, 6, 2] +model = dict( + type='Mask2Former', + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + convert_weights=True, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + panoptic_head=dict( + type='Mask2FormerHead', in_channels=[96, 192, 384, 768]), + init_cfg=None) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optimizer = dict( + type='AdamW', + lr=0.0001, + weight_decay=0.05, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/downstream/mmdetection/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py b/downstream/mmdetection/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py new file mode 100644 index 0000000..0ccbe91 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py @@ -0,0 +1,61 @@ +_base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py'] +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa +depths = [2, 2, 6, 2] +model = dict( + type='Mask2Former', + backbone=dict( + _delete_=True, + type='SwinTransformer', + embed_dims=96, + depths=depths, + num_heads=[3, 6, 12, 24], + window_size=7, + mlp_ratio=4, + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + convert_weights=True, + frozen_stages=-1, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + panoptic_head=dict( + type='Mask2FormerHead', in_channels=[96, 192, 384, 768]), + init_cfg=None) + +# set all layers in backbone to lr_mult=0.1 +# set all norm layers, position_embeding, +# query_embeding, level_embeding to decay_multi=0.0 +backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0) +backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0) +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'backbone.patch_embed.norm': backbone_norm_multi, + 'backbone.norm': backbone_norm_multi, + 'absolute_pos_embed': backbone_embed_multi, + 'relative_position_bias_table': backbone_embed_multi, + 'query_embed': embed_multi, + 'query_feat': embed_multi, + 'level_embed': embed_multi +} +custom_keys.update({ + f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi + for stage_id, num_blocks in enumerate(depths) + for block_id in range(num_blocks) +}) +custom_keys.update({ + f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi + for stage_id in range(len(depths) - 1) +}) +# optimizer +optimizer = dict( + type='AdamW', + lr=0.0001, + weight_decay=0.05, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) diff --git a/downstream/mmdetection/configs/mask2former/metafile.yml b/downstream/mmdetection/configs/mask2former/metafile.yml new file mode 100644 index 0000000..d9f4692 --- /dev/null +++ b/downstream/mmdetection/configs/mask2former/metafile.yml @@ -0,0 +1,223 @@ +Collections: + - Name: Mask2Former + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Weight Decay + Training Resources: 8x A100 GPUs + Architecture: + - Mask2Former + Paper: + URL: https://arxiv.org/pdf/2112.01527 + Title: 'Masked-attention Mask Transformer for Universal Image Segmentation' + README: configs/mask2former/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.23.0/mmdet/models/detectors/mask2former.py#L7 + Version: v2.23.0 + +Models: +- Name: mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic + In Collection: Mask2Former + Config: configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py + Metadata: + Training Memory (GB): 19.1 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 44.5 + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 54.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200-c7b94355.pth +- Name: mask2former_r101_lsj_8x2_50e_coco + In Collection: Mask2Former + Config: configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py + Metadata: + Training Memory (GB): 15.5 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 44.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250-c50b6fa6.pth +- Name: mask2former_r101_lsj_8x2_50e_coco-panoptic + In Collection: Mask2Former + Config: configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py + Metadata: + Training Memory (GB): 16.1 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.4 + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 52.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104-c54e64c9.pth +- Name: mask2former_r50_lsj_8x2_50e_coco-panoptic + In Collection: Mask2Former + Config: configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py + Metadata: + Training Memory (GB): 13.9 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.9 + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 51.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth +- Name: mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic + In Collection: Mask2Former + Config: configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py + Metadata: + Training Memory (GB): 15.9 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 43.4 + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 53.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553-fc567107.pth +- Name: mask2former_r50_lsj_8x2_50e_coco + In Collection: Mask2Former + Config: configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py + Metadata: + Training Memory (GB): 13.7 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth +- Name: mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic + In Collection: Mask2Former + Config: configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py + Metadata: + Training Memory (GB): 21.1 + Iterations: 737500 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 52.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 48.5 + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 57.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949-d4919c44.pth +- Name: mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic + In Collection: Mask2Former + Config: configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py + Metadata: + Training Memory (GB): 25.8 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 46.3 + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 56.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021-3bb8b482.pth +- Name: mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic + In Collection: Mask2Former + Config: configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py + Metadata: + Training Memory (GB): 26.0 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 44.9 + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 55.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244-c149a9e9.pth +- Name: mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco + In Collection: Mask2Former + Config: configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py + Metadata: + Training Memory (GB): 15.3 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 44.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649-4a943037.pth +- Name: mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco + In Collection: Mask2Former + Config: configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py + Metadata: + Training Memory (GB): 18.8 + Iterations: 368750 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 49.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 46.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756-743b7d99.pth diff --git a/downstream/mmdetection/configs/mask_rcnn/README.md b/downstream/mmdetection/configs/mask_rcnn/README.md new file mode 100644 index 0000000..6aad9a2 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/README.md @@ -0,0 +1,59 @@ +# Mask R-CNN + +> [Mask R-CNN](https://arxiv.org/abs/1703.06870) + + + +## Abstract + +We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | caffe | 1x | 4.3 | | 38.0 | 34.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_20200504_231812.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN (FP16) | pytorch | 1x | 3.6 | 24.1 | 38.1 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205_130539.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 39.2 | 35.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_20200505_003907.log.json) | +| R-101-FPN | caffe | 1x | | | 40.4 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758.log.json) | +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 40.8 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_20200505_071027.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 11.3 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_20200506_004702.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | 8.0 | 42.8 | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201_124310.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 42.7 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208.log.json) | +| X-101-32x8d-FPN | pytorch | 1x | - | - | 42.8 | 38.3 | | | + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | caffe | 2x | 4.3 | | 40.3 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_20200504_231822.log.json) | +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 4.3 | | 40.8 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_20200504_163245.log.json) | +| [R-50-FPN](./mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 4.1 | | 40.9 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154.log.json) | +| [R-101-FPN](./mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 5.9 | | 42.9 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339.log.json) | +| [R-101-FPN](./mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 6.1 | | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244.log.json) | +| [x101-32x4d-FPN](./mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 7.3 | | 43.6 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410.log.json) | +| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 1x | - | | 43.6 | 39.0 | | | +| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 10.3 | | 44.3 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042.log.json) | +| [X-101-64x4d-FPN](./mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 10.4 | | 44.5 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447.log.json) | + +## Citation + +```latex +@article{He_2017, + title={Mask R-CNN}, + journal={2017 IEEE International Conference on Computer Vision (ICCV)}, + publisher={IEEE}, + author={He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross}, + year={2017}, + month={Oct} +} +``` diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..95b324f --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..e39781d --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,55 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + depth=101, + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..b7986e8 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 0000000..c9059d5 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,6 @@ +_base_ = './mask_rcnn_r50_fpn_2x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..0696cbe --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,10 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py new file mode 100644 index 0000000..a44c018 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..5a23f8c --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,40 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py new file mode 100644 index 0000000..6308e40 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py @@ -0,0 +1,49 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py new file mode 100644 index 0000000..4f7150c --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..1b48a21 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 0000000..bebbaaa --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe'))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py new file mode 100644 index 0000000..3f8079d --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py @@ -0,0 +1,61 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(requires_grad=False), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + rpn_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + bbox_roi_extractor=dict( + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_roi_extractor=dict( + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..6a6c924 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_wandb_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_wandb_coco.py new file mode 100644 index 0000000..88c8576 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_wandb_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +# Set evaluation interval +evaluation = dict(interval=2) +# Set checkpoint interval +checkpoint_config = dict(interval=4) + +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='MMDetWandbHook', + init_kwargs={ + 'project': 'mmdetection', + 'group': 'maskrcnn-r50-fpn-1x-coco' + }, + interval=50, + log_checkpoint=True, + log_checkpoint_metadata=True, + num_eval_images=100) + ]) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 0000000..932b1f9 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py new file mode 100644 index 0000000..fb8289b --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..b3d9242 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py new file mode 100644 index 0000000..9eb6d57 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..a8b3799 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 0000000..2cd3cee --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_r101_fpn_2x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..b698a7d --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,18 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py new file mode 100644 index 0000000..108ea4e --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py new file mode 100644 index 0000000..6b912f6 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py @@ -0,0 +1,60 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..8ba0e9c --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,85 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnext101_32x8d'))) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..2333b03 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..6074cca --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..9f9cb1c --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,18 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/mask_rcnn/metafile.yml b/downstream/mmdetection/configs/mask_rcnn/metafile.yml new file mode 100644 index 0000000..f74bdf3 --- /dev/null +++ b/downstream/mmdetection/configs/mask_rcnn/metafile.yml @@ -0,0 +1,447 @@ +Collections: + - Name: Mask R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Softmax + - RPN + - Convolution + - Dense Connections + - FPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1703.06870v3 + Title: "Mask R-CNN" + README: configs/mask_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: mask_rcnn_r50_caffe_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth + + - Name: mask_rcnn_r50_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth + + - Name: mask_rcnn_r50_fpn_fp16_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 3.6 + Training Techniques: + - SGD with Momentum + - Weight Decay + - Mixed Precision Training + inference time (ms/im): + - value: 41.49 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP16 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth + + - Name: mask_rcnn_r50_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (ms/im): + - value: 62.11 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth + + - Name: mask_rcnn_r101_caffe_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth + + - Name: mask_rcnn_r101_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth + + - Name: mask_rcnn_r101_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (ms/im): + - value: 74.07 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth + + - Name: mask_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth + + - Name: mask_rcnn_x101_32x4d_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (ms/im): + - value: 88.5 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth + + - Name: mask_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth + + - Name: mask_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth + + - Name: mask_rcnn_x101_32x8d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + + - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth + + - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth + + - Name: mask_rcnn_r50_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 4.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth + + - Name: mask_rcnn_r101_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 6.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth + + - Name: mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth + + - Name: mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 7.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth + + - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + + - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco + Metadata: + Training Memory (GB): 10.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth + + - Name: mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py + Metadata: + Epochs: 36 + Training Memory (GB): 10.4 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth diff --git a/downstream/mmdetection/configs/maskformer/README.md b/downstream/mmdetection/configs/maskformer/README.md new file mode 100644 index 0000000..5d8daa2 --- /dev/null +++ b/downstream/mmdetection/configs/maskformer/README.md @@ -0,0 +1,53 @@ +# MaskFormer + +> [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) + + + +## Abstract + +Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models. + +
    + +
    + +## Introduction + +MaskFormer requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── panoptic_train2017.json +│ │ │ ├── panoptic_train2017 +│ │ │ ├── panoptic_val2017.json +│ │ │ ├── panoptic_val2017 +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +``` + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | PQ | SQ | RQ | PQ_th | SQ_th | RQ_th | PQ_st | SQ_st | RQ_st | Config | Download | detail | +| :------: | :-----: | :-----: | :------: | :------------: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50 | pytorch | 75e | 16.2 | - | 46.854 | 80.617 | 57.085 | 51.089 | 81.511 | 61.853 | 40.463 | 79.269 | 49.888 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956.log.json) | This version was mentioned in Table XI, in paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) | +| Swin-L | pytorch | 300e | 27.2 | - | 53.249 | 81.704 | 64.231 | 58.798 | 82.923 | 70.282 | 44.874 | 79.863 | 55.097 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612-061b4eb8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612.log.json) | - | + +## Citation + +```latex +@inproceedings{cheng2021maskformer, + title={Per-Pixel Classification is Not All You Need for Semantic Segmentation}, + author={Bowen Cheng and Alexander G. Schwing and Alexander Kirillov}, + journal={NeurIPS}, + year={2021} +} +``` diff --git a/downstream/mmdetection/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py b/downstream/mmdetection/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py new file mode 100644 index 0000000..46b3c13 --- /dev/null +++ b/downstream/mmdetection/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py @@ -0,0 +1,238 @@ +_base_ = [ + '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py' +] +num_things_classes = 80 +num_stuff_classes = 53 +num_classes = num_things_classes + num_stuff_classes +model = dict( + type='MaskFormer', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + panoptic_head=dict( + type='MaskFormerHead', + in_channels=[256, 512, 1024, 2048], # pass to pixel_decoder inside + feat_channels=256, + out_channels=256, + num_things_classes=num_things_classes, + num_stuff_classes=num_stuff_classes, + num_queries=100, + pixel_decoder=dict( + type='TransformerEncoderPixelDecoder', + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU'), + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=False), + ffn_cfgs=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.1, + dropout_layer=None, + add_identity=True), + operation_order=('self_attn', 'norm', 'ffn', 'norm'), + norm_cfg=dict(type='LN'), + init_cfg=None, + batch_first=False), + init_cfg=None), + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True)), + enforce_decoder_input_project=False, + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + transformer_decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + attn_drop=0.1, + proj_drop=0.1, + dropout_layer=None, + batch_first=False), + ffn_cfgs=dict( + embed_dims=256, + feedforward_channels=2048, + num_fcs=2, + act_cfg=dict(type='ReLU', inplace=True), + ffn_drop=0.1, + dropout_layer=None, + add_identity=True), + # the following parameter was not used, + # just make current api happy + feedforward_channels=2048, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')), + init_cfg=None), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0, + reduction='mean', + class_weight=[1.0] * num_classes + [0.1]), + loss_mask=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=20.0), + loss_dice=dict( + type='DiceLoss', + use_sigmoid=True, + activate=True, + reduction='mean', + naive_dice=True, + eps=1.0, + loss_weight=1.0)), + panoptic_fusion_head=dict( + type='MaskFormerFusionHead', + num_things_classes=num_things_classes, + num_stuff_classes=num_stuff_classes, + loss_panoptic=None, + init_cfg=None), + train_cfg=dict( + assigner=dict( + type='MaskHungarianAssigner', + cls_cost=dict(type='ClassificationCost', weight=1.0), + mask_cost=dict( + type='FocalLossCost', weight=20.0, binary_input=True), + dice_cost=dict( + type='DiceCost', weight=1.0, pred_act=True, eps=1.0)), + sampler=dict(type='MaskPseudoSampler')), + test_cfg=dict( + panoptic_on=True, + # For now, the dataset does not support + # evaluating semantic segmentation metric. + semantic_on=False, + instance_on=False, + # max_per_image is for instance segmentation. + max_per_image=100, + object_mask_thr=0.8, + iou_thr=0.8, + # In MaskFormer's panoptic postprocessing, + # it will not filter masks whose score is smaller than 0.5 . + filter_low_score=False), + init_cfg=None) + +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadPanopticAnnotations', + with_bbox=True, + with_mask=True, + with_seg=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=1, + workers_per_gpu=1, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +optimizer = dict( + type='AdamW', + lr=0.0001, + weight_decay=0.0001, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1, decay_mult=1.0), + 'query_embed': dict(lr_mult=1.0, decay_mult=0.0) + }, + norm_decay_mult=0.0)) +optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2)) + +# learning policy +lr_config = dict( + policy='step', + gamma=0.1, + by_epoch=True, + step=[50], + warmup='linear', + warmup_by_epoch=False, + warmup_ratio=1.0, # no warmup + warmup_iters=10) +runner = dict(type='EpochBasedRunner', max_epochs=75) diff --git a/downstream/mmdetection/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py b/downstream/mmdetection/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py new file mode 100644 index 0000000..bc23c54 --- /dev/null +++ b/downstream/mmdetection/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py @@ -0,0 +1,67 @@ +_base_ = './maskformer_r50_mstrain_16x1_75e_coco.py' + +pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa +depths = [2, 2, 18, 2] +model = dict( + backbone=dict( + _delete_=True, + type='SwinTransformer', + pretrain_img_size=384, + embed_dims=192, + patch_size=4, + window_size=12, + mlp_ratio=4, + depths=depths, + num_heads=[6, 12, 24, 48], + qkv_bias=True, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0.3, + patch_norm=True, + out_indices=(0, 1, 2, 3), + with_cp=False, + convert_weights=True, + init_cfg=dict(type='Pretrained', checkpoint=pretrained)), + panoptic_head=dict( + in_channels=[192, 384, 768, 1536], # pass to pixel_decoder inside + pixel_decoder=dict( + _delete_=True, + type='PixelDecoder', + norm_cfg=dict(type='GN', num_groups=32), + act_cfg=dict(type='ReLU')), + enforce_decoder_input_project=True)) + +# weight_decay = 0.01 +# norm_weight_decay = 0.0 +# embed_weight_decay = 0.0 +embed_multi = dict(lr_mult=1.0, decay_mult=0.0) +norm_multi = dict(lr_mult=1.0, decay_mult=0.0) +custom_keys = { + 'norm': norm_multi, + 'absolute_pos_embed': embed_multi, + 'relative_position_bias_table': embed_multi, + 'query_embed': embed_multi +} + +# optimizer +optimizer = dict( + type='AdamW', + lr=6e-5, + weight_decay=0.01, + eps=1e-8, + betas=(0.9, 0.999), + paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0)) +optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2)) + +# learning policy +lr_config = dict( + policy='step', + gamma=0.1, + by_epoch=True, + step=[250], + warmup='linear', + warmup_by_epoch=False, + warmup_ratio=1e-6, + warmup_iters=1500) +runner = dict(type='EpochBasedRunner', max_epochs=300) diff --git a/downstream/mmdetection/configs/maskformer/metafile.yml b/downstream/mmdetection/configs/maskformer/metafile.yml new file mode 100644 index 0000000..6530fa1 --- /dev/null +++ b/downstream/mmdetection/configs/maskformer/metafile.yml @@ -0,0 +1,43 @@ +Collections: + - Name: MaskFormer + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Weight Decay + Training Resources: 16x V100 GPUs + Architecture: + - MaskFormer + Paper: + URL: https://arxiv.org/pdf/2107.06278 + Title: 'Per-Pixel Classification is Not All You Need for Semantic Segmentation' + README: configs/maskformer/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/mmdet/models/detectors/maskformer.py#L7 + Version: v2.22.0 + +Models: + - Name: maskformer_r50_mstrain_16x1_75e_coco + In Collection: MaskFormer + Config: configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py + Metadata: + Training Memory (GB): 16.2 + Epochs: 75 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 46.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth + - Name: maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco + In Collection: MaskFormer + Config: configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py + Metadata: + Training Memory (GB): 27.2 + Epochs: 300 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 53.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612-061b4eb8.pth diff --git a/downstream/mmdetection/configs/ms_rcnn/README.md b/downstream/mmdetection/configs/ms_rcnn/README.md new file mode 100644 index 0000000..97bca05 --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/README.md @@ -0,0 +1,36 @@ +# MS R-CNN + +> [Mask Scoring R-CNN](https://arxiv.org/abs/1903.00241) + + + +## Abstract + +Letting a deep network be aware of the quality of its own predictions is an interesting yet important problem. In the task of instance segmentation, the confidence of instance classification is used as mask quality score in most instance segmentation frameworks. However, the mask quality, quantified as the IoU between the instance mask and its ground truth, is usually not well correlated with classification score. In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks. The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score, and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation. By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain with different models, and outperforms the state-of-the-art Mask R-CNN. We hope our simple and effective approach will provide a new direction for improving instance segmentation. + +
    + +
    + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | caffe | 1x | 4.5 | | 38.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848.log.json) | +| R-50-FPN | caffe | 2x | - | - | 38.8 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_20200506_004738.log.json) | +| R-101-FPN | caffe | 1x | 6.5 | | 40.4 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_20200506_004755.log.json) | +| R-101-FPN | caffe | 2x | - | - | 41.1 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_20200506_011134.log.json) | +| R-X101-32x4d | pytorch | 2x | 7.9 | 11.0 | 41.8 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206_100113.log.json) | +| R-X101-64x4d | pytorch | 1x | 11.0 | 8.0 | 43.0 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206_091744.log.json) | +| R-X101-64x4d | pytorch | 2x | 11.0 | 8.0 | 42.6 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308_012247.log.json) | + +## Citation + +```latex +@inproceedings{huang2019msrcnn, + title={Mask Scoring R-CNN}, + author={Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019}, +} +``` diff --git a/downstream/mmdetection/configs/ms_rcnn/metafile.yml b/downstream/mmdetection/configs/ms_rcnn/metafile.yml new file mode 100644 index 0000000..a6c7dc5 --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/metafile.yml @@ -0,0 +1,159 @@ +Collections: + - Name: Mask Scoring R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RPN + - FPN + - ResNet + - RoIAlign + Paper: + URL: https://arxiv.org/abs/1903.00241 + Title: 'Mask Scoring R-CNN' + README: configs/ms_rcnn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_scoring_rcnn.py#L6 + Version: v2.0.0 + +Models: + - Name: ms_rcnn_r50_caffe_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth + + - Name: ms_rcnn_r50_caffe_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth + + - Name: ms_rcnn_r101_caffe_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth + + - Name: ms_rcnn_r101_caffe_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth + + - Name: ms_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (ms/im): + - value: 90.91 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth + + - Name: ms_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 11.0 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth + + - Name: ms_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 11.0 + inference time (ms/im): + - value: 125 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..9b7dcbb --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet101_caffe'))) diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py new file mode 100644 index 0000000..202bcce --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 0000000..5845125 --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + type='MaskScoringRCNN', + roi_head=dict( + type='MaskScoringRoIHead', + mask_iou_head=dict( + type='MaskIoUHead', + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80)), + # model training and testing settings + train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py new file mode 100644 index 0000000..008a70a --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..0a163ce --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + type='MaskScoringRCNN', + roi_head=dict( + type='MaskScoringRoIHead', + mask_iou_head=dict( + type='MaskIoUHead', + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80)), + # model training and testing settings + train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..20479bb --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ms_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d'))) diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 0000000..ee5b734 --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './ms_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d'))) diff --git a/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 0000000..54c605b --- /dev/null +++ b/downstream/mmdetection/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/nas_fcos/README.md b/downstream/mmdetection/configs/nas_fcos/README.md new file mode 100644 index 0000000..def8831 --- /dev/null +++ b/downstream/mmdetection/configs/nas_fcos/README.md @@ -0,0 +1,35 @@ +# NAS-FCOS + +> [NAS-FCOS: Fast Neural Architecture Search for Object Detection](https://arxiv.org/abs/1906.04423) + + + +## Abstract + +The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection. + +
    + +
    + +## Results and Models + +| Head | Backbone | Style | GN-head | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :----------: | :------: | :---: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| NAS-FCOSHead | R-50 | caffe | Y | 1x | | | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520.log.json) | +| FCOSHead | R-50 | caffe | Y | 1x | | | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521.log.json) | + +**Notes:** + +- To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU. + +## Citation + +```latex +@article{wang2019fcos, + title={Nas-fcos: Fast neural architecture search for object detection}, + author={Wang, Ning and Gao, Yang and Chen, Hao and Wang, Peng and Tian, Zhi and Shen, Chunhua}, + journal={arXiv preprint arXiv:1906.04423}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/nas_fcos/metafile.yml b/downstream/mmdetection/configs/nas_fcos/metafile.yml new file mode 100644 index 0000000..1ea28cf --- /dev/null +++ b/downstream/mmdetection/configs/nas_fcos/metafile.yml @@ -0,0 +1,44 @@ +Collections: + - Name: NAS-FCOS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 4x V100 GPUs + Architecture: + - FPN + - NAS-FCOS + - ResNet + Paper: + URL: https://arxiv.org/abs/1906.04423 + Title: 'NAS-FCOS: Fast Neural Architecture Search for Object Detection' + README: configs/nas_fcos/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/detectors/nasfcos.py#L6 + Version: v2.1.0 + +Models: + - Name: nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco + In Collection: NAS-FCOS + Config: configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth + + - Name: nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco + In Collection: NAS-FCOS + Config: configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth diff --git a/downstream/mmdetection/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/downstream/mmdetection/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..a455c92 --- /dev/null +++ b/downstream/mmdetection/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,100 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='NASFCOS', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False, eps=0), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + neck=dict( + type='NASFCOS_FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + norm_cfg=dict(type='BN'), + conv_cfg=dict(type='DCNv2', deform_groups=2)), + bbox_head=dict( + type='FCOSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + norm_cfg=dict(type='GN', num_groups=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) diff --git a/downstream/mmdetection/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/downstream/mmdetection/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 0000000..b779492 --- /dev/null +++ b/downstream/mmdetection/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,99 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='NASFCOS', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False, eps=0), + style='caffe', + init_cfg=dict( + type='Pretrained', + checkpoint='open-mmlab://detectron2/resnet50_caffe')), + neck=dict( + type='NASFCOS_FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + norm_cfg=dict(type='BN'), + conv_cfg=dict(type='DCNv2', deform_groups=2)), + bbox_head=dict( + type='NASFCOSHead', + num_classes=80, + in_channels=256, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + norm_cfg=dict(type='GN', num_groups=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) diff --git a/downstream/mmdetection/configs/nas_fpn/README.md b/downstream/mmdetection/configs/nas_fpn/README.md new file mode 100644 index 0000000..c5acf40 --- /dev/null +++ b/downstream/mmdetection/configs/nas_fpn/README.md @@ -0,0 +1,36 @@ +# NAS-FPN + +> [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://arxiv.org/abs/1904.07392) + + + +## Abstract + +Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in \[32\] and achieves 48.3 AP which surpasses Mask R-CNN \[10\] detection accuracy with less computation time. + +
    + +
    + +## Results and Models + +We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper. + +| Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | 50e | 12.9 | 22.9 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) | +| R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) | + +**Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower. + +## Citation + +```latex +@inproceedings{ghiasi2019fpn, + title={Nas-fpn: Learning scalable feature pyramid architecture for object detection}, + author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + pages={7036--7045}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/nas_fpn/metafile.yml b/downstream/mmdetection/configs/nas_fpn/metafile.yml new file mode 100644 index 0000000..ab8d649 --- /dev/null +++ b/downstream/mmdetection/configs/nas_fpn/metafile.yml @@ -0,0 +1,59 @@ +Collections: + - Name: NAS-FPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - NAS-FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.07392 + Title: 'NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection' + README: configs/nas_fpn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/nas_fpn.py#L67 + Version: v2.0.0 + +Models: + - Name: retinanet_r50_fpn_crop640_50e_coco + In Collection: NAS-FPN + Config: configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py + Metadata: + Training Memory (GB): 12.9 + inference time (ms/im): + - value: 43.67 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth + + - Name: retinanet_r50_nasfpn_crop640_50e_coco + In Collection: NAS-FPN + Config: configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py + Metadata: + Training Memory (GB): 13.2 + inference time (ms/im): + - value: 43.48 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth diff --git a/downstream/mmdetection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py b/downstream/mmdetection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py new file mode 100644 index 0000000..e4408fe --- /dev/null +++ b/downstream/mmdetection/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,85 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +cudnn_benchmark = True +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + relu_before_extra_convs=True, + no_norm_on_lateral=True, + norm_cfg=norm_cfg), + bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), + # training and testing settings + train_cfg=dict(assigner=dict(neg_iou_thr=0.5))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=50) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py b/downstream/mmdetection/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py new file mode 100644 index 0000000..1387a10 --- /dev/null +++ b/downstream/mmdetection/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py @@ -0,0 +1,84 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +cudnn_benchmark = True +# model settings +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + type='RetinaNet', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict(type='NASFPN', stack_times=7, norm_cfg=norm_cfg), + bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), + # training and testing settings + train_cfg=dict(assigner=dict(neg_iou_thr=0.5))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=128), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=50) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/openimages/README.md b/downstream/mmdetection/configs/openimages/README.md new file mode 100644 index 0000000..e5c1c27 --- /dev/null +++ b/downstream/mmdetection/configs/openimages/README.md @@ -0,0 +1,148 @@ +# Open Images Dataset + +> [Open Images Dataset](https://arxiv.org/abs/1811.00982) + + + +## Abstract + + + +#### Open Images v6 + +[Open Images](https://storage.googleapis.com/openimages/web/index.html) is a dataset of ~9M images annotated with image-level labels, +object bounding boxes, object segmentation masks, visual relationships, +and localized narratives: + +- It contains a total of 16M bounding boxes for 600 object classes on + 1.9M images, making it the largest existing dataset with object location + annotations. The boxes have been largely manually drawn by professional + annotators to ensure accuracy and consistency. The images are very diverse + and often contain complex scenes with several objects (8.3 per image on + average). + +- Open Images also offers visual relationship annotations, indicating pairs + of objects in particular relations (e.g. "woman playing guitar", "beer on + table"), object properties (e.g. "table is wooden"), and human actions (e.g. + "woman is jumping"). In total it has 3.3M annotations from 1,466 distinct + relationship triplets. + +- In V5 we added segmentation masks for 2.8M object instances in 350 classes. + Segmentation masks mark the outline of objects, which characterizes their + spatial extent to a much higher level of detail. + +- In V6 we added 675k localized narratives: multimodal descriptions of images + consisting of synchronized voice, text, and mouse traces over the objects being + described. (Note we originally launched localized narratives only on train in V6, + but since July 2020 we also have validation and test covered.) + +- Finally, the dataset is annotated with 59.9M image-level labels spanning 19,957 + classes. + +We believe that having a single dataset with unified annotations for image +classification, object detection, visual relationship detection, instance +segmentation, and multimodal image descriptions will enable to study these +tasks jointly and stimulate progress towards genuine scene understanding. + + + +
    + +
    + +#### Open Images Challenge 2019 + +[Open Images Challenges 2019](https://storage.googleapis.com/openimages/web/challenge2019.html) is based on the V5 release of the Open +Images dataset. The images of the dataset are very varied and +often contain complex scenes with several objects (explore the dataset). + +## Citation + +``` +@article{OpenImages, + author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari}, + title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale}, + year = {2020}, + journal = {IJCV} +} +``` + +## Prepare Dataset + +1. You need to download and extract Open Images dataset. + +2. The Open Images dataset does not have image metas (width and height of the image), + which will be used during evaluation. We suggest to get test image metas before + training/testing by using `tools/misc/get_image_metas.py`. + + **Usage** + + ```shell + python tools/misc/get_image_metas.py ${CONFIG} \ + --out ${OUTPUT FILE NAME} + ``` + +3. The directory should be like this: + + ```none + mmdetection + ├── mmdet + ├── tools + ├── configs + ├── data + │ ├── OpenImages + │ │ ├── annotations + │ │ │ ├── bbox_labels_600_hierarchy.json + │ │ │ ├── class-descriptions-boxable.csv + │ │ │ ├── oidv6-train-annotations-bbox.scv + │ │ │ ├── validation-annotations-bbox.csv + │ │ │ ├── validation-annotations-human-imagelabels-boxable.csv + │ │ │ ├── validation-image-metas.pkl # get from script + │ │ ├── challenge2019 + │ │ │ ├── challenge-2019-train-detection-bbox.txt + │ │ │ ├── challenge-2019-validation-detection-bbox.txt + │ │ │ ├── class_label_tree.np + │ │ │ ├── class_sample_train.pkl + │ │ │ ├── challenge-2019-validation-detection-human-imagelabels.csv # download from official website + │ │ │ ├── challenge-2019-validation-metas.pkl # get from script + │ │ ├── OpenImages + │ │ │ ├── train # training images + │ │ │ ├── test # testing images + │ │ │ ├── validation # validation images + ``` + +**Note**: + +1. The training and validation images of Open Images Challenge dataset are based on + Open Images v6, but the test images are different. +2. The Open Images Challenges annotations are obtained from [TSD](https://github.com/Sense-X/TSD). + You can also download the annotations from [official website](https://storage.googleapis.com/openimages/web/challenge2019_downloads.html), + and set data.train.type=OpenImagesDataset, data.val.type=OpenImagesDataset, and data.test.type=OpenImagesDataset in the config +3. If users do not want to use `validation-annotations-human-imagelabels-boxable.csv` and `challenge-2019-validation-detection-human-imagelabels.csv` + users can set `data.val.load_image_level_labels=False` and `data.test.load_image_level_labels=False` in the config. + Please note that loading image-levels label is the default of Open Images evaluation metric. + More details please refer to the [official website](https://storage.googleapis.com/openimages/web/evaluation.html) + +## Results and Models + +| Architecture | Backbone | Style | Lr schd | Sampler | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------------------------: | :------: | :-----: | :-----: | :-----------------: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Faster R-CNN | R-50 | pytorch | 1x | Group Sampler | 7.7 | - | 51.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_20211130_231159-e87ab7ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_20211130_231159.log.json) | +| Faster R-CNN | R-50 | pytorch | 1x | Class Aware Sampler | 7.7 | - | 60.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_20220306_202424-98c630e5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_20220306_202424.log.json) | +| Faster R-CNN (Challenge 2019) | R-50 | pytorch | 1x | Group Sampler | 7.7 | - | 54.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge_20220114_045100-0e79e5df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge_20220114_045100.log.json) | +| Faster R-CNN (Challenge 2019) | R-50 | pytorch | 1x | Class Aware Sampler | 7.1 | - | 65.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge_20220221_192021-34c402d9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge_20220221_192021.log.json) | +| Retinanet | R-50 | pytorch | 1x | Group Sampler | 6.6 | - | 61.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/retinanet_r50_fpn_32x2_1x_openimages/retinanet_r50_fpn_32x2_1x_openimages_20211223_071954-d2ae5462.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/retinanet_r50_fpn_32x2_1x_openimages/retinanet_r50_fpn_32x2_1x_openimages_20211223_071954.log.json) | +| SSD | VGG16 | pytorch | 36e | Group Sampler | 10.8 | - | 35.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/ssd300_32x8_36e_openimages.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/ssd300_32x8_36e_openimages/ssd300_32x8_36e_openimages_20211224_000232-dce93846.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/ssd300_32x8_36e_openimages/ssd300_32x8_36e_openimages_20211224_000232.log.json) | + +**Notes:** + +- 'cas' is short for 'Class Aware Sampler' + +### Results of consider image level labels + +| Architecture | Sampler | Consider Image Level Labels | box AP | +| :-------------------------------: | :-----------------: | :-------------------------: | :----: | +| Faster R-CNN r50 (Challenge 2019) | Group Sampler | w/o | 62.19 | +| Faster R-CNN r50 (Challenge 2019) | Group Sampler | w/ | 54.87 | +| Faster R-CNN r50 (Challenge 2019) | Class Aware Sampler | w/o | 71.77 | +| Faster R-CNN r50 (Challenge 2019) | Class Aware Sampler | w/ | 64.98 | diff --git a/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py new file mode 100644 index 0000000..3dfc341 --- /dev/null +++ b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/openimages_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict(roi_head=dict(bbox_head=dict(num_classes=601))) + +# Using 32 GPUS while training +optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=26000, + warmup_ratio=1.0 / 64, + step=[8, 11]) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (32 GPUs) x (2 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py new file mode 100644 index 0000000..c8900ad --- /dev/null +++ b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py @@ -0,0 +1,47 @@ +_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py'] + +model = dict( + roi_head=dict(bbox_head=dict(num_classes=500)), + test_cfg=dict(rcnn=dict(score_thr=0.01))) + +# dataset settings +dataset_type = 'OpenImagesChallengeDataset' +data_root = 'data/OpenImages/' +data = dict( + train=dict( + type=dataset_type, + ann_file=data_root + + 'challenge2019/challenge-2019-train-detection-bbox.txt', + img_prefix=data_root + 'OpenImages/', + label_file=data_root + 'challenge2019/cls-label-description.csv', + hierarchy_file=data_root + 'challenge2019/class_label_tree.np'), + val=dict( + type=dataset_type, + ann_file=data_root + + 'challenge2019/challenge-2019-validation-detection-bbox.txt', + img_prefix=data_root + 'OpenImages/', + label_file=data_root + 'challenge2019/cls-label-description.csv', + hierarchy_file=data_root + 'challenge2019/class_label_tree.np', + meta_file=data_root + + 'challenge2019/challenge-2019-validation-metas.pkl', + image_level_ann_file=data_root + + 'challenge2019/challenge-2019-validation-detection-' + 'human-imagelabels.csv'), + test=dict( + type=dataset_type, + ann_file=data_root + + 'challenge2019/challenge-2019-validation-detection-bbox.txt', + img_prefix=data_root + 'OpenImages/', + label_file=data_root + 'challenge2019/cls-label-description.csv', + hierarchy_file=data_root + 'challenge2019/class_label_tree.np', + meta_file=data_root + + 'challenge2019/challenge-2019-validation-metas.pkl', + image_level_ann_file=data_root + + 'challenge2019/challenge-2019-validation-detection-' + 'human-imagelabels.csv')) +evaluation = dict(interval=1, metric='mAP') + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (32 GPUs) x (2 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py new file mode 100644 index 0000000..88d029d --- /dev/null +++ b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py @@ -0,0 +1,5 @@ +_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py'] + +# Use ClassAwareSampler +data = dict( + train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1))) diff --git a/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py new file mode 100644 index 0000000..26bd64e --- /dev/null +++ b/downstream/mmdetection/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py @@ -0,0 +1,5 @@ +_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py'] + +# Use ClassAwareSampler +data = dict( + train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1))) diff --git a/downstream/mmdetection/configs/openimages/metafile.yml b/downstream/mmdetection/configs/openimages/metafile.yml new file mode 100644 index 0000000..9be1726 --- /dev/null +++ b/downstream/mmdetection/configs/openimages/metafile.yml @@ -0,0 +1,102 @@ +Models: + - Name: faster_rcnn_r50_fpn_32x2_1x_openimages + In Collection: Faster R-CNN + Config: configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py + Metadata: + Training Memory (GB): 7.7 + Epochs: 12 + Training Data: Open Images v6 + Training Techniques: + - SGD with Momentum + - Weight Decay + Results: + - Task: Object Detection + Dataset: Open Images v6 + Metrics: + box AP: 51.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_20211130_231159-e87ab7ce.pth + + - Name: retinanet_r50_fpn_32x2_1x_openimages + In Collection: RetinaNet + Config: configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py + Metadata: + Training Memory (GB): 6.6 + Epochs: 12 + Training Data: Open Images v6 + Training Techniques: + - SGD with Momentum + - Weight Decay + Results: + - Task: Object Detection + Dataset: Open Images v6 + Metrics: + box AP: 61.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/retinanet_r50_fpn_32x2_1x_openimages/retinanet_r50_fpn_32x2_1x_openimages_20211223_071954-d2ae5462.pth + + - Name: ssd300_32x8_36e_openimages + In Collection: SSD + Config: configs/openimages/ssd300_32x8_36e_openimages + Metadata: + Training Memory (GB): 10.8 + Epochs: 36 + Training Data: Open Images v6 + Training Techniques: + - SGD with Momentum + - Weight Decay + Results: + - Task: Object Detection + Dataset: Open Images v6 + Metrics: + box AP: 35.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/ssd300_32x8_36e_openimages/ssd300_32x8_36e_openimages_20211224_000232-dce93846.pth + + - Name: faster_rcnn_r50_fpn_32x2_1x_openimages_challenge + In Collection: Faster R-CNN + Config: configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py + Metadata: + Training Memory (GB): 7.7 + Epochs: 12 + Training Data: Open Images Challenge 2019 + Training Techniques: + - SGD with Momentum + - Weight Decay + Results: + - Task: Object Detection + Dataset: Open Images Challenge 2019 + Metrics: + box AP: 54.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge_20220114_045100-0e79e5df.pth + + - Name: faster_rcnn_r50_fpn_32x2_cas_1x_openimages + In Collection: Faster R-CNN + Config: configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py + Metadata: + Training Memory (GB): 7.7 + Epochs: 12 + Training Data: Open Images Challenge 2019 + Training Techniques: + - SGD with Momentum + - Weight Decay + Results: + - Task: Object Detection + Dataset: Open Images Challenge 2019 + Metrics: + box AP: 60.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_20220306_202424-98c630e5.pth + + - Name: faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge + In Collection: Faster R-CNN + Config: configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py + Metadata: + Training Memory (GB): 7.1 + Epochs: 12 + Training Data: Open Images Challenge 2019 + Training Techniques: + - SGD with Momentum + - Weight Decay + Results: + - Task: Object Detection + Dataset: Open Images Challenge 2019 + Metrics: + box AP: 65.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge_20220221_192021-34c402d9.pth diff --git a/downstream/mmdetection/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py b/downstream/mmdetection/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py new file mode 100644 index 0000000..0191aa1 --- /dev/null +++ b/downstream/mmdetection/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/openimages_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict(bbox_head=dict(num_classes=601)) + +optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=26000, + warmup_ratio=1.0 / 64, + step=[8, 11]) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (32 GPUs) x (2 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/openimages/ssd300_32x8_36e_openimages.py b/downstream/mmdetection/configs/openimages/ssd300_32x8_36e_openimages.py new file mode 100644 index 0000000..e2565b9 --- /dev/null +++ b/downstream/mmdetection/configs/openimages/ssd300_32x8_36e_openimages.py @@ -0,0 +1,83 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py', + '../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py' +] +model = dict( + bbox_head=dict( + num_classes=601, + anchor_generator=dict(basesize_ratio_range=(0.2, 0.9)))) +# dataset settings +dataset_type = 'OpenImagesDataset' +data_root = 'data/OpenImages/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True, normed_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, # using 32 GPUS while training. + workers_per_gpu=0, # workers_per_gpu > 0 may occur out of memory + train=dict( + _delete_=True, + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/oidv6-train-annotations-bbox.csv', + img_prefix=data_root + 'OpenImages/train/', + label_file=data_root + + 'annotations/class-descriptions-boxable.csv', + hierarchy_file=data_root + + 'annotations/bbox_labels_600_hierarchy.json', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.04, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=20000, + warmup_ratio=0.001, + step=[8, 11]) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (32 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=256) diff --git a/downstream/mmdetection/configs/paa/README.md b/downstream/mmdetection/configs/paa/README.md new file mode 100644 index 0000000..c8861ec --- /dev/null +++ b/downstream/mmdetection/configs/paa/README.md @@ -0,0 +1,47 @@ +# PAA + +> [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://arxiv.org/abs/2007.08103) + + + +## Abstract + +In object detection, determining which anchors to assign as positive or negative samples, known as anchor assignment, has been revealed as a core procedure that can significantly affect a model's performance. In this paper we propose a novel anchor assignment strategy that adaptively separates anchors into positive and negative samples for a ground truth bounding box according to the model's learning status such that it is able to reason about the separation in a probabilistic manner. To do so we first calculate the scores of anchors conditioned on the model and fit a probability distribution to these scores. The model is then trained with anchors separated into positive and negative samples according to their probabilities. Moreover, we investigate the gap between the training and testing objectives and propose to predict the Intersection-over-Unions of detected boxes as a measure of localization quality to reduce the discrepancy. The combined score of classification and localization qualities serving as a box selection metric in non-maximum suppression well aligns with the proposed anchor assignment strategy and leads significant performance improvements. The proposed methods only add a single convolutional layer to RetinaNet baseline and does not require multiple anchors per location, so are efficient. Experimental results verify the effectiveness of the proposed methods. Especially, our models set new records for single-stage detectors on MS COCO test-dev dataset with various backbones. + +
    + +
    + +## Results and Models + +We provide config files to reproduce the object detection results in the +ECCV 2020 paper for Probabilistic Anchor Assignment with IoU +Prediction for Object Detection. + +| Backbone | Lr schd | Mem (GB) | Score voting | box AP | Config | Download | +| :-------: | :-----: | :------: | :----------: | :----: | :---------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | 12e | 3.7 | True | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json) | +| R-50-FPN | 12e | 3.7 | False | 40.2 | - | | +| R-50-FPN | 18e | 3.7 | True | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1.5x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.log.json) | +| R-50-FPN | 18e | 3.7 | False | 41.2 | - | | +| R-50-FPN | 24e | 3.7 | True | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.log.json) | +| R-50-FPN | 36e | 3.7 | True | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722.log.json) | +| R-101-FPN | 12e | 6.2 | True | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json) | +| R-101-FPN | 12e | 6.2 | False | 42.4 | - | | +| R-101-FPN | 24e | 6.2 | True | 43.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.log.json) | +| R-101-FPN | 36e | 6.2 | True | 45.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202.log.json) | + +**Note**: + +1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.2 mAP. We report the best results. + +## Citation + +```latex +@inproceedings{paa-eccv2020, + title={Probabilistic Anchor Assignment with IoU Prediction for Object Detection}, + author={Kim, Kang and Lee, Hee Seok}, + booktitle = {ECCV}, + year={2020} +} +``` diff --git a/downstream/mmdetection/configs/paa/metafile.yml b/downstream/mmdetection/configs/paa/metafile.yml new file mode 100644 index 0000000..e08b663 --- /dev/null +++ b/downstream/mmdetection/configs/paa/metafile.yml @@ -0,0 +1,104 @@ +Collections: + - Name: PAA + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - Probabilistic Anchor Assignment + - ResNet + Paper: + URL: https://arxiv.org/abs/2007.08103 + Title: 'Probabilistic Anchor Assignment with IoU Prediction for Object Detection' + README: configs/paa/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/detectors/paa.py#L6 + Version: v2.4.0 + +Models: + - Name: paa_r50_fpn_1x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth + + - Name: paa_r50_fpn_1.5x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_1.5x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth + + - Name: paa_r50_fpn_2x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth + + - Name: paa_r50_fpn_mstrain_3x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth + + - Name: paa_r101_fpn_1x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth + + - Name: paa_r101_fpn_2x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth + + - Name: paa_r101_fpn_mstrain_3x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth diff --git a/downstream/mmdetection/configs/paa/paa_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/paa/paa_r101_fpn_1x_coco.py new file mode 100644 index 0000000..94f1c27 --- /dev/null +++ b/downstream/mmdetection/configs/paa/paa_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/paa/paa_r101_fpn_2x_coco.py b/downstream/mmdetection/configs/paa/paa_r101_fpn_2x_coco.py new file mode 100644 index 0000000..641ef76 --- /dev/null +++ b/downstream/mmdetection/configs/paa/paa_r101_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r101_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/paa/paa_r101_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/paa/paa_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..71858ed --- /dev/null +++ b/downstream/mmdetection/configs/paa/paa_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,6 @@ +_base_ = './paa_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/paa/paa_r50_fpn_1.5x_coco.py b/downstream/mmdetection/configs/paa/paa_r50_fpn_1.5x_coco.py new file mode 100644 index 0000000..aabce4a --- /dev/null +++ b/downstream/mmdetection/configs/paa/paa_r50_fpn_1.5x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +lr_config = dict(step=[12, 16]) +runner = dict(type='EpochBasedRunner', max_epochs=18) diff --git a/downstream/mmdetection/configs/paa/paa_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/paa/paa_r50_fpn_1x_coco.py new file mode 100644 index 0000000..4c9c4aa --- /dev/null +++ b/downstream/mmdetection/configs/paa/paa_r50_fpn_1x_coco.py @@ -0,0 +1,70 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='PAA', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='PAAHead', + reg_decoded_bbox=True, + score_voting=True, + topk=9, + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.1, + neg_iou_thr=0.1, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/paa/paa_r50_fpn_2x_coco.py b/downstream/mmdetection/configs/paa/paa_r50_fpn_2x_coco.py new file mode 100644 index 0000000..663d2c0 --- /dev/null +++ b/downstream/mmdetection/configs/paa/paa_r50_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/paa/paa_r50_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/paa/paa_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..91fa28c --- /dev/null +++ b/downstream/mmdetection/configs/paa/paa_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,20 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/pafpn/README.md b/downstream/mmdetection/configs/pafpn/README.md new file mode 100644 index 0000000..ae1e3a3 --- /dev/null +++ b/downstream/mmdetection/configs/pafpn/README.md @@ -0,0 +1,34 @@ +# PAFPN + +> [Path Aggregation Network for Instance Segmentation](https://arxiv.org/abs/1803.01534) + + + +## Abstract + +The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction. These improvements are simple to implement, with subtle extra computational overhead. Our PANet reaches the 1st place in the COCO 2017 Challenge Instance Segmentation task and the 2nd place in Object Detection task without large-batch training. It is also state-of-the-art on MVD and Cityscapes. + +
    + +
    + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 4.0 | 17.2 | 37.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_20200503_105836.log.json) | + +## Citation + +```latex +@inproceedings{liu2018path, + author = {Shu Liu and + Lu Qi and + Haifang Qin and + Jianping Shi and + Jiaya Jia}, + title = {Path Aggregation Network for Instance Segmentation}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2018} +} +``` diff --git a/downstream/mmdetection/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py b/downstream/mmdetection/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py new file mode 100644 index 0000000..b2fdef9 --- /dev/null +++ b/downstream/mmdetection/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + +model = dict( + neck=dict( + type='PAFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/pafpn/metafile.yml b/downstream/mmdetection/configs/pafpn/metafile.yml new file mode 100644 index 0000000..f9cf97c --- /dev/null +++ b/downstream/mmdetection/configs/pafpn/metafile.yml @@ -0,0 +1,38 @@ +Collections: + - Name: PAFPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - PAFPN + Paper: + URL: https://arxiv.org/abs/1803.01534 + Title: 'Path Aggregation Network for Instance Segmentation' + README: configs/pafpn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/pafpn.py#L11 + Version: v2.0.0 + +Models: + - Name: faster_rcnn_r50_pafpn_1x_coco + In Collection: PAFPN + Config: configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (ms/im): + - value: 58.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth diff --git a/downstream/mmdetection/configs/panoptic_fpn/README.md b/downstream/mmdetection/configs/panoptic_fpn/README.md new file mode 100644 index 0000000..12980ce --- /dev/null +++ b/downstream/mmdetection/configs/panoptic_fpn/README.md @@ -0,0 +1,62 @@ +# Panoptic FPN + +> [Panoptic feature pyramid networks](https://arxiv.org/abs/1901.02446) + + + +## Abstract + +The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation. + +
    + +
    + +## Dataset + +PanopticFPN requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ │ ├── panoptic_train2017.json +│ │ │ ├── panoptic_train2017 +│ │ │ ├── panoptic_val2017.json +│ │ │ ├── panoptic_val2017 +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +``` + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | PQ | SQ | RQ | PQ_th | SQ_th | RQ_th | PQ_st | SQ_st | RQ_st | Config | Download | +| :-------: | :-----: | :-----: | :------: | :------------: | :--: | :--: | :--: | :---: | :---: | :---: | :---: | :---: | :---: | :---------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | pytorch | 1x | 4.7 | | 40.2 | 77.8 | 49.3 | 47.8 | 80.9 | 57.5 | 28.9 | 73.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153.log.json) | +| R-50-FPN | pytorch | 3x | - | - | 42.5 | 78.1 | 51.7 | 50.3 | 81.5 | 60.3 | 30.7 | 73.0 | 38.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155.log.json) | +| R-101-FPN | pytorch | 1x | 6.7 | | 42.2 | 78.3 | 51.4 | 50.1 | 81.4 | 59.9 | 30.3 | 73.6 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950.log.json) | +| R-101-FPN | pytorch | 3x | - | - | 44.1 | 78.9 | 53.6 | 52.1 | 81.7 | 62.3 | 32.0 | 74.6 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712-9c99acc4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712.log.json) | + +## Citation + +The base method for panoptic segmentation task. + +```latex +@inproceedings{kirillov2018panopticfpn, + author = { + Alexander Kirillov, + Ross Girshick, + Kaiming He, + Piotr Dollar, + }, + title = {Panoptic Feature Pyramid Networks}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2019} +} +``` diff --git a/downstream/mmdetection/configs/panoptic_fpn/metafile.yml b/downstream/mmdetection/configs/panoptic_fpn/metafile.yml new file mode 100644 index 0000000..8c9d39d --- /dev/null +++ b/downstream/mmdetection/configs/panoptic_fpn/metafile.yml @@ -0,0 +1,70 @@ +Collections: + - Name: PanopticFPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - PanopticFPN + Paper: + URL: https://arxiv.org/pdf/1901.02446 + Title: 'Panoptic feature pyramid networks' + README: configs/panoptic_fpn/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/detectors/panoptic_fpn.py#L7 + Version: v2.16.0 + +Models: + - Name: panoptic_fpn_r50_fpn_1x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 12 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth + + - Name: panoptic_fpn_r50_fpn_mstrain_3x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 36 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth + + - Name: panoptic_fpn_r101_fpn_1x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + Epochs: 12 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 42.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth + + - Name: panoptic_fpn_r101_fpn_mstrain_3x_coco + In Collection: PanopticFPN + Config: configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.5 + Epochs: 36 + Results: + - Task: Panoptic Segmentation + Dataset: COCO + Metrics: + PQ: 44.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712-9c99acc4.pth diff --git a/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py new file mode 100644 index 0000000..78b8079 --- /dev/null +++ b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './panoptic_fpn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..057e481 --- /dev/null +++ b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,6 @@ +_base_ = './panoptic_fpn_r50_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..2995524 --- /dev/null +++ b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py @@ -0,0 +1,33 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_panoptic.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='PanopticFPN', + semantic_head=dict( + type='PanopticFPNHead', + num_things_classes=80, + num_stuff_classes=53, + in_channels=256, + inner_channels=128, + start_level=0, + end_level=4, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), + conv_cfg=None, + loss_seg=dict( + type='CrossEntropyLoss', ignore_index=255, loss_weight=0.5)), + panoptic_fusion_head=dict( + type='HeuristicFusionHead', + num_things_classes=80, + num_stuff_classes=53), + test_cfg=dict( + panoptic=dict( + score_thr=0.6, + max_per_img=100, + mask_thr_binary=0.5, + mask_overlap=0.5, + nms=dict(type='nms', iou_threshold=0.5, class_agnostic=True), + stuff_area_limit=4096))) + +custom_hooks = [] diff --git a/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..b510935 --- /dev/null +++ b/downstream/mmdetection/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,61 @@ +_base_ = './panoptic_fpn_r50_fpn_1x_coco.py' + +# dataset settings +dataset_type = 'CocoPanopticDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadPanopticAnnotations', + with_bbox=True, + with_mask=True, + with_seg=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 4), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + train=dict( + _delete_=True, + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/panoptic_train2017.json', + img_prefix=data_root + 'train2017/', + seg_prefix=data_root + 'annotations/panoptic_train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/pascal_voc/README.md b/downstream/mmdetection/configs/pascal_voc/README.md new file mode 100644 index 0000000..3c09813 --- /dev/null +++ b/downstream/mmdetection/configs/pascal_voc/README.md @@ -0,0 +1,40 @@ +# Pascal VOC + +> [The Pascal Visual Object Classes (VOC) Challenge](https://link.springer.com/article/10.1007/s11263-009-0275-4) + + + +## Abstract + +The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection. + +This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension. + +
    + +
    + +## Results and Models + +| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Faster R-CNN C4 | R-50 | caffe | 18k | | - | 80.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712//home/dong/code_sensetime/2022Q1/mmdetection/work_dirs/prepare_voc/gather/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712_20220314_234327-847a14d2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712_20220314_234327.log.json) | +| Faster R-CNN | R-50 | pytorch | 1x | 2.6 | - | 80.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20220320_192712-54bef0f3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20220320_192712.log.json) | +| Retinanet | R-50 | pytorch | 1x | 2.1 | - | 77.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200617-47cbdd0e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200616_014642.log.json) | +| SSD300 | VGG16 | - | 120e | - | - | 76.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/ssd300_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd300_voc0712/ssd300_voc0712_20220320_194658-17edda1b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd300_voc0712/ssd300_voc0712_20220320_194658.log.json) | +| SSD512 | VGG16 | - | 120e | - | - | 79.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/ssd512_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd512_voc0712/ssd512_voc0712_20220320_194717-03cefefe.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd512_voc0712/ssd512_voc0712_20220320_194717.log.json) | + +## Citation + +```latex +@Article{Everingham10, + author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.", + title = "The Pascal Visual Object Classes (VOC) Challenge", + journal = "International Journal of Computer Vision", + volume = "88", + year = "2010", + number = "2", + month = jun, + pages = "303--338", +} +``` diff --git a/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py b/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py new file mode 100644 index 0000000..7bb1d73 --- /dev/null +++ b/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py @@ -0,0 +1,81 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_c4.py', + '../_base_/default_runtime.py' +] +model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) + +# dataset settings +dataset_type = 'VOCDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576), + (1333, 608), (1333, 640), (1333, 672), (1333, 704), + (1333, 736), (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=[ + data_root + 'VOC2007/ImageSets/Main/trainval.txt', + data_root + 'VOC2012/ImageSets/Main/trainval.txt' + ], + img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'], + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', + img_prefix=data_root + 'VOC2007/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt', + img_prefix=data_root + 'VOC2007/', + pipeline=test_pipeline)) + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=100, + warmup_ratio=0.001, + step=[12000, 16000]) + +# Runner type +runner = dict(type='IterBasedRunner', max_iters=18000) + +checkpoint_config = dict(interval=3000) +evaluation = dict(interval=3000, metric='mAP') diff --git a/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py b/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py new file mode 100644 index 0000000..7866ace --- /dev/null +++ b/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py b/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py new file mode 100644 index 0000000..12eee2c --- /dev/null +++ b/downstream/mmdetection/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py @@ -0,0 +1,75 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) + +CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', + 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') + +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1000, 600), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file='data/voc0712_trainval.json', + img_prefix='data/VOCdevkit', + pipeline=train_pipeline, + classes=CLASSES)), + val=dict( + type=dataset_type, + ann_file='data/voc07_test.json', + img_prefix='data/VOCdevkit', + pipeline=test_pipeline, + classes=CLASSES), + test=dict( + type=dataset_type, + ann_file='data/voc07_test.json', + img_prefix='data/VOCdevkit', + pipeline=test_pipeline, + classes=CLASSES)) +evaluation = dict(interval=1, metric='bbox') + +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/downstream/mmdetection/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py b/downstream/mmdetection/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py new file mode 100644 index 0000000..b4b050d --- /dev/null +++ b/downstream/mmdetection/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(bbox_head=dict(num_classes=20)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/downstream/mmdetection/configs/pascal_voc/ssd300_voc0712.py b/downstream/mmdetection/configs/pascal_voc/ssd300_voc0712.py new file mode 100644 index 0000000..e7008ae --- /dev/null +++ b/downstream/mmdetection/configs/pascal_voc/ssd300_voc0712.py @@ -0,0 +1,74 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict( + bbox_head=dict( + num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2, + 0.9)))) +# dataset settings +dataset_type = 'VOCDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + type='RepeatDataset', times=10, dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 20]) +checkpoint_config = dict(interval=1) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (8 samples per GPU) +auto_scale_lr = dict(base_batch_size=64) diff --git a/downstream/mmdetection/configs/pascal_voc/ssd512_voc0712.py b/downstream/mmdetection/configs/pascal_voc/ssd512_voc0712.py new file mode 100644 index 0000000..f4627c2 --- /dev/null +++ b/downstream/mmdetection/configs/pascal_voc/ssd512_voc0712.py @@ -0,0 +1,57 @@ +_base_ = 'ssd300_voc0712.py' +input_size = 512 +model = dict( + neck=dict( + out_channels=(512, 1024, 512, 256, 256, 256, 256), + level_strides=(2, 2, 2, 2, 1), + level_paddings=(1, 1, 1, 1, 1), + last_kernel_size=4), + bbox_head=dict( + in_channels=(512, 1024, 512, 256, 256, 256, 256), + anchor_generator=dict( + input_size=input_size, + strides=[8, 16, 32, 64, 128, 256, 512], + basesize_ratio_range=(0.15, 0.9), + ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2])))) +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(512, 512), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(512, 512), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/downstream/mmdetection/configs/pisa/README.md b/downstream/mmdetection/configs/pisa/README.md new file mode 100644 index 0000000..c847c85 --- /dev/null +++ b/downstream/mmdetection/configs/pisa/README.md @@ -0,0 +1,50 @@ +# PISA + +> [Prime Sample Attention in Object Detection](https://arxiv.org/abs/1904.04821) + + + +## Abstract + +It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP. Our study suggests that the samples in each mini-batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily mean higher mAP. Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance. We further develop a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) that directs the focus of the training process towards such samples. Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector. Particularly, On the MSCOCO dataset, PISA outperforms the random sampling baseline and hard mining schemes, e.g., OHEM and Focal Loss, consistently by around 2% on both single-stage and two-stage detectors, even with a strong backbone ResNeXt-101. + +
    + +
    + +## Results and Models + +| PISA | Network | Backbone | Lr schd | box AP | mask AP | Config | Download | +| :--: | :----------: | :------------: | :-----: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| × | Faster R-CNN | R-50-FPN | 1x | 36.4 | | - | | +| √ | Faster R-CNN | R-50-FPN | 1x | 38.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json) | +| × | Faster R-CNN | X101-32x4d-FPN | 1x | 40.1 | | - | | +| √ | Faster R-CNN | X101-32x4d-FPN | 1x | 41.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) | +| × | Mask R-CNN | R-50-FPN | 1x | 37.3 | 34.2 | - | | +| √ | Mask R-CNN | R-50-FPN | 1x | 39.1 | 35.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json) | +| × | Mask R-CNN | X101-32x4d-FPN | 1x | 41.1 | 37.1 | - | | +| √ | Mask R-CNN | X101-32x4d-FPN | 1x | | | | | +| × | RetinaNet | R-50-FPN | 1x | 35.6 | | - | | +| √ | RetinaNet | R-50-FPN | 1x | 36.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json) | +| × | RetinaNet | X101-32x4d-FPN | 1x | 39.0 | | - | | +| √ | RetinaNet | X101-32x4d-FPN | 1x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json) | +| × | SSD300 | VGG16 | 1x | 25.6 | | - | | +| √ | SSD300 | VGG16 | 1x | 27.6 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json) | +| × | SSD512 | VGG16 | 1x | 29.3 | | - | | +| √ | SSD512 | VGG16 | 1x | 31.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json) | + +**Notes:** + +- In the original paper, all models are trained and tested on mmdet v1.x, thus results may not be exactly the same with this release on v2.0. +- It is noted PISA only modifies the training pipeline so the inference time remains the same with the baseline. + +## Citation + +```latex +@inproceedings{cao2019prime, + title={Prime sample attention in object detection}, + author={Cao, Yuhang and Chen, Kai and Loy, Chen Change and Lin, Dahua}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2020} +} +``` diff --git a/downstream/mmdetection/configs/pisa/metafile.yml b/downstream/mmdetection/configs/pisa/metafile.yml new file mode 100644 index 0000000..cd43afb --- /dev/null +++ b/downstream/mmdetection/configs/pisa/metafile.yml @@ -0,0 +1,110 @@ +Collections: + - Name: PISA + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - PISA + - RPN + - ResNet + - RoIPool + Paper: + URL: https://arxiv.org/abs/1904.04821 + Title: 'Prime Sample Attention in Object Detection' + README: configs/pisa/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/roi_heads/pisa_roi_head.py#L8 + Version: v2.1.0 + +Models: + - Name: pisa_faster_rcnn_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth + + - Name: pisa_faster_rcnn_x101_32x4d_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth + + - Name: pisa_mask_rcnn_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth + + - Name: pisa_retinanet_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth + + - Name: pisa_retinanet_x101_32x4d_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth + + - Name: pisa_ssd300_coco + In Collection: PISA + Config: configs/pisa/pisa_ssd300_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 27.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth + + - Name: pisa_ssd512_coco + In Collection: PISA + Config: configs/pisa/pisa_ssd512_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 31.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth diff --git a/downstream/mmdetection/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..71e65b0 --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/downstream/mmdetection/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..16edd99 --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/downstream/mmdetection/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 0000000..047a293 --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/downstream/mmdetection/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..2186a8f --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/downstream/mmdetection/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py new file mode 100644 index 0000000..70f89e2 --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' + +model = dict( + bbox_head=dict( + type='PISARetinaHead', + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) diff --git a/downstream/mmdetection/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py b/downstream/mmdetection/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 0000000..b97b672 --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py' + +model = dict( + bbox_head=dict( + type='PISARetinaHead', + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) diff --git a/downstream/mmdetection/configs/pisa/pisa_ssd300_coco.py b/downstream/mmdetection/configs/pisa/pisa_ssd300_coco.py new file mode 100644 index 0000000..b5cc006 --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_ssd300_coco.py @@ -0,0 +1,8 @@ +_base_ = '../ssd/ssd300_coco.py' + +model = dict( + bbox_head=dict(type='PISASSDHead'), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/pisa/pisa_ssd512_coco.py b/downstream/mmdetection/configs/pisa/pisa_ssd512_coco.py new file mode 100644 index 0000000..3219d6d --- /dev/null +++ b/downstream/mmdetection/configs/pisa/pisa_ssd512_coco.py @@ -0,0 +1,8 @@ +_base_ = '../ssd/ssd512_coco.py' + +model = dict( + bbox_head=dict(type='PISASSDHead'), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/point_rend/README.md b/downstream/mmdetection/configs/point_rend/README.md new file mode 100644 index 0000000..183e83d --- /dev/null +++ b/downstream/mmdetection/configs/point_rend/README.md @@ -0,0 +1,33 @@ +# PointRend + +> [PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193) + + + +## Abstract + +We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. + +
    + +
    + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :------: | :---: | :-----: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| R-50-FPN | caffe | 1x | 4.6 | | 38.4 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco_20200612_161407.log.json) | +| R-50-FPN | caffe | 3x | 4.6 | | 41.0 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco_20200614_002632.log.json) | + +Note: All models are trained with multi-scale, the input image shorter side is randomly scaled to one of (640, 672, 704, 736, 768, 800). + +## Citation + +```latex +@InProceedings{kirillov2019pointrend, + title={{PointRend}: Image Segmentation as Rendering}, + author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick}, + journal={ArXiv:1912.08193}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/point_rend/metafile.yml b/downstream/mmdetection/configs/point_rend/metafile.yml new file mode 100644 index 0000000..82aea05 --- /dev/null +++ b/downstream/mmdetection/configs/point_rend/metafile.yml @@ -0,0 +1,54 @@ +Collections: + - Name: PointRend + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - PointRend + - FPN + - ResNet + Paper: + URL: https://arxiv.org/abs/1912.08193 + Title: 'PointRend: Image Segmentation as Rendering' + README: configs/point_rend/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/point_rend.py#L6 + Version: v2.2.0 + +Models: + - Name: point_rend_r50_caffe_fpn_mstrain_1x_coco + In Collection: PointRend + Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth + + - Name: point_rend_r50_caffe_fpn_mstrain_3x_coco + In Collection: PointRend + Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth diff --git a/downstream/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py b/downstream/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 0000000..0c0e563 --- /dev/null +++ b/downstream/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,44 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +# model settings +model = dict( + type='PointRend', + roi_head=dict( + type='PointRendRoIHead', + mask_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='concat', + roi_layer=dict( + _delete_=True, type='SimpleRoIAlign', output_size=14), + out_channels=256, + featmap_strides=[4]), + mask_head=dict( + _delete_=True, + type='CoarseMaskHead', + num_fcs=2, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + point_head=dict( + type='MaskPointHead', + num_fcs=3, + in_channels=256, + fc_channels=256, + num_classes=80, + coarse_pred_each_layer=True, + loss_point=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + mask_size=7, + num_points=14 * 14, + oversample_ratio=3, + importance_sample_ratio=0.75)), + test_cfg=dict( + rcnn=dict( + subdivision_steps=5, + subdivision_num_points=28 * 28, + scale_factor=2))) diff --git a/downstream/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..169278e --- /dev/null +++ b/downstream/mmdetection/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/pvt/README.md b/downstream/mmdetection/configs/pvt/README.md new file mode 100644 index 0000000..1fd090b --- /dev/null +++ b/downstream/mmdetection/configs/pvt/README.md @@ -0,0 +1,57 @@ +# PVT + +> [Pyramid vision transformer: A versatile backbone for dense prediction without convolutions](https://arxiv.org/abs/2102.12122) + + + +## Abstract + +Although using convolutional neural networks (CNNs) as backbones achieves great successes in computer vision, this work investigates a simple backbone network useful for many dense prediction tasks without convolutions. Unlike the recently-proposed Transformer model (e.g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to prior arts. (1) Different from ViT that typically has low-resolution outputs and high computational and memory cost, PVT can be not only trained on dense partitions of the image to achieve high output resolution, which is important for dense predictions but also using a progressive shrinking pyramid to reduce computations of large feature maps. (2) PVT inherits the advantages from both CNN and Transformer, making it a unified backbone in various vision tasks without convolutions by simply replacing CNN backbones. (3) We validate PVT by conducting extensive experiments, showing that it boosts the performance of many downstream tasks, e.g., object detection, semantic, and instance segmentation. For example, with a comparable number of parameters, RetinaNet+PVT achieves 40.4 AP on the COCO dataset, surpassing RetinNet+ResNet50 (36.3 AP) by 4.1 absolute AP. We hope PVT could serve as an alternative and useful backbone for pixel-level predictions and facilitate future researches. + +Transformer recently has shown encouraging progresses in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (abbreviated as PVTv1) by adding three designs, including (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers. +With these modifications, our PVTv2 significantly improves PVTv1 on three tasks e.g., classification, detection, and segmentation. Moreover, PVTv2 achieves comparable or better performances than recent works such as Swin Transformer. We hope this work will facilitate state-of-the-art Transformer researches in computer vision. + +
    + +
    + +## Results and Models + +### RetinaNet (PVTv1) + +| Backbone | Lr schd | Mem (GB) | box AP | Config | Download | +| :--------: | :-----: | :------: | :----: | :--------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| PVT-Tiny | 12e | 8.5 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_t_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-t_fpn_1x_coco/retinanet_pvt-t_fpn_1x_coco_20210831_103110-17b566bd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-t_fpn_1x_coco/retinanet_pvt-t_fpn_1x_coco_20210831_103110.log.json) | +| PVT-Small | 12e | 14.5 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_s_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-s_fpn_1x_coco/retinanet_pvt-s_fpn_1x_coco_20210906_142921-b6c94a5b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-s_fpn_1x_coco/retinanet_pvt-s_fpn_1x_coco_20210906_142921.log.json) | +| PVT-Medium | 12e | 20.9 | 41.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_m_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-m_fpn_1x_coco/retinanet_pvt-m_fpn_1x_coco_20210831_103243-55effa1b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-m_fpn_1x_coco/retinanet_pvt-m_fpn_1x_coco_20210831_103243.log.json) | + +### RetinaNet (PVTv2) + +| Backbone | Lr schd | Mem (GB) | box AP | Config | Download | +| :------: | :-----: | :------: | :----: | :------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| PVTv2-B0 | 12e | 7.4 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b0_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b0_fpn_1x_coco/retinanet_pvtv2-b0_fpn_1x_coco_20210831_103157-13e9aabe.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b0_fpn_1x_coco/retinanet_pvtv2-b0_fpn_1x_coco_20210831_103157.log.json) | +| PVTv2-B1 | 12e | 9.5 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b1_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b1_fpn_1x_coco/retinanet_pvtv2-b1_fpn_1x_coco_20210831_103318-7e169a7d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b1_fpn_1x_coco/retinanet_pvtv2-b1_fpn_1x_coco_20210831_103318.log.json) | +| PVTv2-B2 | 12e | 16.2 | 44.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b2_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b2_fpn_1x_coco/retinanet_pvtv2-b2_fpn_1x_coco_20210901_174843-529f0b9a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b2_fpn_1x_coco/retinanet_pvtv2-b2_fpn_1x_coco_20210901_174843.log.json) | +| PVTv2-B3 | 12e | 23.0 | 46.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b3_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b3_fpn_1x_coco/retinanet_pvtv2-b3_fpn_1x_coco_20210903_151512-8357deff.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b3_fpn_1x_coco/retinanet_pvtv2-b3_fpn_1x_coco_20210903_151512.log.json) | +| PVTv2-B4 | 12e | 17.0 | 46.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b4_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b4_fpn_1x_coco/retinanet_pvtv2-b4_fpn_1x_coco_20210901_170151-83795c86.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b4_fpn_1x_coco/retinanet_pvtv2-b4_fpn_1x_coco_20210901_170151.log.json) | +| PVTv2-B5 | 12e | 18.7 | 46.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b5_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b5_fpn_1x_coco/retinanet_pvtv2-b5_fpn_1x_coco_20210902_201800-3420eb57.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b5_fpn_1x_coco/retinanet_pvtv2-b5_fpn_1x_coco_20210902_201800.log.json) | + +## Citation + +```latex +@article{wang2021pyramid, + title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions}, + author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling}, + journal={arXiv preprint arXiv:2102.12122}, + year={2021} +} +``` + +```latex +@article{wang2021pvtv2, + title={PVTv2: Improved Baselines with Pyramid Vision Transformer}, + author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling}, + journal={arXiv preprint arXiv:2106.13797}, + year={2021} +} +``` diff --git a/downstream/mmdetection/configs/pvt/metafile.yml b/downstream/mmdetection/configs/pvt/metafile.yml new file mode 100644 index 0000000..5884378 --- /dev/null +++ b/downstream/mmdetection/configs/pvt/metafile.yml @@ -0,0 +1,243 @@ +Models: + - Name: retinanet_pvt-t_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvt-t_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.5 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformer + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-t_fpn_1x_coco/retinanet_pvt-t_fpn_1x_coco_20210831_103110-17b566bd.pth + Paper: + URL: https://arxiv.org/abs/2102.12122 + Title: "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L315 + Version: 2.17.0 + + - Name: retinanet_pvt-s_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvt-s_fpn_1x_coco.py + Metadata: + Training Memory (GB): 14.5 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformer + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-s_fpn_1x_coco/retinanet_pvt-s_fpn_1x_coco_20210906_142921-b6c94a5b.pth + Paper: + URL: https://arxiv.org/abs/2102.12122 + Title: "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L315 + Version: 2.17.0 + + - Name: retinanet_pvt-m_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvt-m_fpn_1x_coco.py + Metadata: + Training Memory (GB): 20.9 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformer + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-m_fpn_1x_coco/retinanet_pvt-m_fpn_1x_coco_20210831_103243-55effa1b.pth + Paper: + URL: https://arxiv.org/abs/2102.12122 + Title: "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L315 + Version: 2.17.0 + + - Name: retinanet_pvtv2-b0_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.4 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformerV2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b0_fpn_1x_coco/retinanet_pvtv2-b0_fpn_1x_coco_20210831_103157-13e9aabe.pth + Paper: + URL: https://arxiv.org/abs/2106.13797 + Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543 + Version: 2.17.0 + + - Name: retinanet_pvtv2-b1_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.5 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformerV2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b1_fpn_1x_coco/retinanet_pvtv2-b1_fpn_1x_coco_20210831_103318-7e169a7d.pth + Paper: + URL: https://arxiv.org/abs/2106.13797 + Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543 + Version: 2.17.0 + + - Name: retinanet_pvtv2-b2_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py + Metadata: + Training Memory (GB): 16.2 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformerV2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b2_fpn_1x_coco/retinanet_pvtv2-b2_fpn_1x_coco_20210901_174843-529f0b9a.pth + Paper: + URL: https://arxiv.org/abs/2106.13797 + Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543 + Version: 2.17.0 + + - Name: retinanet_pvtv2-b3_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py + Metadata: + Training Memory (GB): 23.0 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformerV2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b3_fpn_1x_coco/retinanet_pvtv2-b3_fpn_1x_coco_20210903_151512-8357deff.pth + Paper: + URL: https://arxiv.org/abs/2106.13797 + Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543 + Version: 2.17.0 + + - Name: retinanet_pvtv2-b4_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py + Metadata: + Training Memory (GB): 17.0 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformerV2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b4_fpn_1x_coco/retinanet_pvtv2-b4_fpn_1x_coco_20210901_170151-83795c86.pth + Paper: + URL: https://arxiv.org/abs/2106.13797 + Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543 + Version: 2.17.0 + + - Name: retinanet_pvtv2-b5_fpn_1x_coco + In Collection: RetinaNet + Config: configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py + Metadata: + Training Memory (GB): 18.7 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PyramidVisionTransformerV2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b5_fpn_1x_coco/retinanet_pvtv2-b5_fpn_1x_coco_20210902_201800-3420eb57.pth + Paper: + URL: https://arxiv.org/abs/2106.13797 + Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer" + README: configs/pvt/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543 + Version: 2.17.0 diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py new file mode 100644 index 0000000..e299f2a --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = 'retinanet_pvt-t_fpn_1x_coco.py' +model = dict( + backbone=dict( + num_layers=[3, 8, 27, 3], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_large.pth'))) +fp16 = dict(loss_scale=dict(init_scale=512)) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py new file mode 100644 index 0000000..b888f78 --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = 'retinanet_pvt-t_fpn_1x_coco.py' +model = dict( + backbone=dict( + num_layers=[3, 4, 18, 3], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_medium.pth'))) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py new file mode 100644 index 0000000..4660348 --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = 'retinanet_pvt-t_fpn_1x_coco.py' +model = dict( + backbone=dict( + num_layers=[3, 4, 6, 3], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_small.pth'))) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvt-t_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvt-t_fpn_1x_coco.py new file mode 100644 index 0000000..a6cff7d --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvt-t_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='RetinaNet', + backbone=dict( + _delete_=True, + type='PyramidVisionTransformer', + num_layers=[2, 2, 2, 2], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_tiny.pth')), + neck=dict(in_channels=[64, 128, 320, 512])) +# optimizer +optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py new file mode 100644 index 0000000..cbe2295 --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='RetinaNet', + backbone=dict( + _delete_=True, + type='PyramidVisionTransformerV2', + embed_dims=32, + num_layers=[2, 2, 2, 2], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_v2_b0.pth')), + neck=dict(in_channels=[32, 64, 160, 256])) +# optimizer +optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py new file mode 100644 index 0000000..5374c50 --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' +model = dict( + backbone=dict( + embed_dims=64, + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_v2_b1.pth')), + neck=dict(in_channels=[64, 128, 320, 512])) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py new file mode 100644 index 0000000..cf9a18d --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' +model = dict( + backbone=dict( + embed_dims=64, + num_layers=[3, 4, 6, 3], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_v2_b2.pth')), + neck=dict(in_channels=[64, 128, 320, 512])) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py new file mode 100644 index 0000000..7a47f82 --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' +model = dict( + backbone=dict( + embed_dims=64, + num_layers=[3, 4, 18, 3], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_v2_b3.pth')), + neck=dict(in_channels=[64, 128, 320, 512])) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py new file mode 100644 index 0000000..9891d7b --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py @@ -0,0 +1,18 @@ +_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' +model = dict( + backbone=dict( + embed_dims=64, + num_layers=[3, 8, 27, 3], + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_v2_b4.pth')), + neck=dict(in_channels=[64, 128, 320, 512])) +# optimizer +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0001 / 1.4, weight_decay=0.0001) +# dataset settings +data = dict(samples_per_gpu=1, workers_per_gpu=1) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (1 samples per GPU) +auto_scale_lr = dict(base_batch_size=8) diff --git a/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py new file mode 100644 index 0000000..a9fea2e --- /dev/null +++ b/downstream/mmdetection/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py @@ -0,0 +1,19 @@ +_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py' +model = dict( + backbone=dict( + embed_dims=64, + num_layers=[3, 6, 40, 3], + mlp_ratios=(4, 4, 4, 4), + init_cfg=dict(checkpoint='https://github.com/whai362/PVT/' + 'releases/download/v2/pvt_v2_b5.pth')), + neck=dict(in_channels=[64, 128, 320, 512])) +# optimizer +optimizer = dict( + _delete_=True, type='AdamW', lr=0.0001 / 1.4, weight_decay=0.0001) +# dataset settings +data = dict(samples_per_gpu=1, workers_per_gpu=1) + +# NOTE: `auto_scale_lr` is for automatically scaling LR, +# USER SHOULD NOT CHANGE ITS VALUES. +# base_batch_size = (8 GPUs) x (1 samples per GPU) +auto_scale_lr = dict(base_batch_size=8) diff --git a/downstream/mmdetection/configs/queryinst/README.md b/downstream/mmdetection/configs/queryinst/README.md new file mode 100644 index 0000000..ad6e0b3 --- /dev/null +++ b/downstream/mmdetection/configs/queryinst/README.md @@ -0,0 +1,36 @@ +# QueryInst + +> [Instances as Queries](https://openaccess.thecvf.com/content/ICCV2021/html/Fang_Instances_As_Queries_ICCV_2021_paper.html) + + + +## Abstract + +We present QueryInst, a new perspective for instance segmentation. QueryInst is a multi-stage end-to-end system that treats instances of interest as learnable queries, enabling query based object detectors, e.g., Sparse R-CNN, to have strong instance segmentation performance. The attributes of instances such as categories, bounding boxes, instance masks, and instance association embeddings are represented by queries in a unified manner. In QueryInst, a query is shared by both detection and segmentation via dynamic convolutions and driven by parallelly-supervised multi-stage learning. We conduct extensive experiments on three challenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in object detection, instance segmentation, and video instance segmentation tasks. For the first time, we demonstrate that a simple end-to-end query based framework can achieve the state-of-the-art performance in various instance-level recognition tasks. + +
    + +
    + +## Results and Models + +| Model | Backbone | Style | Lr schd | Number of Proposals | Multi-Scale | RandomCrop | box AP | mask AP | Config | Download | +| :-------: | :-------: | :-----: | :-----: | :-----------------: | :---------: | :--------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| QueryInst | R-50-FPN | pytorch | 1x | 100 | False | False | 42.0 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916.log.json) | +| QueryInst | R-50-FPN | pytorch | 3x | 100 | True | False | 44.8 | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643-7837af86.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643.log.json) | +| QueryInst | R-50-FPN | pytorch | 3x | 300 | True | True | 47.5 | 41.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802.log.json) | +| QueryInst | R-101-FPN | pytorch | 3x | 100 | True | False | 46.4 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048-91f9995b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048.log.json) | +| QueryInst | R-101-FPN | pytorch | 3x | 300 | True | True | 49.0 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621.log.json) | + +## Citation + +```latex +@InProceedings{Fang_2021_ICCV, + author = {Fang, Yuxin and Yang, Shusheng and Wang, Xinggang and Li, Yu and Fang, Chen and Shan, Ying and Feng, Bin and Liu, Wenyu}, + title = {Instances As Queries}, + booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2021}, + pages = {6910-6919} +} +``` diff --git a/downstream/mmdetection/configs/queryinst/metafile.yml b/downstream/mmdetection/configs/queryinst/metafile.yml new file mode 100644 index 0000000..da7f0a7 --- /dev/null +++ b/downstream/mmdetection/configs/queryinst/metafile.yml @@ -0,0 +1,100 @@ +Collections: + - Name: QueryInst + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - FPN + - ResNet + - QueryInst + Paper: + URL: https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf + Title: 'Instances as Queries' + README: configs/queryinst/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/queryinst.py + Version: v2.18.0 + +Models: + - Name: queryinst_r50_fpn_1x_coco + In Collection: QueryInst + Config: configs/queryinst/queryinst_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth + + - Name: queryinst_r50_fpn_mstrain_480-800_3x_coco + In Collection: QueryInst + Config: configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643-7837af86.pth + + - Name: queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco + In Collection: QueryInst + Config: configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth + + - Name: queryinst_r101_fpn_mstrain_480-800_3x_coco + In Collection: QueryInst + Config: configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048-91f9995b.pth + + - Name: queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco + In Collection: QueryInst + Config: configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 49.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth diff --git a/downstream/mmdetection/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/downstream/mmdetection/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..fd138f5 --- /dev/null +++ b/downstream/mmdetection/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,7 @@ +_base_ = './queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py b/downstream/mmdetection/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..07cae19 --- /dev/null +++ b/downstream/mmdetection/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py @@ -0,0 +1,7 @@ +_base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py' + +model = dict( + backbone=dict( + depth=101, + init_cfg=dict(type='Pretrained', + checkpoint='torchvision://resnet101'))) diff --git a/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_1x_coco.py b/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_1x_coco.py new file mode 100644 index 0000000..48f5773 --- /dev/null +++ b/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_1x_coco.py @@ -0,0 +1,138 @@ +_base_ = [ + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +num_stages = 6 +num_proposals = 100 +model = dict( + type='QueryInst', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=0, + add_extra_convs='on_input', + num_outs=4), + rpn_head=dict( + type='EmbeddingRPNHead', + num_proposals=num_proposals, + proposal_feature_channel=256), + roi_head=dict( + type='SparseRoIHead', + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + proposal_feature_channel=256, + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='DIIHead', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=3, + feedforward_channels=2048, + in_channels=256, + dropout=0.0, + ffn_act_cfg=dict(type='ReLU', inplace=True), + dynamic_conv_cfg=dict( + type='DynamicConv', + in_channels=256, + feat_channels=64, + out_channels=256, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN')), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages) + ], + mask_head=[ + dict( + type='DynamicMaskHead', + dynamic_conv_cfg=dict( + type='DynamicConv', + in_channels=256, + feat_channels=64, + out_channels=256, + input_feat_shape=14, + with_proj=False, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN')), + num_convs=4, + num_classes=80, + roi_feat_size=14, + in_channels=256, + conv_kernel_size=3, + conv_out_channels=256, + class_agnostic=False, + norm_cfg=dict(type='BN'), + upsample_cfg=dict(type='deconv', scale_factor=2), + loss_mask=dict( + type='DiceLoss', + loss_weight=8.0, + use_sigmoid=True, + activate=False, + eps=1e-5)) for _ in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1, + mask_size=28, + ) for _ in range(num_stages) + ]), + test_cfg=dict( + rpn=None, rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5))) + +# optimizer +optimizer = dict( + _delete_=True, + type='AdamW', + lr=0.0001, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[8, 11], warmup_iters=1000) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..3089b3c --- /dev/null +++ b/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,54 @@ +_base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py' +num_proposals = 300 +model = dict( + rpn_head=dict(num_proposals=num_proposals), + test_cfg=dict( + _delete_=True, + rpn=None, + rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py b/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py new file mode 100644 index 0000000..89e2cd1 --- /dev/null +++ b/downstream/mmdetection/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py @@ -0,0 +1,23 @@ +_base_ = './queryinst_r50_fpn_1x_coco.py' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, value) for value in min_values], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] + +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(policy='step', step=[27, 33]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/downstream/mmdetection/configs/regnet/README.md b/downstream/mmdetection/configs/regnet/README.md new file mode 100644 index 0000000..711ed63 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/README.md @@ -0,0 +1,122 @@ +# RegNet + +> [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678) + + + +## Abstract + +In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs. + +
    + +
    + +## Introduction + +We implement RegNetX and RegNetY models in detection systems and provide their first results on Mask R-CNN, Faster R-CNN and RetinaNet. + +The pre-trained models are converted from [model zoo of pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). + +## Usage + +To use a regnet model, there are two steps to do: + +1. Convert the model to ResNet-style supported by MMDetection +2. Modify backbone and neck in config accordingly + +### Convert model + +We already prepare models of FLOPs from 400M to 12G in our model zoo. + +For more general usage, we also provide script `regnet2mmdet.py` in the tools directory to convert the key of models pretrained by [pycls](https://github.com/facebookresearch/pycls/) to +ResNet-style checkpoints used in MMDetection. + +```bash +python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH} +``` + +This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`. + +### Modify config + +The users can modify the config's `depth` of backbone and corresponding keys in `arch` according to the configs in the [pycls model zoo](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). +The parameter `in_channels` in FPN can be found in the Figure 15 & 16 of the paper (`wi` in the legend). +This directory already provides some configs with their performance, using RegNetX from 800MF to 12GF level. +For other pre-trained models or self-implemented regnet models, the users are responsible to check these parameters by themselves. + +**Note**: Although Fig. 15 & 16 also provide `w0`, `wa`, `wm`, `group_w`, and `bot_mul` for `arch`, they are quantized thus inaccurate, using them sometimes produces different backbone that does not match the key in the pre-trained model. + +## Results and Models + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :----------------------------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | pytorch | 1x | 4.4 | 12.0 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| [RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | pytorch | 1x | 5.0 | | 40.3 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | +| [RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | pytorch | 1x | 5.5 | | 41.5 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json) | +| [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | pytorch | 1x | 6.4 | 10.3 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +| [RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | pytorch | 1x | 6.1 | | 41.0 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json) | +| [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | pytorch | 1x | 7.6 | 9.4 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +| [RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | pytorch | 1x | 6.4 | | 41.7 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json) | +| [RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | pytorch | 1x | 7.4 | | 42.2 | 38 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json) | +| [RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | pytorch | 1x | 5.0 | | 40.3 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json) | + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | pytorch | 1x | 4.0 | 18.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| [RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | pytorch | 1x | 4.5 | | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json) | +| [RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | pytorch | 2x | 4.5 | | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json) | + +### RetinaNet + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-----------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py) | pytorch | 1x | 3.8 | 16.6 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | +| [RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py) | pytorch | 1x | 2.5 | | 35.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json) | +| [RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py) | pytorch | 1x | 3.3 | | 37.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json) | +| [RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py) | pytorch | 1x | 4.2 | | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | + +### Pre-trained models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :---------------: | :---------------------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Faster RCNN | [RegNetX-400MF-FPN](./faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 2.3 | | 37.1 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112.log.json) | +| Faster RCNN | [RegNetX-800MF-FPN](./faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 2.8 | | 38.8 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118.log.json) | +| Faster RCNN | [RegNetX-1.6GF-FPN](./faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 3.4 | | 40.5 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325.log.json) | +| Faster RCNN | [RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 4.4 | | 42.3 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152.log.json) | +| Faster RCNN | [RegNetX-4GF-FPN](./faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 4.9 | | 42.8 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201.log.json) | +| Mask RCNN | [RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 5.0 | | 43.1 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | +| Mask RCNN | [RegNetX-400MF-FPN](./mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 2.5 | | 37.6 | 34.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-8aac57a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443.log.json) | +| Mask RCNN | [RegNetX-800MF-FPN](./mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 2.9 | | 39.5 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-715d51f5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641.log.json) | +| Mask RCNN | [RegNetX-1.6GF-FPN](./mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 3.6 | | 40.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6764cff5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641.log.json) | +| Mask RCNN | [RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 5.0 | | 43.1 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | +| Mask RCNN | [RegNetX-4GF-FPN](./mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 5.1 | | 43.4 | 39.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-00f0331c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621.log.json) | +| Cascade Mask RCNN | [RegNetX-400MF-FPN](./cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 4.3 | | 41.6 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619-5142f449.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619.log.json) | +| Cascade Mask RCNN | [RegNetX-800MF-FPN](./cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 4.8 | | 42.8 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616-dcbd13f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616.log.json) | +| Cascade Mask RCNN | [RegNetX-1.6GF-FPN](./cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 5.4 | | 44.5 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616-75f29a61.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616.log.json) | +| Cascade Mask RCNN | [RegNetX-3.2GF-FPN](./cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 6.4 | | 45.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616-b9c2c58b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616.log.json) | +| Cascade Mask RCNN | [RegNetX-4GF-FPN](./cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | pytorch | 3x | 6.9 | | 45.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034-cbb1be4c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034.log.json) | + +### Notice + +1. The models are trained using a different weight decay, i.e., `weight_decay=5e-5` according to the setting in ImageNet training. This brings improvement of at least 0.7 AP absolute but does not improve the model using ResNet-50. +2. RetinaNets using RegNets are trained with learning rate 0.02 with gradient clip. We find that using learning rate 0.02 could improve the results by at least 0.7 AP absolute and gradient clip is necessary to stabilize the training. However, this does not improve the performance of ResNet-50-FPN RetinaNet. + +## Citation + +```latex +@article{radosavovic2020designing, + title={Designing Network Design Spaces}, + author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, + year={2020}, + eprint={2003.13678}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` diff --git a/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..358d85a --- /dev/null +++ b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..8464571 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,63 @@ +_base_ = [ + '../common/mstrain_3x_coco_instance.py', + '../_base_/models/cascade_mask_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + # Images are converted to float32 directly after loading in PyCls + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict(weight_decay=0.00005) diff --git a/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..2a8990a --- /dev/null +++ b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..3157863 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..41376ad --- /dev/null +++ b/downstream/mmdetection/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..385b5ca --- /dev/null +++ b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 0000000..88d270e --- /dev/null +++ b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,57 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) diff --git a/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py new file mode 100644 index 0000000..612490b --- /dev/null +++ b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..b7e6e1a --- /dev/null +++ b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,61 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict(weight_decay=0.00005) diff --git a/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..0a05f6e --- /dev/null +++ b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..98b3fc2 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..67f448b --- /dev/null +++ b/downstream/mmdetection/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..7970c3c --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py new file mode 100644 index 0000000..ce3661c --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_12gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_12gf')), + neck=dict( + type='FPN', + in_channels=[224, 448, 896, 2240], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 0000000..44bf0d1 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + # Images are converted to float32 directly after loading in PyCls + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 0000000..5b53428 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf'))) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 0000000..aca64d3 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,66 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..c38dfa6 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py new file mode 100644 index 0000000..874d485 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..f0b65ea --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py new file mode 100644 index 0000000..99387d8 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_6.4gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_6.4gf')), + neck=dict( + type='FPN', + in_channels=[168, 392, 784, 1624], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 0000000..335ebab --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,26 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py new file mode 100644 index 0000000..1e7832f --- /dev/null +++ b/downstream/mmdetection/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_8.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_8.0gf')), + neck=dict( + type='FPN', + in_channels=[80, 240, 720, 1920], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/metafile.yml b/downstream/mmdetection/configs/regnet/metafile.yml new file mode 100644 index 0000000..ecd3953 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/metafile.yml @@ -0,0 +1,797 @@ +Models: + - Name: mask_rcnn_regnetx-3.2GF_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-4GF_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-6.4GF_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.1 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-8GF_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-12GF_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.4 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: faster_rcnn_regnetx-3.2GF_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: faster_rcnn_regnetx-3.2GF_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 24 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: retinanet_regnetx-800MF_fpn_1x_coco + In Collection: RetinaNet + Config: configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 2.5 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: retinanet_regnetx-1.6GF_fpn_1x_coco + In Collection: RetinaNet + Config: configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.3 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: retinanet_regnetx-3.2GF_fpn_1x_coco + In Collection: RetinaNet + Config: configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + Epochs: 12 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 2.3 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 2.8 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.4 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.4 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.9 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 2.5 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-8aac57a4.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 2.9 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-715d51f5.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.6 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6764cff5.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6e63e19c.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: Mask R-CNN + Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.1 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-00f0331c.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619-5142f449.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.8 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616-dcbd13f4.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.4 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616-75f29a61.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616-b9c2c58b.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 + + - Name: cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: Cascade R-CNN + Config: configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.9 + Epochs: 36 + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - RegNet + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034-cbb1be4c.pth + Paper: + URL: https://arxiv.org/abs/2003.13678 + Title: 'Designing Network Design Spaces' + README: configs/regnet/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11 + Version: v2.1.0 diff --git a/downstream/mmdetection/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py new file mode 100644 index 0000000..7395c1b --- /dev/null +++ b/downstream/mmdetection/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 0000000..f05307c --- /dev/null +++ b/downstream/mmdetection/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,59 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/downstream/mmdetection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py b/downstream/mmdetection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py new file mode 100644 index 0000000..f6f8989 --- /dev/null +++ b/downstream/mmdetection/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py @@ -0,0 +1,17 @@ +_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + init_cfg=dict( + type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/downstream/mmdetection/configs/reppoints/README.md b/downstream/mmdetection/configs/reppoints/README.md new file mode 100644 index 0000000..acf8e47 --- /dev/null +++ b/downstream/mmdetection/configs/reppoints/README.md @@ -0,0 +1,59 @@ +# RepPoints + +> [RepPoints: Point Set Representation for Object Detection](https://arxiv.org/abs/1904.11490) + + + +## Abstract + +Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present RepPoints(representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 AP50 on the COCO test-dev detection benchmark, using ResNet-101 model. + +
    + +
    + +## Introdution + +By [Ze Yang](https://yangze.tech/), [Shaohui Liu](http://b1ueber2y.me/), and [Han Hu](https://ancientmooner.github.io/). + +We provide code support and configuration files to reproduce the results in the paper for +["RepPoints: Point Set Representation for Object Detection"](https://arxiv.org/abs/1904.11490) on COCO object detection. + +**RepPoints**, initially described in [arXiv](https://arxiv.org/abs/1904.11490), is a new representation method for visual objects, on which visual understanding tasks are typically centered. Visual object representation, aiming at both geometric description and appearance feature extraction, is conventionally achieved by `bounding box + RoIPool (RoIAlign)`. The bounding box representation is convenient to use; however, it provides only a rectangular localization of objects that lacks geometric precision and may consequently degrade feature quality. Our new representation, RepPoints, models objects by a `point set` instead of a `bounding box`, which learns to adaptively position themselves over an object in a manner that circumscribes the object’s `spatial extent` and enables `semantically aligned feature extraction`. This richer and more flexible representation maintains the convenience of bounding boxes while facilitating various visual understanding applications. This repo demonstrated the effectiveness of RepPoints for COCO object detection. + +Another feature of this repo is the demonstration of an `anchor-free detector`, which can be as effective as state-of-the-art anchor-based detection methods. The anchor-free detector can utilize either `bounding box` or `RepPoints` as the basic object representation. + +## Results and Models + +The results on COCO 2017val are shown in the table below. + +| Method | Backbone | GN | Anchor | convert func | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------: | :-----------: | :-: | :----: | :----------: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| BBox | R-50-FPN | Y | single | - | 1x | 3.9 | 15.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json) | +| BBox | R-50-FPN | Y | none | - | 1x | 3.9 | 15.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | N | none | moment | 1x | 3.3 | 18.5 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 1x | 3.9 | 17.5 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 2x | 3.9 | - | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329_150020.log.json) | +| RepPoints | R-101-FPN | Y | none | moment | 2x | 5.8 | 13.7 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329_132205.log.json) | +| RepPoints | R-101-FPN-DCN | Y | none | moment | 2x | 5.9 | 12.1 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132134.log.json) | +| RepPoints | X-101-FPN-DCN | Y | none | moment | 2x | 7.1 | 9.3 | 44.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132201.log.json) | + +**Notes:** + +- `R-xx`, `X-xx` denote the ResNet and ResNeXt architectures, respectively. +- `DCN` denotes replacing 3x3 conv with the 3x3 deformable convolution in `c3-c5` stages of backbone. +- `none` in the `anchor` column means 2-d `center point` (x,y) is used to represent the initial object hypothesis. `single` denotes one 4-d anchor box (x,y,w,h) with IoU based label assign criterion is adopted. +- `moment`, `partial MinMax`, `MinMax` in the `convert func` column are three functions to convert a point set to a pseudo box. +- Note the results here are slightly different from those reported in the paper, due to framework change. While the original paper uses an [MXNet](https://mxnet.apache.org/) implementation, we re-implement the method in [PyTorch](https://pytorch.org/) based on mmdetection. + +## Citation + +```latex +@inproceedings{yang2019reppoints, + title={RepPoints: Point Set Representation for Object Detection}, + author={Yang, Ze and Liu, Shaohui and Hu, Han and Wang, Liwei and Lin, Stephen}, + booktitle={The IEEE International Conference on Computer Vision (ICCV)}, + month={Oct}, + year={2019} +} +``` diff --git a/downstream/mmdetection/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py b/downstream/mmdetection/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py new file mode 100644 index 0000000..b24c8db --- /dev/null +++ b/downstream/mmdetection/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True)) diff --git a/downstream/mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py b/downstream/mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py new file mode 100644 index 0000000..8d5013d --- /dev/null +++ b/downstream/mmdetection/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +model = dict( + bbox_head=dict(transform_method='minmax', use_grid_points=True), + # training and testing settings + train_cfg=dict( + init=dict( + assigner=dict( + _delete_=True, + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1)))) diff --git a/downstream/mmdetection/configs/reppoints/metafile.yml b/downstream/mmdetection/configs/reppoints/metafile.yml new file mode 100644 index 0000000..cd4312c --- /dev/null +++ b/downstream/mmdetection/configs/reppoints/metafile.yml @@ -0,0 +1,181 @@ +Collections: + - Name: RepPoints + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x V100 GPUs + Architecture: + - Group Normalization + - FPN + - RepPoints + - ResNet + Paper: + URL: https://arxiv.org/abs/1904.11490 + Title: 'RepPoints: Point Set Representation for Object Detection' + README: configs/reppoints/README.md + Code: + URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/reppoints_detector.py#L9 + Version: v2.0.0 + +Models: + - Name: bbox_r50_grid_fpn_gn-neck+head_1x_coco + In Collection: RepPoints + Config: configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 62.89 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth + + - Name: bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco + In Collection: RepPoints + Config: configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 64.94 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth + + - Name: reppoints_moment_r50_fpn_1x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.3 + inference time (ms/im): + - value: 54.05 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth + + - Name: reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 57.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth + + - Name: reppoints_moment_r50_fpn_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (ms/im): + - value: 57.14 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth + + - Name: reppoints_moment_r101_fpn_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 5.8 + inference time (ms/im): + - value: 72.99 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth + + - Name: reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 5.9 + inference time (ms/im): + - value: 82.64 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth + + - Name: reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (ms/im): + - value: 107.53 + hardware: V100 + backend: PyTorch + batch size: 1 + mode: FP32 + resolution: (800, 1333) + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth diff --git a/downstream/mmdetection/configs/reppoints/reppoints.png b/downstream/mmdetection/configs/reppoints/reppoints.png new file mode 100644 index 0000000000000000000000000000000000000000..a9306d9ba6c659a670822213bf198099f9e125b1 GIT binary patch literal 1198109 zcmaHT2{_d2`+nz~)+4ltlI65#8QPF#)TvMjS;jU7A=_B8Gh?*Mu^$O1%Sf{C%gh)I zQ7B^UgE1qdVa5!?h(W)1qVMm2zQ61GcU@g=6YuAJ-{*Pm=f3af6MkD?NAP#?-?wbp 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