init

.circleci/config.yml

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+version: 2.1
+
+jobs:
+  python_lint:
+    docker:
+      - image: circleci/python:3.7
+    steps:
+      - checkout
+      - run:
+          command: |
+            pip install --user --progress-bar off flake8 typing
+            flake8 .
+  test:
+    docker:
+      - image: circleci/python:3.7
+    steps:
+      - checkout
+      - run:
+          command: |
+            pip install --user --progress-bar off pytest
+            pip install --user --progress-bar off torch torchvision
+            pip install --user --progress-bar off timm==0.3.2
+            pytest .
+
+workflows:
+  build:
+    jobs:
+      - python_lint

.gitignore

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+*.swp
+**/__pycache__/**
+imnet_resnet50_scratch/timm_temp/
+.dumbo.json
+checkpoints/

LICENSE

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+MIT License
+
+Copyright (c) 2021 Shoufa Chen
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# A MLP-like Architecture for Dense Prediction
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+![Python 3.8](https://img.shields.io/badge/python-3.8-green.svg)
+
+
+
+<p align="middle">
+  <img src="figures/teaser.png" height="300" />
+  &nbsp;&nbsp;&nbsp;&nbsp;
+  <img src="figures/flops.png" height="300" />
+</p>
+
+# Updates
+
+- (22/07/2021) Initial release.
+
+
+
+# Model Zoo
+
+We provide CycleMLP models pretrained on ImageNet 2012.
+
+| Model                | Parameters | FLOPs    | Top 1 Acc. | Download |
+| :------------------- | :--------- | :------- | :--------- | :------- |
+| CycleMLP-B1          | 15M        |  2.1G    |  78.9%     |          |
+| CycleMLP-B2          | 27M        |  3.9G    |  81.6%     |          |
+| CycleMLP-B3          | 38M        |  6.9G    |  82.4%     |          |
+| CycleMLP-B4          | 52M        |  10.1G   |  83.0%     |          |
+| CycleMLP-B5          | 76M        |  12.3G   |  83.2%     |          |
+
+
+# Usage
+
+
+## Install
+
+- PyTorch 1.7.0+ and torchvision 0.8.1+
+- [timm](https://github.com/rwightman/pytorch-image-models/tree/c2ba229d995c33aaaf20e00a5686b4dc857044be):
+```
+pip install 'git+https://github.com/rwightman/pytorch-image-models@c2ba229d995c33aaaf20e00a5686b4dc857044be'
+
+or
+
+git clone https://github.com/rwightman/pytorch-image-models
+cd pytorch-image-models
+git checkout c2ba229d995c33aaaf20e00a5686b4dc857044be
+pip install -e .
+```
+- fvcore (optional, for FLOPs calculation)
+- mmcv, mmdetection, mmsegmentation (optional)
+
+## Data preparation
+
+Download and extract ImageNet train and val images from http://image-net.org/.
+The directory structure is:
+
+```
+│path/to/imagenet/
+├──train/
+│  ├── n01440764
+│  │   ├── n01440764_10026.JPEG
+│  │   ├── n01440764_10027.JPEG
+│  │   ├── ......
+│  ├── ......
+├──val/
+│  ├── n01440764
+│  │   ├── ILSVRC2012_val_00000293.JPEG
+│  │   ├── ILSVRC2012_val_00002138.JPEG
+│  │   ├── ......
+│  ├── ......
+```
+
+## Evaluation
+To evaluate a pre-trained CycleMLP-B5 on ImageNet val with a single GPU run:
+```
+python main.py --eval --model CycleMLP_B5 --resume path/to/CycleMLP_B5.pth --data-path /path/to/imagenet
+```
+
+
+## Training
+
+To train CycleMLP-B5 on ImageNet on a single node with 8 gpus for 300 epochs run:
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env main.py --model CycleMLP_B5 --batch-size 128 --data-path /path/to/imagenet --output_dir /path/to/save
+```
+
+
+# License
+
+CycleMLP is released under MIT License.