add lvis support

configs/_base_/datasets/lvis_instance.py

@@ -0,0 +1,19 @@
+_base_ = 'coco_instance.py'
+dataset_type = 'LVISDataset'
+data_root = 'data/lvis/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=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'])

configs/lvis/README.md

@@ -0,0 +1,24 @@
+# LVIS dataset
+
+## Introduction
+```
+@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}
+}
+```
+
+## Common Setting
+* All experiments use oversample strategy [here](../../docs/tutorials/new_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
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: |
+|    R-50-FPN     | pytorch |   2x    | -        | -              | 26.1   | 25.9    | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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://open-mmlab.s3.ap-northeast-2.amazonaws.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    | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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://open-mmlab.s3.ap-northeast-2.amazonaws.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    | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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://open-mmlab.s3.ap-northeast-2.amazonaws.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    | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.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://open-mmlab.s3.ap-northeast-2.amazonaws.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)  |

configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis.py

@@ -0,0 +1,2 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis.py'
+model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis.py

@@ -0,0 +1,42 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/lvis_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))
+dataset_type = 'LVISDataset'
+data_root = 'data/lvis/'
+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(
+        _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/',
+            pipeline=train_pipeline)))

configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis.py

@@ -0,0 +1,13 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis.py'
+model = dict(
+    pretrained='open-mmlab://resnext101_32x4d',
+    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'))

configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis.py

@@ -0,0 +1,13 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis.py'
+model = dict(
+    pretrained='open-mmlab://resnext101_64x4d',
+    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'))

docs/install.md

@@ -53,11 +53,12 @@ cd mmdetection
 ```
 
 d. Install build requirements and then install mmdetection.
-(We install pycocotools via the github repo instead of pypi because the pypi version is old and not compatible with the latest numpy.)
+(We install our forked version of pycocotools via the github repo instead of pypi
+for better compatibility with our repo.)
 
 ```shell
 pip install -r requirements/build.txt
-pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
+pip install "git+https://github.com/open-mmlab/cocoapi.git#subdirectory=PythonAPI"
 pip install -v -e .  # or "python setup.py develop"
 ```
 
@@ -129,7 +130,7 @@ conda install -c pytorch pytorch torchvision -y
 git clone https://github.com/open-mmlab/mmdetection.git
 cd mmdetection
 pip install -r requirements/build.txt
-pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
+pip install "git+https://github.com/open-mmlab/cocoapi.git#subdirectory=PythonAPI"
 pip install -v -e .
 ```
 

docs/tutorials/new_dataset.md

@@ -228,7 +228,7 @@ dataset_A_train = dict(
     )
 ```
 
-### Repeat factor dataset
+### Class balanced dataset
 
 We use `ClassBalancedDataset` as wrapper to repeat the dataset based on category
 frequency. The dataset to repeat needs to instantiate function `self.get_cat_ids(idx)`

mmdet/datasets/__init__.py

@@ -4,15 +4,16 @@
 from .custom import CustomDataset
 from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
                                RepeatDataset)
+from .lvis import LVISDataset
 from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
 from .voc import VOCDataset
 from .wider_face import WIDERFaceDataset
 from .xml_style import XMLDataset
 
 __all__ = [
     'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOCDataset',
-    'CityscapesDataset', 'GroupSampler', 'DistributedGroupSampler',
-    'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
-    'ClassBalancedDataset', 'WIDERFaceDataset', 'DATASETS', 'PIPELINES',
-    'build_dataset'
+    'CityscapesDataset', 'LVISDataset', 'GroupSampler',
+    'DistributedGroupSampler', 'DistributedSampler', 'build_dataloader',
+    'ConcatDataset', 'RepeatDataset', 'ClassBalancedDataset',
+    'WIDERFaceDataset', 'DATASETS', 'PIPELINES', 'build_dataset'
 ]

mmdet/datasets/coco.py

@@ -35,26 +35,26 @@ class CocoDataset(CustomDataset):
 
     def load_annotations(self, ann_file):
         self.coco = COCO(ann_file)
-        self.cat_ids = self.coco.getCatIds(catNms=self.CLASSES)
+        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
         self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
-        self.img_ids = self.coco.getImgIds()
+        self.img_ids = self.coco.get_img_ids()
         data_infos = []
         for i in self.img_ids:
-            info = self.coco.loadImgs([i])[0]
+            info = self.coco.load_imgs([i])[0]
             info['filename'] = info['file_name']
             data_infos.append(info)
         return data_infos
 
     def get_ann_info(self, idx):
         img_id = self.data_infos[idx]['id']
-        ann_ids = self.coco.getAnnIds(imgIds=[img_id])
-        ann_info = self.coco.loadAnns(ann_ids)
+        ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
+        ann_info = self.coco.load_anns(ann_ids)
         return self._parse_ann_info(self.data_infos[idx], ann_info)
 
     def get_cat_ids(self, idx):
         img_id = self.data_infos[idx]['id']
-        ann_ids = self.coco.getAnnIds(imgIds=[img_id])
-        ann_info = self.coco.loadAnns(ann_ids)
+        ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
+        ann_info = self.coco.load_anns(ann_ids)
         return [ann['category_id'] for ann in ann_info]
 
     def _filter_imgs(self, min_size=32):
@@ -83,12 +83,12 @@ def get_subset_by_classes(self):
 
         ids = set()
         for i, class_id in enumerate(self.cat_ids):
-            ids |= set(self.coco.catToImgs[class_id])
+            ids |= set(self.coco.cat_img_map[class_id])
         self.img_ids = list(ids)
 
         data_infos = []
         for i in self.img_ids:
-            info = self.coco.loadImgs([i])[0]
+            info = self.coco.load_imgs([i])[0]
             info['filename'] = info['file_name']
             data_infos.append(info)
         return data_infos
@@ -268,8 +268,8 @@ def results2json(self, results, outfile_prefix):
     def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
         gt_bboxes = []
         for i in range(len(self.img_ids)):
-            ann_ids = self.coco.getAnnIds(imgIds=self.img_ids[i])
-            ann_info = self.coco.loadAnns(ann_ids)
+            ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i])
+            ann_info = self.coco.load_anns(ann_ids)
             if len(ann_info) == 0:
                 gt_bboxes.append(np.zeros((0, 4)))
                 continue