[Enchance] Update FAQ docs

.dev_scripts/gather_models.py

@@ -53,6 +53,14 @@ def get_final_epoch(config):
     return cfg.runner.max_epochs
 
 
+def get_best_epoch(exp_dir):
+    best_epoch_full_path = list(
+        sorted(glob.glob(osp.join(exp_dir, 'best_*.pth'))))[-1]
+    best_epoch_model_path = best_epoch_full_path.split('/')[-1]
+    best_epoch = best_epoch_model_path.split('_')[-1].split('.')[0]
+    return best_epoch_model_path, int(best_epoch)
+
+
 def get_real_epoch(config):
     cfg = mmcv.Config.fromfile('./configs/' + config)
     epoch = cfg.runner.max_epochs
@@ -160,6 +168,10 @@ def parse_args():
         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
@@ -187,10 +199,13 @@ def main():
     for used_config in used_configs:
         exp_dir = osp.join(models_root, used_config)
         # check whether the exps is finished
-        final_epoch = get_final_epoch(used_config)
-        final_model = 'epoch_{}.pth'.format(final_epoch)
-        model_path = osp.join(exp_dir, final_model)
+        if args.best is True:
+            final_model, final_epoch = get_best_epoch(exp_dir)
+        else:
+            final_epoch = get_final_epoch(used_config)
+            final_model = 'epoch_{}.pth'.format(final_epoch)
 
+        model_path = osp.join(exp_dir, final_model)
         # skip if the model is still training
         if not osp.exists(model_path):
             continue
@@ -221,6 +236,7 @@ def main():
                 results=model_performance,
                 epochs=final_epoch,
                 model_time=model_time,
+                final_model=final_model,
                 log_json_path=osp.split(log_json_path)[-1]))
 
     # publish model for each checkpoint
@@ -234,7 +250,7 @@ def main():
         model_name += '_' + model['model_time']
         publish_model_path = osp.join(model_publish_dir, model_name)
         trained_model_path = osp.join(models_root, model['config'],
-                                      'epoch_{}.pth'.format(model['epochs']))
+                                      model['final_model'])
 
         # convert model
         final_model_path = process_checkpoint(trained_model_path,
@@ -254,9 +270,9 @@ def main():
         config_path = osp.join(
             'configs',
             config_path) if 'configs' not in config_path else config_path
-        target_cconfig_path = osp.split(config_path)[-1]
-        shutil.copy(config_path,
-                    osp.join(model_publish_dir, target_cconfig_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)

README.md

@@ -15,7 +15,7 @@
 
 [📘Documentation](https://mmdetection.readthedocs.io/en/v2.18.1/) |
 [🛠️Installation](https://mmdetection.readthedocs.io/en/v2.18.1/get_started.html) |
-[👀Model Zoo](https://mmdetection.readthedocs.io/zh_CN/v2.18.1/model_zoo.html) |
+[👀Model Zoo](https://mmdetection.readthedocs.io/en/v2.18.1/model_zoo.html) |
 [🆕Update News](https://mmdetection.readthedocs.io/en/v2.18.1/changelog.html) |
 [🚀Ongoing Projects](https://github.com/open-mmlab/mmdetection/projects) |
 [🤔Reporting Issues](https://github.com/open-mmlab/mmdetection/issues/new/choose)
@@ -205,3 +205,4 @@ If you use this toolbox or benchmark in your research, please cite this project.
 - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
 - [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
 - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.

README_zh-CN.md

@@ -203,6 +203,7 @@ MMDetection 是一款由来自不同高校和企业的研发人员共同参与
 - [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
 - [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
 
 ## 欢迎加入 OpenMMLab 社区
 

configs/lad/README.md

@@ -0,0 +1,32 @@
+# Improving Object Detection by Label Assignment Distillation
+
+<!-- [ALGORITHM] -->
+
+```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}
+}
+```
+
+## 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.

configs/lad/lad_r101_paa_r50_fpn_coco_1x.py

@@ -0,0 +1,121 @@
+_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.)

configs/lad/lad_r50_paa_r101_fpn_coco_1x.py

@@ -0,0 +1,120 @@
+_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.)