添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py

+_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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py

+_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
+model = dict(
+    pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py

+_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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py

+_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(
+    pretrained='open-mmlab://jhu/resnet50_gn_ws',
+    backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg),
+    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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py

+_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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py

+_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(
+    pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws',
+    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))

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py

+_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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py

+_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(
+    pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws',
+    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))

openmmlab_test/mmdetection-speed_xinpian/configs/gn+ws/metafile.yml

+Collections:
+  - Name: Weight Standardization
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - Group Normalization
+        - Weight Standardization
+    Paper: https://arxiv.org/abs/1903.10520
+    README: configs/gn+ws/README.md
+
+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 (s/im): 0.08547
+      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 (s/im): 0.11111
+      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 (s/im): 0.09709
+      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 (s/im): 0.13158
+      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 (s/im): 0.09524
+      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 (s/im): 0.11628
+      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 (s/im): 0.10753
+      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 (s/im): 0.14085
+      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

openmmlab_test/mmdetection-speed_xinpian/configs/gn/README.md

+# Group Normalization
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```latex
+@inproceedings{wu2018group,
+  title={Group Normalization},
+  author={Wu, Yuxin and He, Kaiming},
+  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
+  year={2018}
+}
+```
+
+## 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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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.**

openmmlab_test/mmdetection-speed_xinpian/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py

+_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
+model = dict(
+    pretrained='open-mmlab://detectron/resnet101_gn', backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py

+_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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    pretrained='open-mmlab://detectron/resnet50_gn',
+    backbone=dict(norm_cfg=norm_cfg),
+    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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py

+_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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    pretrained='open-mmlab://contrib/resnet50_gn',
+    backbone=dict(norm_cfg=norm_cfg),
+    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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py

+_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)

openmmlab_test/mmdetection-speed_xinpian/configs/gn/metafile.yml

+Collections:
+  - Name: Group Normalization
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - Group Normalization
+    Paper: https://arxiv.org/abs/1803.08494
+    README: configs/gn/README.md
+
+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 (s/im): 0.09091
+      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
+      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 (s/im): 0.11111
+      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
+      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 (s/im): 0.09174
+      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
+      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

openmmlab_test/mmdetection-speed_xinpian/configs/grid_rcnn/README.md

+# Grid R-CNN
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```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}
+}
+```
+
+## 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) &#124; [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) &#124; [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) &#124; [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) &#124; [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.

openmmlab_test/mmdetection-speed_xinpian/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py

+_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
+
+model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py

+_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)