添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py

+_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))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py

+_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))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py

+_base_ = './ga_retinanet_r50_fpn_1x_coco.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'))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py

+_base_ = './ga_retinanet_r50_fpn_1x_coco.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'))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py

+_base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py'
+# model settings
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet101_caffe',
+    backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py

+_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))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py

+_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))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py

+_base_ = './ga_rpn_r50_fpn_1x_coco.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'))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py

+_base_ = './ga_rpn_r50_fpn_1x_coco.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'))

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

+Collections:
+  - Name: Guided Anchoring
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - FPN
+        - Guided Anchoring
+        - ResNet
+    Paper: https://arxiv.org/abs/1901.03278
+    README: configs/guided_anchoring/README.md
+
+Models:
+  - 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 (s/im): 0.10309
+      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 (s/im): 0.13699
+      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 (s/im): 0.05952
+      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 (s/im): 0.07752
+      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 (s/im): 0.09434
+      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 (s/im): 0.12987
+      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

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

+# High-resolution networks (HRNets) for object detection
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```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}
+}
+```
+
+## 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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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) &#124; [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).

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py

+_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    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)))),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py

+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    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)))),
+    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)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py

+_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w40',
+    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)))),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py

+_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    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)))),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py

+_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    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)))),
+    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)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py

+_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w40',
+    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)))),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py

+_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
+# model settings
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w18',
+    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)))),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py

+_base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py'
+
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    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)))),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))