添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py

+_base_ = [
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='GridRCNN',
+    pretrained='torchvision://resnet50',
+    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'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='GridRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            with_reg=False,
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False),
+        grid_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        grid_head=dict(
+            type='GridHead',
+            grid_points=9,
+            num_convs=8,
+            in_channels=256,
+            point_feat_channels=64,
+            norm_cfg=dict(type='GN', num_groups=36),
+            loss_grid=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=15))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_radius=1,
+            pos_weight=-1,
+            max_num_grid=192,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.03,
+            nms=dict(type='nms', iou_threshold=0.3),
+            max_per_img=100)))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=3665,
+    warmup_ratio=1.0 / 80,
+    step=[17, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=25)

openmmlab_test/mmdetection-speed_xinpian/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py

+_base_ = './grid_rcnn_r50_fpn_gn-head_2x_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,
+        style='pytorch'))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=3665,
+    warmup_ratio=1.0 / 80,
+    step=[17, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=25)

openmmlab_test/mmdetection-speed_xinpian/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py

+_base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_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,
+        style='pytorch'))

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

+Collections:
+  - Name: Grid R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - RPN
+        - Dilated Convolution
+        - ResNet
+        - RoIAlign
+    Paper: https://arxiv.org/abs/1906.05688
+    README: configs/grid_rcnn/README.md
+
+Models:
+  - Name: grid_rcnn_r50_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.1
+      inference time (s/im): 0.06667
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: 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
+
+  - Name: grid_rcnn_r101_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (s/im): 0.07937
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.5
+    Weights: 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
+
+  - Name: grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 8.3
+      inference time (s/im): 0.09259
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+    Weights: 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
+
+  - Name: grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.3
+      inference time (s/im): 0.12987
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+    Weights: 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

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

+# GRoIE
+
+## A novel Region of Interest Extraction Layer for Instance Segmentation
+
+By Leonardo Rossi, Akbar Karimi and Andrea Prati from
+[IMPLab](http://implab.ce.unipr.it/).
+
+We provide configs to reproduce the results in the paper for
+"*A novel Region of Interest Extraction Layer for Instance Segmentation*"
+on COCO object detection.
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+This paper is motivated by the need to overcome to the limitations of existing
+RoI extractors which select only one (the best) layer from FPN.
+
+Our intuition is that all the layers of FPN retain useful information.
+
+Therefore, the proposed layer (called Generic RoI Extractor - **GRoIE**)
+introduces non-local building blocks and attention mechanisms to boost the
+performance.
+
+## Results and models
+
+The results on COCO 2017 minival (5k images) are shown in the below table.
+You can find
+[here](https://drive.google.com/drive/folders/19ssstbq_h0Z1cgxHmJYFO8s1arf3QJbT)
+the trained models.
+
+### Application of GRoIE to different architectures
+
+| Backbone  | Method            | Lr schd | box AP | mask AP |  Config | Download|
+| :-------: | :--------------: | :-----: | :----: | :-----: | :-------:| :--------:|
+| R-50-FPN  | Faster Original  |   1x    |  37.4  |         | [config](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) |
+| R-50-FPN  | + GRoIE          |   1x    |  38.3  |         | [config](./faster_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) |
+| R-50-FPN  | Grid R-CNN       |   1x    |  39.1  |         | [config](./grid_rcnn_r50_fpn_gn-head_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059-64f00ee8.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059.log.json) |
+| R-50-FPN  | + GRoIE          |   1x    |    |         | [config](./grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py)||
+| R-50-FPN  | Mask R-CNN       |   1x    |  38.2  |  34.7   | [config](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) |
+| R-50-FPN  | + GRoIE          |   1x    |  39.0  |  36.0   | [config](./mask_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) |
+| R-50-FPN  | GC-Net           |   1x    |  40.7  |  36.5   | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) |
+| R-50-FPN  | + GRoIE          |   1x    |  41.0  |  37.8   | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) |
+| R-101-FPN | GC-Net           |   1x    |  42.2  |  37.8   | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) |
+| R-101-FPN | + GRoIE          |   1x    |   |    | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) |
+
+## Citation
+
+If you use this work or benchmark in your research, please cite this project.
+
+```latex
+@misc{rossi2020novel,
+    title={A novel Region of Interest Extraction Layer for Instance Segmentation},
+    author={Leonardo Rossi and Akbar Karimi and Andrea Prati},
+    year={2020},
+    eprint={2004.13665},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
+
+## Contact
+
+The implementation of GROI is currently maintained by
+[Leonardo Rossi](https://github.com/hachreak/).

openmmlab_test/mmdetection-speed_xinpian/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))

openmmlab_test/mmdetection-speed_xinpian/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py

+_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        grid_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))

openmmlab_test/mmdetection-speed_xinpian/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py

+_base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        mask_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))

openmmlab_test/mmdetection-speed_xinpian/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        mask_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))

openmmlab_test/mmdetection-speed_xinpian/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py

+_base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        mask_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))

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

+Collections:
+  - Name: GRoIE
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - Generic RoI Extractor
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+    Paper: https://arxiv.org/abs/2004.13665
+    README: configs/groie/README.md
+
+Models:
+  - Name: faster_rcnn_r50_fpn_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth
+
+  - Name: grid_rcnn_r50_fpn_gn-head_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+
+  - Name: mask_rcnn_r50_fpn_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:   37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth
+
+  - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth

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

+# Region Proposal by Guided Anchoring
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278).
+
+```latex
+@inproceedings{wang2019region,
+    title={Region Proposal by Guided Anchoring},
+    author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin},
+    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+    year={2019}
+}
+```
+
+## Results and Models
+
+The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).
+
+| Method |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 | Config | Download |
+| :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :------: | :--------: |
+| GA-RPN |    R-50-FPN     |  caffe  |   1x    |   5.3    |      15.8      |  68.4   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json)   |
+| GA-RPN |    R-101-FPN    |  caffe  |   1x    |   7.3    |      13.0      |  69.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) |
+| GA-RPN | X-101-32x4d-FPN | pytorch |   1x    |   8.5    |      10.0      |  70.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) |
+| GA-RPN | X-101-64x4d-FPN | pytorch |   1x    |   7.1    |      7.5       |  71.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) |
+
+|     Method     |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+| :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
+| GA-Faster RCNN |    R-50-FPN     |  caffe  |   1x    |   5.5    |                |  39.6  |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py) | [model](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) &#124; [log](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.log.json)           |
+| GA-Faster RCNN |    R-101-FPN    |  caffe  |   1x    |   7.5    |                |  41.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) |
+| GA-Faster RCNN | X-101-32x4d-FPN | pytorch |   1x    |   8.7    |      9.7       |  43.0  |            [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json)            |
+| GA-Faster RCNN | X-101-64x4d-FPN | pytorch |   1x    |   11.8   |      7.3       |  43.9  |            [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json)            |
+|  GA-RetinaNet  |    R-50-FPN     |  caffe  |   1x    |   3.5    |      16.8      |  36.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json)       |
+|  GA-RetinaNet  |    R-101-FPN    |  caffe  |   1x    |   5.5    |      12.9      |  39.0  |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json)      |
+|  GA-RetinaNet  | X-101-32x4d-FPN | pytorch |   1x    |   6.9    |      10.6      |  40.5  |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json)      |
+|  GA-RetinaNet  | X-101-64x4d-FPN | pytorch |   1x    |   9.9    |      7.7       |  41.3  |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) | [model](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) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json)      |
+
+- In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.
+
+- Performance on COCO test-dev benchmark are shown as follows.
+
+|     Method     | Backbone  | Style | Lr schd | Aug Train | Score thr |  AP   | AP_50 | AP_75 | AP_small | AP_medium | AP_large | Download |
+| :------------: | :-------: | :---: | :-----: | :-------: | :-------: | :---: | :---: | :---: | :------: | :-------: | :------: | :------: |
+| GA-Faster RCNN | R-101-FPN | caffe |   1x    |     F     |   0.05    |       |       |       |          |           |          |          |
+| GA-Faster RCNN | R-101-FPN | caffe |   1x    |     F     |   0.001   |       |       |       |          |           |          |          |
+|  GA-RetinaNet  | R-101-FPN | caffe |   1x    |     F     |   0.05    |       |       |       |          |           |          |          |
+|  GA-RetinaNet  | R-101-FPN | caffe |   2x    |     T     |   0.05    |       |       |       |          |           |          |          |

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py

+_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet50_caffe',
+    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=False),
+        norm_eval=True,
+        style='caffe'),
+    roi_head=dict(
+        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
+    # model training and testing settings
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
+            sampler=dict(num=256))),
+    test_cfg=dict(rcnn=dict(score_thr=1e-3)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+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='LoadProposals', num_max_proposals=300),
+    dict(type='LoadAnnotations', with_bbox=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', 'proposals', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=None),
+    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', 'proposals']),
+        ])
+]
+data = dict(
+    train=dict(
+        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl',
+        pipeline=train_pipeline),
+    val=dict(
+        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline),
+    test=dict(
+        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py

+_base_ = './ga_faster_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet101_caffe',
+    backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_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)),
+    roi_head=dict(
+        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
+    # 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),
+        rpn_proposal=dict(nms_post=1000, max_per_img=300),
+        rcnn=dict(
+            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
+            sampler=dict(type='RandomSampler', num=256))),
+    test_cfg=dict(
+        rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_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)),
+    roi_head=dict(
+        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
+    # 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),
+        rpn_proposal=dict(nms_post=1000, max_per_img=300),
+        rcnn=dict(
+            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
+            sampler=dict(type='RandomSampler', num=256))),
+    test_cfg=dict(
+        rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py

+_base_ = './ga_faster_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_faster_x101_64x4d_fpn_1x_coco.py

+_base_ = './ga_faster_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_retinanet_r101_caffe_fpn_1x_coco.py

+_base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet101_caffe',
+    backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py

+_base_ = '../_base_/default_runtime.py'
+
+# model settings
+model = dict(
+    type='RetinaNet',
+    pretrained='open-mmlab://detectron2/resnet101_caffe',
+    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=False),
+        norm_eval=True,
+        style='caffe'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs=True,
+        num_outs=5),
+    bbox_head=dict(
+        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(
+        type='MaxIoUAssigner',
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        min_pos_iou=0.0,
+        ignore_iof_thr=-1),
+    allowed_border=-1,
+    pos_weight=-1,
+    center_ratio=0.2,
+    ignore_ratio=0.5,
+    debug=False)
+test_cfg = dict(
+    nms_pre=1000,
+    min_bbox_size=0,
+    score_thr=0.05,
+    nms=dict(type='nms', iou_threshold=0.5),
+    max_per_img=100)
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+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),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 960)],
+        keep_ratio=True,
+        multiscale_mode='range'),
+    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']),
+]
+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(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[16, 22])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=24)