添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py

+_base_ = './fovea_r50_fpn_4x4_2x_coco.py'
+model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))

openmmlab_test/mmdetection-speed_xinpian/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py

+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='FOVEA',
+    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,
+        start_level=1,
+        num_outs=5,
+        add_extra_convs='on_input'),
+    bbox_head=dict(
+        type='FoveaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        base_edge_list=[16, 32, 64, 128, 256],
+        scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
+        sigma=0.4,
+        with_deform=False,
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=1.50,
+            alpha=0.4,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(
+        nms_pre=1000,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+data = dict(samples_per_gpu=4, workers_per_gpu=4)
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)

openmmlab_test/mmdetection-speed_xinpian/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py

+_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

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

+Collections:
+  - Name: FoveaBox
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 4x NVIDIA V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+    Paper: https://arxiv.org/abs/1904.03797
+    README: configs/foveabox/README.md
+
+Models:
+  - Name: fovea_r50_fpn_4x4_1x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.6
+      inference time (s/im): 0.04149
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth
+
+  - Name: fovea_r50_fpn_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.6
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth
+
+  - Name: fovea_align_r50_fpn_gn-head_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 8.1
+      inference time (s/im): 0.05155
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth
+
+  - Name: fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 8.1
+      inference time (s/im): 0.05464
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth
+
+  - Name: fovea_r101_fpn_4x4_1x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      inference time (s/im): 0.05747
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth
+
+  - Name: fovea_r101_fpn_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.7
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth
+
+  - Name: fovea_align_r101_fpn_gn-head_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.7
+      inference time (s/im): 0.06803
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth
+
+  - Name: fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.7
+      inference time (s/im): 0.06803
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/README.md

+# Mixed Precision Training
+
+## Introduction
+
+<!-- [OTHERS] -->
+
+```latex
+@article{micikevicius2017mixed,
+  title={Mixed precision training},
+  author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others},
+  journal={arXiv preprint arXiv:1710.03740},
+  year={2017}
+}
+```
+
+## Results and Models
+
+| Architecture | Backbone  | Style   | Conv         | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
+|:------------:|:---------:|:-------:|:------------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
+| Faster R-CNN | R-50      | pytorch |   -          | 1x      | 3.4      | 28.8           | 37.5   | -       |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) |
+| Mask   R-CNN | R-50      | pytorch |   -          | 1x      | 3.6      | 24.1           | 38.1   | 34.7    |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205_130539.log.json) |
+| Mask   R-CNN | R-50      | pytorch | dconv(c3-c5) | 1x      | 3.0      |                | 41.9   | 37.5    |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247.log.json)  |
+| Mask   R-CNN | R-50      | pytorch | mdconv(c3-c5)| 1x      | 3.1      |                | 42.0   | 37.6    |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434.log.json)  |
+| Retinanet    | R-50      | pytorch |   -          | 1x      | 2.8      | 31.6           | 36.4   |         |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702_020127.log.json) |

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/cascade_mask_rcnn_r50_fpn_1x_fp16_coco.py

+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/dh_faster_rcnn_r50_fpn_1x_fp16_coco.py

+_base_ = '../double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/faster_rcnn_r50_fpn_mdpool_1x_fp16_coco.py

+_base_ = '../dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_fp16_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
+
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
+
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_fp16_coco.py

+_base_ = '../resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/metafile.yml

+Collections:
+  - Name: FP16
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - Mixed Precision Training
+      Training Resources: 8x NVIDIA V100 GPUs
+    Paper: https://arxiv.org/abs/1710.03740
+    README: configs/fp16/README.md
+
+Models:
+  - Name: faster_rcnn_r50_fpn_fp16_1x_coco
+    In Collection: FP16
+    Config: configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.4
+      inference time (s/im): 0.03472
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth
+
+  - Name: mask_rcnn_r50_fpn_fp16_1x_coco
+    In Collection: FP16
+    Config: configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.6
+      inference time (s/im): 0.04149
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  34.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth
+
+  - Name: retinanet_r50_fpn_fp16_1x_coco
+    In Collection: FP16
+    Config: configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 2.8
+      inference time (s/im): 0.03165
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/retinanet_r50_caffe_fpn_1x_fp16_coco.py

+_base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py

+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/ssd300_fp16_coco.py

+_base_ = '../ssd/ssd300_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/vfnet_r50_fpn_1x_fp16_coco.py

+_base_ = '../vfnet/vfnet_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)

openmmlab_test/mmdetection-speed_xinpian/configs/fp16/yolov3_d53_mstrain-416_273e_fp16_coco.py

+_base_ = '../yolo/yolov3_d53_mstrain-416_273e_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)
\ No newline at end of file