添加openmmlab测试用例

openmmlab_test/mmdetection-speed_xinpian/configs/paa/paa_r50_fpn_2x_coco.py

+_base_ = './paa_r50_fpn_1x_coco.py'
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/paa/paa_r50_fpn_mstrain_3x_coco.py

+_base_ = './paa_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)

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

+# Path Aggregation Network for Instance Segmentation
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```
+@inproceedings{liu2018path,
+  author = {Shu Liu and
+            Lu Qi and
+            Haifang Qin and
+            Jianping Shi and
+            Jiaya Jia},
+  title = {Path Aggregation Network for Instance Segmentation},
+  booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year = {2018}
+}
+```
+
+## Results and Models
+
+## Results and Models
+
+| Backbone      | style      | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
+|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:|
+| R-50-FPN      | pytorch    | 1x      | 4.0      | 17.2           | 37.5   |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_20200503_105836.log.json) |

openmmlab_test/mmdetection-speed_xinpian/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    neck=dict(
+        type='PAFPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5))

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

+Collections:
+  - Name: PAFPN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PAFPN
+    Paper: https://arxiv.org/abs/1803.01534
+    README: configs/pafpn/README.md
+
+Models:
+  - Name: faster_rcnn_r50_pafpn_1x_coco
+    In Collection: PAFPN
+    Config: configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (s/im): 0.05814
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth

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

+# PASCAL VOC Dataset
+
+<!-- [DATASET] -->
+
+```
+@Article{Everingham10,
+   author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
+   title = "The Pascal Visual Object Classes (VOC) Challenge",
+   journal = "International Journal of Computer Vision",
+   volume = "88",
+   year = "2010",
+   number = "2",
+   month = jun,
+   pages = "303--338",
+}
+```
+
+## Results and Models
+
+| Architecture | Backbone  | Style   | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
+|:------------:|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:|
+| Faster R-CNN | R-50      | pytorch | 1x      | 2.6   | -          | 79.5  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/20200623_015208.log.json) |
+| Retinanet    | R-50      | pytorch | 1x      | 2.1   | -          | 77.3  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200617-47cbdd0e.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200616_014642.log.json) |

openmmlab_test/mmdetection-speed_xinpian/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py

+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+# actual epoch = 3 * 3 = 9
+lr_config = dict(policy='step', step=[3])
+# runtime settings
+runner = dict(
+    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12

openmmlab_test/mmdetection-speed_xinpian/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py

+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
+
+CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+           'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
+           'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1000, 600),
+        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='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file='data/voc0712_trainval.json',
+            img_prefix='data/VOCdevkit',
+            pipeline=train_pipeline,
+            classes=CLASSES)),
+    val=dict(
+        type=dataset_type,
+        ann_file='data/voc07_test.json',
+        img_prefix='data/VOCdevkit',
+        pipeline=test_pipeline,
+        classes=CLASSES),
+    test=dict(
+        type=dataset_type,
+        ann_file='data/voc07_test.json',
+        img_prefix='data/VOCdevkit',
+        pipeline=test_pipeline,
+        classes=CLASSES))
+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=None)
+# learning policy
+# actual epoch = 3 * 3 = 9
+lr_config = dict(policy='step', step=[3])
+# runtime settings
+runner = dict(
+    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12

openmmlab_test/mmdetection-speed_xinpian/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py

+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(bbox_head=dict(num_classes=20))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+# actual epoch = 3 * 3 = 9
+lr_config = dict(policy='step', step=[3])
+# runtime settings
+runner = dict(
+    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12

openmmlab_test/mmdetection-speed_xinpian/configs/pascal_voc/ssd300_voc0712.py

+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    bbox_head=dict(
+        num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
+                                                                    0.9))))
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=3,
+    train=dict(
+        type='RepeatDataset', times=10, dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict()
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[16, 20])
+checkpoint_config = dict(interval=1)
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=24)

openmmlab_test/mmdetection-speed_xinpian/configs/pascal_voc/ssd512_voc0712.py

+_base_ = 'ssd300_voc0712.py'
+input_size = 512
+model = dict(
+    backbone=dict(input_size=input_size),
+    bbox_head=dict(
+        in_channels=(512, 1024, 512, 256, 256, 256, 256),
+        anchor_generator=dict(
+            input_size=input_size,
+            strides=[8, 16, 32, 64, 128, 256, 512],
+            basesize_ratio_range=(0.15, 0.9),
+            ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]))))
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 512),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))

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

+# Prime Sample Attention in Object Detection
+
+## Introduction
+
+<!-- [ALGORITHM] -->
+
+```latex
+@inproceedings{cao2019prime,
+  title={Prime sample attention in object detection},
+  author={Cao, Yuhang and Chen, Kai and Loy, Chen Change and Lin, Dahua},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2020}
+}
+```
+
+## Results and models
+
+| PISA | Network | Backbone            | Lr schd | box AP | mask AP | Config | Download |
+|:----:|:-------:|:-------------------:|:-------:|:------:|:-------:|:------:|:--------:|
+| ×    | Faster R-CNN | R-50-FPN       | 1x      | 36.4   |         | - |
+| √    | Faster R-CNN | R-50-FPN       | 1x      | 38.4   |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json)  |
+| ×    | Faster R-CNN | X101-32x4d-FPN | 1x      | 40.1   |         | - |
+| √    | Faster R-CNN | X101-32x4d-FPN | 1x      | 41.9   |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) |
+| ×    | Mask   R-CNN | R-50-FPN       | 1x      | 37.3   | 34.2    | - |
+| √    | Mask   R-CNN | R-50-FPN       | 1x      | 39.1   | 35.2    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json) |
+| ×    | Mask   R-CNN | X101-32x4d-FPN | 1x      | 41.1   | 37.1    | - |
+| √    | Mask   R-CNN | X101-32x4d-FPN | 1x      |        |         |   |
+| ×    | RetinaNet    | R-50-FPN       | 1x      | 35.6   |         | - |
+| √    | RetinaNet    | R-50-FPN       | 1x      | 36.9   |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json) |
+| ×    | RetinaNet    | X101-32x4d-FPN | 1x      | 39.0   |         | - |
+| √    | RetinaNet    | X101-32x4d-FPN | 1x      | 40.7   |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json) |
+| ×    | SSD300       | VGG16          | 1x      | 25.6   |         | - |
+| √    | SSD300       | VGG16          | 1x      | 27.6   |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json) |
+| ×    | SSD300       | VGG16          | 1x      | 29.3   |         | - |
+| √    | SSD300       | VGG16          | 1x      | 31.8   |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json)  |
+
+**Notes:**
+
+- In the original paper, all models are trained and tested on mmdet v1.x, thus results may not be exactly the same with this release on v2.0.
+- It is noted PISA only modifies the training pipeline so the inference time remains the same with the baseline.

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

+Collections:
+  - Name: PISA
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - FPN
+        - PISA
+        - RPN
+        - ResNet
+        - RoIPool
+    Paper: https://arxiv.org/abs/1904.04821
+    README: configs/pisa/README.md
+
+Models:
+  - Name: pisa_faster_rcnn_r50_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth
+
+  - Name: pisa_faster_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth
+
+  - Name: pisa_mask_rcnn_r50_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 35.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth
+
+  - Name: pisa_retinanet_r50_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth
+
+  - Name: pisa_retinanet_x101_32x4d_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth
+
+  - Name: pisa_ssd300_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_ssd300_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 27.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth
+
+  - Name: pisa_ssd512_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_ssd512_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 31.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth

openmmlab_test/mmdetection-speed_xinpian/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))

openmmlab_test/mmdetection-speed_xinpian/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py

+_base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))

openmmlab_test/mmdetection-speed_xinpian/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))

openmmlab_test/mmdetection-speed_xinpian/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py

+_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))

openmmlab_test/mmdetection-speed_xinpian/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py

+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+
+model = dict(
+    bbox_head=dict(
+        type='PISARetinaHead',
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
+    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))

openmmlab_test/mmdetection-speed_xinpian/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py

+_base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py'
+
+model = dict(
+    bbox_head=dict(
+        type='PISARetinaHead',
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
+    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))

openmmlab_test/mmdetection-speed_xinpian/configs/pisa/pisa_ssd300_coco.py

+_base_ = '../ssd/ssd300_coco.py'
+
+model = dict(
+    bbox_head=dict(type='PISASSDHead'),
+    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
+
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))