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Add support for DetectoRS. (open-mmlab#3064)
* Add support for DetectoRS. * Fix Travis CI build failure and remove loading rfp_pretrained for test * Revise the code according to the PR reviews: * remove unnecessary `,` * add docstring to ASPP and ConvAWS2d * make args of ASPP configurable * simplify the code of ConvAWS2d * import nn and functional for SAConv2d * rewrite rfp_conv with build_conv_layer and constant_init * rename rfp_inp with rfp_inplanes * reformat code to pass Travis CI build * Revise codes according to the PR reviews: * Create new backbone DetectoRS_ResNe(X)t to incorporate RFP and SAC. * Initialize modules with mmcv.cnn. * Revise the codes of RFP. * Revise codes according to the PR comments: * Add docstrings to Bottleneck in mmdet/models/backbones/detectors_resnet.py. * Add docstrings to _load_from_state_dict in mmdet/ops/conv_ws.py. * Initialize the switch of SAC with mmcv. * minior refactor and docstring * update doc * update README * move init into init_weights() * update model zoo * add gfl Co-authored-by: Jiarui XU <[email protected]>
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‎README.md

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- [x] [Mixed Precision (FP16) Training](configs/fp16/README.md)
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- [x] [InstaBoost](configs/instaboost/README.md)
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- [x] [GRoIE](configs/groie/README.md)
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- [x] [DetectoRS](configs/detectors/README.md)
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- [x] [Generalized Focal Loss](configs/gfl/README.md)
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Some other methods are also supported in [projects using MMDetection](./docs/projects.md).
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‎configs/detectors/README.md

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# DetectoRS
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## Introduction
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We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf).
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```BibTeX
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@article{qiao2020detectors,
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title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution},
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author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan},
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journal={arXiv preprint arXiv:2006.02334},
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year={2020}
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}
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```
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## Results and Models
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DetectoRS includes two major components:
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- Recursive Feature Pyramid (RFP).
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- Switchable Atrous Convolution (SAC).
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They can be used independently.
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Combining them together results in DetectoRS.
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The results on COCO 2017 val are shown in the below table.
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| Method | Detector | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Download |
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|:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:-------:|:--------:|
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| RFP | Cascade + ResNet-50 | 1x | 7.5 | - | 44.8 | | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json) |
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| SAC | Cascade + ResNet-50 | 1x | 5.6 | - | 45.0| | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json) |
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| DetectoRS | Cascade + ResNet-50 | 1x | 9.9 | - | 46.9 | | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-0db1ab6a.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200624_103448.log.json) |
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| RFP | HTC + ResNet-50 | 1x | 11.2 | - | 46.6 | 40.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json) |
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| SAC | HTC + ResNet-50 | 1x | 9.3 | - | 46.4 | 40.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json) |
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| DetectoRS | HTC + ResNet-50 | 1x | 13.6 | - | 49.1 | 42.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) &#124; [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json) |
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*Note*: This is a re-implementation based on MMDetection-V2.
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The original implementation is based on MMDetection-V1.

‎configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py

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_base_ = [
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'../_base_/models/cascade_rcnn_r50_fpn.py',
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'../_base_/datasets/coco_detection.py',
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
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]
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model = dict(
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backbone=dict(
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type='DetectoRS_ResNet',
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conv_cfg=dict(type='ConvAWS'),
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output_img=True),
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neck=dict(
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type='RFP',
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rfp_steps=2,
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aspp_out_channels=64,
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aspp_dilations=(1, 3, 6, 1),
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rfp_backbone=dict(
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rfp_inplanes=256,
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type='DetectoRS_ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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conv_cfg=dict(type='ConvAWS'),
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pretrained='torchvision://resnet50',
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style='pytorch')))

‎configs/detectors/cascade_rcnn_r50_sac_1x_coco.py

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_base_ = [
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'../_base_/models/cascade_rcnn_r50_fpn.py',
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'../_base_/datasets/coco_detection.py',
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
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]
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model = dict(
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backbone=dict(
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type='DetectoRS_ResNet',
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conv_cfg=dict(type='ConvAWS'),
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sac=dict(type='SAC', use_deform=True),
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stage_with_sac=(False, True, True, True)))

‎configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py

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_base_ = [
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'../_base_/models/cascade_rcnn_r50_fpn.py',
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'../_base_/datasets/coco_detection.py',
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'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
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]
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model = dict(
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backbone=dict(
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type='DetectoRS_ResNet',
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conv_cfg=dict(type='ConvAWS'),
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sac=dict(type='SAC', use_deform=True),
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stage_with_sac=(False, True, True, True),
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output_img=True),
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neck=dict(
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type='RFP',
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rfp_steps=2,
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aspp_out_channels=64,
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aspp_dilations=(1, 3, 6, 1),
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rfp_backbone=dict(
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rfp_inplanes=256,
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type='DetectoRS_ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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conv_cfg=dict(type='ConvAWS'),
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sac=dict(type='SAC', use_deform=True),
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stage_with_sac=(False, True, True, True),
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pretrained='torchvision://resnet50',
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style='pytorch')))

‎configs/detectors/detectors_htc_r50_1x_coco.py

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_base_ = '../htc/htc_r50_fpn_1x_coco.py'
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model = dict(
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backbone=dict(
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type='DetectoRS_ResNet',
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conv_cfg=dict(type='ConvAWS'),
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sac=dict(type='SAC', use_deform=True),
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stage_with_sac=(False, True, True, True),
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output_img=True),
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neck=dict(
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type='RFP',
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rfp_steps=2,
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aspp_out_channels=64,
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aspp_dilations=(1, 3, 6, 1),
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rfp_backbone=dict(
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rfp_inplanes=256,
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type='DetectoRS_ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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conv_cfg=dict(type='ConvAWS'),
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sac=dict(type='SAC', use_deform=True),
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stage_with_sac=(False, True, True, True),
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pretrained='torchvision://resnet50',
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style='pytorch')))

‎configs/detectors/htc_r50_rfp_1x_coco.py

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_base_ = '../htc/htc_r50_fpn_1x_coco.py'
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model = dict(
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backbone=dict(
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type='DetectoRS_ResNet',
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conv_cfg=dict(type='ConvAWS'),
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output_img=True),
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neck=dict(
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type='RFP',
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rfp_steps=2,
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aspp_out_channels=64,
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aspp_dilations=(1, 3, 6, 1),
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rfp_backbone=dict(
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rfp_inplanes=256,
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type='DetectoRS_ResNet',
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depth=50,
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num_stages=4,
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out_indices=(0, 1, 2, 3),
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frozen_stages=1,
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norm_cfg=dict(type='BN', requires_grad=True),
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norm_eval=True,
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conv_cfg=dict(type='ConvAWS'),
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pretrained='torchvision://resnet50',
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style='pytorch')))

‎configs/detectors/htc_r50_sac_1x_coco.py

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_base_ = '../htc/htc_r50_fpn_1x_coco.py'
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model = dict(
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backbone=dict(
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type='DetectoRS_ResNet',
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conv_cfg=dict(type='ConvAWS'),
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sac=dict(type='SAC', use_deform=True),
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stage_with_sac=(False, True, True, True)))

‎docs/model_zoo.md

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### PointRend
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Please refer to [PointRend](https://github.com/open-mmlab/mmdetection/blob/master/configs/point_rend) for details.
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### DetectoRS
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Please refer to [DetectoRS](https://github.com/open-mmlab/mmdetection/blob/master/configs/detectors) for details.
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### Generalized Focal Loss
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Please refer to [Generalized Focal Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/gfl) for details.
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### Other datasets
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We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face).

‎mmdet/models/backbones/__init__.py

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from .detectors_resnet import DetectoRS_ResNet
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from .detectors_resnext import DetectoRS_ResNeXt
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from .hourglass import HourglassNet
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from .hrnet import HRNet
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from .regnet import RegNet
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__all__ = [
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'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet', 'Res2Net',
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'HourglassNet'
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'HourglassNet', 'DetectoRS_ResNet', 'DetectoRS_ResNeXt'
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]

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