Code for "High-Resolution Representations for Labeling Pixels and Reg…

…ions" (#610) * support HRNet * add zip * remove zip files * remove zip datasets in config * modify format and shorten lines * fix line to long * support conv_cfg and update conv layer * revise the backbone network and neck * update format and pretrained mode * fix flake8 error * update modules following review suggestions * revert some changes for adapting to pretrained models * update hrnet and hrfpn * remove unused import * remove unused import * finish testing * change pretrained model link to open-mmlab * fix docstring and convert models * update README and model links * modify configs and README * support loss evaluator * update model urls * format hrnet.py * format hrfpn.py * add 20e for cascade config
cizhenshi · May 22, 2019 · 3cb84ac · 3cb84ac
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diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md
@@ -0,0 +1,54 @@
+# High-resolution networks (HRNets) for object detection
+
+## Introduction
+
+```
+@inproceedings{SunXLW19,
+  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
+  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
+  booktitle={CVPR},
+  year={2019}
+}
+
+@article{SunZJCXLMWLW19,
+  title={High-Resolution Representations for Labeling Pixels and Regions},
+  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao 
+  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
+  journal   = {CoRR},
+  volume    = {abs/1904.04514},
+  year={2019}
+}
+```
+
+## Results and Models
+
+Faster R-CNN
+
+| Backbone|#Params|GFLOPs|Lr sched|mAP|Download|
+| :--:|:--:|:--:|:--:|:--:|:--:|
+| HRNetV2-W18 |26.2M|159.1| 1x | 36.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/faster_rcnn_hrnetv2_w18_fpn_1x_20190522-e368c387.pth)|
+| HRNetV2-W18 |26.2M|159.1| 20-23-24e | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/faster_rcnn_hrnetv2_w18_fpn_20_23_24e_20190522-ed3c0293.pth)|
+| HRNetV2-W32 |45.0M|245.3| 1x | 39.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/faster_rcnn_hrnetv2_w32_fpn_1x_20190522-d22f1fef.pth)|
+| HRNetV2-W32 |45.0M|245.3| 20-23-24e | 40.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/faster_rcnn_hrnetv2_w32_fpn_20_23_24e_20190522-2d67a5eb.pth)|
+| HRNetV2-W40 |60.5M|314.9| 1x | 40.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/faster_rcnn_hrnetv2_w40_fpn_1x_20190522-30502318.pth)|
+| HRNetV2-W40 |60.5M|314.9| 20-23-24e | 41.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/faster_rcnn_hrnetv2_w40_fpn_20_23_24e_20190522-050a7c7f.pth)|
+
+
+Mask R-CNN
+
+|Backbone|Lr sched|mask mAP|box mAP|Download|
+|:--:|:--:|:--:|:--:|:--:|
+| HRNetV2-W18 | 1x | 34.2 | 37.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/mask_rcnn_hrnetv2_w18_fpn_1x_20190522-c8ad459f.pth)|
+| HRNetV2-W18 | 20-23-24e | 35.7 | 39.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/mask_rcnn_hrnetv2_w18_fpn_20_23_24e_20190522-5c11b7f2.pth)|
+| HRNetV2-W32 | 1x | 36.8 | 40.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/mask_rcnn_hrnetv2_w32_fpn_1x_20190522-374aaa00.pth)|
+| HRNetV2-W32 | 20-23-24e | 37.6 | 42.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/mask_rcnn_hrnetv2_w32_fpn_20_23_24e_20190522-4dd02a79.pth)|
+
+Cascade R-CNN
+
+|Backbone|Lr sched|mAP|Download|
+|:--:|:--:|:--:|:--:|
+| HRNetV2-W32 | 20e | 43.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/hrnet/cascade_rcnn_hrnetv2_w32_fpn_20e_20190522-55bec4ee.pth)|
+
+**Note:**
+
+- HRNetV2 ImageNet pretrained models are in [HRNets for Image Classification](https://github.com/HRNet/HRNet-Image-Classification).
diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e.py
@@ -0,0 +1,268 @@
+# model settings
+model = dict(
+    type='CascadeRCNN',
+    num_stages=3,
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256)))),
+    neck=dict(
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_scales=[8],
+        anchor_ratios=[0.5, 1.0, 2.0],
+        anchor_strides=[4, 8, 16, 32, 64],
+        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)),
+    bbox_roi_extractor=dict(
+        type='SingleRoIExtractor',
+        roi_layer=dict(
+            type='RoIAlign',
+            out_size=7,
+            sample_num=2),
+        out_channels=256,
+        featmap_strides=[4, 8, 16, 32]),
+    bbox_head=[
+        dict(
+            type='SharedFCBBoxHead',
+            num_fcs=2,
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=81,
+            target_means=[0., 0., 0., 0.],
+            target_stds=[0.1, 0.1, 0.2, 0.2],
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0),
+            loss_bbox=dict(
+                type='SmoothL1Loss',
+                beta=1.0,
+                loss_weight=1.0)),
+        dict(
+            type='SharedFCBBoxHead',
+            num_fcs=2,
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=81,
+            target_means=[0., 0., 0., 0.],
+            target_stds=[0.05, 0.05, 0.1, 0.1],
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0),
+            loss_bbox=dict(
+                type='SmoothL1Loss',
+                beta=1.0,
+                loss_weight=1.0)),
+        dict(
+            type='SharedFCBBoxHead',
+            num_fcs=2,
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=81,
+            target_means=[0., 0., 0., 0.],
+            target_stds=[0.033, 0.033, 0.067, 0.067],
+            reg_class_agnostic=True,
+            loss_cls=dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0),
+            loss_bbox=dict(
+                type='SmoothL1Loss',
+                beta=1.0,
+                loss_weight=1.0)),
+    ])
+# model training and testing settings
+train_cfg = dict(
+    rpn=dict(
+        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_across_levels=False,
+        nms_pre=2000,
+        nms_post=2000,
+        max_num=2000,
+        nms_thr=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_weight=-1,
+            debug=False),
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.6,
+                neg_iou_thr=0.6,
+                min_pos_iou=0.6,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False),
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.7,
+                min_pos_iou=0.7,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)
+    ],
+    stage_loss_weights=[1, 0.5, 0.25])
+test_cfg = dict(
+    rpn=dict(
+        nms_across_levels=False,
+        nms_pre=1000,
+        nms_post=1000,
+        max_num=1000,
+        nms_thr=0.7,
+        min_bbox_size=0),
+    rcnn=dict(
+        score_thr=0.05,
+        nms=dict(type='nms', iou_thr=0.5),
+        max_per_img=100),
+    keep_all_stages=False)
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53],
+    std=[58.395, 57.12, 57.375],
+    to_rgb=True)
+data = dict(
+    imgs_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/',
+        img_scale=(1333, 800),
+        img_norm_cfg=img_norm_cfg,
+        size_divisor=32,
+        flip_ratio=0.5,
+        with_mask=False,
+        with_crowd=True,
+        with_label=True),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        img_scale=(1333, 800),
+        img_norm_cfg=img_norm_cfg,
+        size_divisor=32,
+        flip_ratio=0,
+        with_mask=False,
+        with_crowd=True,
+        with_label=True),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        img_scale=(1333, 800),
+        img_norm_cfg=img_norm_cfg,
+        size_divisor=32,
+        flip_ratio=0,
+        with_mask=False,
+        with_label=False,
+        test_mode=True))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, 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, 19])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+total_epochs = 20
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+work_dir = './work_dirs/cascade_rcnn_hrnetv2p_w32'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]