Support COCO-Wholebody (#133)

open-mmlab · Sep 19, 2020 · 0d2568d · 0d2568d
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diff --git a/.gitignore b/.gitignore
@@ -106,6 +106,7 @@ venv.bak/
 
 # custom
 mmpose/version.py
+/models
 /data
 .vscode
 .idea

diff --git a/configs/top_down/darkpose/README.md b/configs/top_down/darkpose/README.md
@@ -24,3 +24,11 @@
 | [dark_pose_hrnet_w32](/configs/top_down/darkpose/coco/hrnet_w32_coco_384x288_dark.py) | 384x288 | 0.767 | 0.909 | 0.832 | 0.816 | 0.944 | [ckpt](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w32_coco_384x288_dark-459422a4_20200812.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w32_coco_384x288_dark_20200812.log.json) |
 | [dark_pose_hrnet_w48](/configs/top_down/darkpose/coco/hrnet_w48_coco_256x192_dark.py) | 256x192 | 0.764 | 0.907 | 0.830 | 0.814 | 0.943 | [ckpt](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192_dark-8cba3197_20200812.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192_dark_20200812.log.json) |
 | [dark_pose_hrnet_w48](/configs/top_down/darkpose/coco/hrnet_w48_coco_384x288_dark.py) | 384x288 | 0.773 | 0.910 | 0.833 | 0.820 | 0.946 | [ckpt](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_384x288_dark-741844ba_20200812.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w48_coco_384x288_dark_20200812.log.json) |
+
+### Results on COCO-WholeBody val2017 with detector having human AP of 56.4 on COCO val2017 dataset
+
+| Arch  | Input Size | Body AP | Body AR | Foot AP | Foot AR | Face AP | Face AR  | Hand AP | Hand AR | Whole AP | Whole AR | ckpt | log |
+| :---- | :--------: | :-----: | :-----: | :-----: | :-----: | :-----: | :------: | :-----: | :-----: | :------: |:-------: |:------: | :------: |
+| [dark_pose_hrnet_w48+](/configs/top_down/darkpose/coco-wholebody/hrnet_w48_coco_wholebody_384x288_dark.py)  | 384x288 | 0.742 | 0.807 | 0.705 | 0.804 | 0.840 | 0.892 | 0.602 | 0.694 | 0.661 | 0.743 | [ckpt](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192-c78dce93_20200708.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmpose/top_down/hrnet/hrnet_w32_coco_256x192_20200708.log.json) |
+
+Note: `+` means the model is first pre-trained on original COCO dataset, and then fine-tuned on COCO-WholeBody dataset. We find this will lead to better performance.
diff --git a/configs/top_down/darkpose/coco-wholebody/hrnet_w32_coco_wholebody_256x192_dark.py b/configs/top_down/darkpose/coco-wholebody/hrnet_w32_coco_wholebody_256x192_dark.py
@@ -0,0 +1,172 @@
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=10)
+evaluation = dict(interval=10, metric='mAP', key_indicator='AP')
+
+optimizer = dict(
+    type='Adam',
+    lr=5e-4,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[170, 200])
+total_epochs = 210
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+
+channel_cfg = dict(
+    num_output_channels=133,
+    dataset_joints=133,
+    dataset_channel=[
+        list(range(133)),
+    ],
+    inference_channel=list(range(133)))
+
+# model settings
+model = dict(
+    type='TopDown',
+    pretrained='models/pytorch/imagenet/hrnet_w32-36af842e.pth',
+    backbone=dict(
+        type='HRNet',
+        in_channels=3,
+        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))),
+    ),
+    keypoint_head=dict(
+        type='TopDownSimpleHead',
+        in_channels=32,
+        out_channels=channel_cfg['num_output_channels'],
+        num_deconv_layers=0,
+        extra=dict(final_conv_kernel=1, ),
+    ),
+    train_cfg=dict(),
+    test_cfg=dict(
+        flip_test=True,
+        post_process=True,
+        shift_heatmap=True,
+        unbiased_decoding=True,
+        modulate_kernel=11),
+    loss_pose=dict(type='JointsMSELoss', use_target_weight=True))
+
+data_cfg = dict(
+    image_size=[192, 256],
+    heatmap_size=[48, 64],
+    num_output_channels=channel_cfg['num_output_channels'],
+    num_joints=channel_cfg['dataset_joints'],
+    dataset_channel=channel_cfg['dataset_channel'],
+    inference_channel=channel_cfg['inference_channel'],
+    soft_nms=False,
+    nms_thr=1.0,
+    oks_thr=0.9,
+    vis_thr=0.2,
+    bbox_thr=1.0,
+    use_gt_bbox=False,
+    image_thr=0.0,
+    bbox_file='data/coco/person_detection_results/'
+    'COCO_val2017_detections_AP_H_56_person.json',
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownRandomFlip', flip_prob=0.5),
+    dict(
+        type='TopDownHalfBodyTransform',
+        num_joints_half_body=8,
+        prob_half_body=0.3),
+    dict(
+        type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='TopDownGenerateTarget', sigma=2, unbiased_encoding=True),
+    dict(
+        type='Collect',
+        keys=['img', 'target', 'target_weight'],
+        meta_keys=[
+            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+            'rotation', 'bbox_score', 'flip_pairs'
+        ]),
+]
+
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(
+        type='Collect',
+        keys=[
+            'img',
+        ],
+        meta_keys=[
+            'image_file', 'center', 'scale', 'rotation', 'bbox_score',
+            'flip_pairs'
+        ]),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/coco'
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type='TopDownCocoWholeBodyDataset',
+        ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json',
+        img_prefix=f'{data_root}/train2017/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline),
+    val=dict(
+        type='TopDownCocoWholeBodyDataset',
+        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+    test=dict(
+        type='TopDownCocoWholeBodyDataset',
+        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+)
diff --git a/configs/top_down/darkpose/coco-wholebody/hrnet_w32_coco_wholebody_384x288_dark.py b/configs/top_down/darkpose/coco-wholebody/hrnet_w32_coco_wholebody_384x288_dark.py
@@ -0,0 +1,172 @@
+log_level = 'INFO'
+load_from = None
+resume_from = None
+dist_params = dict(backend='nccl')
+workflow = [('train', 1)]
+checkpoint_config = dict(interval=10)
+evaluation = dict(interval=10, metric='mAP', key_indicator='AP')
+
+optimizer = dict(
+    type='Adam',
+    lr=5e-4,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[170, 200])
+total_epochs = 210
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+
+channel_cfg = dict(
+    num_output_channels=133,
+    dataset_joints=133,
+    dataset_channel=[
+        list(range(133)),
+    ],
+    inference_channel=list(range(133)))
+
+# model settings
+model = dict(
+    type='TopDown',
+    pretrained='models/pytorch/imagenet/hrnet_w32-36af842e.pth',
+    backbone=dict(
+        type='HRNet',
+        in_channels=3,
+        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))),
+    ),
+    keypoint_head=dict(
+        type='TopDownSimpleHead',
+        in_channels=32,
+        out_channels=channel_cfg['num_output_channels'],
+        num_deconv_layers=0,
+        extra=dict(final_conv_kernel=1, ),
+    ),
+    train_cfg=dict(),
+    test_cfg=dict(
+        flip_test=True,
+        post_process=True,
+        shift_heatmap=True,
+        unbiased_decoding=True,
+        modulate_kernel=11),
+    loss_pose=dict(type='JointsMSELoss', use_target_weight=True))
+
+data_cfg = dict(
+    image_size=[288, 384],
+    heatmap_size=[72, 96],
+    num_output_channels=channel_cfg['num_output_channels'],
+    num_joints=channel_cfg['dataset_joints'],
+    dataset_channel=channel_cfg['dataset_channel'],
+    inference_channel=channel_cfg['inference_channel'],
+    soft_nms=False,
+    nms_thr=1.0,
+    oks_thr=0.9,
+    vis_thr=0.2,
+    bbox_thr=1.0,
+    use_gt_bbox=False,
+    image_thr=0.0,
+    bbox_file='data/coco/person_detection_results/'
+    'COCO_val2017_detections_AP_H_56_person.json',
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownRandomFlip', flip_prob=0.5),
+    dict(
+        type='TopDownHalfBodyTransform',
+        num_joints_half_body=8,
+        prob_half_body=0.3),
+    dict(
+        type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(type='TopDownGenerateTarget', sigma=3, unbiased_encoding=True),
+    dict(
+        type='Collect',
+        keys=['img', 'target', 'target_weight'],
+        meta_keys=[
+            'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
+            'rotation', 'bbox_score', 'flip_pairs'
+        ]),
+]
+
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='TopDownAffine'),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(
+        type='Collect',
+        keys=[
+            'img',
+        ],
+        meta_keys=[
+            'image_file', 'center', 'scale', 'rotation', 'bbox_score',
+            'flip_pairs'
+        ]),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/coco'
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=2,
+    train=dict(
+        type='TopDownCocoWholeBodyDataset',
+        ann_file=f'{data_root}/annotations/coco_wholebody_train_v1.0.json',
+        img_prefix=f'{data_root}/train2017/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline),
+    val=dict(
+        type='TopDownCocoWholeBodyDataset',
+        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+    test=dict(
+        type='TopDownCocoWholeBodyDataset',
+        ann_file=f'{data_root}/annotations/coco_wholebody_val_v1.0.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+)