Bottomup

configs/bottom_up/higherhrnet/coco/higher_hrnet32_coco_512x512.py

@@ -198,14 +198,3 @@
         data_cfg=data_cfg,
         pipeline=val_pipeline),
 )
-
-loss = dict(
-    type='MultiLossFactory',
-    num_stages=2,
-    ae_loss_type='exp',
-    with_ae_loss=[True, False],
-    push_loss_factor=[0.001, 0.001],
-    pull_loss_factor=[0.001, 0.001],
-    with_heatmaps_loss=[True, True],
-    heatmaps_loss_factor=[1.0, 1.0],
-)

configs/bottom_up/higherhrnet/coco/higher_hrnet32_coco_640x640.py

@@ -199,14 +199,3 @@
         data_cfg=data_cfg,
         pipeline=val_pipeline),
 )
-
-loss = dict(
-    type='MultiLossFactory',
-    num_stages=2,
-    ae_loss_type='exp',
-    with_ae_loss=[True, False],
-    push_loss_factor=[0.001, 0.001],
-    pull_loss_factor=[0.001, 0.001],
-    with_heatmaps_loss=[True, True],
-    heatmaps_loss_factor=[1.0, 1.0],
-)

configs/bottom_up/higherhrnet/coco/higher_hrnet48_coco_512x512.py

@@ -199,14 +199,3 @@
         data_cfg=data_cfg,
         pipeline=val_pipeline),
 )
-
-loss = dict(
-    type='MultiLossFactory',
-    num_stages=2,
-    ae_loss_type='exp',
-    with_ae_loss=[True, False],
-    push_loss_factor=[0.001, 0.001],
-    pull_loss_factor=[0.001, 0.001],
-    with_heatmaps_loss=[True, True],
-    heatmaps_loss_factor=[1.0, 1.0],
-)

configs/bottom_up/hrnet/README.md

@@ -0,0 +1,35 @@
+# Associative Embedding (AE) + HRNet
+
+## Introduction
+```
+@inproceedings{newell2017associative,
+  title={Associative embedding: End-to-end learning for joint detection and grouping},
+  author={Newell, Alejandro and Huang, Zhiao and Deng, Jia},
+  booktitle={Advances in neural information processing systems},
+  pages={2277--2287},
+  year={2017}
+}
+@inproceedings{sun2019deep,
+  title={Deep high-resolution representation learning for human pose estimation},
+  author={Sun, Ke and Xiao, Bin and Liu, Dong and Wang, Jingdong},
+  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
+  pages={5693--5703},
+  year={2019}
+}
+```
+
+## Results and models
+
+### Results on COCO val2017 without multi-scale test
+
+| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
+| :----------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
+| [HRNet-w32](/configs/bottom_up/hrnet/coco/hrnet_w32_coco_512x512.py)  | 512x512 | 0.654 | 0.863 | 0.720 | 0.710 | 0.892 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w32_coco_512x512_20200816.log.json) |
+| [HRNet-w48](/configs/bottom_up/hrnet/coco/hrnet_w48_coco_512x512.py)  | 512x512 | 0.665 | 0.860 | 0.727 | 0.716 | 0.889 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w48_coco_512x512-cf72fcdf_20200816.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w48_coco_512x512_20200816.log.json) |
+
+### Results on COCO val2017 with multi-scale test with scales [2, 1, 0.5].
+
+| Arch | Input Size | AP | AP<sup>50</sup> | AP<sup>75</sup> | AR | AR<sup>50</sup> | ckpt | log |
+| :----------------- | :-----------: | :------: | :------: | :------: | :------: | :------: |:------: |:------: |
+| [HRNet-w32](/configs/bottom_up/hrnet/coco/hrnet_w32_coco_512x512.py)  | 512x512 | 0.698 | 0.877 | 0.760 | 0.748 | 0.907 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w32_coco_512x512-bcb8c247_20200816.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w32_coco_512x512_20200816.log.json) |
+| [HRNet-w48](/configs/bottom_up/hrnet/coco/hrnet_w48_coco_512x512.py)  | 512x512 | 0.712 | 0.880 | 0.771 | 0.757 | 0.909 | [ckpt](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w48_coco_512x512-cf72fcdf_20200816.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmpose/bottom_up/hrnet_w48_coco_512x512_20200816.log.json) |

configs/bottom_up/hrnet/coco/hrnet_w32_coco_512x512.py

@@ -0,0 +1,196 @@
+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=100, metric='mAP')
+
+optimizer = dict(
+    type='Adam',
+    lr=0.0015,
+)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[200, 260])
+total_epochs = 300
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+
+channel_cfg = dict(
+    dataset_joints=17,
+    dataset_channel=[
+        [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
+    ],
+    inference_channel=[
+        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
+    ])
+
+data_cfg = dict(
+    image_size=512,
+    base_size=256,
+    base_sigma=2,
+    heatmap_size=[128],
+    num_joints=channel_cfg['dataset_joints'],
+    dataset_channel=channel_cfg['dataset_channel'],
+    inference_channel=channel_cfg['inference_channel'],
+    num_scales=1,
+    scale_aware_sigma=False,
+)
+
+# model settings
+model = dict(
+    type='BottomUp',
+    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='BottomUpSimpleHead',
+        in_channels=32,
+        num_joints=17,
+        num_deconv_layers=0,
+        tag_per_joint=True,
+        with_ae_loss=[True],
+        extra=dict(final_conv_kernel=1, )),
+    train_cfg=dict(
+        num_joints=channel_cfg['dataset_joints'],
+        img_size=data_cfg['image_size']),
+    test_cfg=dict(
+        num_joints=channel_cfg['dataset_joints'],
+        max_num_people=30,
+        scale_factor=[1],
+        with_heatmaps=[True],
+        with_ae=[True],
+        project2image=True,
+        nms_kernel=5,
+        nms_padding=2,
+        tag_per_joint=True,
+        detection_threshold=0.1,
+        tag_threshold=1,
+        use_detection_val=True,
+        ignore_too_much=False,
+        adjust=True,
+        refine=True,
+        flip_test=True),
+    loss_pose=dict(
+        type='MultiLossFactory',
+        num_joints=17,
+        num_stages=1,
+        ae_loss_type='exp',
+        with_ae_loss=[True],
+        push_loss_factor=[0.001],
+        pull_loss_factor=[0.001],
+        with_heatmaps_loss=[True],
+        heatmaps_loss_factor=[1.0],
+    ),
+)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='BottomUpRandomAffine',
+        rot_factor=30,
+        scale_factor=[0.75, 1.5],
+        scale_type='short',
+        trans_factor=40),
+    dict(type='BottomUpRandomFlip', flip_prob=0.5),
+    dict(type='ToTensor'),
+    dict(
+        type='NormalizeTensor',
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]),
+    dict(
+        type='BottomUpGenerateTarget',
+        sigma=2,
+        max_num_people=30,
+    ),
+    dict(
+        type='Collect',
+        keys=['img', 'joints', 'targets', 'masks'],
+        meta_keys=[]),
+]
+
+val_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='BottomUpGetImgSize', test_scale_factor=[1]),
+    dict(
+        type='BottomUpResizeAlign',
+        transforms=[
+            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', 'aug_data', 'test_scale_factor', 'base_size',
+            'center', 'scale', 'flip_index'
+        ]),
+]
+
+test_pipeline = val_pipeline
+
+data_root = 'data/coco'
+data = dict(
+    samples_per_gpu=24,
+    workers_per_gpu=1,
+    train=dict(
+        type='BottomUpCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
+        img_prefix=f'{data_root}/train2017/',
+        data_cfg=data_cfg,
+        pipeline=train_pipeline),
+    val=dict(
+        type='BottomUpCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+    test=dict(
+        type='BottomUpCocoDataset',
+        ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
+        img_prefix=f'{data_root}/val2017/',
+        data_cfg=data_cfg,
+        pipeline=val_pipeline),
+)