[Fix] Fix the incorrect labels for training vis_head with combined datasets

configs/body_2d_keypoint/topdown_heatmap/coco/resnet_coco.md

@@ -60,3 +60,9 @@ Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 da
 | [pose_resnet_101](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb32-210e_coco-384x288.py) |  384x288   | 0.749 |      0.906      |      0.817      | 0.799 |      0.941      | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb64-210e_coco-256x192-065d3625_20220926.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res101_8xb64-210e_coco-256x192_20220926.log) |
 | [pose_resnet_152](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-256x192.py) |  256x192   | 0.736 |      0.904      |      0.818      | 0.791 |      0.942      | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-256x192-0345f330_20220928.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-256x192_20220928.log) |
 | [pose_resnet_152](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-384x288.py) |  384x288   | 0.750 |      0.908      |      0.821      | 0.800 |      0.942      | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-384x288-7fbb906f_20220927.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res152_8xb32-210e_coco-384x288_20220927.log) |
+
+The following model is equipped with a visibility prediction head and has been trained using COCO and AIC datasets.
+
+| Arch                                          | Input Size |  AP   | AP<sup>50</sup> | AP<sup>75</sup> |  AR   | AR<sup>50</sup> |                     ckpt                      |                      log                      |
+| :-------------------------------------------- | :--------: | :---: | :-------------: | :-------------: | :---: | :-------------: | :-------------------------------------------: | :-------------------------------------------: |
+| [pose_resnet_50](/configs/body_2d_keypoint/topdown_heatmap/coco/td-hm-vis_res50_8xb64-210e_coco-aic-256x192-merge.py) |  256x192   | 0.729 |      0.900      |      0.807      | 0.783 |      0.938      | [ckpt](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm-vis_res50_8xb64-210e_coco-aic-256x192-merge-21815b2c_20230726.pth) | [log](https://download.openmmlab.com/mmpose/v1/body_2d_keypoint/topdown_heatmap/coco/td-hm_res50_8xb64-210e_coco-256x192_20220923.log) |

configs/body_2d_keypoint/topdown_heatmap/coco/td-hm-vis_res50_8xb64-210e_coco-aic-256x192-merge.py

@@ -0,0 +1,167 @@
+_base_ = ['../../../_base_/default_runtime.py']
+
+# runtime
+train_cfg = dict(max_epochs=210, val_interval=10)
+
+# optimizer
+optim_wrapper = dict(optimizer=dict(
+    type='Adam',
+    lr=5e-4,
+))
+
+# learning policy
+param_scheduler = [
+    dict(
+        type='LinearLR', begin=0, end=500, start_factor=0.001,
+        by_epoch=False),  # warm-up
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=210,
+        milestones=[170, 200],
+        gamma=0.1,
+        by_epoch=True)
+]
+
+# automatically scaling LR based on the actual training batch size
+auto_scale_lr = dict(base_batch_size=512)
+
+# hooks
+default_hooks = dict(checkpoint=dict(save_best='coco/AP', rule='greater'))
+
+# codec settings
+codec = dict(
+    type='MSRAHeatmap', input_size=(192, 256), heatmap_size=(48, 64), sigma=2)
+
+# model settings
+model = dict(
+    type='TopdownPoseEstimator',
+    data_preprocessor=dict(
+        type='PoseDataPreprocessor',
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        bgr_to_rgb=True),
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+    ),
+    head=dict(
+        type='VisPredictHead',
+        loss=dict(
+            type='BCELoss',
+            use_target_weight=True,
+            use_sigmoid=True,
+            loss_weight=1e-3,
+        ),
+        pose_cfg=dict(
+            type='HeatmapHead',
+            in_channels=2048,
+            out_channels=17,
+            loss=dict(type='KeypointMSELoss', use_target_weight=True),
+            decoder=codec)),
+    test_cfg=dict(
+        flip_test=True,
+        flip_mode='heatmap',
+        shift_heatmap=True,
+    ))
+
+# base dataset settings
+dataset_type = 'CocoDataset'
+data_mode = 'topdown'
+data_root = 'data/coco/'
+
+# pipelines
+train_pipeline = [
+    dict(type='LoadImage'),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='RandomFlip', direction='horizontal'),
+    dict(type='RandomHalfBody'),
+    dict(type='RandomBBoxTransform'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(type='PackPoseInputs')
+]
+val_pipeline = [
+    dict(type='LoadImage'),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='TopdownAffine', input_size=codec['input_size']),
+    dict(type='PackPoseInputs')
+]
+
+# train datasets
+dataset_coco = dict(
+    type=dataset_type,
+    data_root=data_root,
+    data_mode=data_mode,
+    ann_file='annotations/person_keypoints_train2017.json',
+    data_prefix=dict(img='train2017/'),
+    pipeline=[],
+)
+
+dataset_aic = dict(
+    type='AicDataset',
+    data_root='data/aic/',
+    data_mode=data_mode,
+    ann_file='annotations/aic_train.json',
+    data_prefix=dict(img='ai_challenger_keypoint_train_20170902/'
+                     'keypoint_train_images_20170902/'),
+    pipeline=[
+        dict(
+            type='KeypointConverter',
+            num_keypoints=17,
+            mapping=[
+                (0, 6),
+                (1, 8),
+                (2, 10),
+                (3, 5),
+                (4, 7),
+                (5, 9),
+                (6, 12),
+                (7, 14),
+                (8, 16),
+                (9, 11),
+                (10, 13),
+                (11, 15),
+            ])
+    ],
+)
+
+# data loaders
+train_dataloader = dict(
+    batch_size=64,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='CombinedDataset',
+        metainfo=dict(from_file='configs/_base_/datasets/coco.py'),
+        datasets=[dataset_coco, dataset_aic],
+        pipeline=train_pipeline,
+        test_mode=False,
+    ))
+val_dataloader = dict(
+    batch_size=32,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False, round_up=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_mode=data_mode,
+        ann_file='annotations/person_keypoints_val2017.json',
+        bbox_file='data/coco/person_detection_results/'
+        'COCO_val2017_detections_AP_H_56_person.json',
+        data_prefix=dict(img='val2017/'),
+        test_mode=True,
+        pipeline=val_pipeline,
+    ))
+test_dataloader = val_dataloader
+
+# evaluators
+val_evaluator = dict(
+    type='CocoMetric',
+    # score_mode='bbox',
+    ann_file=data_root + 'annotations/person_keypoints_val2017.json')
+test_evaluator = val_evaluator

docs/en/advanced_guides/implement_new_models.md

@@ -79,3 +79,27 @@ class YourNewHead(BaseHead):
 ```
 
 Finally, please remember to import your new prediction head in `[__init__.py](https://github.com/open-mmlab/mmpose/blob/main/mmpose/models/heads/__init__.py)` .
+
+### Head with Keypoints Visibility Prediction
+
+Many models predict keypoint visibility based on confidence in coordinate predictions. However, this approach is suboptimal. Our [`VisPredictHead`](https://github.com/open-mmlab/mmpose/blob/dev-1.x/mmpose/models/heads/hybrid_heads/vis_head.py) wrapper enables heads to directly predict keypoint visibility from ground truth training data, improving reliability. To add visibility prediction, wrap your head module with VisPredictHead in the config file.
+
+```python
+model=dict(
+     ...
+     head=dict(
+          type='VisPredictHead',
+          loss=dict(
+               type='BCELoss',
+               use_target_weight=True,
+               use_sigmoid=True,
+               loss_weight=1e-3),
+          pose_cfg=dict(
+               type='HeatmapHead',
+               in_channels=2048,
+               out_channels=17,
+               loss=dict(type='KeypointMSELoss', use_target_weight=True),
+               decoder=codec)),
+     ...
+)
+```

docs/zh_cn/advanced_guides/implement_new_models.md

@@ -78,3 +78,27 @@ class YourNewHead(BaseHead):
 ```
 
 最后，请记得在 [heads/\_\_init\_\_.py](https://github.com/open-mmlab/mmpose/blob/main/mmpose/models/heads/__init__.py) 中导入你的新预测头部。
+
+### 关键点可见性预测头部
+
+许多模型都是通过对关键点坐标预测的置信度来判断关键点的可见性的。然而，这种解决方案并非最优。我们提供了一个叫做 [`VisPredictHead`](https://github.com/open-mmlab/mmpose/blob/dev-1.x/mmpose/models/heads/hybrid_heads/vis_head.py) 的头部模块包装器，使得头部模块能够直接预测关键点的可见性。这个包装器是用训练数据中关键点可见性真值来训练的。因此，其预测会更加可靠。用户可以通过修改配置文件来对自己的头部模块加上这个包装器。下面是一个例子:
+
+```python
+model=dict(
+     ...
+     head=dict(
+          type='VisPredictHead',
+          loss=dict(
+               type='BCELoss',
+               use_target_weight=True,
+               use_sigmoid=True,
+               loss_weight=1e-3),
+          pose_cfg=dict(
+               type='HeatmapHead',
+               in_channels=2048,
+               out_channels=17,
+               loss=dict(type='KeypointMSELoss', use_target_weight=True),
+               decoder=codec)),
+     ...
+)
+```

mmpose/datasets/transforms/common_transforms.py

@@ -340,7 +340,7 @@ def _random_select_half_body(self, keypoints_visible: np.ndarray,
 
         Args:
             keypoints_visible (np.ndarray, optional): The visibility of
-                keypoints in shape (N, K, 1).
+                keypoints in shape (N, K, 1) or (N, K, 2).
             upper_body_ids (list): The list of upper body keypoint indices
             lower_body_ids (list): The list of lower body keypoint indices
 
@@ -349,6 +349,9 @@ def _random_select_half_body(self, keypoints_visible: np.ndarray,
             of each instance. ``None`` means not applying half-body transform.
         """
 
+        if keypoints_visible.ndim == 3:
+            keypoints_visible = keypoints_visible[..., 0]
+
         half_body_ids = []
 
         for visible in keypoints_visible:
@@ -390,7 +393,6 @@ def transform(self, results: Dict) -> Optional[dict]:
         Returns:
             dict: The result dict.
         """
-
         half_body_ids = self._random_select_half_body(
             keypoints_visible=results['keypoints_visible'],
             upper_body_ids=results['upper_body_ids'],
@@ -952,6 +954,10 @@ def transform(self, results: Dict) -> Optional[dict]:
                 ' \'keypoints\' in the results.')
 
         keypoints_visible = results['keypoints_visible']
+        if keypoints_visible.ndim == 3 and keypoints_visible.shape[2] == 2:
+            keypoints_visible, keypoints_visible_weights = \
+                keypoints_visible[..., 0], keypoints_visible[..., 1]
+            results['keypoints_visible_weights'] = keypoints_visible_weights
 
         # Encoded items from the encoder(s) will be updated into the results.
         # Please refer to the document of the specific codec for details about
@@ -1031,16 +1037,6 @@ def transform(self, results: Dict) -> Optional[dict]:
 
         results.update(encoded)
 
-        if results.get('keypoint_weights', None) is not None:
-            results['transformed_keypoints_visible'] = results[
-                'keypoint_weights']
-        elif results.get('keypoints', None) is not None:
-            results['transformed_keypoints_visible'] = results[
-                'keypoints_visible']
-        else:
-            raise ValueError('GenerateTarget requires \'keypoint_weights\' or'
-                             ' \'keypoints_visible\' in the results.')
-
         return results
 
     def __repr__(self) -> str:

mmpose/datasets/transforms/converting.py

@@ -87,13 +87,18 @@ def __init__(self, num_keypoints: int,
         self.interpolation = interpolation
 
     def transform(self, results: dict) -> dict:
+        """Transforms the keypoint results to match the target keypoints."""
         num_instances = results['keypoints'].shape[0]
 
+        # Initialize output arrays
         keypoints = np.zeros((num_instances, self.num_keypoints, 2))
         keypoints_visible = np.zeros((num_instances, self.num_keypoints))
 
-        # When paired source_indexes are input,
-        # perform interpolation with self.source_index and self.source_index2
+        # Create a mask to weight visibility loss
+        keypoints_visible_weights = keypoints_visible.copy()
+        keypoints_visible_weights[:, self.target_index] = 1.0
+
+        # Interpolate keypoints if pairs of source indexes provided
         if self.interpolation:
             keypoints[:, self.target_index] = 0.5 * (
                 results['keypoints'][:, self.source_index] +
@@ -102,15 +107,19 @@ def transform(self, results: dict) -> dict:
             keypoints_visible[:, self.target_index] = results[
                 'keypoints_visible'][:, self.source_index] * \
                 results['keypoints_visible'][:, self.source_index2]
+
+        # Otherwise just copy from the source index
         else:
             keypoints[:,
                       self.target_index] = results['keypoints'][:, self.
                                                                 source_index]
             keypoints_visible[:, self.target_index] = results[
                 'keypoints_visible'][:, self.source_index]
 
+        # Update the results dict
         results['keypoints'] = keypoints
-        results['keypoints_visible'] = keypoints_visible
+        results['keypoints_visible'] = np.stack(
+            [keypoints_visible, keypoints_visible_weights], axis=2)
         return results
 
     def __repr__(self) -> str:

mmpose/datasets/transforms/formatting.py

@@ -128,7 +128,7 @@ class PackPoseInputs(BaseTransform):
         'keypoint_y_labels': 'keypoint_y_labels',
         'keypoint_weights': 'keypoint_weights',
         'instance_coords': 'instance_coords',
-        'transformed_keypoints_visible': 'keypoints_visible',
+        'keypoints_visible_weights': 'keypoints_visible_weights'
     }
 
     # items in `field_mapping_table` will be packed into
@@ -195,10 +195,6 @@ def transform(self, results: dict) -> dict:
         if self.pack_transformed and 'transformed_keypoints' in results:
             gt_instances.set_field(results['transformed_keypoints'],
                                    'transformed_keypoints')
-        if self.pack_transformed and \
-                'transformed_keypoints_visible' in results:
-            gt_instances.set_field(results['transformed_keypoints_visible'],
-                                   'transformed_keypoints_visible')
 
         data_sample.gt_instances = gt_instances