[Feature] Support multi-dataset evaluation

docs/en/user_guides/train_and_test.md

@@ -390,3 +390,121 @@ Here are the environment variables that can be used to configure the slurm job.
 | `GPUS_PER_NODE` | The number of GPUs to be allocated per node. Defaults to 8.                                                |
 | `CPUS_PER_TASK` | The number of CPUs to be allocated per task (Usually one GPU corresponds to one task). Defaults to 5.      |
 | `SRUN_ARGS`     | The other arguments of `srun`. Available options can be found [here](https://slurm.schedmd.com/srun.html). |
+
+## Custom Testing Features
+
+### Test with Custom Metrics
+
+If you're looking to assess models using unique metrics not already supported by MMPose, you'll need to code these metrics yourself and include them in your config file. For guidance on how to accomplish this, check out our [customized evaluation guide](https://mmpose.readthedocs.io/en/latest/advanced_guides/customize_evaluation.html).
+
+### Evaluating Across Multiple Datasets
+
+MMPose offers a handy tool known as `MultiDatasetEvaluator` for streamlined assessment across multiple datasets. Setting up this evaluator in your config file is a breeze. Below is a quick example demonstrating how to evaluate a model using both the COCO and AIC datasets:
+
+```python
+# Set up validation datasets
+coco_val = dict(type='CocoDataset', ...)
+aic_val = dict(type='AicDataset', ...)
+val_dataset = dict(
+        type='CombinedDataset',
+        datasets=[coco_val, aic_val],
+        pipeline=val_pipeline,
+        ...)
+
+# configurate the evaluator
+val_evaluator = dict(
+    type='MultiDatasetEvaluator',
+    metrics=[  # metrics for each dataset
+        dict(type='CocoMetric',
+             ann_file='data/coco/annotations/person_keypoints_val2017.json'),
+        dict(type='CocoMetric',
+            ann_file='data/aic/annotations/aic_val.json',
+            use_area=False,
+            prefix='aic')
+    ],
+    # the number and order of datasets must align with metrics
+    datasets=[coco_val, aic_val],
+    )
+```
+
+Keep in mind that different datasets, like COCO and AIC, have various keypoint definitions. Yet, the model's output keypoints are standardized. This results in a discrepancy between the model outputs and the actual ground truth. To address this, you can employ `KeypointConverter` to align the keypoint configurations between different datasets. Here’s a full example that shows how to leverage `KeypointConverter` to align AIC keypoints with COCO keypoints:
+
+```python
+aic_to_coco_converter = 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),
+            ])
+
+# val datasets
+coco_val = dict(
+    type='CocoDataset',
+    data_root='data/coco/',
+    data_mode='topdown',
+    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=[],
+)
+
+aic_val = dict(
+        type='AicDataset',
+        data_root='data/aic/',
+        data_mode=data_mode,
+        ann_file='annotations/aic_val.json',
+        data_prefix=dict(img='ai_challenger_keypoint_validation_20170911/'
+                         'keypoint_validation_images_20170911/'),
+        test_mode=True,
+        pipeline=[],
+    )
+
+val_dataset = dict(
+        type='CombinedDataset',
+        metainfo=dict(from_file='configs/_base_/datasets/coco.py'),
+        datasets=[coco_val, aic_val],
+        pipeline=val_pipeline,
+        test_mode=True,
+    )
+
+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=val_dataset)
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='MultiDatasetEvaluator',
+    metrics=[
+        dict(type='CocoMetric',
+             ann_file=data_root + 'annotations/person_keypoints_val2017.json'),
+        dict(type='CocoMetric',
+            ann_file='data/aic/annotations/aic_val.json',
+            use_area=False,
+            gt_converter=aic_to_coco_converter,
+            prefix='aic')
+    ],
+    datasets=val_dataset['datasets'],
+    )
+
+test_evaluator = val_evaluator
+```
+
+For further clarification on converting AIC keypoints to COCO keypoints, please consult [this guide](https://mmpose.readthedocs.io/en/latest/user_guides/mixed_datasets.html#merge-aic-into-coco).

docs/zh_cn/user_guides/train_and_test.md

@@ -376,3 +376,123 @@ NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR bash tools/dist_
 | `GPUS_PER_NODE` | 每台机器使用的 GPU 总数，默认为 8                                        |
 | `CPUS_PER_TASK` | 每个任务分配的 CPU 总数（通常为 1 张 GPU 对应 1 个任务进程），默认为 5   |
 | `SRUN_ARGS`     | `srun` 的其他参数，可选项见 [这里](https://slurm.schedmd.com/srun.html). |
+
+## 自定义测试
+
+### 用自定义度量进行测试
+
+如果您希望使用 MMPose 中尚未支持的独特度量来评估模型，您将需要自己编写这些度量并将它们包含在您的配置文件中。关于如何实现这一点的指导，请查看我们的 [自定义评估指南](https://mmpose.readthedocs.io/zh_CN/dev-1.x/advanced_guides/customize_evaluation.html)。
+
+### 在多个数据集上进行评估
+
+MMPose 提供了一个名为 `MultiDatasetEvaluator` 的便捷工具，用于在多个数据集上进行简化评估。在配置文件中设置此评估器非常简单。下面是一个快速示例，演示如何使用 COCO 和 AIC 数据集评估模型：
+
+```python
+# 设置验证数据集
+coco_val = dict(type='CocoDataset', ...)
+
+aic_val = dict(type='AicDataset', ...)
+
+val_dataset = dict(
+        type='CombinedDataset',
+        datasets=[coco_val, aic_val],
+        pipeline=val_pipeline,
+        ...)
+
+# 配置评估器
+val_evaluator = dict(
+    type='MultiDatasetEvaluator',
+    metrics=[  # 为每个数据集配置度量
+        dict(type='CocoMetric',
+             ann_file='data/coco/annotations/person_keypoints_val2017.json'),
+        dict(type='CocoMetric',
+            ann_file='data/aic/annotations/aic_val.json',
+            use_area=False,
+            prefix='aic')
+    ],
+    # 数据集个数和顺序与度量必须匹配
+    datasets=[coco_val, aic_val],
+    )
+```
+
+同的数据集（如 COCO 和 AIC）具有不同的关键点定义。然而，模型的输出关键点是标准化的。这导致了模型输出与真值之间关键点顺序的差异。为解决这一问题，您可以使用 `KeypointConverter` 来对齐不同数据集之间的关键点顺序。下面是一个完整示例，展示了如何利用 `KeypointConverter` 来对齐 AIC 关键点与 COCO 关键点：
+
+```python
+aic_to_coco_converter = 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),
+            ])
+
+# val datasets
+coco_val = dict(
+    type='CocoDataset',
+    data_root='data/coco/',
+    data_mode='topdown',
+    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=[],
+)
+
+aic_val = dict(
+        type='AicDataset',
+        data_root='data/aic/',
+        data_mode=data_mode,
+        ann_file='annotations/aic_val.json',
+        data_prefix=dict(img='ai_challenger_keypoint_validation_20170911/'
+                         'keypoint_validation_images_20170911/'),
+        test_mode=True,
+        pipeline=[],
+    )
+
+val_dataset = dict(
+        type='CombinedDataset',
+        metainfo=dict(from_file='configs/_base_/datasets/coco.py'),
+        datasets=[coco_val, aic_val],
+        pipeline=val_pipeline,
+        test_mode=True,
+    )
+
+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=val_dataset)
+
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='MultiDatasetEvaluator',
+    metrics=[
+        dict(type='CocoMetric',
+             ann_file=data_root + 'annotations/person_keypoints_val2017.json'),
+        dict(type='CocoMetric',
+            ann_file='data/aic/annotations/aic_val.json',
+            use_area=False,
+            gt_converter=aic_to_coco_converter,
+            prefix='aic')
+    ],
+    datasets=val_dataset['datasets'],
+    )
+
+test_evaluator = val_evaluator
+```
+
+如需进一步了解如何将 AIC 关键点转换为 COCO 关键点，请查阅 [该指南](https://mmpose.readthedocs.io/zh_CN/dev-1.x/user_guides/mixed_datasets.html#aic-coco)。

mmpose/datasets/datasets/base/base_coco_style_dataset.py

@@ -166,7 +166,7 @@ def get_data_info(self, idx: int) -> dict:
 
         # Add metainfo items that are required in the pipeline and the model
         metainfo_keys = [
-            'upper_body_ids', 'lower_body_ids', 'flip_pairs',
+            'dataset_name', 'upper_body_ids', 'lower_body_ids', 'flip_pairs',
             'dataset_keypoint_weights', 'flip_indices', 'skeleton_links'
         ]
 

mmpose/datasets/transforms/converting.py

@@ -83,7 +83,7 @@ def __init__(self, num_keypoints: int,
             self.source_index2 = src2
 
         self.source_index = src1
-        self.target_index = target_index
+        self.target_index = list(target_index)
         self.interpolation = interpolation
 
     def transform(self, results: dict) -> dict:
@@ -122,6 +122,44 @@ def transform(self, results: dict) -> dict:
             [keypoints_visible, keypoints_visible_weights], axis=2)
         return results
 
+    def transform_sigmas(self, sigmas: Union[List, np.ndarray]):
+        """Transforms the sigmas based on the mapping."""
+        list_input = False
+        if isinstance(sigmas, list):
+            sigmas = np.array(sigmas)
+            list_input = True
+
+        new_sigmas = np.ones(self.num_keypoints, dtype=sigmas.dtype)
+        new_sigmas[self.target_index] = sigmas[self.source_index]
+
+        if list_input:
+            new_sigmas = new_sigmas.tolist()
+
+        return new_sigmas
+
+    def transform_ann(self, ann_info: Union[dict, list]):
+        """Transforms the annotations based on the mapping."""
+
+        list_input = True
+        if not isinstance(ann_info, list):
+            ann_info = [ann_info]
+            list_input = False
+
+        for ann in ann_info:
+            if 'keypoints' in ann:
+                keypoints = np.array(ann['keypoints']).reshape(-1, 3)
+                new_keypoints = np.zeros((self.num_keypoints, 3),
+                                         dtype=keypoints.dtype)
+                new_keypoints[self.target_index] = keypoints[self.source_index]
+                ann['keypoints'] = new_keypoints.reshape(-1).tolist()
+            if 'num_keypoints' in ann:
+                ann['num_keypoints'] = self.num_keypoints
+
+        if not list_input:
+            ann_info = ann_info[0]
+
+        return ann_info
+
     def __repr__(self) -> str:
         """print the basic information of the transform.
 

mmpose/datasets/transforms/formatting.py

@@ -148,7 +148,7 @@ def __init__(self,
                             'crowd_index', 'ori_shape', 'img_shape',
                             'input_size', 'input_center', 'input_scale',
                             'flip', 'flip_direction', 'flip_indices',
-                            'raw_ann_info'),
+                            'raw_ann_info', 'dataset_name'),
                  pack_transformed=False):
         self.meta_keys = meta_keys
         self.pack_transformed = pack_transformed

mmpose/evaluation/__init__.py

@@ -1,3 +1,4 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+from .evaluators import *  # noqa: F401,F403
 from .functional import *  # noqa: F401,F403
 from .metrics import *  # noqa: F401,F403

mmpose/evaluation/evaluators/__init__.py

@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .mutli_dataset_evaluator import MultiDatasetEvaluator
+
+__all__ = ['MultiDatasetEvaluator']