Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_animal_dataset.py

@@ -0,0 +1,500 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import pytest
+from mmcv import Config
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+from tests.utils.data_utils import convert_db_to_output
+
+
+def test_animal_horse10_dataset():
+    dataset = 'AnimalHorse10Dataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/horse10.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=22,
+        dataset_joints=22,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 21
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 21
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/horse10/test_horse10.json',
+        img_prefix='tests/data/horse10/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/horse10/test_horse10.json',
+        img_prefix='tests/data/horse10/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'horse10'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 3
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK'])
+    assert_almost_equal(infos['PCK'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_animal_fly_dataset():
+    dataset = 'AnimalFlyDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/fly.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=32,
+        dataset_joints=32,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31
+        ])
+
+    data_cfg = dict(
+        image_size=[192, 192],
+        heatmap_size=[48, 48],
+        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'])
+
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/fly/test_fly.json',
+        img_prefix='tests/data/fly/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/fly/test_fly.json',
+        img_prefix='tests/data/fly/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'fly'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+
+    infos = custom_dataset.evaluate(results, metric=['PCK'])
+    assert_almost_equal(infos['PCK'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_animal_locust_dataset():
+    dataset = 'AnimalLocustDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/locust.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=35,
+        dataset_joints=35,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
+                34
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34
+        ])
+
+    data_cfg = dict(
+        image_size=[160, 160],
+        heatmap_size=[40, 40],
+        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'])
+
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/locust/test_locust.json',
+        img_prefix='tests/data/locust/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/locust/test_locust.json',
+        img_prefix='tests/data/locust/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'locust'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+
+    infos = custom_dataset.evaluate(results, metric=['PCK'])
+    assert_almost_equal(infos['PCK'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_animal_zebra_dataset():
+    dataset = 'AnimalZebraDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/zebra.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=9,
+        dataset_joints=9,
+        dataset_channel=[
+            [0, 1, 2, 3, 4, 5, 6, 7, 8],
+        ],
+        inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8])
+
+    data_cfg = dict(
+        image_size=[160, 160],
+        heatmap_size=[40, 40],
+        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'])
+
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/zebra/test_zebra.json',
+        img_prefix='tests/data/zebra/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/zebra/test_zebra.json',
+        img_prefix='tests/data/zebra/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'zebra'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK'])
+    assert_almost_equal(infos['PCK'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_animal_ATRW_dataset():
+    dataset = 'AnimalATRWDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/atrw.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=15,
+        dataset_joints=15,
+        dataset_channel=[
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
+        ],
+        inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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,
+        use_gt_bbox=True,
+        det_bbox_thr=0.0,
+        bbox_file='',
+    )
+
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/atrw/test_atrw.json',
+        img_prefix='tests/data/atrw/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/atrw/test_atrw.json',
+        img_prefix='tests/data/atrw/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'atrw'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric=['PCK'])
+
+
+def test_animal_Macaque_dataset():
+    dataset = 'AnimalMacaqueDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/macaque.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=17,
+        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=[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,
+        use_gt_bbox=True,
+        det_bbox_thr=0.0,
+        bbox_file='',
+    )
+
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/macaque/test_macaque.json',
+        img_prefix='tests/data/macaque/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/macaque/test_macaque.json',
+        img_prefix='tests/data/macaque/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'macaque'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric=['PCK'])
+
+
+def test_animalpose_dataset():
+    dataset = 'AnimalPoseDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/animalpose.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=20,
+        dataset_joints=20,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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,
+        use_gt_bbox=True,
+        det_bbox_thr=0.0,
+        bbox_file='',
+    )
+
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/animalpose/test_animalpose.json',
+        img_prefix='tests/data/animalpose/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/animalpose/test_animalpose.json',
+        img_prefix='tests/data/animalpose/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'animalpose'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric=['PCK'])
+
+
+def test_ap10k_dataset():
+    dataset = 'AnimalAP10KDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/ap10k.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=17,
+        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=[256, 256],
+        heatmap_size=[64, 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,
+        use_gt_bbox=True,
+        det_bbox_thr=0.0,
+        bbox_file='',
+    )
+
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/ap10k/test_ap10k.json',
+        img_prefix='tests/data/ap10k/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/ap10k/test_ap10k.json',
+        img_prefix='tests/data/ap10k/',
+        data_cfg=data_cfg_copy,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'ap10k'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+
+    for output in results:
+        # as there is only one box in each image for test
+        output['bbox_ids'] = [0 for _ in range(len(output['bbox_ids']))]
+
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric=['PCK'])

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_body3d_dataset.py

@@ -0,0 +1,347 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+
+import numpy as np
+from mmcv import Config
+
+from mmpose.datasets import DATASETS
+from mmpose.datasets.builder import build_dataset
+
+
+def test_body3d_h36m_dataset():
+    # Test Human3.6M dataset
+    dataset = 'Body3DH36MDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/h36m.py').dataset_info
+
+    # test single-frame input
+    data_cfg = dict(
+        num_joints=17,
+        seq_len=1,
+        seq_frame_interval=1,
+        joint_2d_src='pipeline',
+        joint_2d_det_file=None,
+        causal=False,
+        need_camera_param=True,
+        camera_param_file='tests/data/h36m/cameras.pkl')
+
+    _ = dataset_class(
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=data_cfg,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=data_cfg,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    assert custom_dataset.dataset_name == 'h36m'
+    assert custom_dataset.test_mode is True
+    _ = custom_dataset[0]
+
+    results = []
+    for result in custom_dataset:
+        results.append({
+            'preds': result['target'][None, ...],
+            'target_image_paths': [result['target_image_path']],
+        })
+
+    metrics = ['mpjpe', 'p-mpjpe', 'n-mpjpe']
+    infos = custom_dataset.evaluate(results, metric=metrics)
+
+    np.testing.assert_almost_equal(infos['MPJPE'], 0.0)
+    np.testing.assert_almost_equal(infos['P-MPJPE'], 0.0)
+    np.testing.assert_almost_equal(infos['N-MPJPE'], 0.0)
+
+    # test multi-frame input with joint_2d_src = 'detection'
+    data_cfg = dict(
+        num_joints=17,
+        seq_len=27,
+        seq_frame_interval=1,
+        causal=True,
+        temporal_padding=True,
+        joint_2d_src='detection',
+        joint_2d_det_file='tests/data/h36m/test_h36m_2d_detection.npy',
+        need_camera_param=True,
+        camera_param_file='tests/data/h36m/cameras.pkl')
+
+    _ = dataset_class(
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=data_cfg,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=data_cfg,
+        dataset_info=dataset_info,
+        pipeline=[],
+        test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    _ = custom_dataset[0]
+
+    results = []
+    for result in custom_dataset:
+        results.append({
+            'preds': result['target'][None, ...],
+            'target_image_paths': [result['target_image_path']],
+        })
+
+    metrics = ['mpjpe', 'p-mpjpe', 'n-mpjpe']
+    infos = custom_dataset.evaluate(results, metric=metrics)
+
+    np.testing.assert_almost_equal(infos['MPJPE'], 0.0)
+    np.testing.assert_almost_equal(infos['P-MPJPE'], 0.0)
+    np.testing.assert_almost_equal(infos['N-MPJPE'], 0.0)
+
+
+def test_body3d_semi_supervision_dataset():
+    # Test Body3d Semi-supervision Dataset
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/h36m.py').dataset_info
+
+    # load labeled dataset
+    labeled_data_cfg = dict(
+        num_joints=17,
+        seq_len=27,
+        seq_frame_interval=1,
+        causall=False,
+        temporal_padding=True,
+        joint_2d_src='gt',
+        subset=1,
+        subjects=['S1'],
+        need_camera_param=True,
+        camera_param_file='tests/data/h36m/cameras.pkl')
+    labeled_dataset_cfg = dict(
+        type='Body3DH36MDataset',
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=labeled_data_cfg,
+        dataset_info=dataset_info,
+        pipeline=[])
+
+    # load unlabled data
+    unlabeled_data_cfg = dict(
+        num_joints=17,
+        seq_len=27,
+        seq_frame_interval=1,
+        causal=False,
+        temporal_padding=True,
+        joint_2d_src='gt',
+        subjects=['S5', 'S7', 'S8'],
+        need_camera_param=True,
+        camera_param_file='tests/data/h36m/cameras.pkl',
+        need_2d_label=True)
+    unlabeled_dataset_cfg = dict(
+        type='Body3DH36MDataset',
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=unlabeled_data_cfg,
+        dataset_info=dataset_info,
+        pipeline=[
+            dict(
+                type='Collect',
+                keys=[('input_2d', 'unlabeled_input')],
+                meta_name='metas',
+                meta_keys=[])
+        ])
+
+    # combine labeled and unlabeled dataset to form a new dataset
+    dataset = 'Body3DSemiSupervisionDataset'
+    dataset_class = DATASETS.get(dataset)
+    custom_dataset = dataset_class(labeled_dataset_cfg, unlabeled_dataset_cfg)
+    item = custom_dataset[0]
+    assert custom_dataset.labeled_dataset.dataset_name == 'h36m'
+    assert 'unlabeled_input' in item.keys()
+
+    unlabeled_dataset = build_dataset(unlabeled_dataset_cfg)
+    assert len(unlabeled_dataset) == len(custom_dataset)
+
+
+def test_body3d_mpi_inf_3dhp_dataset():
+    # Test MPI-INF-3DHP dataset
+    dataset = 'Body3DMpiInf3dhpDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/mpi_inf_3dhp.py').dataset_info
+
+    # Test single-frame input on trainset
+    single_frame_train_data_cfg = dict(
+        num_joints=17,
+        seq_len=1,
+        seq_frame_interval=1,
+        joint_2d_src='pipeline',
+        joint_2d_det_file=None,
+        causal=False,
+        need_camera_param=True,
+        camera_param_file='tests/data/mpi_inf_3dhp/cameras_train.pkl')
+
+    # Test single-frame input on testset
+    single_frame_test_data_cfg = dict(
+        num_joints=17,
+        seq_len=1,
+        seq_frame_interval=1,
+        joint_2d_src='gt',
+        joint_2d_det_file=None,
+        causal=False,
+        need_camera_param=True,
+        camera_param_file='tests/data/mpi_inf_3dhp/cameras_test.pkl')
+
+    # Test multi-frame input on trainset
+    multi_frame_train_data_cfg = dict(
+        num_joints=17,
+        seq_len=27,
+        seq_frame_interval=1,
+        joint_2d_src='gt',
+        joint_2d_det_file=None,
+        causal=True,
+        temporal_padding=True,
+        need_camera_param=True,
+        camera_param_file='tests/data/mpi_inf_3dhp/cameras_train.pkl')
+
+    # Test multi-frame input on testset
+    multi_frame_test_data_cfg = dict(
+        num_joints=17,
+        seq_len=27,
+        seq_frame_interval=1,
+        joint_2d_src='pipeline',
+        joint_2d_det_file=None,
+        causal=False,
+        temporal_padding=True,
+        need_camera_param=True,
+        camera_param_file='tests/data/mpi_inf_3dhp/cameras_test.pkl')
+
+    ann_files = [
+        'tests/data/mpi_inf_3dhp/test_3dhp_train.npz',
+        'tests/data/mpi_inf_3dhp/test_3dhp_test.npz'
+    ] * 2
+    data_cfgs = [
+        single_frame_train_data_cfg, single_frame_test_data_cfg,
+        multi_frame_train_data_cfg, multi_frame_test_data_cfg
+    ]
+
+    for ann_file, data_cfg in zip(ann_files, data_cfgs):
+        _ = dataset_class(
+            ann_file=ann_file,
+            img_prefix='tests/data/mpi_inf_3dhp',
+            data_cfg=data_cfg,
+            pipeline=[],
+            dataset_info=dataset_info,
+            test_mode=False)
+
+        custom_dataset = dataset_class(
+            ann_file=ann_file,
+            img_prefix='tests/data/mpi_inf_3dhp',
+            data_cfg=data_cfg,
+            pipeline=[],
+            dataset_info=dataset_info,
+            test_mode=True)
+
+        assert custom_dataset.test_mode is True
+        _ = custom_dataset[0]
+
+        results = []
+        for result in custom_dataset:
+            results.append({
+                'preds': result['target'][None, ...],
+                'target_image_paths': [result['target_image_path']],
+            })
+
+        metrics = ['mpjpe', 'p-mpjpe', '3dpck', 'p-3dpck', '3dauc', 'p-3dauc']
+        infos = custom_dataset.evaluate(results, metric=metrics)
+
+        np.testing.assert_almost_equal(infos['MPJPE'], 0.0)
+        np.testing.assert_almost_equal(infos['P-MPJPE'], 0.0)
+        np.testing.assert_almost_equal(infos['3DPCK'], 100.)
+        np.testing.assert_almost_equal(infos['P-3DPCK'], 100.)
+        np.testing.assert_almost_equal(infos['3DAUC'], 30 / 31 * 100)
+        np.testing.assert_almost_equal(infos['P-3DAUC'], 30 / 31 * 100)
+
+
+def test_body3dmview_direct_panoptic_dataset():
+    # Test Mview-Panoptic dataset
+    dataset = 'Body3DMviewDirectPanopticDataset'
+    dataset_class = DATASETS.get(dataset)
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/panoptic_body3d.py').dataset_info
+    space_size = [8000, 8000, 2000]
+    space_center = [0, -500, 800]
+    cube_size = [80, 80, 20]
+    train_data_cfg = dict(
+        image_size=[960, 512],
+        heatmap_size=[[240, 128]],
+        space_size=space_size,
+        space_center=space_center,
+        cube_size=cube_size,
+        num_joints=15,
+        seq_list=['160906_band1', '160906_band2'],
+        cam_list=[(0, 12), (0, 6)],
+        num_cameras=2,
+        seq_frame_interval=1,
+        subset='train',
+        need_2d_label=True,
+        need_camera_param=True,
+        root_id=2)
+
+    test_data_cfg = dict(
+        image_size=[960, 512],
+        heatmap_size=[[240, 128]],
+        num_joints=15,
+        space_size=space_size,
+        space_center=space_center,
+        cube_size=cube_size,
+        seq_list=['160906_band1', '160906_band2'],
+        cam_list=[(0, 12), (0, 6)],
+        num_cameras=2,
+        seq_frame_interval=1,
+        subset='validation',
+        need_2d_label=True,
+        need_camera_param=True,
+        root_id=2)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        _ = dataset_class(
+            ann_file=tmpdir + '/tmp_train.pkl',
+            img_prefix='tests/data/panoptic_body3d/',
+            data_cfg=train_data_cfg,
+            pipeline=[],
+            dataset_info=dataset_info,
+            test_mode=False)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        test_dataset = dataset_class(
+            ann_file=tmpdir + '/tmp_validation.pkl',
+            img_prefix='tests/data/panoptic_body3d',
+            data_cfg=test_data_cfg,
+            pipeline=[],
+            dataset_info=dataset_info,
+            test_mode=False)
+
+    import copy
+    gt_num = test_dataset.db_size // test_dataset.num_cameras
+    results = []
+    for i in range(gt_num):
+        index = test_dataset.num_cameras * i
+        db_rec = copy.deepcopy(test_dataset.db[index])
+        joints_3d = db_rec['joints_3d']
+        joints_3d_vis = db_rec['joints_3d_visible']
+        num_gts = len(joints_3d)
+        gt_pose = -np.ones((1, 10, test_dataset.num_joints, 5))
+
+        if num_gts > 0:
+            gt_pose[0, :num_gts, :, :3] = np.array(joints_3d)
+            gt_pose[0, :num_gts, :, 3] = np.array(joints_3d_vis)[:, :, 0] - 1.0
+            gt_pose[0, :num_gts, :, 4] = 1.0
+
+        results.append(dict(pose_3d=gt_pose, sample_id=[i]))
+    _ = test_dataset.evaluate(results, metric=['mAP', 'mpjpe'])

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_bottom_up_dataset.py

@@ -0,0 +1,334 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+from mmcv import Config
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+
+
+def convert_coco_to_output(coco, is_wholebody=False):
+    results = []
+    for img_id in coco.getImgIds():
+        preds = []
+        scores = []
+        image = coco.imgs[img_id]
+        ann_ids = coco.getAnnIds(img_id)
+        for ann_id in ann_ids:
+            obj = coco.anns[ann_id]
+            if is_wholebody:
+                keypoints = np.array(obj['keypoints'] + obj['foot_kpts'] +
+                                     obj['face_kpts'] + obj['lefthand_kpts'] +
+                                     obj['righthand_kpts']).reshape(-1, 3)
+            else:
+                keypoints = np.array(obj['keypoints']).reshape((-1, 3))
+            K = keypoints.shape[0]
+            if sum(keypoints[:, 2]) == 0:
+                continue
+            preds.append(
+                np.concatenate((keypoints[:, :2], np.ones(
+                    [K, 1]), np.ones([K, 1]) * ann_id),
+                               axis=1))
+            scores.append(1)
+        image_paths = []
+        image_paths.append(image['file_name'])
+
+        output = {}
+        output['preds'] = np.stack(preds)
+        output['scores'] = scores
+        output['image_paths'] = image_paths
+        output['output_heatmap'] = None
+
+        results.append(output)
+
+    return results
+
+
+def test_bottom_up_COCO_dataset():
+    dataset = 'BottomUpCocoDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/coco.py').dataset_info
+    # test COCO datasets
+    dataset_class = DATASETS.get(dataset)
+
+    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, 17],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17
+        ])
+
+    data_cfg = dict(
+        image_size=512,
+        base_size=256,
+        base_sigma=2,
+        heatmap_size=[128, 256],
+        num_joints=channel_cfg['dataset_joints'],
+        dataset_channel=channel_cfg['dataset_channel'],
+        inference_channel=channel_cfg['inference_channel'],
+        num_scales=2,
+        scale_aware_sigma=False,
+        use_nms=True)
+
+    _ = dataset_class(
+        ann_file='tests/data/coco/test_coco.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/coco/test_coco.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    assert custom_dataset.dataset_name == 'coco'
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+
+    results = convert_coco_to_output(custom_dataset.coco)
+
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='PCK')
+
+
+def test_bottom_up_CrowdPose_dataset():
+    dataset = 'BottomUpCrowdPoseDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/crowdpose.py').dataset_info
+    # test CrowdPose datasets
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=14,
+        dataset_joints=14,
+        dataset_channel=[
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
+        ],
+        inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
+
+    data_cfg = dict(
+        image_size=512,
+        base_size=256,
+        base_sigma=2,
+        heatmap_size=[128, 256],
+        num_joints=channel_cfg['dataset_joints'],
+        dataset_channel=channel_cfg['dataset_channel'],
+        inference_channel=channel_cfg['inference_channel'],
+        num_scales=2,
+        scale_aware_sigma=False)
+
+    _ = dataset_class(
+        ann_file='tests/data/crowdpose/test_crowdpose.json',
+        img_prefix='tests/data/crowdpose/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/crowdpose/test_crowdpose.json',
+        img_prefix='tests/data/crowdpose/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    assert custom_dataset.dataset_name == 'crowdpose'
+
+    image_id = 103319
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 2
+    _ = custom_dataset[0]
+
+    results = convert_coco_to_output(custom_dataset.coco)
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='PCK')
+
+
+def test_bottom_up_MHP_dataset():
+    dataset = 'BottomUpMhpDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/mhp.py').dataset_info
+    # test MHP datasets
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        dataset_joints=16,
+        dataset_channel=[
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15
+        ])
+
+    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,
+    )
+
+    _ = dataset_class(
+        ann_file='tests/data/mhp/test_mhp.json',
+        img_prefix='tests/data/mhp/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/mhp/test_mhp.json',
+        img_prefix='tests/data/mhp/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    assert custom_dataset.dataset_name == 'mhp'
+
+    image_id = 2889
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 2
+    _ = custom_dataset[0]
+
+    results = convert_coco_to_output(custom_dataset.coco)
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='PCK')
+
+
+def test_bottom_up_AIC_dataset():
+    dataset = 'BottomUpAicDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/aic.py').dataset_info
+    # test MHP datasets
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=14,
+        dataset_joints=14,
+        dataset_channel=[
+            [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
+        ],
+        inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])
+
+    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,
+    )
+
+    _ = dataset_class(
+        ann_file='tests/data/aic/test_aic.json',
+        img_prefix='tests/data/aic/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/aic/test_aic.json',
+        img_prefix='tests/data/aic/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    assert custom_dataset.dataset_name == 'aic'
+
+    image_id = 1
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 3
+    _ = custom_dataset[0]
+
+    results = convert_coco_to_output(custom_dataset.coco)
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='PCK')
+
+
+def test_bottom_up_COCO_wholebody_dataset():
+    dataset = 'BottomUpCocoWholeBodyDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/coco_wholebody.py').dataset_info
+    # test COCO-wholebody datasets
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=133,
+        dataset_joints=133,
+        dataset_channel=[
+            list(range(133)),
+        ],
+        inference_channel=list(range(133)))
+
+    data_cfg = dict(
+        image_size=512,
+        base_size=256,
+        base_sigma=2,
+        heatmap_size=[128, 256],
+        num_joints=channel_cfg['dataset_joints'],
+        dataset_channel=channel_cfg['dataset_channel'],
+        inference_channel=channel_cfg['inference_channel'],
+        num_scales=2,
+        scale_aware_sigma=False,
+    )
+
+    _ = dataset_class(
+        ann_file='tests/data/coco/test_coco_wholebody.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/coco/test_coco_wholebody.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'coco_wholebody'
+
+    image_id = 785
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 4
+    _ = custom_dataset[0]
+
+    results = convert_coco_to_output(custom_dataset.coco, is_wholebody=True)
+    infos = custom_dataset.evaluate(results, metric='mAP')
+    assert_almost_equal(infos['AP'], 1.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='PCK')

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_dataset_info.py

@@ -0,0 +1,77 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmpose.datasets import DatasetInfo
+
+
+def test_dataset_info():
+    dataset_info = dict(
+        dataset_name='zebra',
+        paper_info=dict(
+            author='Graving, Jacob M and Chae, Daniel and Naik, Hemal and '
+            'Li, Liang and Koger, Benjamin and Costelloe, Blair R and '
+            'Couzin, Iain D',
+            title='DeepPoseKit, a software toolkit for fast and robust '
+            'animal pose estimation using deep learning',
+            container='Elife',
+            year='2019',
+            homepage='https://github.com/jgraving/DeepPoseKit-Data',
+        ),
+        keypoint_info={
+            0:
+            dict(name='snout', id=0, color=[255, 255, 255], type='', swap=''),
+            1:
+            dict(name='head', id=1, color=[255, 255, 255], type='', swap=''),
+            2:
+            dict(name='neck', id=2, color=[255, 255, 255], type='', swap=''),
+            3:
+            dict(
+                name='forelegL1',
+                id=3,
+                color=[255, 255, 255],
+                type='',
+                swap='forelegR1'),
+            4:
+            dict(
+                name='forelegR1',
+                id=4,
+                color=[255, 255, 255],
+                type='',
+                swap='forelegL1'),
+            5:
+            dict(
+                name='hindlegL1',
+                id=5,
+                color=[255, 255, 255],
+                type='',
+                swap='hindlegR1'),
+            6:
+            dict(
+                name='hindlegR1',
+                id=6,
+                color=[255, 255, 255],
+                type='',
+                swap='hindlegL1'),
+            7:
+            dict(
+                name='tailbase', id=7, color=[255, 255, 255], type='',
+                swap=''),
+            8:
+            dict(
+                name='tailtip', id=8, color=[255, 255, 255], type='', swap='')
+        },
+        skeleton_info={
+            0: dict(link=('head', 'snout'), id=0, color=[255, 255, 255]),
+            1: dict(link=('neck', 'head'), id=1, color=[255, 255, 255]),
+            2: dict(link=('forelegL1', 'neck'), id=2, color=[255, 255, 255]),
+            3: dict(link=('forelegR1', 'neck'), id=3, color=[255, 255, 255]),
+            4:
+            dict(link=('hindlegL1', 'tailbase'), id=4, color=[255, 255, 255]),
+            5:
+            dict(link=('hindlegR1', 'tailbase'), id=5, color=[255, 255, 255]),
+            6: dict(link=('tailbase', 'neck'), id=6, color=[255, 255, 255]),
+            7: dict(link=('tailtip', 'tailbase'), id=7, color=[255, 255, 255])
+        },
+        joint_weights=[1.] * 9,
+        sigmas=[])
+
+    dataset_info = DatasetInfo(dataset_info)
+    assert dataset_info.keypoint_num == len(dataset_info.flip_index)

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_dataset_wrapper.py

@@ -0,0 +1,67 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv import Config
+
+from mmpose.datasets.builder import build_dataset
+
+
+def test_concat_dataset():
+    # build COCO-like dataset config
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/coco.py').dataset_info
+
+    channel_cfg = dict(
+        num_output_channels=17,
+        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=[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,
+        use_gt_bbox=True,
+        det_bbox_thr=0.0,
+        bbox_file='tests/data/coco/test_coco_det_AP_H_56.json',
+    )
+
+    dataset_cfg = dict(
+        type='TopDownCocoDataset',
+        ann_file='tests/data/coco/test_coco.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info)
+
+    dataset = build_dataset(dataset_cfg)
+
+    # Case 1: build ConcatDataset explicitly
+    concat_dataset_cfg = dict(
+        type='ConcatDataset', datasets=[dataset_cfg, dataset_cfg])
+    concat_dataset = build_dataset(concat_dataset_cfg)
+    assert len(concat_dataset) == 2 * len(dataset)
+
+    # Case 2: build ConcatDataset from cfg sequence
+    concat_dataset = build_dataset([dataset_cfg, dataset_cfg])
+    assert len(concat_dataset) == 2 * len(dataset)
+
+    # Case 3: build ConcatDataset from ann_file sequence
+    concat_dataset_cfg = dataset_cfg.copy()
+    for key in ['ann_file', 'type', 'img_prefix', 'dataset_info']:
+        val = concat_dataset_cfg[key]
+        concat_dataset_cfg[key] = [val] * 2
+    for key in ['num_joints', 'dataset_channel']:
+        val = concat_dataset_cfg['data_cfg'][key]
+        concat_dataset_cfg['data_cfg'][key] = [val] * 2
+    concat_dataset = build_dataset(concat_dataset_cfg)
+    assert len(concat_dataset) == 2 * len(dataset)

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_face_dataset.py

@@ -0,0 +1,284 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+from unittest.mock import MagicMock
+
+import pytest
+from mmcv import Config
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+from tests.utils.data_utils import convert_db_to_output
+
+
+def test_face_300W_dataset():
+    dataset = 'Face300WDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/300w.py').dataset_info
+    # test Face 300W datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=68,
+        dataset_joints=68,
+        dataset_channel=[
+            list(range(68)),
+        ],
+        inference_channel=list(range(68)))
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/300w/test_300w.json',
+        img_prefix='tests/data/300w/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/300w/test_300w.json',
+        img_prefix='tests/data/300w/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == '300w'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['NME'])
+    assert_almost_equal(infos['NME'], 0.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_face_coco_wholebody_dataset():
+    dataset = 'FaceCocoWholeBodyDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/coco_wholebody_face.py').dataset_info
+    # test Face wholebody datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=68,
+        dataset_joints=68,
+        dataset_channel=[
+            list(range(68)),
+        ],
+        inference_channel=list(range(68)))
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/coco/test_coco_wholebody.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/coco/test_coco_wholebody.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['NME'])
+    assert_almost_equal(infos['NME'], 0.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_face_AFLW_dataset():
+    dataset = 'FaceAFLWDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/aflw.py').dataset_info
+    # test Face AFLW datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=19,
+        dataset_joints=19,
+        dataset_channel=[
+            list(range(19)),
+        ],
+        inference_channel=list(range(19)))
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/aflw/test_aflw.json',
+        img_prefix='tests/data/aflw/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/aflw/test_aflw.json',
+        img_prefix='tests/data/aflw/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'aflw'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['NME'])
+    assert_almost_equal(infos['NME'], 0.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_face_WFLW_dataset():
+    dataset = 'FaceWFLWDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/wflw.py').dataset_info
+    # test Face WFLW datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=98,
+        dataset_joints=98,
+        dataset_channel=[
+            list(range(98)),
+        ],
+        inference_channel=list(range(98)))
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/wflw/test_wflw.json',
+        img_prefix='tests/data/wflw/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/wflw/test_wflw.json',
+        img_prefix='tests/data/wflw/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'wflw'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['NME'])
+    assert_almost_equal(infos['NME'], 0.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_face_COFW_dataset():
+    dataset = 'FaceCOFWDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/cofw.py').dataset_info
+    # test Face COFW datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=29,
+        dataset_joints=29,
+        dataset_channel=[
+            list(range(29)),
+        ],
+        inference_channel=list(range(29)))
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/cofw/test_cofw.json',
+        img_prefix='tests/data/cofw/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/cofw/test_cofw.json',
+        img_prefix='tests/data/cofw/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'cofw'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['NME'])
+    assert_almost_equal(infos['NME'], 0.0)
+
+    with pytest.raises(KeyError):
+        _ = custom_dataset.evaluate(results, metric='mAP')

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_fashion_dataset.py

@@ -0,0 +1,70 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from unittest.mock import MagicMock
+
+import pytest
+from mmcv import Config
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+from tests.utils.data_utils import convert_db_to_output
+
+
+def test_deepfashion_dataset():
+    dataset = 'DeepFashionDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/deepfashion_full.py').dataset_info
+    # test JHMDB datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=8,
+        dataset_joints=8,
+        dataset_channel=[
+            [0, 1, 2, 3, 4, 5, 6, 7],
+        ],
+        inference_channel=[0, 1, 2, 3, 4, 5, 6, 7])
+
+    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,
+        use_gt_bbox=True,
+        det_bbox_thr=0.0,
+        image_thr=0.0,
+        bbox_file='')
+
+    # Test gt bbox
+    custom_dataset = dataset_class(
+        ann_file='tests/data/fld/test_fld.json',
+        img_prefix='tests/data/fld/',
+        subset='full',
+        data_cfg=data_cfg,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'deepfashion_full'
+
+    image_id = 128
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 2
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
+    assert_almost_equal(infos['PCK'], 1.0)
+    assert_almost_equal(infos['AUC'], 0.95)
+    assert_almost_equal(infos['EPE'], 0.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_hand_dataset.py

@@ -0,0 +1,456 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import pytest
+from mmcv import Config
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+from tests.utils.data_utils import convert_db_to_output
+
+
+def test_OneHand10K_dataset():
+    dataset = 'OneHand10KDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/onehand10k.py').dataset_info
+
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=21,
+        dataset_joints=21,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/onehand10k/test_onehand10k.json',
+        img_prefix='tests/data/onehand10k/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/onehand10k/test_onehand10k.json',
+        img_prefix='tests/data/onehand10k/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'onehand10k'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
+    assert_almost_equal(infos['PCK'], 1.0)
+    assert_almost_equal(infos['AUC'], 0.95)
+    assert_almost_equal(infos['EPE'], 0.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_hand_coco_wholebody_dataset():
+    dataset = 'HandCocoWholeBodyDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/coco_wholebody_hand.py').dataset_info
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=21,
+        dataset_joints=21,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/coco/test_coco_wholebody.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/coco/test_coco_wholebody.json',
+        img_prefix='tests/data/coco/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
+    assert_almost_equal(infos['PCK'], 1.0)
+    assert_almost_equal(infos['AUC'], 0.95)
+    assert_almost_equal(infos['EPE'], 0.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_FreiHand2D_dataset():
+    dataset = 'FreiHandDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/freihand2d.py').dataset_info
+
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=21,
+        dataset_joints=21,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ])
+
+    data_cfg = dict(
+        image_size=[224, 224],
+        heatmap_size=[56, 56],
+        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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/freihand/test_freihand.json',
+        img_prefix='tests/data/freihand/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/freihand/test_freihand.json',
+        img_prefix='tests/data/freihand/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'freihand'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 8
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
+    assert_almost_equal(infos['PCK'], 1.0)
+    assert_almost_equal(infos['AUC'], 0.95)
+    assert_almost_equal(infos['EPE'], 0.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_RHD2D_dataset():
+    dataset = 'Rhd2DDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/rhd2d.py').dataset_info
+
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=21,
+        dataset_joints=21,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/rhd/test_rhd.json',
+        img_prefix='tests/data/rhd/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/rhd/test_rhd.json',
+        img_prefix='tests/data/rhd/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'rhd2d'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 3
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
+    assert_almost_equal(infos['PCK'], 1.0)
+    assert_almost_equal(infos['AUC'], 0.95)
+    assert_almost_equal(infos['EPE'], 0.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_Panoptic2D_dataset():
+    dataset = 'PanopticDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/panoptic_hand2d.py').dataset_info
+
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=21,
+        dataset_joints=21,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/panoptic/test_panoptic.json',
+        img_prefix='tests/data/panoptic/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/panoptic/test_panoptic.json',
+        img_prefix='tests/data/panoptic/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'panoptic_hand2d'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCKh', 'EPE', 'AUC'])
+    assert_almost_equal(infos['PCKh'], 1.0)
+    assert_almost_equal(infos['AUC'], 0.95)
+    assert_almost_equal(infos['EPE'], 0.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_InterHand2D_dataset():
+    dataset = 'InterHand2DDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/interhand2d.py').dataset_info
+
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=21,
+        dataset_joints=21,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',
+        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',
+        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',
+        img_prefix='tests/data/interhand2.6m/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',
+        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',
+        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',
+        img_prefix='tests/data/interhand2.6m/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'interhand2d'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    assert len(custom_dataset.db) == 6
+
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(custom_dataset.db)
+    infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
+    print(infos, flush=True)
+    assert_almost_equal(infos['PCK'], 1.0)
+    assert_almost_equal(infos['AUC'], 0.95)
+    assert_almost_equal(infos['EPE'], 0.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')
+
+
+def test_InterHand3D_dataset():
+    dataset = 'InterHand3DDataset'
+    dataset_info = Config.fromfile(
+        'configs/_base_/datasets/interhand3d.py').dataset_info
+
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=42,
+        dataset_joints=42,
+        dataset_channel=[
+            [
+                0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+                18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
+                34, 35, 36, 37, 38, 39, 40, 41
+            ],
+        ],
+        inference_channel=[
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
+            36, 37, 38, 39, 40, 41
+        ])
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        heatmap_size=[64, 64, 64],
+        heatmap3d_depth_bound=400.0,
+        heatmap_size_root=64,
+        root_depth_bound=400.0,
+        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'])
+    # Test
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    _ = dataset_class(
+        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',
+        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',
+        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',
+        img_prefix='tests/data/interhand2.6m/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=True)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json',
+        camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json',
+        joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json',
+        img_prefix='tests/data/interhand2.6m/',
+        data_cfg=data_cfg_copy,
+        pipeline=[],
+        dataset_info=dataset_info,
+        test_mode=False)
+
+    assert custom_dataset.dataset_name == 'interhand3d'
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    assert len(custom_dataset.db) == 4
+
+    _ = custom_dataset[0]
+
+    results = convert_db_to_output(
+        custom_dataset.db, keys=['rel_root_depth', 'hand_type'], is_3d=True)
+    infos = custom_dataset.evaluate(
+        results, metric=['MRRPE', 'MPJPE', 'Handedness_acc'])
+    assert_almost_equal(infos['MRRPE'], 0.0, decimal=5)
+    assert_almost_equal(infos['MPJPE_all'], 0.0, decimal=5)
+    assert_almost_equal(infos['MPJPE_single'], 0.0, decimal=5)
+    assert_almost_equal(infos['MPJPE_interacting'], 0.0, decimal=5)
+    assert_almost_equal(infos['Handedness_acc'], 1.0)
+
+    with pytest.raises(KeyError):
+        infos = custom_dataset.evaluate(results, metric='mAP')

third_party/hamer/third-party/ViTPose/tests/test_datasets/test_mesh_dataset.py

@@ -0,0 +1,127 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+
+from mmpose.datasets import DATASETS
+
+
+def test_mesh_Mosh_dataset():
+    # test Mosh dataset
+    dataset = 'MoshDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/mosh/test_mosh.npz', pipeline=[])
+
+    _ = custom_dataset[0]
+
+
+def test_mesh_H36M_dataset():
+    # test H36M dataset
+    dataset = 'MeshH36MDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        iuv_size=[64, 64],
+        num_joints=24,
+        use_IUV=True,
+        uv_type='BF')
+    _ = dataset_class(
+        ann_file='tests/data/h36m/test_h36m.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=data_cfg,
+        pipeline=[],
+        test_mode=False)
+
+    custom_dataset = dataset_class(
+        ann_file='tests/data/h36m/test_h36m.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=data_cfg,
+        pipeline=[],
+        test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    _ = custom_dataset[0]
+
+    # test evaluation
+    outputs = []
+    for item in custom_dataset:
+        pred = dict(
+            keypoints_3d=item['joints_3d'][None, ...],
+            image_path=item['image_file'])
+        outputs.append(pred)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        eval_result = custom_dataset.evaluate(outputs, tmpdir)
+    assert 'MPJPE' in eval_result
+    assert 'MPJPE-PA' in eval_result
+
+
+def test_mesh_Mix_dataset():
+    # test mesh Mix dataset
+
+    dataset = 'MeshMixDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    data_cfg = dict(
+        image_size=[256, 256],
+        iuv_size=[64, 64],
+        num_joints=24,
+        use_IUV=True,
+        uv_type='BF')
+
+    custom_dataset = dataset_class(
+        configs=[
+            dict(
+                ann_file='tests/data/h36m/test_h36m.npz',
+                img_prefix='tests/data/h36m',
+                data_cfg=data_cfg,
+                pipeline=[]),
+            dict(
+                ann_file='tests/data/h36m/test_h36m.npz',
+                img_prefix='tests/data/h36m',
+                data_cfg=data_cfg,
+                pipeline=[]),
+        ],
+        partition=[0.6, 0.4])
+
+    _ = custom_dataset[0]
+
+
+def test_mesh_Adversarial_dataset():
+    # test mesh Adversarial dataset
+
+    # load train dataset
+    data_cfg = dict(
+        image_size=[256, 256],
+        iuv_size=[64, 64],
+        num_joints=24,
+        use_IUV=True,
+        uv_type='BF')
+    train_dataset = dict(
+        type='MeshMixDataset',
+        configs=[
+            dict(
+                ann_file='tests/data/h36m/test_h36m.npz',
+                img_prefix='tests/data/h36m',
+                data_cfg=data_cfg,
+                pipeline=[]),
+            dict(
+                ann_file='tests/data/h36m/test_h36m.npz',
+                img_prefix='tests/data/h36m',
+                data_cfg=data_cfg,
+                pipeline=[]),
+        ],
+        partition=[0.6, 0.4])
+
+    # load adversarial dataset
+    adversarial_dataset = dict(
+        type='MoshDataset',
+        ann_file='tests/data/mosh/test_mosh.npz',
+        pipeline=[])
+
+    # combine train and adversarial dataset to form a new dataset
+    dataset = 'MeshAdversarialDataset'
+    dataset_class = DATASETS.get(dataset)
+    custom_dataset = dataset_class(train_dataset, adversarial_dataset)
+    item = custom_dataset[0]
+    assert 'mosh_theta' in item.keys()