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_backward_compatibility/test_dataset_info_compatibility/test_animal_dataset_compatibility.py

@@ -0,0 +1,415 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import tempfile
+
+import pytest
+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_compatibility():
+    dataset = 'AnimalHorse10Dataset'
+    dataset_class = DATASETS.get(dataset)
+
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/horse10/test_horse10.json',
+            img_prefix='tests/data/horse10/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/horse10/test_horse10.json',
+            img_prefix='tests/data/horse10/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 3
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
+        assert_almost_equal(infos['PCK'], 1.0)
+
+        with pytest.raises(KeyError):
+            infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+
+
+def test_animal_fly_dataset_compatibility():
+    dataset = 'AnimalFlyDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/fly/test_fly.json',
+            img_prefix='tests/data/fly/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/fly/test_fly.json',
+            img_prefix='tests/data/fly/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
+        assert_almost_equal(infos['PCK'], 1.0)
+
+        with pytest.raises(KeyError):
+            infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+
+
+def test_animal_locust_dataset_compatibility():
+    dataset = 'AnimalLocustDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/locust/test_locust.json',
+            img_prefix='tests/data/locust/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/locust/test_locust.json',
+            img_prefix='tests/data/locust/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
+        assert_almost_equal(infos['PCK'], 1.0)
+
+        with pytest.raises(KeyError):
+            infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+
+
+def test_animal_zebra_dataset_compatibility():
+    dataset = 'AnimalZebraDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/zebra/test_zebra.json',
+            img_prefix='tests/data/zebra/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/zebra/test_zebra.json',
+            img_prefix='tests/data/zebra/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
+        assert_almost_equal(infos['PCK'], 1.0)
+
+        with pytest.raises(KeyError):
+            infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+
+
+def test_animal_ATRW_dataset_compatibility():
+    dataset = 'AnimalATRWDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/atrw/test_atrw.json',
+            img_prefix='tests/data/atrw/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/atrw/test_atrw.json',
+            img_prefix='tests/data/atrw/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
+
+
+def test_animal_Macaque_dataset_compatibility():
+    dataset = 'AnimalMacaqueDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/macaque/test_macaque.json',
+            img_prefix='tests/data/macaque/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/macaque/test_macaque.json',
+            img_prefix='tests/data/macaque/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
+
+
+def test_animalpose_dataset_compatibility():
+    dataset = 'AnimalPoseDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/animalpose/test_animalpose.json',
+            img_prefix='tests/data/animalpose/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/animalpose/test_animalpose.json',
+            img_prefix='tests/data/animalpose/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_body3d_dataset_compatibility.py

@@ -0,0 +1,266 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+
+import numpy as np
+import pytest
+
+from mmpose.datasets import DATASETS
+from mmpose.datasets.builder import build_dataset
+
+
+def test_body3d_h36m_dataset_compatibility():
+    # Test Human3.6M dataset
+    dataset = 'Body3DH36MDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    # 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')
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/h36m/test_h36m_body3d.npz',
+            img_prefix='tests/data/h36m',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/h36m/test_h36m_body3d.npz',
+            img_prefix='tests/data/h36m',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    _ = custom_dataset[0]
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        outputs = []
+        for result in custom_dataset:
+            outputs.append({
+                'preds': result['target'][None, ...],
+                'target_image_paths': [result['target_image_path']],
+            })
+
+        metrics = ['mpjpe', 'p-mpjpe', 'n-mpjpe']
+        infos = custom_dataset.evaluate(outputs, tmpdir, 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')
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/h36m/test_h36m_body3d.npz',
+            img_prefix='tests/data/h36m',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/h36m/test_h36m_body3d.npz',
+            img_prefix='tests/data/h36m',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    _ = custom_dataset[0]
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        outputs = []
+        for result in custom_dataset:
+            outputs.append({
+                'preds': result['target'][None, ...],
+                'target_image_paths': [result['target_image_path']],
+            })
+
+        metrics = ['mpjpe', 'p-mpjpe', 'n-mpjpe']
+        infos = custom_dataset.evaluate(outputs, tmpdir, 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_compatibility():
+    # Test Body3d Semi-supervision Dataset
+
+    # 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 = dict(
+        type='Body3DH36MDataset',
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=labeled_data_cfg,
+        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 = dict(
+        type='Body3DH36MDataset',
+        ann_file='tests/data/h36m/test_h36m_body3d.npz',
+        img_prefix='tests/data/h36m',
+        data_cfg=unlabeled_data_cfg,
+        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)
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(labeled_dataset, unlabeled_dataset)
+    item = custom_dataset[0]
+    assert 'unlabeled_input' in item.keys()
+
+    unlabeled_dataset = build_dataset(unlabeled_dataset)
+    assert len(unlabeled_dataset) == len(custom_dataset)
+
+
+def test_body3d_mpi_inf_3dhp_dataset_compatibility():
+    # Test MPI-INF-3DHP dataset
+    dataset = 'Body3DMpiInf3dhpDataset'
+    dataset_class = DATASETS.get(dataset)
+
+    # 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):
+        with pytest.warns(DeprecationWarning):
+            _ = dataset_class(
+                ann_file=ann_file,
+                img_prefix='tests/data/mpi_inf_3dhp',
+                data_cfg=data_cfg,
+                pipeline=[],
+                test_mode=False)
+
+        with pytest.warns(DeprecationWarning):
+            custom_dataset = dataset_class(
+                ann_file=ann_file,
+                img_prefix='tests/data/mpi_inf_3dhp',
+                data_cfg=data_cfg,
+                pipeline=[],
+                test_mode=True)
+
+        assert custom_dataset.test_mode is True
+        _ = custom_dataset[0]
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            outputs = []
+            for result in custom_dataset:
+                outputs.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(outputs, tmpdir, 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)

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_bottom_up_dataset_compatibility.py

@@ -0,0 +1,325 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+
+import numpy as np
+import pytest
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+
+
+def convert_coco_to_output(coco, is_wholebody=False):
+    outputs = []
+    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
+
+        outputs.append(output)
+
+    return outputs
+
+
+def test_bottom_up_COCO_dataset_compatibility():
+    dataset = 'BottomUpCocoDataset'
+    # 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)
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/coco/test_coco.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/coco/test_coco.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+    assert custom_dataset.dataset_name == 'coco'
+
+    outputs = convert_coco_to_output(custom_dataset.coco)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_bottom_up_CrowdPose_dataset_compatibility():
+    dataset = 'BottomUpCrowdPoseDataset'
+    # 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)
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/crowdpose/test_crowdpose.json',
+            img_prefix='tests/data/crowdpose/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/crowdpose/test_crowdpose.json',
+            img_prefix='tests/data/crowdpose/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    image_id = 103319
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 2
+    _ = custom_dataset[0]
+    assert custom_dataset.dataset_name == 'crowdpose'
+
+    outputs = convert_coco_to_output(custom_dataset.coco)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_bottom_up_MHP_dataset_compatibility():
+    dataset = 'BottomUpMhpDataset'
+    # 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,
+    )
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/mhp/test_mhp.json',
+            img_prefix='tests/data/mhp/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/mhp/test_mhp.json',
+            img_prefix='tests/data/mhp/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    image_id = 2889
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 2
+    _ = custom_dataset[0]
+    assert custom_dataset.dataset_name == 'mhp'
+
+    outputs = convert_coco_to_output(custom_dataset.coco)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_bottom_up_AIC_dataset_compatibility():
+    dataset = 'BottomUpAicDataset'
+    # 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,
+    )
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/aic/test_aic.json',
+            img_prefix='tests/data/aic/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/aic/test_aic.json',
+            img_prefix='tests/data/aic/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    image_id = 1
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 3
+    _ = custom_dataset[0]
+
+    outputs = convert_coco_to_output(custom_dataset.coco)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_bottom_up_COCO_wholebody_dataset_compatibility():
+    dataset = 'BottomUpCocoWholeBodyDataset'
+    # 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,
+    )
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/coco/test_coco_wholebody.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/coco/test_coco_wholebody.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            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]
+
+    outputs = convert_coco_to_output(custom_dataset.coco, is_wholebody=True)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_deprecated_dataset_base.py

@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+
+from mmpose.datasets.datasets.animal.animal_base_dataset import \
+    AnimalBaseDataset
+from mmpose.datasets.datasets.body3d.body3d_base_dataset import \
+    Body3DBaseDataset
+from mmpose.datasets.datasets.bottom_up.bottom_up_base_dataset import \
+    BottomUpBaseDataset
+from mmpose.datasets.datasets.face.face_base_dataset import FaceBaseDataset
+from mmpose.datasets.datasets.fashion.fashion_base_dataset import \
+    FashionBaseDataset
+from mmpose.datasets.datasets.hand.hand_base_dataset import HandBaseDataset
+from mmpose.datasets.datasets.top_down.topdown_base_dataset import \
+    TopDownBaseDataset
+
+
+@pytest.mark.parametrize('BaseDataset',
+                         (AnimalBaseDataset, BottomUpBaseDataset,
+                          FaceBaseDataset, FashionBaseDataset, HandBaseDataset,
+                          TopDownBaseDataset, Body3DBaseDataset))
+def test_dataset_base_class(BaseDataset):
+    with pytest.raises(ImportError):
+
+        class Dataset(BaseDataset):
+            pass
+
+        _ = Dataset()

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_face_dataset_compatibility.py

@@ -0,0 +1,170 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import tempfile
+from unittest.mock import MagicMock
+
+import pytest
+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_compatibility():
+    dataset = 'Face300WDataset'
+    # 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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/300w/test_300w.json',
+            img_prefix='tests/data/300w/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/300w/test_300w.json',
+            img_prefix='tests/data/300w/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['NME'])
+        assert_almost_equal(infos['NME'], 0.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+
+
+def test_face_AFLW_dataset_compatibility():
+    dataset = 'FaceAFLWDataset'
+    # 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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/aflw/test_aflw.json',
+            img_prefix='tests/data/aflw/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/aflw/test_aflw.json',
+            img_prefix='tests/data/aflw/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['NME'])
+        assert_almost_equal(infos['NME'], 0.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+
+
+def test_face_WFLW_dataset_compatibility():
+    dataset = 'FaceWFLWDataset'
+    # 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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/wflw/test_wflw.json',
+            img_prefix='tests/data/wflw/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/wflw/test_wflw.json',
+            img_prefix='tests/data/wflw/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 2
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['NME'])
+        assert_almost_equal(infos['NME'], 0.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'mAP')

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_fashion_dataset_compatibility.py

@@ -0,0 +1,69 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+from unittest.mock import MagicMock
+
+import pytest
+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_compatibility():
+    dataset = 'DeepFashionDataset'
+    # 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
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/fld/test_fld.json',
+            img_prefix='tests/data/fld/',
+            subset='full',
+            data_cfg=data_cfg,
+            pipeline=[],
+            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]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['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(outputs, tmpdir, 'mAP')

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_hand_dataset_compatibility.py

@@ -0,0 +1,388 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import tempfile
+
+import pytest
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+from tests.utils.data_utils import convert_db_to_output
+
+
+def test_top_down_OneHand10K_dataset_compatibility():
+    dataset = 'OneHand10KDataset'
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/onehand10k/test_onehand10k.json',
+            img_prefix='tests/data/onehand10k/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/onehand10k/test_onehand10k.json',
+            img_prefix='tests/data/onehand10k/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['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(outputs, tmpdir, 'mAP')
+
+
+def test_top_down_FreiHand_dataset_compatibility():
+    dataset = 'FreiHandDataset'
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/freihand/test_freihand.json',
+            img_prefix='tests/data/freihand/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/freihand/test_freihand.json',
+            img_prefix='tests/data/freihand/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 8
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['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(outputs, tmpdir, 'mAP')
+
+
+def test_top_down_RHD_dataset_compatibility():
+    dataset = 'Rhd2DDataset'
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/rhd/test_rhd.json',
+            img_prefix='tests/data/rhd/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/rhd/test_rhd.json',
+            img_prefix='tests/data/rhd/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 3
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['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(outputs, tmpdir, 'mAP')
+
+
+def test_top_down_Panoptic_dataset_compatibility():
+    dataset = 'PanopticDataset'
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/panoptic/test_panoptic.json',
+            img_prefix='tests/data/panoptic/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/panoptic/test_panoptic.json',
+            img_prefix='tests/data/panoptic/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir,
+                                        ['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(outputs, tmpdir, 'mAP')
+
+
+def test_top_down_InterHand2D_dataset_compatibility():
+    dataset = 'InterHand2DDataset'
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = 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=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        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=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    assert len(custom_dataset.db) == 6
+
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['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(outputs, tmpdir, 'mAP')
+
+
+def test_top_down_InterHand3D_dataset_compatibility():
+    dataset = 'InterHand3DDataset'
+    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)
+    with pytest.warns(DeprecationWarning):
+        _ = 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=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        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=[],
+            test_mode=False)
+
+    assert custom_dataset.test_mode is False
+    assert custom_dataset.num_images == 4
+    assert len(custom_dataset.db) == 4
+
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(
+        custom_dataset.db, keys=['rel_root_depth', 'hand_type'], is_3d=True)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir,
+                                        ['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(outputs, tmpdir, 'mAP')

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_inference_compatibility.py

@@ -0,0 +1,156 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+
+from mmpose.apis import (extract_pose_sequence, get_track_id,
+                         inference_bottom_up_pose_model,
+                         inference_pose_lifter_model,
+                         inference_top_down_pose_model, init_pose_model,
+                         vis_3d_pose_result, vis_pose_result,
+                         vis_pose_tracking_result)
+
+
+def test_inference_without_dataset_info():
+    # Top down
+    pose_model = init_pose_model(
+        'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
+        'coco/res50_coco_256x192.py',
+        None,
+        device='cpu')
+
+    if 'dataset_info' in pose_model.cfg:
+        _ = pose_model.cfg.pop('dataset_info')
+
+    image_name = 'tests/data/coco/000000000785.jpg'
+    person_result = []
+    person_result.append({'bbox': [50, 50, 50, 100]})
+
+    with pytest.warns(DeprecationWarning):
+        pose_results, _ = inference_top_down_pose_model(
+            pose_model, image_name, person_result, format='xywh')
+
+    with pytest.warns(DeprecationWarning):
+        vis_pose_result(pose_model, image_name, pose_results)
+
+    with pytest.raises(NotImplementedError):
+        with pytest.warns(DeprecationWarning):
+            pose_results, _ = inference_top_down_pose_model(
+                pose_model,
+                image_name,
+                person_result,
+                format='xywh',
+                dataset='test')
+
+    # Bottom up
+    pose_model = init_pose_model(
+        'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'
+        'coco/res50_coco_512x512.py',
+        None,
+        device='cpu')
+    if 'dataset_info' in pose_model.cfg:
+        _ = pose_model.cfg.pop('dataset_info')
+
+    image_name = 'tests/data/coco/000000000785.jpg'
+
+    with pytest.warns(DeprecationWarning):
+        pose_results, _ = inference_bottom_up_pose_model(
+            pose_model, image_name)
+    with pytest.warns(DeprecationWarning):
+        vis_pose_result(pose_model, image_name, pose_results)
+
+    # Top down tracking
+    pose_model = init_pose_model(
+        'configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/'
+        'coco/res50_coco_256x192.py',
+        None,
+        device='cpu')
+
+    if 'dataset_info' in pose_model.cfg:
+        _ = pose_model.cfg.pop('dataset_info')
+
+    image_name = 'tests/data/coco/000000000785.jpg'
+    person_result = [{'bbox': [50, 50, 50, 100]}]
+
+    with pytest.warns(DeprecationWarning):
+        pose_results, _ = inference_top_down_pose_model(
+            pose_model, image_name, person_result, format='xywh')
+
+    pose_results, _ = get_track_id(pose_results, [], next_id=0)
+
+    with pytest.warns(DeprecationWarning):
+        vis_pose_tracking_result(pose_model, image_name, pose_results)
+
+    with pytest.raises(NotImplementedError):
+        with pytest.warns(DeprecationWarning):
+            vis_pose_tracking_result(
+                pose_model, image_name, pose_results, dataset='test')
+
+    # Bottom up tracking
+    pose_model = init_pose_model(
+        'configs/body/2d_kpt_sview_rgb_img/associative_embedding/'
+        'coco/res50_coco_512x512.py',
+        None,
+        device='cpu')
+
+    if 'dataset_info' in pose_model.cfg:
+        _ = pose_model.cfg.pop('dataset_info')
+
+    image_name = 'tests/data/coco/000000000785.jpg'
+    with pytest.warns(DeprecationWarning):
+        pose_results, _ = inference_bottom_up_pose_model(
+            pose_model, image_name)
+
+    pose_results, next_id = get_track_id(pose_results, [], next_id=0)
+
+    with pytest.warns(DeprecationWarning):
+        vis_pose_tracking_result(
+            pose_model,
+            image_name,
+            pose_results,
+            dataset='BottomUpCocoDataset')
+
+    # Pose lifting
+    pose_model = init_pose_model(
+        'configs/body/3d_kpt_sview_rgb_img/pose_lift/'
+        'h36m/simplebaseline3d_h36m.py',
+        None,
+        device='cpu')
+
+    pose_det_result = {
+        'keypoints': np.zeros((17, 3)),
+        'bbox': [50, 50, 50, 50],
+        'track_id': 0,
+        'image_name': 'tests/data/h36m/S1_Directions_1.54138969_000001.jpg',
+    }
+
+    if 'dataset_info' in pose_model.cfg:
+        _ = pose_model.cfg.pop('dataset_info')
+
+    pose_results_2d = [[pose_det_result]]
+
+    dataset = pose_model.cfg.data['test']['type']
+
+    pose_results_2d = extract_pose_sequence(
+        pose_results_2d, frame_idx=0, causal=False, seq_len=1, step=1)
+
+    with pytest.warns(DeprecationWarning):
+        _ = inference_pose_lifter_model(
+            pose_model, pose_results_2d, dataset, with_track_id=False)
+
+    with pytest.warns(DeprecationWarning):
+        pose_lift_results = inference_pose_lifter_model(
+            pose_model, pose_results_2d, dataset, with_track_id=True)
+
+    for res in pose_lift_results:
+        res['title'] = 'title'
+    with pytest.warns(DeprecationWarning):
+        vis_3d_pose_result(
+            pose_model,
+            pose_lift_results,
+            img=pose_results_2d[0][0]['image_name'],
+            dataset=dataset)
+
+    with pytest.raises(NotImplementedError):
+        with pytest.warns(DeprecationWarning):
+            _ = inference_pose_lifter_model(
+                pose_model, pose_results_2d, dataset='test')

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_dataset_info_compatibility/test_top_down_dataset_compatibility.py

@@ -0,0 +1,748 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import tempfile
+from unittest.mock import MagicMock
+
+import pytest
+from numpy.testing import assert_almost_equal
+
+from mmpose.datasets import DATASETS
+from tests.utils.data_utils import convert_db_to_output
+
+
+def test_top_down_COCO_dataset_compatibility():
+    dataset = 'TopDownCocoDataset'
+    # test COCO datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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',
+    )
+    # Test det bbox
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    data_cfg_copy['use_gt_bbox'] = False
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/coco/test_coco.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/coco/test_coco.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/coco/test_coco.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'coco'
+
+    image_id = 785
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 4
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_top_down_MHP_dataset_compatibility():
+    dataset = 'TopDownMhpDataset'
+    # test MHP datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=16,
+        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=[192, 256],
+        heatmap_size=[48, 64],
+        num_output_channels=channel_cfg['num_output_channels'],
+        num_joints=channel_cfg['dataset_joints'],
+        dataset_channel=channel_cfg['dataset_channel'],
+        inference_channel=channel_cfg['inference_channel'],
+        soft_nms=False,
+        nms_thr=1.0,
+        oks_thr=0.9,
+        vis_thr=0.2,
+        bbox_thr=1.0,
+        use_gt_bbox=True,
+        det_bbox_thr=0.0,
+        bbox_file='',
+    )
+
+    # Test det bbox
+    with pytest.raises(AssertionError):
+        data_cfg_copy = copy.deepcopy(data_cfg)
+        data_cfg_copy['use_gt_bbox'] = False
+
+        with pytest.warns(DeprecationWarning):
+            _ = dataset_class(
+                ann_file='tests/data/mhp/test_mhp.json',
+                img_prefix='tests/data/mhp/',
+                data_cfg=data_cfg_copy,
+                pipeline=[],
+                test_mode=True)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/mhp/test_mhp.json',
+            img_prefix='tests/data/mhp/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/mhp/test_mhp.json',
+            img_prefix='tests/data/mhp/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is 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]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_top_down_PoseTrack18_dataset_compatibility():
+    dataset = 'TopDownPoseTrack18Dataset'
+    # test PoseTrack datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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/posetrack18/annotations/'
+        'test_posetrack18_human_detections.json',
+    )
+    # Test det bbox
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    data_cfg_copy['use_gt_bbox'] = False
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/posetrack18/annotations/'
+            'test_posetrack18_val.json',
+            img_prefix='tests/data/posetrack18/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/posetrack18/annotations/'
+            'test_posetrack18_val.json',
+            img_prefix='tests/data/posetrack18/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/posetrack18/annotations/'
+            'test_posetrack18_val.json',
+            img_prefix='tests/data/posetrack18/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'posetrack18'
+
+    image_id = 10128340000
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 3
+    _ = custom_dataset[0]
+
+
+def test_top_down_CrowdPose_dataset_compatibility():
+    dataset = 'TopDownCrowdPoseDataset'
+    # test CrowdPose datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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=[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/crowdpose/test_crowdpose_det_AP_40.json',
+    )
+    # Test det bbox
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    data_cfg_copy['use_gt_bbox'] = False
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/crowdpose/test_crowdpose.json',
+            img_prefix='tests/data/crowdpose/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/crowdpose/test_crowdpose.json',
+            img_prefix='tests/data/crowdpose/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/crowdpose/test_crowdpose.json',
+            img_prefix='tests/data/crowdpose/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is 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]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_top_down_COCO_wholebody_dataset_compatibility():
+    dataset = 'TopDownCocoWholeBodyDataset'
+    # test COCO datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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=[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',
+    )
+    # Test det bbox
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    data_cfg_copy['use_gt_bbox'] = False
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/coco/test_coco_wholebody.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=True)
+
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/coco/test_coco_wholebody.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/coco/test_coco_wholebody.json',
+            img_prefix='tests/data/coco/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            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]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_top_down_OCHuman_dataset_compatibility():
+    dataset = 'TopDownOCHumanDataset'
+    # test OCHuman datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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='',
+    )
+
+    with pytest.raises(AssertionError):
+        # Test det bbox
+        data_cfg_copy = copy.deepcopy(data_cfg)
+        data_cfg_copy['use_gt_bbox'] = False
+        with pytest.warns(DeprecationWarning):
+            _ = dataset_class(
+                ann_file='tests/data/ochuman/test_ochuman.json',
+                img_prefix='tests/data/ochuman/',
+                data_cfg=data_cfg_copy,
+                pipeline=[],
+                test_mode=True)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/ochuman/test_ochuman.json',
+            img_prefix='tests/data/ochuman/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'ochuman'
+
+    image_id = 1
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 3
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_top_down_MPII_dataset_compatibility():
+    dataset = 'TopDownMpiiDataset'
+    # test COCO datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    channel_cfg = dict(
+        num_output_channels=16,
+        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=[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 det bbox
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/mpii/test_mpii.json',
+            img_prefix='tests/data/mpii/',
+            data_cfg=data_cfg_copy,
+            pipeline=[])
+
+    assert len(custom_dataset) == 5
+    assert custom_dataset.dataset_name == 'mpii'
+    _ = custom_dataset[0]
+
+
+def test_top_down_MPII_TRB_dataset_compatibility():
+    dataset = 'TopDownMpiiTrbDataset'
+    # test MPII TRB datasets
+    dataset_class = DATASETS.get(dataset)
+
+    channel_cfg = dict(
+        num_output_channels=40,
+        dataset_joints=40,
+        dataset_channel=[list(range(40))],
+        inference_channel=list(range(40)))
+
+    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'])
+
+    data_cfg_copy = copy.deepcopy(data_cfg)
+    with pytest.warns(DeprecationWarning):
+        _ = dataset_class(
+            ann_file='tests/data/mpii/test_mpii_trb.json',
+            img_prefix='tests/data/mpii/',
+            data_cfg=data_cfg_copy,
+            pipeline=[],
+            test_mode=False)
+
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/mpii/test_mpii_trb.json',
+            img_prefix='tests/data/mpii/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'mpii_trb'
+    _ = custom_dataset[0]
+
+
+def test_top_down_AIC_dataset_compatibility():
+    dataset = 'TopDownAicDataset'
+    # test AIC datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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=[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='')
+
+    with pytest.raises(AssertionError):
+        # Test det bbox
+        data_cfg_copy = copy.deepcopy(data_cfg)
+        data_cfg_copy['use_gt_bbox'] = False
+        with pytest.warns(DeprecationWarning):
+            _ = dataset_class(
+                ann_file='tests/data/aic/test_aic.json',
+                img_prefix='tests/data/aic/',
+                data_cfg=data_cfg_copy,
+                pipeline=[],
+                test_mode=True)
+
+        with pytest.warns(DeprecationWarning):
+            _ = dataset_class(
+                ann_file='tests/data/aic/test_aic.json',
+                img_prefix='tests/data/aic/',
+                data_cfg=data_cfg_copy,
+                pipeline=[],
+                test_mode=False)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/aic/test_aic.json',
+            img_prefix='tests/data/aic/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is 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]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+        assert_almost_equal(infos['AP'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'PCK')
+
+
+def test_top_down_JHMDB_dataset_compatibility():
+    dataset = 'TopDownJhmdbDataset'
+    # test JHMDB datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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=[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='')
+
+    with pytest.raises(AssertionError):
+        # Test det bbox
+        data_cfg_copy = copy.deepcopy(data_cfg)
+        data_cfg_copy['use_gt_bbox'] = False
+        with pytest.warns(DeprecationWarning):
+            _ = dataset_class(
+                ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
+                img_prefix='tests/data/jhmdb/',
+                data_cfg=data_cfg_copy,
+                pipeline=[],
+                test_mode=True)
+
+        with pytest.warns(DeprecationWarning):
+            _ = dataset_class(
+                ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
+                img_prefix='tests/data/jhmdb/',
+                data_cfg=data_cfg_copy,
+                pipeline=[],
+                test_mode=False)
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/jhmdb/test_jhmdb_sub1.json',
+            img_prefix='tests/data/jhmdb/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'jhmdb'
+
+    image_id = 2290001
+    assert image_id in custom_dataset.img_ids
+    assert len(custom_dataset.img_ids) == 3
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['PCK'])
+        assert_almost_equal(infos['Mean PCK'], 1.0)
+
+        infos = custom_dataset.evaluate(outputs, tmpdir, ['tPCK'])
+        assert_almost_equal(infos['Mean tPCK'], 1.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'mAP')
+
+
+def test_top_down_h36m_dataset_compatibility():
+    dataset = 'TopDownH36MDataset'
+    # test AIC datasets
+    dataset_class = DATASETS.get(dataset)
+    dataset_class.load_annotations = MagicMock()
+    dataset_class.coco = MagicMock()
+
+    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'])
+
+    # Test gt bbox
+    with pytest.warns(DeprecationWarning):
+        custom_dataset = dataset_class(
+            ann_file='tests/data/h36m/h36m_coco.json',
+            img_prefix='tests/data/h36m/',
+            data_cfg=data_cfg,
+            pipeline=[],
+            test_mode=True)
+
+    assert custom_dataset.test_mode is True
+    assert custom_dataset.dataset_name == 'h36m'
+
+    image_id = 1
+    assert image_id in custom_dataset.img_ids
+    _ = custom_dataset[0]
+
+    outputs = convert_db_to_output(custom_dataset.db)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        infos = custom_dataset.evaluate(outputs, tmpdir, 'EPE')
+        assert_almost_equal(infos['EPE'], 0.0)
+
+        with pytest.raises(KeyError):
+            _ = custom_dataset.evaluate(outputs, tmpdir, 'AUC')

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_eval_hook_compatibility.py

@@ -0,0 +1,46 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import unittest.mock as mock
+
+import pytest
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+from mmpose.core import DistEvalHook, EvalHook
+
+
+class ExampleDataset(Dataset):
+
+    def __init__(self):
+        self.index = 0
+        self.eval_result = [0.1, 0.4, 0.3, 0.7, 0.2, 0.05, 0.4, 0.6]
+
+    def __getitem__(self, idx):
+        results = dict(imgs=torch.tensor([1]))
+        return results
+
+    def __len__(self):
+        return 1
+
+    @mock.create_autospec
+    def evaluate(self, results, res_folder=None, logger=None):
+        pass
+
+
+def test_old_fashion_eval_hook_parameters():
+
+    data_loader = DataLoader(
+        ExampleDataset(),
+        batch_size=1,
+        sampler=None,
+        num_workers=0,
+        shuffle=False)
+
+    # test argument "key_indicator"
+    with pytest.warns(DeprecationWarning):
+        _ = EvalHook(data_loader, key_indicator='AP')
+    with pytest.warns(DeprecationWarning):
+        _ = DistEvalHook(data_loader, key_indicator='AP')
+
+    # test argument "gpu_collect"
+    with pytest.warns(DeprecationWarning):
+        _ = EvalHook(data_loader, save_best='AP', gpu_collect=False)

third_party/hamer/third-party/ViTPose/tests/test_backward_compatibility/test_registry_compatibility.py

@@ -0,0 +1,10 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# flake8: noqa
+import pytest
+
+
+def test_old_fashion_registry_importing():
+    with pytest.warns(DeprecationWarning):
+        from mmpose.models.registry import BACKBONES, HEADS, LOSSES, NECKS, POSENETS  # isort: skip
+    with pytest.warns(DeprecationWarning):
+        from mmpose.datasets.registry import DATASETS, PIPELINES  # noqa: F401