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