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import copy |
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import pytest |
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import torch |
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from mmcv import Config |
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from numpy.testing import assert_almost_equal |
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from mmpose.datasets import DATASETS |
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def test_NVGesture_dataset(): |
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dataset = 'NVGestureDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/nvgesture.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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data_cfg = dict( |
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video_size=[320, 240], |
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modality=['rgb', 'depth'], |
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bbox_file='tests/data/nvgesture/bboxes.json', |
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) |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/nvgesture/test_nvgesture.lst', |
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vid_prefix='tests/data/nvgesture/', |
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data_cfg=data_cfg_copy, |
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pipeline=[], |
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dataset_info=dataset_info, |
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test_mode=True) |
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custom_dataset = dataset_class( |
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ann_file='tests/data/nvgesture/test_nvgesture.lst', |
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vid_prefix='tests/data/nvgesture/', |
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data_cfg=data_cfg_copy, |
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pipeline=[], |
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dataset_info=dataset_info, |
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test_mode=False) |
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assert custom_dataset.dataset_name == 'nvgesture' |
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assert custom_dataset.test_mode is False |
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assert len(custom_dataset) == 1 |
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sample = custom_dataset[0] |
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sample['logits'] = { |
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modal: torch.zeros(1, 25, 1) |
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for modal in sample['modality'] |
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} |
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sample['logits']['rgb'][:, sample['label']] = 1 |
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sample['logits']['depth'][:, (sample['label'] + 1) % 25] = 1 |
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sample['label'] = torch.tensor([sample['label']]).long() |
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infos = custom_dataset.evaluate([sample], metric=['AP']) |
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assert_almost_equal(infos['AP_rgb'], 1.0) |
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assert_almost_equal(infos['AP_depth'], 0.0) |
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assert_almost_equal(infos['AP_mean'], 0.5) |
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with pytest.raises(KeyError): |
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infos = custom_dataset.evaluate([sample], metric='mAP') |
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