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from unittest.mock import MagicMock |
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import pytest |
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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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from tests.utils.data_utils import convert_db_to_output |
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def test_deepfashion_dataset(): |
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dataset = 'DeepFashionDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/deepfashion_full.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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dataset_class.load_annotations = MagicMock() |
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dataset_class.coco = MagicMock() |
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channel_cfg = dict( |
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num_output_channels=8, |
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dataset_joints=8, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7], |
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], |
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inference_channel=[0, 1, 2, 3, 4, 5, 6, 7]) |
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data_cfg = dict( |
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image_size=[192, 256], |
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heatmap_size=[48, 64], |
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num_output_channels=channel_cfg['num_output_channels'], |
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num_joints=channel_cfg['dataset_joints'], |
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dataset_channel=channel_cfg['dataset_channel'], |
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inference_channel=channel_cfg['inference_channel'], |
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soft_nms=False, |
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nms_thr=1.0, |
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oks_thr=0.9, |
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vis_thr=0.2, |
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use_gt_bbox=True, |
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det_bbox_thr=0.0, |
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image_thr=0.0, |
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bbox_file='') |
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custom_dataset = dataset_class( |
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ann_file='tests/data/fld/test_fld.json', |
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img_prefix='tests/data/fld/', |
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subset='full', |
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data_cfg=data_cfg, |
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pipeline=[], |
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dataset_info=dataset_info, |
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test_mode=True) |
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assert custom_dataset.test_mode is True |
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assert custom_dataset.dataset_name == 'deepfashion_full' |
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image_id = 128 |
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assert image_id in custom_dataset.img_ids |
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assert len(custom_dataset.img_ids) == 2 |
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_ = custom_dataset[0] |
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results = convert_db_to_output(custom_dataset.db) |
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infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC']) |
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assert_almost_equal(infos['PCK'], 1.0) |
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assert_almost_equal(infos['AUC'], 0.95) |
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assert_almost_equal(infos['EPE'], 0.0) |
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with pytest.raises(KeyError): |
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infos = custom_dataset.evaluate(results, metric='mAP') |
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