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