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import copy |
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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_top_down_COCO_dataset(): |
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dataset = 'TopDownCocoDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/coco.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=17, |
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dataset_joints=17, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
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], |
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inference_channel=[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
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]) |
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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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bbox_file='tests/data/coco/test_coco_det_AP_H_56.json', |
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) |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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data_cfg_copy['use_gt_bbox'] = False |
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_ = dataset_class( |
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ann_file='tests/data/coco/test_coco.json', |
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img_prefix='tests/data/coco/', |
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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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_ = dataset_class( |
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ann_file='tests/data/coco/test_coco.json', |
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img_prefix='tests/data/coco/', |
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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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custom_dataset = dataset_class( |
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ann_file='tests/data/coco/test_coco.json', |
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img_prefix='tests/data/coco/', |
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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 == 'coco' |
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image_id = 785 |
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assert image_id in custom_dataset.img_ids |
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assert len(custom_dataset.img_ids) == 4 |
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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='mAP') |
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assert_almost_equal(infos['AP'], 1.0) |
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infos = custom_dataset.evaluate(results, metric='mAP', rle_score=True) |
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assert_almost_equal(infos['AP'], 1.0) |
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with pytest.raises(KeyError): |
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_ = custom_dataset.evaluate(results, metric='PCK') |
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del custom_dataset.coco.dataset['annotations'] |
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with pytest.warns(UserWarning): |
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_ = custom_dataset.evaluate(results, metric='mAP') |
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def test_top_down_MHP_dataset(): |
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dataset = 'TopDownMhpDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/mhp.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=16, |
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dataset_joints=16, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], |
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], |
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inference_channel=[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 |
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]) |
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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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bbox_thr=1.0, |
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use_gt_bbox=True, |
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det_bbox_thr=0.0, |
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bbox_file='', |
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) |
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with pytest.raises(AssertionError): |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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data_cfg_copy['use_gt_bbox'] = False |
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_ = dataset_class( |
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ann_file='tests/data/mhp/test_mhp.json', |
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img_prefix='tests/data/mhp/', |
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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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_ = dataset_class( |
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ann_file='tests/data/mhp/test_mhp.json', |
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img_prefix='tests/data/mhp/', |
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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=False) |
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custom_dataset = dataset_class( |
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ann_file='tests/data/mhp/test_mhp.json', |
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img_prefix='tests/data/mhp/', |
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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 == 'mhp' |
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image_id = 2889 |
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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='mAP') |
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assert_almost_equal(infos['AP'], 1.0) |
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with pytest.raises(KeyError): |
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_ = custom_dataset.evaluate(results, metric='PCK') |
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def test_top_down_PoseTrack18_dataset(): |
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dataset = 'TopDownPoseTrack18Dataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/posetrack18.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=17, |
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dataset_joints=17, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
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], |
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inference_channel=[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
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]) |
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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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bbox_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_human_detections.json', |
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) |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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data_cfg_copy['use_gt_bbox'] = False |
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_ = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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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_ = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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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custom_dataset = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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 == 'posetrack18' |
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image_id = 10128340000 |
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assert image_id in custom_dataset.img_ids |
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assert len(custom_dataset.img_ids) == 3 |
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assert len(custom_dataset) == 14 |
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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='mAP') |
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assert_almost_equal(infos['Total AP'], 100) |
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with pytest.raises(KeyError): |
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_ = custom_dataset.evaluate(results, metric='PCK') |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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data_cfg_copy['use_gt_bbox'] = False |
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custom_dataset = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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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assert len(custom_dataset) == 278 |
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results = convert_db_to_output(custom_dataset.db) |
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infos = custom_dataset.evaluate(results, metric='mAP') |
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assert_almost_equal(infos['Total AP'], 0.) |
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with pytest.raises(KeyError): |
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_ = custom_dataset.evaluate(results, metric='PCK') |
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def test_top_down_PoseTrack18Video_dataset(): |
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dataset = 'TopDownPoseTrack18VideoDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/posetrack18.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=17, |
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dataset_joints=17, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], |
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], |
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inference_channel=[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 |
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]) |
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data_cfg = dict( |
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image_size=[288, 384], |
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heatmap_size=[72, 96], |
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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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use_nms=True, |
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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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bbox_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_human_detections.json', |
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frame_index_rand=True, |
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frame_index_range=[-2, 2], |
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num_adj_frames=1, |
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frame_indices_test=[-2, 2, -1, 1, 0], |
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frame_weight_train=(0.0, 1.0), |
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frame_weight_test=(0.3, 0.1, 0.25, 0.25, 0.1), |
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) |
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with pytest.raises(ValueError): |
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_ = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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data_cfg=data_cfg, |
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pipeline=[], |
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dataset_info=None, |
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test_mode=False) |
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_ = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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=False) |
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custom_dataset = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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 == 'posetrack18' |
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assert custom_dataset.ph_fill_len == 6 |
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image_id = 10128340000 |
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assert image_id in custom_dataset.img_ids |
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assert len(custom_dataset.img_ids) == 3 |
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assert len(custom_dataset) == 14 |
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_ = custom_dataset[0] |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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data_cfg_copy['use_gt_bbox'] = False |
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custom_dataset = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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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assert custom_dataset.frame_indices_test == [-2, -1, 0, 1, 2] |
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assert len(custom_dataset) == 278 |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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data_cfg_copy['frame_index_rand'] = False |
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data_cfg_copy['frame_indices_train'] = [0, -1] |
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custom_dataset = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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.frame_indices_train == [-1, 0] |
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results = convert_db_to_output(custom_dataset.db) |
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infos = custom_dataset.evaluate(results, metric='mAP') |
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assert_almost_equal(infos['Total AP'], 100) |
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with pytest.raises(KeyError): |
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_ = custom_dataset.evaluate(results, metric='PCK') |
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data_cfg_copy = copy.deepcopy(data_cfg) |
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data_cfg_copy['use_gt_bbox'] = False |
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custom_dataset = dataset_class( |
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ann_file='tests/data/posetrack18/annotations/' |
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'test_posetrack18_val.json', |
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img_prefix='tests/data/posetrack18/', |
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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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results = convert_db_to_output(custom_dataset.db) |
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infos = custom_dataset.evaluate(results, metric='mAP') |
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assert_almost_equal(infos['Total AP'], 0) |
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with pytest.raises(KeyError): |
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_ = custom_dataset.evaluate(results, metric='PCK') |
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def test_top_down_CrowdPose_dataset(): |
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dataset = 'TopDownCrowdPoseDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/crowdpose.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=14, |
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dataset_joints=14, |
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dataset_channel=[ |
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[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], |
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], |
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inference_channel=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]) |
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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, |
|
|
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', |
|
|
) |
|
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg) |
|
|
data_cfg_copy['use_gt_bbox'] = False |
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/crowdpose/test_crowdpose.json', |
|
|
img_prefix='tests/data/crowdpose/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=True) |
|
|
|
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/crowdpose/test_crowdpose.json', |
|
|
img_prefix='tests/data/crowdpose/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=False) |
|
|
|
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/crowdpose/test_crowdpose.json', |
|
|
img_prefix='tests/data/crowdpose/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
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] |
|
|
|
|
|
results = convert_db_to_output(custom_dataset.db) |
|
|
infos = custom_dataset.evaluate(results, metric='mAP') |
|
|
assert_almost_equal(infos['AP'], 1.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
_ = custom_dataset.evaluate(results, metric='PCK') |
|
|
|
|
|
|
|
|
def test_top_down_COCO_wholebody_dataset(): |
|
|
dataset = 'TopDownCocoWholeBodyDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/coco_wholebody.py').dataset_info |
|
|
|
|
|
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', |
|
|
) |
|
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg) |
|
|
data_cfg_copy['use_gt_bbox'] = False |
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/coco/test_coco_wholebody.json', |
|
|
img_prefix='tests/data/coco/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=True) |
|
|
|
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/coco/test_coco_wholebody.json', |
|
|
img_prefix='tests/data/coco/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=False) |
|
|
|
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/coco/test_coco_wholebody.json', |
|
|
img_prefix='tests/data/coco/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
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] |
|
|
|
|
|
results = convert_db_to_output(custom_dataset.db) |
|
|
infos = custom_dataset.evaluate(results, metric='mAP') |
|
|
assert_almost_equal(infos['AP'], 1.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
_ = custom_dataset.evaluate(results, metric='PCK') |
|
|
|
|
|
|
|
|
def test_top_down_halpe_dataset(): |
|
|
dataset = 'TopDownHalpeDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/halpe.py').dataset_info |
|
|
|
|
|
dataset_class = DATASETS.get(dataset) |
|
|
dataset_class.load_annotations = MagicMock() |
|
|
dataset_class.coco = MagicMock() |
|
|
|
|
|
channel_cfg = dict( |
|
|
num_output_channels=136, |
|
|
dataset_joints=136, |
|
|
dataset_channel=[ |
|
|
list(range(136)), |
|
|
], |
|
|
inference_channel=list(range(136))) |
|
|
|
|
|
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', |
|
|
) |
|
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg) |
|
|
data_cfg_copy['use_gt_bbox'] = False |
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/halpe/test_halpe.json', |
|
|
img_prefix='tests/data/coco/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=True) |
|
|
|
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/halpe/test_halpe.json', |
|
|
img_prefix='tests/data/coco/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=False) |
|
|
|
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/halpe/test_halpe.json', |
|
|
img_prefix='tests/data/coco/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=True) |
|
|
|
|
|
assert custom_dataset.test_mode is True |
|
|
assert custom_dataset.dataset_name == 'halpe' |
|
|
|
|
|
image_id = 785 |
|
|
assert image_id in custom_dataset.img_ids |
|
|
assert len(custom_dataset.img_ids) == 4 |
|
|
_ = custom_dataset[0] |
|
|
|
|
|
results = convert_db_to_output(custom_dataset.db) |
|
|
infos = custom_dataset.evaluate(results, metric='mAP') |
|
|
assert_almost_equal(infos['AP'], 1.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
_ = custom_dataset.evaluate(results, metric='PCK') |
|
|
|
|
|
|
|
|
def test_top_down_OCHuman_dataset(): |
|
|
dataset = 'TopDownOCHumanDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/ochuman.py').dataset_info |
|
|
|
|
|
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): |
|
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg) |
|
|
data_cfg_copy['use_gt_bbox'] = False |
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/ochuman/test_ochuman.json', |
|
|
img_prefix='tests/data/ochuman/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=True) |
|
|
|
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/ochuman/test_ochuman.json', |
|
|
img_prefix='tests/data/ochuman/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
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] |
|
|
|
|
|
results = convert_db_to_output(custom_dataset.db) |
|
|
infos = custom_dataset.evaluate(results, metric='mAP') |
|
|
assert_almost_equal(infos['AP'], 1.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
_ = custom_dataset.evaluate(results, metric='PCK') |
|
|
|
|
|
|
|
|
def test_top_down_MPII_dataset(): |
|
|
dataset = 'TopDownMpiiDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/mpii.py').dataset_info |
|
|
|
|
|
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'], |
|
|
) |
|
|
|
|
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg) |
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/mpii/test_mpii.json', |
|
|
img_prefix='tests/data/mpii/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
) |
|
|
|
|
|
assert len(custom_dataset) == 5 |
|
|
assert custom_dataset.dataset_name == 'mpii' |
|
|
_ = custom_dataset[0] |
|
|
|
|
|
|
|
|
def test_top_down_MPII_TRB_dataset(): |
|
|
dataset = 'TopDownMpiiTrbDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/mpii_trb.py').dataset_info |
|
|
|
|
|
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) |
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/mpii/test_mpii_trb.json', |
|
|
img_prefix='tests/data/mpii/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=False) |
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/mpii/test_mpii_trb.json', |
|
|
img_prefix='tests/data/mpii/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
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(): |
|
|
dataset = 'TopDownAicDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/aic.py').dataset_info |
|
|
|
|
|
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): |
|
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg) |
|
|
data_cfg_copy['use_gt_bbox'] = False |
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/aic/test_aic.json', |
|
|
img_prefix='tests/data/aic/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=True) |
|
|
|
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/aic/test_aic.json', |
|
|
img_prefix='tests/data/aic/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=False) |
|
|
|
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/aic/test_aic.json', |
|
|
img_prefix='tests/data/aic/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
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] |
|
|
|
|
|
results = convert_db_to_output(custom_dataset.db) |
|
|
infos = custom_dataset.evaluate(results, metric='mAP') |
|
|
assert_almost_equal(infos['AP'], 1.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
_ = custom_dataset.evaluate(results, metric='PCK') |
|
|
|
|
|
|
|
|
def test_top_down_JHMDB_dataset(): |
|
|
dataset = 'TopDownJhmdbDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/jhmdb.py').dataset_info |
|
|
|
|
|
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): |
|
|
|
|
|
data_cfg_copy = copy.deepcopy(data_cfg) |
|
|
data_cfg_copy['use_gt_bbox'] = False |
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/jhmdb/test_jhmdb_sub1.json', |
|
|
img_prefix='tests/data/jhmdb/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=True) |
|
|
|
|
|
_ = dataset_class( |
|
|
ann_file='tests/data/jhmdb/test_jhmdb_sub1.json', |
|
|
img_prefix='tests/data/jhmdb/', |
|
|
data_cfg=data_cfg_copy, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
test_mode=False) |
|
|
|
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/jhmdb/test_jhmdb_sub1.json', |
|
|
img_prefix='tests/data/jhmdb/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
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] |
|
|
|
|
|
results = convert_db_to_output(custom_dataset.db) |
|
|
infos = custom_dataset.evaluate(results, metric=['PCK']) |
|
|
assert_almost_equal(infos['Mean PCK'], 1.0) |
|
|
|
|
|
infos = custom_dataset.evaluate(results, metric=['tPCK']) |
|
|
assert_almost_equal(infos['Mean tPCK'], 1.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
_ = custom_dataset.evaluate(results, metric='mAP') |
|
|
|
|
|
|
|
|
def test_top_down_h36m_dataset(): |
|
|
dataset = 'TopDownH36MDataset' |
|
|
dataset_info = Config.fromfile( |
|
|
'configs/_base_/datasets/h36m.py').dataset_info |
|
|
|
|
|
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']) |
|
|
|
|
|
|
|
|
custom_dataset = dataset_class( |
|
|
ann_file='tests/data/h36m/h36m_coco.json', |
|
|
img_prefix='tests/data/h36m/', |
|
|
data_cfg=data_cfg, |
|
|
pipeline=[], |
|
|
dataset_info=dataset_info, |
|
|
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] |
|
|
|
|
|
results = convert_db_to_output(custom_dataset.db) |
|
|
infos = custom_dataset.evaluate(results, metric='EPE') |
|
|
assert_almost_equal(infos['EPE'], 0.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
_ = custom_dataset.evaluate(results, metric='AUC') |
|
|
|
