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import copy |
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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_OneHand10K_dataset(): |
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dataset = 'OneHand10KDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/onehand10k.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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channel_cfg = dict( |
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num_output_channels=21, |
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dataset_joints=21, |
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dataset_channel=[ |
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[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, |
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18, 19, 20 |
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], |
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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, 17, 18, |
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19, 20 |
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]) |
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data_cfg = dict( |
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image_size=[256, 256], |
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heatmap_size=[64, 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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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/onehand10k/test_onehand10k.json', |
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img_prefix='tests/data/onehand10k/', |
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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/onehand10k/test_onehand10k.json', |
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img_prefix='tests/data/onehand10k/', |
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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 == 'onehand10k' |
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assert custom_dataset.test_mode is False |
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assert custom_dataset.num_images == 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=['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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def test_hand_coco_wholebody_dataset(): |
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dataset = 'HandCocoWholeBodyDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/coco_wholebody_hand.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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channel_cfg = dict( |
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num_output_channels=21, |
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dataset_joints=21, |
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dataset_channel=[ |
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[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, |
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18, 19, 20 |
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], |
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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, 17, 18, |
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19, 20 |
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]) |
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data_cfg = dict( |
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image_size=[256, 256], |
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heatmap_size=[64, 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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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/coco/test_coco_wholebody.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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custom_dataset = dataset_class( |
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ann_file='tests/data/coco/test_coco_wholebody.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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assert custom_dataset.test_mode is False |
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assert custom_dataset.num_images == 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=['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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def test_FreiHand2D_dataset(): |
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dataset = 'FreiHandDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/freihand2d.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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channel_cfg = dict( |
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num_output_channels=21, |
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dataset_joints=21, |
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dataset_channel=[ |
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[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, |
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18, 19, 20 |
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], |
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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, 17, 18, |
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19, 20 |
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]) |
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data_cfg = dict( |
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image_size=[224, 224], |
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heatmap_size=[56, 56], |
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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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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/freihand/test_freihand.json', |
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img_prefix='tests/data/freihand/', |
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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/freihand/test_freihand.json', |
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img_prefix='tests/data/freihand/', |
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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 == 'freihand' |
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assert custom_dataset.test_mode is False |
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assert custom_dataset.num_images == 8 |
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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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def test_RHD2D_dataset(): |
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dataset = 'Rhd2DDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/rhd2d.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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channel_cfg = dict( |
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num_output_channels=21, |
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dataset_joints=21, |
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dataset_channel=[ |
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[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, |
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18, 19, 20 |
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], |
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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, 17, 18, |
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19, 20 |
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]) |
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data_cfg = dict( |
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image_size=[256, 256], |
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heatmap_size=[64, 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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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/rhd/test_rhd.json', |
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img_prefix='tests/data/rhd/', |
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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/rhd/test_rhd.json', |
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img_prefix='tests/data/rhd/', |
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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 == 'rhd2d' |
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assert custom_dataset.test_mode is False |
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assert custom_dataset.num_images == 3 |
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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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def test_Panoptic2D_dataset(): |
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dataset = 'PanopticDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/panoptic_hand2d.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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channel_cfg = dict( |
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num_output_channels=21, |
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dataset_joints=21, |
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dataset_channel=[ |
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[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, |
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18, 19, 20 |
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], |
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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, 17, 18, |
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19, 20 |
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]) |
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data_cfg = dict( |
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image_size=[256, 256], |
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heatmap_size=[64, 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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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/panoptic/test_panoptic.json', |
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img_prefix='tests/data/panoptic/', |
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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/panoptic/test_panoptic.json', |
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img_prefix='tests/data/panoptic/', |
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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 == 'panoptic_hand2d' |
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assert custom_dataset.test_mode is False |
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assert custom_dataset.num_images == 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=['PCKh', 'EPE', 'AUC']) |
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assert_almost_equal(infos['PCKh'], 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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def test_InterHand2D_dataset(): |
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dataset = 'InterHand2DDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/interhand2d.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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channel_cfg = dict( |
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num_output_channels=21, |
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dataset_joints=21, |
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dataset_channel=[ |
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[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, |
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18, 19, 20 |
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], |
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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, 17, 18, |
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19, 20 |
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]) |
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data_cfg = dict( |
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image_size=[256, 256], |
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heatmap_size=[64, 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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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json', |
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camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json', |
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joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json', |
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img_prefix='tests/data/interhand2.6m/', |
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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/interhand2.6m/test_interhand2.6m_data.json', |
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camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json', |
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joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json', |
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img_prefix='tests/data/interhand2.6m/', |
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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 == 'interhand2d' |
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assert custom_dataset.test_mode is False |
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assert custom_dataset.num_images == 4 |
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assert len(custom_dataset.db) == 6 |
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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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print(infos, flush=True) |
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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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def test_InterHand3D_dataset(): |
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dataset = 'InterHand3DDataset' |
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dataset_info = Config.fromfile( |
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'configs/_base_/datasets/interhand3d.py').dataset_info |
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dataset_class = DATASETS.get(dataset) |
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channel_cfg = dict( |
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num_output_channels=42, |
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dataset_joints=42, |
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dataset_channel=[ |
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[ |
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0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, |
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18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, |
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34, 35, 36, 37, 38, 39, 40, 41 |
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], |
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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, 17, 18, |
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19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, |
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36, 37, 38, 39, 40, 41 |
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]) |
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data_cfg = dict( |
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image_size=[256, 256], |
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heatmap_size=[64, 64, 64], |
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heatmap3d_depth_bound=400.0, |
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heatmap_size_root=64, |
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root_depth_bound=400.0, |
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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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data_cfg_copy = copy.deepcopy(data_cfg) |
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_ = dataset_class( |
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ann_file='tests/data/interhand2.6m/test_interhand2.6m_data.json', |
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camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json', |
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joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json', |
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img_prefix='tests/data/interhand2.6m/', |
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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/interhand2.6m/test_interhand2.6m_data.json', |
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camera_file='tests/data/interhand2.6m/test_interhand2.6m_camera.json', |
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joint_file='tests/data/interhand2.6m/test_interhand2.6m_joint_3d.json', |
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img_prefix='tests/data/interhand2.6m/', |
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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 == 'interhand3d' |
|
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assert custom_dataset.test_mode is False |
|
|
assert custom_dataset.num_images == 4 |
|
|
assert len(custom_dataset.db) == 4 |
|
|
|
|
|
_ = custom_dataset[0] |
|
|
|
|
|
results = convert_db_to_output( |
|
|
custom_dataset.db, keys=['rel_root_depth', 'hand_type'], is_3d=True) |
|
|
infos = custom_dataset.evaluate( |
|
|
results, metric=['MRRPE', 'MPJPE', 'Handedness_acc']) |
|
|
assert_almost_equal(infos['MRRPE'], 0.0, decimal=5) |
|
|
assert_almost_equal(infos['MPJPE_all'], 0.0, decimal=5) |
|
|
assert_almost_equal(infos['MPJPE_single'], 0.0, decimal=5) |
|
|
assert_almost_equal(infos['MPJPE_interacting'], 0.0, decimal=5) |
|
|
assert_almost_equal(infos['Handedness_acc'], 1.0) |
|
|
|
|
|
with pytest.raises(KeyError): |
|
|
infos = custom_dataset.evaluate(results, metric='mAP') |
|
|
|
