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456 lines
15 KiB
456 lines
15 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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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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# Test
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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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# Test
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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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# Test
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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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# Test
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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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# Test
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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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# Test
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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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# Test
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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
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assert custom_dataset.num_images == 4
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assert len(custom_dataset.db) == 4
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_ = custom_dataset[0]
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results = convert_db_to_output(
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custom_dataset.db, keys=['rel_root_depth', 'hand_type'], is_3d=True)
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infos = custom_dataset.evaluate(
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results, metric=['MRRPE', 'MPJPE', 'Handedness_acc'])
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assert_almost_equal(infos['MRRPE'], 0.0, decimal=5)
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assert_almost_equal(infos['MPJPE_all'], 0.0, decimal=5)
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assert_almost_equal(infos['MPJPE_single'], 0.0, decimal=5)
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assert_almost_equal(infos['MPJPE_interacting'], 0.0, decimal=5)
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assert_almost_equal(infos['Handedness_acc'], 1.0)
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with pytest.raises(KeyError):
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infos = custom_dataset.evaluate(results, metric='mAP')
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