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284 lines
8.9 KiB
284 lines
8.9 KiB
# Copyright (c) OpenMMLab. All rights reserved.
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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_face_300W_dataset():
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dataset = 'Face300WDataset'
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/300w.py').dataset_info
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# test Face 300W datasets
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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=68,
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dataset_joints=68,
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dataset_channel=[
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list(range(68)),
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],
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inference_channel=list(range(68)))
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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/300w/test_300w.json',
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img_prefix='tests/data/300w/',
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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/300w/test_300w.json',
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img_prefix='tests/data/300w/',
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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 == '300w'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 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=['NME'])
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assert_almost_equal(infos['NME'], 0.0)
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with pytest.raises(KeyError):
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_ = custom_dataset.evaluate(results, metric='mAP')
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def test_face_coco_wholebody_dataset():
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dataset = 'FaceCocoWholeBodyDataset'
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/coco_wholebody_face.py').dataset_info
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# test Face wholebody datasets
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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=68,
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dataset_joints=68,
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dataset_channel=[
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list(range(68)),
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],
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inference_channel=list(range(68)))
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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=['NME'])
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assert_almost_equal(infos['NME'], 0.0)
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with pytest.raises(KeyError):
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_ = custom_dataset.evaluate(results, metric='mAP')
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def test_face_AFLW_dataset():
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dataset = 'FaceAFLWDataset'
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/aflw.py').dataset_info
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# test Face AFLW datasets
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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=19,
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dataset_joints=19,
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dataset_channel=[
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list(range(19)),
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],
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inference_channel=list(range(19)))
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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/aflw/test_aflw.json',
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img_prefix='tests/data/aflw/',
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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/aflw/test_aflw.json',
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img_prefix='tests/data/aflw/',
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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 == 'aflw'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 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=['NME'])
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assert_almost_equal(infos['NME'], 0.0)
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with pytest.raises(KeyError):
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_ = custom_dataset.evaluate(results, metric='mAP')
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def test_face_WFLW_dataset():
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dataset = 'FaceWFLWDataset'
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/wflw.py').dataset_info
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# test Face WFLW datasets
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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=98,
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dataset_joints=98,
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dataset_channel=[
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list(range(98)),
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],
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inference_channel=list(range(98)))
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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/wflw/test_wflw.json',
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img_prefix='tests/data/wflw/',
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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/wflw/test_wflw.json',
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img_prefix='tests/data/wflw/',
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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 == 'wflw'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 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=['NME'])
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assert_almost_equal(infos['NME'], 0.0)
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with pytest.raises(KeyError):
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_ = custom_dataset.evaluate(results, metric='mAP')
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def test_face_COFW_dataset():
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dataset = 'FaceCOFWDataset'
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dataset_info = Config.fromfile(
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'configs/_base_/datasets/cofw.py').dataset_info
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# test Face COFW datasets
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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=29,
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dataset_joints=29,
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dataset_channel=[
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list(range(29)),
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],
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inference_channel=list(range(29)))
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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/cofw/test_cofw.json',
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img_prefix='tests/data/cofw/',
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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/cofw/test_cofw.json',
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img_prefix='tests/data/cofw/',
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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 == 'cofw'
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assert custom_dataset.test_mode is False
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assert custom_dataset.num_images == 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=['NME'])
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assert_almost_equal(infos['NME'], 0.0)
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with pytest.raises(KeyError):
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_ = custom_dataset.evaluate(results, metric='mAP')
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