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Samuel Callahan
SCallahan/
lung-ultrasound-scan-targ
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[c1b1c5]: / ViTPose / tests / test_datasets / test_face_dataset.py

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# Copyright (c) OpenMMLab. All rights reserved.

import copy

from unittest.mock import MagicMock



import pytest

from mmcv import Config

from numpy.testing import assert_almost_equal



from mmpose.datasets import DATASETS

from tests.utils.data_utils import convert_db_to_output





def test_face_300W_dataset():

    dataset = 'Face300WDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/300w.py').dataset_info

    # test Face 300W datasets

    dataset_class = DATASETS.get(dataset)

    dataset_class.load_annotations = MagicMock()

    dataset_class.coco = MagicMock()



    channel_cfg = dict(

        num_output_channels=68,

        dataset_joints=68,

        dataset_channel=[

            list(range(68)),

        ],

        inference_channel=list(range(68)))



    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'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/300w/test_300w.json',

        img_prefix='tests/data/300w/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/300w/test_300w.json',

        img_prefix='tests/data/300w/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == '300w'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 2

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['NME'])

    assert_almost_equal(infos['NME'], 0.0)



    with pytest.raises(KeyError):

        _ = custom_dataset.evaluate(results, metric='mAP')





def test_face_coco_wholebody_dataset():

    dataset = 'FaceCocoWholeBodyDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/coco_wholebody_face.py').dataset_info

    # test Face wholebody datasets

    dataset_class = DATASETS.get(dataset)

    dataset_class.load_annotations = MagicMock()

    dataset_class.coco = MagicMock()



    channel_cfg = dict(

        num_output_channels=68,

        dataset_joints=68,

        dataset_channel=[

            list(range(68)),

        ],

        inference_channel=list(range(68)))



    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'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = 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)



    custom_dataset = 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)



    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 4

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['NME'])

    assert_almost_equal(infos['NME'], 0.0)



    with pytest.raises(KeyError):

        _ = custom_dataset.evaluate(results, metric='mAP')





def test_face_AFLW_dataset():

    dataset = 'FaceAFLWDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/aflw.py').dataset_info

    # test Face AFLW datasets

    dataset_class = DATASETS.get(dataset)

    dataset_class.load_annotations = MagicMock()

    dataset_class.coco = MagicMock()



    channel_cfg = dict(

        num_output_channels=19,

        dataset_joints=19,

        dataset_channel=[

            list(range(19)),

        ],

        inference_channel=list(range(19)))



    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'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/aflw/test_aflw.json',

        img_prefix='tests/data/aflw/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/aflw/test_aflw.json',

        img_prefix='tests/data/aflw/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'aflw'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 2

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['NME'])

    assert_almost_equal(infos['NME'], 0.0)



    with pytest.raises(KeyError):

        _ = custom_dataset.evaluate(results, metric='mAP')





def test_face_WFLW_dataset():

    dataset = 'FaceWFLWDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/wflw.py').dataset_info

    # test Face WFLW datasets

    dataset_class = DATASETS.get(dataset)

    dataset_class.load_annotations = MagicMock()

    dataset_class.coco = MagicMock()



    channel_cfg = dict(

        num_output_channels=98,

        dataset_joints=98,

        dataset_channel=[

            list(range(98)),

        ],

        inference_channel=list(range(98)))



    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'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/wflw/test_wflw.json',

        img_prefix='tests/data/wflw/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/wflw/test_wflw.json',

        img_prefix='tests/data/wflw/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'wflw'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 2

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['NME'])

    assert_almost_equal(infos['NME'], 0.0)



    with pytest.raises(KeyError):

        _ = custom_dataset.evaluate(results, metric='mAP')





def test_face_COFW_dataset():

    dataset = 'FaceCOFWDataset'

    dataset_info = Config.fromfile(

        'configs/_base_/datasets/cofw.py').dataset_info

    # test Face COFW datasets

    dataset_class = DATASETS.get(dataset)

    dataset_class.load_annotations = MagicMock()

    dataset_class.coco = MagicMock()



    channel_cfg = dict(

        num_output_channels=29,

        dataset_joints=29,

        dataset_channel=[

            list(range(29)),

        ],

        inference_channel=list(range(29)))



    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'])

    # Test

    data_cfg_copy = copy.deepcopy(data_cfg)

    _ = dataset_class(

        ann_file='tests/data/cofw/test_cofw.json',

        img_prefix='tests/data/cofw/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=True)



    custom_dataset = dataset_class(

        ann_file='tests/data/cofw/test_cofw.json',

        img_prefix='tests/data/cofw/',

        data_cfg=data_cfg_copy,

        pipeline=[],

        dataset_info=dataset_info,

        test_mode=False)



    assert custom_dataset.dataset_name == 'cofw'

    assert custom_dataset.test_mode is False

    assert custom_dataset.num_images == 2

    _ = custom_dataset[0]



    results = convert_db_to_output(custom_dataset.db)

    infos = custom_dataset.evaluate(results, metric=['NME'])

    assert_almost_equal(infos['NME'], 0.0)



    with pytest.raises(KeyError):

        _ = custom_dataset.evaluate(results, metric='mAP')