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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')
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