|
|
|
|
|
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 |
|
|
|
|
|
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']) |
|
|
|
|
|
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 |
|
|
|
|
|
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']) |
|
|
|
|
|
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 |
|
|
|
|
|
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']) |
|
|
|
|
|
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 |
|
|
|
|
|
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']) |
|
|
|
|
|
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 |
|
|
|
|
|
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']) |
|
|
|
|
|
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') |
|
|
|
