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# Copyright (c) OpenMMLab. All rights reserved.
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_deepfashion_dataset():
dataset = 'DeepFashionDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/deepfashion_full.py').dataset_info
# test JHMDB datasets
dataset_class = DATASETS.get(dataset)
dataset_class.load_annotations = MagicMock()
dataset_class.coco = MagicMock()
channel_cfg = dict(
num_output_channels=8,
dataset_joints=8,
dataset_channel=[
[0, 1, 2, 3, 4, 5, 6, 7],
],
inference_channel=[0, 1, 2, 3, 4, 5, 6, 7])
data_cfg = dict(
image_size=[192, 256],
heatmap_size=[48, 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'],
soft_nms=False,
nms_thr=1.0,
oks_thr=0.9,
vis_thr=0.2,
use_gt_bbox=True,
det_bbox_thr=0.0,
image_thr=0.0,
bbox_file='')
# Test gt bbox
custom_dataset = dataset_class(
ann_file='tests/data/fld/test_fld.json',
img_prefix='tests/data/fld/',
subset='full',
data_cfg=data_cfg,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
assert custom_dataset.test_mode is True
assert custom_dataset.dataset_name == 'deepfashion_full'
image_id = 128
assert image_id in custom_dataset.img_ids
assert len(custom_dataset.img_ids) == 2
_ = custom_dataset[0]
results = convert_db_to_output(custom_dataset.db)
infos = custom_dataset.evaluate(results, metric=['PCK', 'EPE', 'AUC'])
assert_almost_equal(infos['PCK'], 1.0)
assert_almost_equal(infos['AUC'], 0.95)
assert_almost_equal(infos['EPE'], 0.0)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate(results, metric='mAP')
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