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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import pytest
import torch
from mmcv import Config
from numpy.testing import assert_almost_equal
from mmpose.datasets import DATASETS
def test_NVGesture_dataset():
dataset = 'NVGestureDataset'
dataset_info = Config.fromfile(
'configs/_base_/datasets/nvgesture.py').dataset_info
dataset_class = DATASETS.get(dataset)
data_cfg = dict(
video_size=[320, 240],
modality=['rgb', 'depth'],
bbox_file='tests/data/nvgesture/bboxes.json',
)
# Test
data_cfg_copy = copy.deepcopy(data_cfg)
_ = dataset_class(
ann_file='tests/data/nvgesture/test_nvgesture.lst',
vid_prefix='tests/data/nvgesture/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=True)
custom_dataset = dataset_class(
ann_file='tests/data/nvgesture/test_nvgesture.lst',
vid_prefix='tests/data/nvgesture/',
data_cfg=data_cfg_copy,
pipeline=[],
dataset_info=dataset_info,
test_mode=False)
assert custom_dataset.dataset_name == 'nvgesture'
assert custom_dataset.test_mode is False
assert len(custom_dataset) == 1
sample = custom_dataset[0]
# make pseudo prediction for evaluation
sample['logits'] = {
modal: torch.zeros(1, 25, 1)
for modal in sample['modality']
}
sample['logits']['rgb'][:, sample['label']] = 1
sample['logits']['depth'][:, (sample['label'] + 1) % 25] = 1
sample['label'] = torch.tensor([sample['label']]).long()
infos = custom_dataset.evaluate([sample], metric=['AP'])
assert_almost_equal(infos['AP_rgb'], 1.0)
assert_almost_equal(infos['AP_depth'], 0.0)
assert_almost_equal(infos['AP_mean'], 0.5)
with pytest.raises(KeyError):
infos = custom_dataset.evaluate([sample], metric='mAP')
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