def test_audio_dataset(self):
audio_dataset = AudioDataset(self.audio_ann_file,
self.audio_pipeline,
data_prefix=self.data_prefix)
audio_infos = audio_dataset.video_infos
wav_path = osp.join(self.data_prefix, 'test.wav')
assert audio_infos == [
dict(audio_path=wav_path, total_frames=100, label=127)
] * 2
def test_audio_pipeline(self):
target_keys = [
'audio_path', 'label', 'start_index', 'modality', 'audios_shape',
'length', 'sample_rate', 'total_frames'
]
# Audio dataset not in test mode
audio_dataset = AudioDataset(self.audio_ann_file,
self.audio_pipeline,
data_prefix=self.data_prefix,
test_mode=False)
result = audio_dataset[0]
assert assert_dict_has_keys(result, target_keys)
# Audio dataset in test mode
audio_dataset = AudioDataset(self.audio_ann_file,
self.audio_pipeline,
data_prefix=self.data_prefix,
test_mode=True)
result = audio_dataset[0]
assert assert_dict_has_keys(result, target_keys)
def test_audio_evaluate(self):
audio_dataset = AudioDataset(self.audio_ann_file,
self.audio_pipeline,
data_prefix=self.data_prefix)
with pytest.raises(TypeError):
# results must be a list
audio_dataset.evaluate('0.5')
with pytest.raises(AssertionError):
# The length of results must be equal to the dataset len
audio_dataset.evaluate([0] * 5)
with pytest.raises(TypeError):
# topk must be int or tuple of int
audio_dataset.evaluate(
[0] * len(audio_dataset),
metric_options=dict(top_k_accuracy=dict(topk=1.)))
with pytest.raises(KeyError):
# unsupported metric
audio_dataset.evaluate([0] * len(audio_dataset), metrics='iou')
# evaluate top_k_accuracy and mean_class_accuracy metric
results = [np.array([0.1, 0.5, 0.4])] * 2
eval_result = audio_dataset.evaluate(
results, metrics=['top_k_accuracy', 'mean_class_accuracy'])
assert set(eval_result) == set(
['top1_acc', 'top5_acc', 'mean_class_accuracy'])
