forked from huawei/mindspore2022
200 lines
8.0 KiB
Python
200 lines
8.0 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""
|
|
Test SpeechCommands dataset operators
|
|
"""
|
|
import pytest
|
|
import numpy as np
|
|
|
|
import mindspore.dataset as ds
|
|
import mindspore.dataset.audio.transforms as audio
|
|
from mindspore import log as logger
|
|
|
|
DATA_DIR = "../data/dataset/testSpeechCommandsData/"
|
|
|
|
|
|
def test_speech_commands_basic():
|
|
"""
|
|
Feature: SpeechCommands Dataset
|
|
Description: Read all files
|
|
Expectation: Output the amount of files
|
|
"""
|
|
logger.info("Test SpeechCommandsDataset Op.")
|
|
|
|
# case 1: test loading whole dataset
|
|
data1 = ds.SpeechCommandsDataset(DATA_DIR)
|
|
num_iter1 = 0
|
|
for _ in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_iter1 += 1
|
|
assert num_iter1 == 3
|
|
|
|
# case 2: test num_samples
|
|
data2 = ds.SpeechCommandsDataset(DATA_DIR, num_samples=3)
|
|
num_iter2 = 0
|
|
for _ in data2.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_iter2 += 1
|
|
assert num_iter2 == 3
|
|
|
|
# case 3: test repeat
|
|
data3 = ds.SpeechCommandsDataset(DATA_DIR, num_samples=2)
|
|
data3 = data3.repeat(5)
|
|
num_iter3 = 0
|
|
for _ in data3.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_iter3 += 1
|
|
assert num_iter3 == 10
|
|
|
|
|
|
def test_speech_commands_sequential_sampler():
|
|
"""
|
|
Feature: SpeechCommands Dataset
|
|
Description: Use SequentialSampler to sample data.
|
|
Expectation: The number of samplers returned by dict_iterator is equal to the requested number of samples.
|
|
"""
|
|
logger.info("Test SpeechCommandsDataset with SequentialSampler.")
|
|
num_samples = 2
|
|
sampler = ds.SequentialSampler(num_samples=num_samples)
|
|
data1 = ds.SpeechCommandsDataset(DATA_DIR, sampler=sampler)
|
|
data2 = ds.SpeechCommandsDataset(DATA_DIR, shuffle=False, num_samples=num_samples)
|
|
sample_rate_list1, sample_rate_list2 = [], []
|
|
num_iter = 0
|
|
for item1, item2 in zip(data1.create_dict_iterator(num_epochs=1, output_numpy=True),
|
|
data2.create_dict_iterator(num_epochs=1, output_numpy=True)):
|
|
sample_rate_list1.append(item1["sample_rate"])
|
|
sample_rate_list2.append(item2["sample_rate"])
|
|
num_iter += 1
|
|
np.testing.assert_array_equal(sample_rate_list1, sample_rate_list2)
|
|
assert num_iter == num_samples
|
|
|
|
|
|
def test_speech_commands_exception():
|
|
"""
|
|
Feature: SpeechCommands Dataset
|
|
Description: Test error cases for SpeechCommandsDataset
|
|
Expectation: Error message
|
|
"""
|
|
logger.info("Test error cases for SpeechCommandsDataset.")
|
|
error_msg_1 = "sampler and shuffle cannot be specified at the same time."
|
|
with pytest.raises(RuntimeError, match=error_msg_1):
|
|
ds.SpeechCommandsDataset(DATA_DIR, shuffle=False, sampler=ds.PKSampler(3))
|
|
|
|
error_msg_2 = "sampler and sharding cannot be specified at the same time."
|
|
with pytest.raises(RuntimeError, match=error_msg_2):
|
|
ds.SpeechCommandsDataset(DATA_DIR, sampler=ds.PKSampler(3), num_shards=2, shard_id=0)
|
|
|
|
error_msg_3 = "num_shards is specified and currently requires shard_id as well."
|
|
with pytest.raises(RuntimeError, match=error_msg_3):
|
|
ds.SpeechCommandsDataset(DATA_DIR, num_shards=10)
|
|
|
|
error_msg_4 = "shard_id is specified but num_shards is not."
|
|
with pytest.raises(RuntimeError, match=error_msg_4):
|
|
ds.SpeechCommandsDataset(DATA_DIR, shard_id=0)
|
|
|
|
error_msg_5 = "Input shard_id is not within the required interval."
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.SpeechCommandsDataset(DATA_DIR, num_shards=5, shard_id=-1)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.SpeechCommandsDataset(DATA_DIR, num_shards=5, shard_id=5)
|
|
with pytest.raises(ValueError, match=error_msg_5):
|
|
ds.SpeechCommandsDataset(DATA_DIR, num_shards=2, shard_id=5)
|
|
|
|
error_msg_6 = "num_parallel_workers exceeds."
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.SpeechCommandsDataset(DATA_DIR, shuffle=False, num_parallel_workers=0)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.SpeechCommandsDataset(DATA_DIR, shuffle=False, num_parallel_workers=256)
|
|
with pytest.raises(ValueError, match=error_msg_6):
|
|
ds.SpeechCommandsDataset(DATA_DIR, shuffle=False, num_parallel_workers=-2)
|
|
|
|
error_msg_7 = "Argument shard_id."
|
|
with pytest.raises(TypeError, match=error_msg_7):
|
|
ds.SpeechCommandsDataset(DATA_DIR, num_shards=2, shard_id="0")
|
|
|
|
def exception_func(item):
|
|
raise Exception("Error occur!")
|
|
|
|
error_msg_8 = "The corresponding data files."
|
|
with pytest.raises(RuntimeError, match=error_msg_8):
|
|
data = ds.SpeechCommandsDataset(DATA_DIR)
|
|
data = data.map(operations=exception_func, input_columns=["waveform"], num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
with pytest.raises(RuntimeError, match=error_msg_8):
|
|
data = ds.SpeechCommandsDataset(DATA_DIR)
|
|
data = data.map(operations=exception_func, input_columns=["sample_rate"], num_parallel_workers=1)
|
|
for _ in data.__iter__():
|
|
pass
|
|
|
|
|
|
def test_speech_commands_usage():
|
|
"""
|
|
Feature: SpeechCommands Dataset
|
|
Description: Usage Test
|
|
Expectation: Get the result of each function
|
|
"""
|
|
logger.info("Test SpeechCommandsDataset usage flag.")
|
|
|
|
def test_config(usage, speech_commands_path=DATA_DIR):
|
|
try:
|
|
data = ds.SpeechCommandsDataset(speech_commands_path, usage=usage)
|
|
num_rows = 0
|
|
for _ in data.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
num_rows += 1
|
|
except (ValueError, TypeError, RuntimeError) as e:
|
|
return str(e)
|
|
return num_rows
|
|
|
|
# test the usage of SpeechCommands
|
|
assert test_config("test") == 1
|
|
assert test_config("train") == 1
|
|
assert test_config("valid") == 1
|
|
assert test_config("all") == 3
|
|
assert "usage is not within the valid set of ['train', 'test', 'valid', 'all']." in test_config("invalid")
|
|
|
|
# change this directory to the folder that contains all SpeechCommands files
|
|
all_speech_commands = None
|
|
if all_speech_commands is not None:
|
|
assert test_config("test", all_speech_commands) == 11005
|
|
assert test_config("valid", all_speech_commands) == 9981
|
|
assert test_config("train", all_speech_commands) == 84843
|
|
assert test_config("all", all_speech_commands) == 105829
|
|
assert ds.SpeechCommandsDataset(all_speech_commands, usage="test").get_dataset_size() == 11005
|
|
assert ds.SpeechCommandsDataset(all_speech_commands, usage="valid").get_dataset_size() == 9981
|
|
assert ds.SpeechCommandsDataset(all_speech_commands, usage="train").get_dataset_size() == 84843
|
|
assert ds.SpeechCommandsDataset(all_speech_commands, usage="all").get_dataset_size() == 105829
|
|
|
|
|
|
def test_speech_commands_pipeline():
|
|
"""
|
|
Feature: Pipeline test
|
|
Description: Read a sample
|
|
Expectation: Test BandBiquad by pipeline
|
|
"""
|
|
dataset = ds.SpeechCommandsDataset(DATA_DIR, num_samples=1)
|
|
band_biquad_op = audio.BandBiquad(8000, 200.0)
|
|
# Filtered waveform by bandbiquad
|
|
dataset = dataset.map(input_columns=["waveform"], operations=band_biquad_op, num_parallel_workers=4)
|
|
i = 0
|
|
for _ in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
|
|
i += 1
|
|
assert i == 1
|
|
|
|
|
|
if __name__ == '__main__':
|
|
test_speech_commands_basic()
|
|
test_speech_commands_sequential_sampler()
|
|
test_speech_commands_exception()
|
|
test_speech_commands_usage()
|
|
test_speech_commands_pipeline()
|