246 lines
13 KiB
Python
246 lines
13 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing GriffinLim op in DE
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"""
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import numpy as np
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import pytest
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import mindspore.dataset as ds
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import mindspore.dataset.audio as c_audio
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from mindspore import log as logger
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DATA_DIR = "../data/dataset/audiorecord/"
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def count_unequal_element(data_expected, data_me, rtol, atol):
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assert data_expected.shape == data_me.shape
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total_count = len(data_expected.flatten())
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error = np.abs(data_expected - data_me)
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greater = np.greater(error, atol + np.abs(data_expected) * rtol)
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loss_count = np.count_nonzero(greater)
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assert (loss_count / total_count) < rtol, \
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"\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \
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format(data_expected[greater], data_me[greater], error[greater])
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def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True):
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if np.any(np.isnan(data_expected)):
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assert np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan)
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elif not np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan):
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count_unequal_element(data_expected, data_me, rtol, atol)
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def test_griffin_lim_pipeline():
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"""
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Feature: GriffinLim
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Description: Test GriffinLim cpp op in pipeline
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Expectation: Equal results from Mindspore and benchmark
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"""
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# <101, 6>
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in_data = np.load(DATA_DIR + "griffinlim_101x6.npy")[np.newaxis, :]
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out_expect = np.load(DATA_DIR + "griffinlim_101x6_out.npy")
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dataset = ds.NumpySlicesDataset(in_data, column_names=["multi_dimensional_data"], shuffle=False)
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transforms = [c_audio.GriffinLim(n_fft=200, rand_init=False)]
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dataset = dataset.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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out_put = item["multi_dimensional_data"]
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allclose_nparray(out_put, out_expect, 0.001, 0.001)
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# <151, 8>
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in_data = np.load(DATA_DIR + "griffinlim_151x8.npy")[np.newaxis, :]
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out_expect = np.load(DATA_DIR + "griffinlim_151x8_out.npy")
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dataset = ds.NumpySlicesDataset(in_data, column_names=["multi_dimensional_data"], shuffle=False)
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=20, win_length=240, hop_length=120, rand_init=False, power=1.2)]
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dataset = dataset.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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out_put = item["multi_dimensional_data"]
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allclose_nparray(out_put, out_expect, 0.001, 0.001)
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# <2, 301, 4> hop_length greater than half of win_length
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in_data = np.load(DATA_DIR + "griffinlim_2x301x4.npy")[np.newaxis, :]
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out_expect = np.load(DATA_DIR + "griffinlim_2x301x4_out.npy")
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dataset = ds.NumpySlicesDataset(in_data, column_names=["multi_dimensional_data"], shuffle=False)
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transforms = [c_audio.GriffinLim(n_fft=600, n_iter=10, win_length=240, hop_length=130, rand_init=False)]
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dataset = dataset.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
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out_put = item["multi_dimensional_data"]
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allclose_nparray(out_put, out_expect, 0.001, 0.001)
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def test_griffin_lim_pipeline_invalid_param_range():
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"""
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Feature: GriffinLim
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Description: Test GriffinLim with invalid input parameters
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Expectation: Throw correct error and message
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"""
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logger.info("test GriffinLim op with default values")
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in_data = np.load(DATA_DIR + "griffinlim_151x8.npy")[np.newaxis, :]
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data1 = ds.NumpySlicesDataset(in_data, column_names=["multi_dimensional_data"], shuffle=False)
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with pytest.raises(ValueError, match=r"Input n_fft is not within the required interval of \[1, 2147483647\]."):
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transforms = [c_audio.GriffinLim(n_fft=-10)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(ValueError, match=r"Input n_iter is not within the required interval of \[1, 2147483647\]."):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=-10)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(ValueError, match=r"Input win_length is not within the required interval of \[0, 2147483647\]."):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=-10)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(ValueError,
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match=r"Input win_length should be no more than n_fft, but got win_length: 400 " +
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r"and n_fft: 300."):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=400)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(ValueError, match=r"Input hop_length is not within the required interval of \[0, 2147483647\]."):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=0, hop_length=-10)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(ValueError, match=r"Input power is not within the required interval of \(0, 16777216\]."):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=0, hop_length=0, power=-3)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(ValueError, match=r"Input momentum is not within the required interval of \[0, 16777216\]."):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=0, hop_length=0, power=2, momentum=-10)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(ValueError, match=r"Input length is not within the required interval of \[0, 2147483647\]."):
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transforms = [
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c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=0, hop_length=0, power=2, momentum=0.9, length=-2)
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]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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def test_griffin_lim_pipeline_invalid_param_constraint():
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"""
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Feature: GriffinLim
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Description: Test GriffinLim with invalid input parameters
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Expectation: Throw RuntimeError
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"""
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logger.info("test GriffinLim op with default values")
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in_data = np.load(DATA_DIR + "griffinlim_151x8.npy")[np.newaxis, :]
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data1 = ds.NumpySlicesDataset(in_data, column_names=["multi_dimensional_data"], shuffle=False)
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with pytest.raises(RuntimeError,
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match=r"Unexpected error. map operation: \[GriffinLim\] failed. " +
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r"GriffinLim: the frequency of the input should equal to n_fft / 2 \+ 1"):
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transforms = [c_audio.GriffinLim(n_fft=100)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(RuntimeError,
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match=r"Unexpected error. map operation: \[GriffinLim\] failed. " +
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r"GriffinLim: the frequency of the input should equal to n_fft / 2 \+ 1"):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=0, hop_length=120)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(RuntimeError,
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match=r"Syntax error. GriffinLim: momentum equal to or greater than 1 can be unstable, " +
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"but got: 1.000000"):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=0, hop_length=0, power=2, momentum=1)]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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def test_griffin_lim_pipeline_invalid_param_type():
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"""
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Feature: GriffinLim
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Description: Test GriffinLim with invalid input parameters
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Expectation: Throw correct error and message
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"""
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logger.info("test GriffinLim op with default values")
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in_data = np.load(DATA_DIR + "griffinlim_151x8.npy")[np.newaxis, :]
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data1 = ds.NumpySlicesDataset(in_data, column_names=["multi_dimensional_data"], shuffle=False)
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with pytest.raises(TypeError,
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match=r"Argument window_type with value type is not of type " +
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r"\[<enum \'WindowType\'>\], but got <class \'str\'>."):
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transforms = [c_audio.GriffinLim(n_fft=300, n_iter=10, win_length=0, hop_length=0, window_type="type")]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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with pytest.raises(TypeError,
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match=r"Argument rand_init with value true is not of type \[<class \'bool\'>\], " +
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r"but got <class \'str\'>."):
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transforms = [
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c_audio.GriffinLim(n_fft=300,
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n_iter=10,
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win_length=0,
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hop_length=0,
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power=2,
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momentum=0.9,
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length=0,
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rand_init='true')
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]
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data1 = data1.map(operations=transforms, input_columns=["multi_dimensional_data"])
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for item in data1.create_dict_iterator(num_epochs=1, output_numpy=True):
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_ = item["multi_dimensional_data"]
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def test_griffin_lim_eager():
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"""
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Feature: GriffinLim
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Description: Test GriffinLim cpp op with eager mode
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Expectation: Equal results from Mindspore and benchmark
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"""
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# <freq, time>
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spectrogram = np.load(DATA_DIR + "griffinlim_101x6.npy").astype(np.float64)
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out_expect = np.load(DATA_DIR + "griffinlim_101x6_out.npy").astype(np.float64)
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out_ms = c_audio.GriffinLim(n_fft=200, rand_init=False)(spectrogram)
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allclose_nparray(out_ms, out_expect, 0.001, 0.001)
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# <1, freq, time>
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spectrogram = np.load(DATA_DIR + "griffinlim_1x201x6.npy").astype(np.float64)
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out_expect = np.load(DATA_DIR + "griffinlim_1x201x6_out.npy").astype(np.float64)
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out_ms = c_audio.GriffinLim(rand_init=False)(spectrogram)
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allclose_nparray(out_ms, out_expect, 0.001, 0.001)
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# <2, freq, time>
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spectrogram = np.load(DATA_DIR + "griffinlim_2x301x6.npy").astype(np.float64)
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out_expect = np.load(DATA_DIR + "griffinlim_2x301x6_out.npy").astype(np.float64)
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out_ms = c_audio.GriffinLim(n_fft=600, rand_init=False)(spectrogram)
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allclose_nparray(out_ms, out_expect, 0.001, 0.001)
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if __name__ == "__main__":
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test_griffin_lim_pipeline()
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test_griffin_lim_pipeline_invalid_param_range()
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test_griffin_lim_pipeline_invalid_param_constraint()
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test_griffin_lim_pipeline_invalid_param_type()
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test_griffin_lim_eager()
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