def test_compose(input, idx, axis):
t = transforms.Compose(
[transforms.Crop(idx, axis=axis),
transforms.Normalize(axis=axis)])
expected_output = F.crop(input, idx, axis=axis)
expected_output = F.normalize(expected_output, axis=axis)
assert np.array_equal(t(input), expected_output)
def test_stft(input, window_size, hop_size, axis):
expected_output = input
samples = input.shape[axis]
spectrogram = F.stft(input, window_size, hop_size, axis=axis)
magnitude, phase = librosa.magphase(spectrogram)
output = F.istft(spectrogram, window_size, hop_size, axis=axis)
output = F.crop(output, (0, samples), axis=axis)
np.testing.assert_almost_equal(output, expected_output, decimal=6)
def test_expand(input, size, axis, method):
t = transforms.Expand(size=size, axis=axis, method=method)
if method == 'pad':
assert np.array_equal(t(input), F.pad(
input, (0, size - input.shape[axis]), axis=axis))
else:
assert np.array_equal(t(input), F.crop(F.replicate(
input,
repetitions=size // input.shape[axis] + 1,
axis=axis), (0, size), axis=axis))
def test_crop(input, idx, axis, expected_output):
output = F.crop(input, idx, axis=axis)
assert np.array_equal(output, expected_output)
def test_randomcrop(input, size, axis):
t = transforms.RandomCrop(size, axis=axis)
t.fix_randomization = True
assert np.array_equal(t(input), t(input))
assert np.array_equal(t(input), F.crop(input, t.idx, axis=t.axis))