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Merged
merged 31 commits into from
Jul 24, 2019
Merged

[BC] Standardization of Transforms/Functionals #152

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jamarshon Jul 18, 2019
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Merge branch 'master' into standardization
jamarshon Jul 18, 2019
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small push to save progress
jamarshon Jul 22, 2019
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small push to save progress
jamarshon Jul 22, 2019
e9c805f
fix test
jamarshon Jul 22, 2019
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Merge branch 'master' into standardization
jamarshon Jul 22, 2019
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jamarshon Jul 23, 2019
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remove trailing zero
jamarshon Jul 23, 2019
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apply feedback: remove scale and lc2cl
jamarshon Jul 24, 2019
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apply feedback: remove downmix
jamarshon Jul 24, 2019
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apply feedback: remove downmix
jamarshon Jul 24, 2019
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apply feedback: rearrange functions
jamarshon Jul 24, 2019
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apply feedback: rearrange functions
jamarshon Jul 24, 2019
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Merge branch 'master' into standardization
jamarshon Jul 24, 2019
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merge: delete stft
jamarshon Jul 24, 2019
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merge
jamarshon Jul 24, 2019
a7aa440
remove batch support for istft
jamarshon Jul 24, 2019
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docstring
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remove unused xfail
jamarshon Jul 24, 2019
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Revert "remove batch support for istft"
jamarshon Jul 24, 2019
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rename batch to channel
jamarshon Jul 24, 2019
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54 changes: 2 additions & 52 deletions test/test_functional.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,8 @@

import torch
import torchaudio
import torchaudio.functional as F
import pytest
import unittest
import test.common_utils

Expand All @@ -11,10 +13,6 @@
import numpy as np
import librosa

import pytest
import torchaudio.functional as F
xfail = pytest.mark.xfail


class TestFunctional(unittest.TestCase):
data_sizes = [(2, 20), (3, 15), (4, 10)]
Expand Down Expand Up @@ -197,54 +195,6 @@ def _num_stft_bins(signal_len, fft_len, hop_length, pad):
return (signal_len + 2 * pad - fft_len + hop_length) // hop_length


@pytest.mark.parametrize('fft_length', [512])
@pytest.mark.parametrize('hop_length', [256])
@pytest.mark.parametrize('waveform', [
(torch.randn(1, 100000)),
(torch.randn(1, 2, 100000)),
pytest.param(torch.randn(1, 100), marks=xfail(raises=RuntimeError)),
])
@pytest.mark.parametrize('pad_mode', [
# 'constant',
'reflect',
])
@unittest.skipIf(not IMPORT_LIBROSA, 'Librosa is not available')
def test_stft(waveform, fft_length, hop_length, pad_mode):
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Why might want to bring this back / add it to core later on for the regular torch.stft

"""
Test STFT for multi-channel signals.

Padding: Value in having padding outside of torch.stft?
"""
pad = fft_length // 2
window = torch.hann_window(fft_length)
complex_spec = F.stft(waveform,
fft_length=fft_length,
hop_length=hop_length,
window=window,
pad_mode=pad_mode)
mag_spec, phase_spec = F.magphase(complex_spec)

# == Test shape
expected_size = list(waveform.size()[:-1])
expected_size += [fft_length // 2 + 1, _num_stft_bins(
waveform.size(-1), fft_length, hop_length, pad), 2]
assert complex_spec.dim() == waveform.dim() + 2
assert complex_spec.size() == torch.Size(expected_size)

# == Test values
fft_config = dict(n_fft=fft_length, hop_length=hop_length, pad_mode=pad_mode)
# note that librosa *automatically* pad with fft_length // 2.
expected_complex_spec = np.apply_along_axis(librosa.stft, -1,
waveform.numpy(), **fft_config)
expected_mag_spec, _ = librosa.magphase(expected_complex_spec)
# Convert torch to np.complex
complex_spec = complex_spec.numpy()
complex_spec = complex_spec[..., 0] + 1j * complex_spec[..., 1]

assert np.allclose(complex_spec, expected_complex_spec, atol=1e-5)
assert np.allclose(mag_spec.numpy(), expected_mag_spec, atol=1e-5)


@pytest.mark.parametrize('rate', [0.5, 1.01, 1.3])
@pytest.mark.parametrize('complex_specgrams', [
torch.randn(1, 2, 1025, 400, 2),
Expand Down
104 changes: 12 additions & 92 deletions test/test_jit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,40 +30,18 @@ def _test_script_module(self, tensor, f, *args):

self.assertTrue(torch.allclose(jit_out, py_out))

def test_torchscript_scale(self):
@torch.jit.script
def jit_method(tensor, factor):
# type: (Tensor, int) -> Tensor
return F.scale(tensor, factor)

tensor = torch.rand((10, 1))
factor = 2

jit_out = jit_method(tensor, factor)
py_out = F.scale(tensor, factor)

self.assertTrue(torch.allclose(jit_out, py_out))

@unittest.skipIf(not RUN_CUDA, "no CUDA")
def test_scriptmodule_scale(self):
tensor = torch.rand((10, 1), device="cuda")

self._test_script_module(tensor, transforms.Scale)

def test_torchscript_pad_trim(self):
@torch.jit.script
def jit_method(tensor, ch_dim, max_len, len_dim, fill_value):
# type: (Tensor, int, int, int, float) -> Tensor
return F.pad_trim(tensor, ch_dim, max_len, len_dim, fill_value)
def jit_method(tensor, max_len, fill_value):
# type: (Tensor, int, float) -> Tensor
return F.pad_trim(tensor, max_len, fill_value)

tensor = torch.rand((10, 1))
ch_dim = 1
tensor = torch.rand((1, 10))
max_len = 5
len_dim = 0
fill_value = 3.

jit_out = jit_method(tensor, ch_dim, max_len, len_dim, fill_value)
py_out = F.pad_trim(tensor, ch_dim, max_len, len_dim, fill_value)
jit_out = jit_method(tensor, max_len, fill_value)
py_out = F.pad_trim(tensor, max_len, fill_value)

self.assertTrue(torch.allclose(jit_out, py_out))

Expand All @@ -74,45 +52,6 @@ def test_scriptmodule_pad_trim(self):

self._test_script_module(tensor, transforms.PadTrim, max_len)

def test_torchscript_downmix_mono(self):
@torch.jit.script
def jit_method(tensor, ch_dim):
# type: (Tensor, int) -> Tensor
return F.downmix_mono(tensor, ch_dim)

tensor = torch.rand((10, 1))
ch_dim = 1

jit_out = jit_method(tensor, ch_dim)
py_out = F.downmix_mono(tensor, ch_dim)

self.assertTrue(torch.allclose(jit_out, py_out))

@unittest.skipIf(not RUN_CUDA, "no CUDA")
def test_scriptmodule_downmix_mono(self):
tensor = torch.rand((1, 10), device="cuda")

self._test_script_module(tensor, transforms.DownmixMono)

def test_torchscript_LC2CL(self):
@torch.jit.script
def jit_method(tensor):
# type: (Tensor) -> Tensor
return F.LC2CL(tensor)

tensor = torch.rand((10, 1))

jit_out = jit_method(tensor)
py_out = F.LC2CL(tensor)

self.assertTrue(torch.allclose(jit_out, py_out))

@unittest.skipIf(not RUN_CUDA, "no CUDA")
def test_scriptmodule_LC2CL(self):
tensor = torch.rand((10, 1), device="cuda")

self._test_script_module(tensor, transforms.LC2CL)

def test_torchscript_spectrogram(self):
@torch.jit.script
def jit_method(sig, pad, window, n_fft, hop, ws, power, normalize):
Expand Down Expand Up @@ -167,7 +106,7 @@ def jit_method(spec, multiplier, amin, db_multiplier, top_db):
# type: (Tensor, float, float, float, Optional[float]) -> Tensor
return F.spectrogram_to_DB(spec, multiplier, amin, db_multiplier, top_db)

spec = torch.rand((10, 1))
spec = torch.rand((6, 201))
multiplier = 10.
amin = 1e-10
db_multiplier = 0.
Expand All @@ -180,7 +119,7 @@ def jit_method(spec, multiplier, amin, db_multiplier, top_db):

@unittest.skipIf(not RUN_CUDA, "no CUDA")
def test_scriptmodule_SpectrogramToDB(self):
spec = torch.rand((10, 1), device="cuda")
spec = torch.rand((6, 201), device="cuda")

self._test_script_module(spec, transforms.SpectrogramToDB)

Expand Down Expand Up @@ -211,32 +150,13 @@ def test_scriptmodule_MelSpectrogram(self):

self._test_script_module(tensor, transforms.MelSpectrogram)

def test_torchscript_BLC2CBL(self):
@torch.jit.script
def jit_method(tensor):
# type: (Tensor) -> Tensor
return F.BLC2CBL(tensor)

tensor = torch.rand((10, 1000, 1))

jit_out = jit_method(tensor)
py_out = F.BLC2CBL(tensor)

self.assertTrue(torch.allclose(jit_out, py_out))

@unittest.skipIf(not RUN_CUDA, "no CUDA")
def test_scriptmodule_BLC2CBL(self):
tensor = torch.rand((10, 1000, 1), device="cuda")

self._test_script_module(tensor, transforms.BLC2CBL)

def test_torchscript_mu_law_encoding(self):
@torch.jit.script
def jit_method(tensor, qc):
# type: (Tensor, int) -> Tensor
return F.mu_law_encoding(tensor, qc)

tensor = torch.rand((10, 1))
tensor = torch.rand((1, 10))
qc = 256

jit_out = jit_method(tensor, qc)
Expand All @@ -246,7 +166,7 @@ def jit_method(tensor, qc):

@unittest.skipIf(not RUN_CUDA, "no CUDA")
def test_scriptmodule_MuLawEncoding(self):
tensor = torch.rand((10, 1), device="cuda")
tensor = torch.rand((1, 10), device="cuda")

self._test_script_module(tensor, transforms.MuLawEncoding)

Expand All @@ -256,7 +176,7 @@ def jit_method(tensor, qc):
# type: (Tensor, int) -> Tensor
return F.mu_law_expanding(tensor, qc)

tensor = torch.rand((10, 1))
tensor = torch.rand((1, 10))
qc = 256

jit_out = jit_method(tensor, qc)
Expand All @@ -266,7 +186,7 @@ def jit_method(tensor, qc):

@unittest.skipIf(not RUN_CUDA, "no CUDA")
def test_scriptmodule_MuLawExpanding(self):
tensor = torch.rand((10, 1), device="cuda")
tensor = torch.rand((1, 10), device="cuda")

self._test_script_module(tensor, transforms.MuLawExpanding)

Expand Down
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