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Functionalgputest #1475

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merged 7 commits into from
Apr 26, 2021
Merged

Functionalgputest #1475

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49f745f
functional cpu test generalized for gpu
Apr 22, 2021
72ca075
functional cpu test generalized for gpu
Apr 22, 2021
a1243a2
Merge branch 'functionalgputest' of https://github.com/msaroufim/audi…
msaroufim Apr 22, 2021
bf9b5e7
Merge branch 'functionalgputest' of https://github.com/msaroufim/audi…
msaroufim Apr 22, 2021
8a62b67
Merge branch 'functionalgputest' of https://github.com/msaroufim/audi…
msaroufim Apr 22, 2021
cad19b1
fixed linting issues
msaroufim Apr 23, 2021
b1f3ab2
Merge remote-tracking branch 'upstream/master' into functionalgputest
msaroufim Apr 26, 2021
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6 changes: 3 additions & 3 deletions test/torchaudio_unittest/functional/functional_cpu_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,18 +16,18 @@ def test_lfilter_9th_order_filter_stability(self):
super().test_lfilter_9th_order_filter_stability()


class TestFunctionalFloat64(Functional, FunctionalCPUOnly, PytorchTestCase):
class TestFunctionalFloat64(Functional, PytorchTestCase):
dtype = torch.float64
device = torch.device('cpu')


class TestFunctionalComplex64(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase):
class TestFunctionalComplex64(FunctionalComplex, PytorchTestCase):
complex_dtype = torch.complex64
real_dtype = torch.float32
device = torch.device('cpu')


class TestFunctionalComplex128(FunctionalComplex, FunctionalCPUOnly, PytorchTestCase):
class TestFunctionalComplex128(FunctionalComplex, PytorchTestCase):
complex_dtype = torch.complex128
real_dtype = torch.float64
device = torch.device('cpu')
Expand Down
130 changes: 65 additions & 65 deletions test/torchaudio_unittest/functional/functional_impl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,73 +93,17 @@ def test_spectogram_grad_at_zero(self, power):
spec.sum().backward()
assert not x.grad.isnan().sum()


class FunctionalComplex(TestBaseMixin):
complex_dtype = None
real_dtype = None
device = None

@nested_params(
[0.5, 1.01, 1.3],
[True, False],
)
def test_phase_vocoder_shape(self, rate, test_pseudo_complex):
"""Verify the output shape of phase vocoder"""
hop_length = 256
num_freq = 1025
num_frames = 400
batch_size = 2

torch.random.manual_seed(42)
spec = torch.randn(
batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device)
if test_pseudo_complex:
spec = torch.view_as_real(spec)

phase_advance = torch.linspace(
0,
np.pi * hop_length,
num_freq,
dtype=self.real_dtype, device=self.device)[..., None]

spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance)

assert spec.dim() == spec_stretch.dim()
expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))])
output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape
assert output_shape == expected_shape


class FunctionalCPUOnly(TestBaseMixin):
def test_create_fb_matrix_no_warning_high_n_freq(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
F.create_fb_matrix(288, 0, 8000, 128, 16000)
assert len(w) == 0

def test_create_fb_matrix_no_warning_low_n_mels(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
F.create_fb_matrix(201, 0, 8000, 89, 16000)
assert len(w) == 0

def test_create_fb_matrix_warning(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
F.create_fb_matrix(201, 0, 8000, 128, 16000)
assert len(w) == 1

def test_compute_deltas_one_channel(self):
specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]])
expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]])
specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device)
expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device)
computed = F.compute_deltas(specgram, win_length=3)
self.assertEqual(computed, expected)

def test_compute_deltas_two_channels(self):
specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0]]])
[1.0, 2.0, 3.0, 4.0]]], dtype=self.dtype, device=self.device)
expected = torch.tensor([[[0.5, 1.0, 1.0, 0.5],
[0.5, 1.0, 1.0, 0.5]]])
[0.5, 1.0, 1.0, 0.5]]], dtype=self.dtype, device=self.device)
computed = F.compute_deltas(specgram, win_length=3)
self.assertEqual(computed, expected)

Expand Down Expand Up @@ -190,7 +134,7 @@ def test_amplitude_to_DB_reversible(self, shape):
db_mult = math.log10(max(amin, ref))

torch.manual_seed(0)
spec = torch.rand(*shape) * 200
spec = torch.rand(*shape, dtype=self.dtype, device=self.device) * 200

# Spectrogram amplitude -> DB -> amplitude
db = F.amplitude_to_DB(spec, amplitude_mult, amin, db_mult, top_db=None)
Expand Down Expand Up @@ -218,7 +162,7 @@ def test_amplitude_to_DB_top_db_clamp(self, shape):
# each spectrogram still need to be predictable. The max determines the
# decibel cutoff, and the distance from the min must be large enough
# that it triggers a clamp.
spec = torch.rand(*shape)
spec = torch.rand(*shape, dtype=self.dtype, device=self.device)
# Ensure each spectrogram has a min of 0 and a max of 1.
spec -= spec.amin([-2, -1])[..., None, None]
spec /= spec.amax([-2, -1])[..., None, None]
Expand All @@ -245,7 +189,7 @@ def test_amplitude_to_DB_top_db_clamp(self, shape):
)
def test_complex_norm(self, shape, power):
torch.random.manual_seed(42)
complex_tensor = torch.randn(*shape)
complex_tensor = torch.randn(*shape, dtype=self.dtype, device=self.device)
expected_norm_tensor = complex_tensor.pow(2).sum(-1).pow(power / 2)
norm_tensor = F.complex_norm(complex_tensor, power)
self.assertEqual(norm_tensor, expected_norm_tensor, atol=1e-5, rtol=1e-5)
Expand All @@ -255,7 +199,7 @@ def test_complex_norm(self, shape, power):
)
def test_mask_along_axis(self, shape, mask_param, mask_value, axis):
torch.random.manual_seed(42)
specgram = torch.randn(*shape)
specgram = torch.randn(*shape, dtype=self.dtype, device=self.device)
mask_specgram = F.mask_along_axis(specgram, mask_param, mask_value, axis)

other_axis = 1 if axis == 2 else 2
Expand All @@ -271,7 +215,7 @@ def test_mask_along_axis(self, shape, mask_param, mask_value, axis):
@parameterized.expand(list(itertools.product([100], [0., 30.], [2, 3])))
def test_mask_along_axis_iid(self, mask_param, mask_value, axis):
torch.random.manual_seed(42)
specgrams = torch.randn(4, 2, 1025, 400)
specgrams = torch.randn(4, 2, 1025, 400, dtype=self.dtype, device=self.device)

mask_specgrams = F.mask_along_axis_iid(specgrams, mask_param, mask_value, axis)

Expand All @@ -282,3 +226,59 @@ def test_mask_along_axis_iid(self, mask_param, mask_value, axis):

assert mask_specgrams.size() == specgrams.size()
assert (num_masked_columns < mask_param).sum() == num_masked_columns.numel()


class FunctionalComplex(TestBaseMixin):
complex_dtype = None
real_dtype = None
device = None

@nested_params(
[0.5, 1.01, 1.3],
[True, False],
)
def test_phase_vocoder_shape(self, rate, test_pseudo_complex):
"""Verify the output shape of phase vocoder"""
hop_length = 256
num_freq = 1025
num_frames = 400
batch_size = 2

torch.random.manual_seed(42)
spec = torch.randn(
batch_size, num_freq, num_frames, dtype=self.complex_dtype, device=self.device)
if test_pseudo_complex:
spec = torch.view_as_real(spec)

phase_advance = torch.linspace(
0,
np.pi * hop_length,
num_freq,
dtype=self.real_dtype, device=self.device)[..., None]

spec_stretch = F.phase_vocoder(spec, rate=rate, phase_advance=phase_advance)

assert spec.dim() == spec_stretch.dim()
expected_shape = torch.Size([batch_size, num_freq, int(np.ceil(num_frames / rate))])
output_shape = (torch.view_as_complex(spec_stretch) if test_pseudo_complex else spec_stretch).shape
assert output_shape == expected_shape


class FunctionalCPUOnly(TestBaseMixin):
def test_create_fb_matrix_no_warning_high_n_freq(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
F.create_fb_matrix(288, 0, 8000, 128, 16000)
assert len(w) == 0

def test_create_fb_matrix_no_warning_low_n_mels(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
F.create_fb_matrix(201, 0, 8000, 89, 16000)
assert len(w) == 0

def test_create_fb_matrix_warning(self):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
F.create_fb_matrix(201, 0, 8000, 128, 16000)
assert len(w) == 1