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Add autograd test for T.Spectrogram/T.MelSpectrogram (#1340)
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from torchaudio_unittest.common_utils import PytorchTestCase | ||
from .autograd_test_impl import AutogradTestMixin | ||
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class AutogradCPUTest(AutogradTestMixin, PytorchTestCase): | ||
device = 'cpu' |
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from torchaudio_unittest.common_utils import ( | ||
PytorchTestCase, | ||
skipIfNoCuda, | ||
) | ||
from .autograd_test_impl import AutogradTestMixin | ||
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@skipIfNoCuda | ||
class AutogradCUDATest(AutogradTestMixin, PytorchTestCase): | ||
device = 'cuda' |
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from typing import List | ||
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from parameterized import parameterized | ||
import torch | ||
from torch.autograd import gradcheck, gradgradcheck | ||
import torchaudio.transforms as T | ||
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from torchaudio_unittest.common_utils import ( | ||
TestBaseMixin, | ||
get_whitenoise, | ||
) | ||
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class AutogradTestMixin(TestBaseMixin): | ||
def assert_grad( | ||
self, | ||
transform: torch.nn.Module, | ||
inputs: List[torch.Tensor], | ||
*, | ||
nondet_tol: float = 0.0, | ||
): | ||
transform = transform.to(dtype=torch.float64, device=self.device) | ||
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inputs_ = [] | ||
for i in inputs: | ||
i.requires_grad = True | ||
inputs_.append(i.to(dtype=torch.float64, device=self.device)) | ||
assert gradcheck(transform, inputs_) | ||
assert gradgradcheck(transform, inputs_, nondet_tol=nondet_tol) | ||
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@parameterized.expand([ | ||
({'pad': 0, 'normalized': False, 'power': None}, ), | ||
({'pad': 3, 'normalized': False, 'power': None}, ), | ||
({'pad': 0, 'normalized': True, 'power': None}, ), | ||
({'pad': 3, 'normalized': True, 'power': None}, ), | ||
({'pad': 0, 'normalized': False, 'power': 1.0}, ), | ||
({'pad': 3, 'normalized': False, 'power': 1.0}, ), | ||
({'pad': 0, 'normalized': True, 'power': 1.0}, ), | ||
({'pad': 3, 'normalized': True, 'power': 1.0}, ), | ||
({'pad': 0, 'normalized': False, 'power': 2.0}, ), | ||
({'pad': 3, 'normalized': False, 'power': 2.0}, ), | ||
({'pad': 0, 'normalized': True, 'power': 2.0}, ), | ||
({'pad': 3, 'normalized': True, 'power': 2.0}, ), | ||
]) | ||
def test_spectrogram(self, kwargs): | ||
# replication_pad1d_backward_cuda is not deteministic and | ||
# gives very small (~2.7756e-17) difference. | ||
# | ||
# See https://github.com/pytorch/pytorch/issues/54093 | ||
transform = T.Spectrogram(**kwargs) | ||
waveform = get_whitenoise(sample_rate=8000, duration=0.05, n_channels=2) | ||
self.assert_grad(transform, [waveform], nondet_tol=1e-10) | ||
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def test_melspectrogram(self): | ||
# replication_pad1d_backward_cuda is not deteministic and | ||
# gives very small (~2.7756e-17) difference. | ||
# | ||
# See https://github.com/pytorch/pytorch/issues/54093 | ||
sample_rate = 8000 | ||
transform = T.MelSpectrogram(sample_rate=sample_rate) | ||
waveform = get_whitenoise(sample_rate=sample_rate, duration=0.05, n_channels=2) | ||
self.assert_grad(transform, [waveform], nondet_tol=1e-10) |