Add autograd test for T.Spectrogram/T.MelSpectrogram

test/torchaudio_unittest/transforms/autograd_cpu_test.py

@@ -0,0 +1,6 @@
+from torchaudio_unittest.common_utils import PytorchTestCase
+from .autograd_test_impl import AutogradTestMixin
+
+
+class AutogradCPUTest(AutogradTestMixin, PytorchTestCase):
+    device = 'cpu'

test/torchaudio_unittest/transforms/autograd_cuda_test.py

@@ -0,0 +1,10 @@
+from torchaudio_unittest.common_utils import (
+    PytorchTestCase,
+    skipIfNoCuda,
+)
+from .autograd_test_impl import AutogradTestMixin
+
+
+@skipIfNoCuda
+class AutogradCUDATest(AutogradTestMixin, PytorchTestCase):
+    device = 'cuda'

test/torchaudio_unittest/transforms/autograd_test_impl.py

@@ -0,0 +1,62 @@
+from typing import List
+
+from parameterized import parameterized
+import torch
+from torch.autograd import gradcheck, gradgradcheck
+import torchaudio.transforms as T
+
+from torchaudio_unittest.common_utils import (
+    TestBaseMixin,
+    get_whitenoise,
+)
+
+
+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)
+
+        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)
+
+    @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)
+
+    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)