skip cuda test

Author: nateanl

test/torchaudio_unittest/prototype/functional/autograd_cpu_test.py

@@ -1,9 +1,14 @@
 import torch
 from torchaudio_unittest.common_utils import PytorchTestCase
 
-from .autograd_test_impl import AutogradTestImpl
+from .autograd_test_impl import AutogradTestImpl, AutogradTestRIRImpl
 
 
 class TestAutogradCPUFloat64(AutogradTestImpl, PytorchTestCase):
     dtype = torch.float64
     device = torch.device("cpu")
+
+
+class TestAutogradRIRCPUFloat64(AutogradTestRIRImpl, PytorchTestCase):
+    dtype = torch.float64
+    device = torch.device("cpu")

test/torchaudio_unittest/prototype/functional/autograd_test_impl.py

@@ -32,6 +32,8 @@ def test_add_noise(self):
         self.assertTrue(gradcheck(F.add_noise, (waveform, noise, lengths, snr)))
         self.assertTrue(gradgradcheck(F.add_noise, (waveform, noise, lengths, snr)))
 
+
+class AutogradTestRIRImpl(TestBaseMixin):
     @parameterized.expand([(2, 1), (3, 4)])
     def test_simulate_rir_ism(self, D, channel):
         room = torch.rand(D, dtype=self.dtype, device=self.device, requires_grad=True)

test/torchaudio_unittest/prototype/functional/functional_cpu_test.py

@@ -1,7 +1,7 @@
 import torch
 from torchaudio_unittest.common_utils import PytorchTestCase
 
-from .functional_test_impl import FunctionalTestImpl
+from .functional_test_impl import FunctionalTestImpl, FunctionalTestRIRImpl
 
 
 class FunctionalFloat32CPUTest(FunctionalTestImpl, PytorchTestCase):
@@ -12,3 +12,13 @@ class FunctionalFloat32CPUTest(FunctionalTestImpl, PytorchTestCase):
 class FunctionalFloat64CPUTest(FunctionalTestImpl, PytorchTestCase):
     dtype = torch.float64
     device = torch.device("cpu")
+
+
+class FunctionalRIRFloat32Test(FunctionalTestRIRImpl, PytorchTestCase):
+    dtype = torch.float32
+    device = torch.device("cpu")
+
+
+class FunctionalRIRFloat64Test(FunctionalTestRIRImpl, PytorchTestCase):
+    dtype = torch.float64
+    device = torch.device("cpu")

test/torchaudio_unittest/prototype/functional/functional_test_impl.py

@@ -1,4 +1,5 @@
 import numpy as np
+
 try:
     import pyroomacoustics as pra
 except Exception:
@@ -112,6 +113,8 @@ def test_add_noise_length_check(self):
         with self.assertRaisesRegex(ValueError, "Length dimensions"):
             F.add_noise(waveform, noise, lengths, snr)
 
+
+class FunctionalTestRIRImpl(TestBaseMixin):
     @skipIfNoModule("pyroomacoustics")
     @parameterized.expand([(2, 1), (3, 4)])
     def test_simulate_rir_ism_single_band(self, D, channel):

test/torchaudio_unittest/prototype/functional/torchscript_consistency_cpu_test.py

@@ -1,7 +1,7 @@
 import torch
 from torchaudio_unittest.common_utils import PytorchTestCase
 
-from .torchscript_consistency_test_impl import TorchScriptConsistencyTestImpl
+from .torchscript_consistency_test_impl import TorchScriptConsistencyTestImpl, TorchScriptConsistencyTestRIRImpl
 
 
 class TorchScriptConsistencyCPUFloat32Test(TorchScriptConsistencyTestImpl, PytorchTestCase):
@@ -12,3 +12,13 @@ class TorchScriptConsistencyCPUFloat32Test(TorchScriptConsistencyTestImpl, Pytor
 class TorchScriptConsistencyCPUFloat64Test(TorchScriptConsistencyTestImpl, PytorchTestCase):
     dtype = torch.float64
     device = torch.device("cpu")
+
+
+class TorchScriptConsistencyRIRCPUFloat32Test(TorchScriptConsistencyTestRIRImpl, PytorchTestCase):
+    dtype = torch.float32
+    device = torch.device("cpu")
+
+
+class TorchScriptConsistencyRIRCPUFloat64Test(TorchScriptConsistencyTestRIRImpl, PytorchTestCase):
+    dtype = torch.float64
+    device = torch.device("cpu")

test/torchaudio_unittest/prototype/functional/torchscript_consistency_test_impl.py

@@ -49,6 +49,27 @@ def test_add_noise(self):
 
         self._assert_consistency(F.add_noise, (waveform, noise, lengths, snr))
 
+
+class TorchScriptConsistencyTestRIRImpl(TestBaseMixin):
+    def _assert_consistency(self, func, inputs, shape_only=False):
+        inputs_ = []
+        for i in inputs:
+            if torch.is_tensor(i):
+                i = i.to(device=self.device, dtype=self.dtype)
+            inputs_.append(i)
+        ts_func = torch_script(func)
+
+        torch.random.manual_seed(40)
+        output = func(*inputs_)
+
+        torch.random.manual_seed(40)
+        ts_output = ts_func(*inputs_)
+
+        if shape_only:
+            ts_output = ts_output.shape
+            output = output.shape
+        self.assertEqual(ts_output, output)
+
     @parameterized.expand([(2, 1), (3, 4)])
     def test_simulate_rir_ism_single_band(self, D, channel):
         room_dim = torch.rand(D, dtype=self.dtype, device=self.device) + 5