Adopt PyTorch's test util to transforms test (#652)

test/test_transforms.py

@@ -2,6 +2,7 @@ import math
 import unittest
 
 import torch
+from torch.testing._internal.common_utils import TestCase
 import torchaudio
 import torchaudio.transforms as transforms
 import torchaudio.functional as F
@@ -9,7 +10,7 @@ import torchaudio.functional as F
 import common_utils
 
 
-class Tester(unittest.TestCase):
+class Tester(TestCase):
 
     # create a sinewave signal for testing
     sample_rate = 16000
@@ -49,7 +50,7 @@ class Tester(unittest.TestCase):
         mag_to_db_torch = mag_to_db_transform(torch.abs(waveform))
         power_to_db_torch = power_to_db_transform(torch.pow(waveform, 2))
 
-        torch.testing.assert_allclose(mag_to_db_torch, power_to_db_torch)
+        self.assertEqual(mag_to_db_torch, power_to_db_torch)
 
     def test_melscale_load_save(self):
         specgram = torch.ones(1, 1000, 100)
@@ -63,7 +64,7 @@ class Tester(unittest.TestCase):
         fb_copy = melscale_transform_copy.fb
 
         self.assertEqual(fb_copy.size(), (1000, 128))
-        torch.testing.assert_allclose(fb, fb_copy)
+        self.assertEqual(fb, fb_copy)
 
     def test_melspectrogram_load_save(self):
         waveform = self.waveform.float()
@@ -79,10 +80,10 @@ class Tester(unittest.TestCase):
         fb = mel_spectrogram_transform.mel_scale.fb
         fb_copy = mel_spectrogram_transform_copy.mel_scale.fb
 
-        torch.testing.assert_allclose(window, window_copy)
+        self.assertEqual(window, window_copy)
         # the default for n_fft = 400 and n_mels = 128
         self.assertEqual(fb_copy.size(), (201, 128))
-        torch.testing.assert_allclose(fb, fb_copy)
+        self.assertEqual(fb, fb_copy)
 
     def test_mel2(self):
         top_db = 80.
@@ -205,7 +206,7 @@ class Tester(unittest.TestCase):
         computed_transform = transform(specgram)
 
         computed_functional = F.compute_deltas(specgram, win_length=win_length)
-        torch.testing.assert_allclose(computed_functional, computed_transform, atol=atol, rtol=rtol)
+        self.assertEqual(computed_functional, computed_transform, atol=atol, rtol=rtol)
 
     def test_compute_deltas_twochannel(self):
         specgram = torch.tensor([1., 2., 3., 4.]).repeat(1, 2, 1)
@@ -214,7 +215,7 @@ class Tester(unittest.TestCase):
         transform = transforms.ComputeDeltas(win_length=3)
         computed = transform(specgram)
         assert computed.shape == expected.shape, (computed.shape, expected.shape)
-        torch.testing.assert_allclose(computed, expected, atol=1e-6, rtol=1e-8)
+        self.assertEqual(computed, expected, atol=1e-6, rtol=1e-8)
 
 
 if __name__ == '__main__':