Extract batch test from test_transforms and move to the dedicated module (#501)

test/test_batch_consistency.py

@@ -7,6 +7,7 @@ import torchaudio
 import torchaudio.functional as F
 
 import common_utils
+from common_utils import AudioBackendScope, BACKENDS
 
 
 def _test_batch_shape(functional, tensor, *args, **kwargs):
@@ -102,3 +103,212 @@ class TestFunctional(unittest.TestCase):
             [[0., 0.], [0., 0.], [0., 0.], [0., 0.], [0., 0.]]
         ])
         _test_batch(F.istft, stft, n_fft=4, length=4)
+
+
+class TestTransforms(unittest.TestCase):
+    """Test suite for classes defined in `transforms` module"""
+    def test_batch_AmplitudeToDB(self):
+        spec = torch.rand((6, 201))
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.AmplitudeToDB()(spec).repeat(3, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.AmplitudeToDB()(spec.repeat(3, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    def test_batch_Resample(self):
+        waveform = torch.randn(2, 2786)
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.Resample()(waveform).repeat(3, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.Resample()(waveform.repeat(3, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    def test_batch_MelScale(self):
+        specgram = torch.randn(2, 31, 2786)
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.MelScale()(specgram).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.MelScale()(specgram.repeat(3, 1, 1, 1))
+
+        # shape = (3, 2, 201, 1394)
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    def test_batch_InverseMelScale(self):
+        n_mels = 32
+        n_stft = 5
+        mel_spec = torch.randn(2, n_mels, 32) ** 2
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.InverseMelScale(n_stft, n_mels)(mel_spec).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.InverseMelScale(n_stft, n_mels)(mel_spec.repeat(3, 1, 1, 1))
+
+        # shape = (3, 2, n_mels, 32)
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+
+        # Because InverseMelScale runs SGD on randomly initialized values so they do not yield
+        # exactly same result. For this reason, tolerance is very relaxed here.
+        assert torch.allclose(computed, expected, atol=1.0)
+
+    def test_batch_compute_deltas(self):
+        specgram = torch.randn(2, 31, 2786)
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.ComputeDeltas()(specgram).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.ComputeDeltas()(specgram.repeat(3, 1, 1, 1))
+
+        # shape = (3, 2, 201, 1394)
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    def test_batch_mulaw(self):
+        test_filepath = os.path.join(
+            common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
+        waveform, _ = torchaudio.load(test_filepath)  # (2, 278756), 44100
+
+        # Single then transform then batch
+        waveform_encoded = torchaudio.transforms.MuLawEncoding()(waveform)
+        expected = waveform_encoded.unsqueeze(0).repeat(3, 1, 1)
+
+        # Batch then transform
+        waveform_batched = waveform.unsqueeze(0).repeat(3, 1, 1)
+        computed = torchaudio.transforms.MuLawEncoding()(waveform_batched)
+
+        # shape = (3, 2, 201, 1394)
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+        # Single then transform then batch
+        waveform_decoded = torchaudio.transforms.MuLawDecoding()(waveform_encoded)
+        expected = waveform_decoded.unsqueeze(0).repeat(3, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.MuLawDecoding()(computed)
+
+        # shape = (3, 2, 201, 1394)
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    def test_batch_spectrogram(self):
+        test_filepath = os.path.join(
+            common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
+        waveform, _ = torchaudio.load(test_filepath)  # (2, 278756), 44100
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.Spectrogram()(waveform).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.Spectrogram()(waveform.repeat(3, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    def test_batch_melspectrogram(self):
+        test_filepath = os.path.join(
+            common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
+        waveform, _ = torchaudio.load(test_filepath)  # (2, 278756), 44100
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.MelSpectrogram()(waveform).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.MelSpectrogram()(waveform.repeat(3, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    @unittest.skipIf("sox" not in BACKENDS, "sox not available")
+    @AudioBackendScope("sox")
+    def test_batch_mfcc(self):
+        test_filepath = os.path.join(
+            common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.mp3')
+        waveform, _ = torchaudio.load(test_filepath)
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.MFCC()(waveform).repeat(3, 1, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.MFCC()(waveform.repeat(3, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected, atol=1e-5)
+
+    def test_batch_TimeStretch(self):
+        test_filepath = os.path.join(
+            common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
+        waveform, _ = torchaudio.load(test_filepath)  # (2, 278756), 44100
+
+        kwargs = {
+            'n_fft': 2048,
+            'hop_length': 512,
+            'win_length': 2048,
+            'window': torch.hann_window(2048),
+            'center': True,
+            'pad_mode': 'reflect',
+            'normalized': True,
+            'onesided': True,
+        }
+        rate = 2
+
+        complex_specgrams = torch.stft(waveform, **kwargs)
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.TimeStretch(
+            fixed_rate=rate,
+            n_freq=1025,
+            hop_length=512,
+        )(complex_specgrams).repeat(3, 1, 1, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.TimeStretch(
+            fixed_rate=rate,
+            n_freq=1025,
+            hop_length=512,
+        )(complex_specgrams.repeat(3, 1, 1, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected, atol=1e-5)
+
+    def test_batch_Fade(self):
+        test_filepath = os.path.join(
+            common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
+        waveform, _ = torchaudio.load(test_filepath)  # (2, 278756), 44100
+        fade_in_len = 3000
+        fade_out_len = 3000
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.Fade(fade_in_len, fade_out_len)(waveform).repeat(3, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.Fade(fade_in_len, fade_out_len)(waveform.repeat(3, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)
+
+    def test_batch_Vol(self):
+        test_filepath = os.path.join(
+            common_utils.TEST_DIR_PATH, 'assets', 'steam-train-whistle-daniel_simon.wav')
+        waveform, _ = torchaudio.load(test_filepath)  # (2, 278756), 44100
+
+        # Single then transform then batch
+        expected = torchaudio.transforms.Vol(gain=1.1)(waveform).repeat(3, 1, 1)
+
+        # Batch then transform
+        computed = torchaudio.transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1))
+
+        assert computed.shape == expected.shape, (computed.shape, expected.shape)
+        assert torch.allclose(computed, expected)

test/test_transforms.py

@@ -44,18 +44,6 @@ class Tester(unittest.TestCase):
         waveform_exp = transforms.MuLawDecoding(quantization_channels)(waveform_mu)
         self.assertTrue(waveform_exp.min() >= -1. and waveform_exp.max() <= 1.)
 
-    def test_batch_AmplitudeToDB(self):
-        spec = torch.rand((6, 201))
-
-        # Single then transform then batch
-        expected = transforms.AmplitudeToDB()(spec).repeat(3, 1, 1)
-
-        # Batch then transform
-        computed = transforms.AmplitudeToDB()(spec.repeat(3, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
     def test_AmplitudeToDB(self):
         waveform, sample_rate = torchaudio.load(self.test_filepath)
 
@@ -175,18 +163,6 @@ class Tester(unittest.TestCase):
 
         self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))
 
-    def test_batch_Resample(self):
-        waveform = torch.randn(2, 2786)
-
-        # Single then transform then batch
-        expected = transforms.Resample()(waveform).repeat(3, 1, 1)
-
-        # Batch then transform
-        computed = transforms.Resample()(waveform.repeat(3, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
     def test_resample_size(self):
         input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
         waveform, sample_rate = torchaudio.load(input_path)
@@ -242,174 +218,6 @@ class Tester(unittest.TestCase):
         computed = transform(specgram)
         self.assertTrue(computed.shape == specgram.shape, (computed.shape, specgram.shape))
 
-    def test_batch_MelScale(self):
-        specgram = torch.randn(2, 31, 2786)
-
-        # Single then transform then batch
-        expected = transforms.MelScale()(specgram).repeat(3, 1, 1, 1)
-
-        # Batch then transform
-        computed = transforms.MelScale()(specgram.repeat(3, 1, 1, 1))
-
-        # shape = (3, 2, 201, 1394)
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
-    def test_batch_InverseMelScale(self):
-        n_fft = 8
-        n_mels = 32
-        n_stft = 5
-        mel_spec = torch.randn(2, n_mels, 32) ** 2
-
-        # Single then transform then batch
-        expected = transforms.InverseMelScale(n_stft, n_mels)(mel_spec).repeat(3, 1, 1, 1)
-
-        # Batch then transform
-        computed = transforms.InverseMelScale(n_stft, n_mels)(mel_spec.repeat(3, 1, 1, 1))
-
-        # shape = (3, 2, n_mels, 32)
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-
-        # Because InverseMelScale runs SGD on randomly initialized values so they do not yield
-        # exactly same result. For this reason, tolerance is very relaxed here.
-        self.assertTrue(torch.allclose(computed, expected, atol=1.0))
-
-    def test_batch_compute_deltas(self):
-        specgram = torch.randn(2, 31, 2786)
-
-        # Single then transform then batch
-        expected = transforms.ComputeDeltas()(specgram).repeat(3, 1, 1, 1)
-
-        # Batch then transform
-        computed = transforms.ComputeDeltas()(specgram.repeat(3, 1, 1, 1))
-
-        # shape = (3, 2, 201, 1394)
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
-    def test_batch_mulaw(self):
-        waveform, sample_rate = torchaudio.load(self.test_filepath)  # (2, 278756), 44100
-
-        # Single then transform then batch
-        waveform_encoded = transforms.MuLawEncoding()(waveform)
-        expected = waveform_encoded.unsqueeze(0).repeat(3, 1, 1)
-
-        # Batch then transform
-        waveform_batched = waveform.unsqueeze(0).repeat(3, 1, 1)
-        computed = transforms.MuLawEncoding()(waveform_batched)
-
-        # shape = (3, 2, 201, 1394)
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
-        # Single then transform then batch
-        waveform_decoded = transforms.MuLawDecoding()(waveform_encoded)
-        expected = waveform_decoded.unsqueeze(0).repeat(3, 1, 1)
-
-        # Batch then transform
-        computed = transforms.MuLawDecoding()(computed)
-
-        # shape = (3, 2, 201, 1394)
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
-    def test_batch_spectrogram(self):
-        waveform, sample_rate = torchaudio.load(self.test_filepath)
-
-        # Single then transform then batch
-        expected = transforms.Spectrogram()(waveform).repeat(3, 1, 1, 1)
-
-        # Batch then transform
-        computed = transforms.Spectrogram()(waveform.repeat(3, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
-    def test_batch_melspectrogram(self):
-        waveform, sample_rate = torchaudio.load(self.test_filepath)
-
-        # Single then transform then batch
-        expected = transforms.MelSpectrogram()(waveform).repeat(3, 1, 1, 1)
-
-        # Batch then transform
-        computed = transforms.MelSpectrogram()(waveform.repeat(3, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
-    @unittest.skipIf("sox" not in BACKENDS, "sox not available")
-    @AudioBackendScope("sox")
-    def test_batch_mfcc(self):
-        test_filepath = os.path.join(
-            self.test_dirpath, 'assets', 'steam-train-whistle-daniel_simon.mp3'
-        )
-        waveform, sample_rate = torchaudio.load(test_filepath)
-
-        # Single then transform then batch
-        expected = transforms.MFCC()(waveform).repeat(3, 1, 1, 1)
-
-        # Batch then transform
-        computed = transforms.MFCC()(waveform.repeat(3, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected, atol=1e-5))
-
-    def test_batch_TimeStretch(self):
-        waveform, sample_rate = torchaudio.load(self.test_filepath)
-
-        kwargs = {
-            'n_fft': 2048,
-            'hop_length': 512,
-            'win_length': 2048,
-            'window': torch.hann_window(2048),
-            'center': True,
-            'pad_mode': 'reflect',
-            'normalized': True,
-            'onesided': True,
-        }
-        rate = 2
-
-        complex_specgrams = torch.stft(waveform, **kwargs)
-
-        # Single then transform then batch
-        expected = transforms.TimeStretch(fixed_rate=rate,
-                                          n_freq=1025,
-                                          hop_length=512)(complex_specgrams).repeat(3, 1, 1, 1, 1)
-
-        # Batch then transform
-        computed = transforms.TimeStretch(fixed_rate=rate,
-                                          n_freq=1025,
-                                          hop_length=512)(complex_specgrams.repeat(3, 1, 1, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected, atol=1e-5))
-
-    def test_batch_Fade(self):
-        waveform, sample_rate = torchaudio.load(self.test_filepath)
-        fade_in_len = 3000
-        fade_out_len = 3000
-
-        # Single then transform then batch
-        expected = transforms.Fade(fade_in_len, fade_out_len)(waveform).repeat(3, 1, 1)
-
-        # Batch then transform
-        computed = transforms.Fade(fade_in_len, fade_out_len)(waveform.repeat(3, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
-    def test_batch_Vol(self):
-        waveform, sample_rate = torchaudio.load(self.test_filepath)
-
-        # Single then transform then batch
-        expected = transforms.Vol(gain=1.1)(waveform).repeat(3, 1, 1)
-
-        # Batch then transform
-        computed = transforms.Vol(gain=1.1)(waveform.repeat(3, 1, 1))
-
-        self.assertTrue(computed.shape == expected.shape, (computed.shape, expected.shape))
-        self.assertTrue(torch.allclose(computed, expected))
-
 
 if __name__ == '__main__':
     unittest.main()