Make TestCases backend-aware (#719)

* Make tests backend aware by introducing TorchaudioTestCase and reset backend for each TestCase.

* Set backends for the test cases that require specific backend.

test/README.md

@@ -44,6 +44,16 @@ The following test modules are defined for corresponding `torchaudio` module/fun
 
 ## Adding test
 
+The following is the current practice of torchaudio test suite.
+
+1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils.py) has some data generator functions.
+1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA.
+  - Set class memeber `dtype`, `device` and `backend` for the desired behavior.
+  - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail.
+  - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability.
+1. If you are writing tests that should pass on diffrent dtype/devices, write a common class inheriting `common_utils.TestBaseMixin`, then inherit `common_utils.PytorchTestCase` and define class attributes (`dtype` / `device` / `backend`) there. See [Torchscript consistency test implementation](./torchscript_consistency_impl.py) and test definitions for [CPU](./torchscript_consistency_cpu_test.py) and [CUDA](./torchscript_consistency_cuda_test.py) devices.
+1. For numerically comparing Tensors, use `assertEqual` method from `common_utils.PytorchTestCase` class. This method has a better support for a wide variety of Tensor types.
+
 When you add a new feature(functional/transform), consider the following
 
 1. When you add a new feature, please make it Torchscript-able and batch-consistent unless it degrades the performance. Please add the tests to see if the new feature meet these requirements.

test/common_utils.py

@@ -1,12 +1,11 @@
 import os
 import tempfile
 import unittest
-from typing import Iterable, Union
-from contextlib import contextmanager
+from typing import Union
 from shutil import copytree
 
 import torch
-from torch.testing._internal.common_utils import TestCase
+from torch.testing._internal.common_utils import TestCase as PytorchTestCase
 import torchaudio
 
 _TEST_DIR_PATH = os.path.dirname(os.path.realpath(__file__))
@@ -55,24 +54,14 @@ def random_float_tensor(seed, size, a=22695477, c=1, m=2 ** 32):
     return torch.tensor(arr).float().view(size) / m
 
 
-@contextmanager
-def AudioBackendScope(new_backend):
-    previous_backend = torchaudio.get_audio_backend()
-    try:
-        torchaudio.set_audio_backend(new_backend)
-        yield
-    finally:
-        torchaudio.set_audio_backend(previous_backend)
-
-
 def filter_backends_with_mp3(backends):
     # Filter out backends that do not support mp3
     test_filepath = get_asset_path('steam-train-whistle-daniel_simon.mp3')
 
     def supports_mp3(backend):
+        torchaudio.set_audio_backend(backend)
         try:
-            with AudioBackendScope(backend):
-                torchaudio.load(test_filepath)
+            torchaudio.load(test_filepath)
             return True
         except (RuntimeError, ImportError):
             return False
@@ -83,21 +72,38 @@ def supports_mp3(backend):
 BACKENDS_MP3 = filter_backends_with_mp3(BACKENDS)
 
 
+def set_audio_backend(backend):
+    """Allow additional backend value, 'default'"""
+    if backend == 'default':
+        if 'sox' in BACKENDS:
+            be = 'sox'
+        elif 'soundfile' in BACKENDS:
+            be = 'soundfile'
+        else:
+            raise unittest.SkipTest('No default backend available')
+    else:
+        be = backend
+
+    torchaudio.set_audio_backend(be)
+
+
 class TestBaseMixin:
+    """Mixin to provide consistent way to define device/dtype/backend aware TestCase"""
     dtype = None
     device = None
+    backend = None
 
+    def setUp(self):
+        super().setUp()
+        set_audio_backend(self.backend)
 
-skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available')
 
+class TorchaudioTestCase(TestBaseMixin, PytorchTestCase):
+    pass
 
-def common_test_class_parameters(
-    dtypes: Iterable[str] = ("float32", "float64"),
-    devices: Iterable[str] = ("cpu", "cuda"),
-):
-    for device in devices:
-        for dtype in dtypes:
-            yield {"device": torch.device(device), "dtype": getattr(torch, dtype)}
+
+skipIfNoSoxBackend = unittest.skipIf('sox' not in BACKENDS, 'Sox backend not available')
+skipIfNoCuda = unittest.skipIf(not torch.cuda.is_available(), reason='CUDA not available')
 
 
 def get_whitenoise(

test/functional_cpu_test.py

@@ -10,17 +10,17 @@
 from .functional_impl import Lfilter
 
 
-class TestLFilterFloat32(Lfilter, common_utils.TestCase):
+class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase):
     dtype = torch.float32
     device = torch.device('cpu')
 
 
-class TestLFilterFloat64(Lfilter, common_utils.TestCase):
+class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase):
     dtype = torch.float64
     device = torch.device('cpu')
 
 
-class TestComputeDeltas(unittest.TestCase):
+class TestComputeDeltas(common_utils.TorchaudioTestCase):
     """Test suite for correctness of compute_deltas"""
     def test_one_channel(self):
         specgram = torch.tensor([[[1.0, 2.0, 3.0, 4.0]]])
@@ -57,7 +57,7 @@ def _test_istft_is_inverse_of_stft(kwargs):
             _compare_estimate(sound, estimate)
 
 
-class TestIstft(unittest.TestCase):
+class TestIstft(common_utils.TorchaudioTestCase):
     """Test suite for correctness of istft with various input"""
     number_of_trials = 100
 
@@ -273,7 +273,9 @@ def test_linearity_of_istft4(self):
         self._test_linearity_of_istft(data_size, kwargs4, atol=1e-5, rtol=1e-8)
 
 
-class TestDetectPitchFrequency(unittest.TestCase):
+class TestDetectPitchFrequency(common_utils.TorchaudioTestCase):
+    backend = 'default'
+
     def test_pitch(self):
         test_filepath_100 = common_utils.get_asset_path("100Hz_44100Hz_16bit_05sec.wav")
         test_filepath_440 = common_utils.get_asset_path("440Hz_44100Hz_16bit_05sec.wav")
@@ -294,7 +296,7 @@ def test_pitch(self):
             self.assertFalse(s)
 
 
-class TestDB_to_amplitude(unittest.TestCase):
+class TestDB_to_amplitude(common_utils.TorchaudioTestCase):
     def test_DB_to_amplitude(self):
         # Make some noise
         x = torch.rand(1000)

test/functional_cuda_test.py

@@ -5,12 +5,12 @@
 
 
 @common_utils.skipIfNoCuda
-class TestLFilterFloat32(Lfilter, common_utils.TestCase):
+class TestLFilterFloat32(Lfilter, common_utils.PytorchTestCase):
     dtype = torch.float32
     device = torch.device('cuda')
 
 
 @common_utils.skipIfNoCuda
-class TestLFilterFloat64(Lfilter, common_utils.TestCase):
+class TestLFilterFloat64(Lfilter, common_utils.PytorchTestCase):
     dtype = torch.float64
     device = torch.device('cuda')

test/kaldi_compatibility_cpu_test.py

@@ -4,11 +4,11 @@
 from .kaldi_compatibility_impl import Kaldi
 
 
-class TestKaldiFloat32(Kaldi, common_utils.TestCase):
+class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase):
     dtype = torch.float32
     device = torch.device('cpu')
 
 
-class TestKaldiFloat64(Kaldi, common_utils.TestCase):
+class TestKaldiFloat64(Kaldi, common_utils.PytorchTestCase):
     dtype = torch.float64
     device = torch.device('cpu')

test/kaldi_compatibility_cuda_test.py

@@ -5,12 +5,12 @@
 
 
 @common_utils.skipIfNoCuda
-class TestKaldiFloat32(Kaldi, common_utils.TestCase):
+class TestKaldiFloat32(Kaldi, common_utils.PytorchTestCase):
     dtype = torch.float32
     device = torch.device('cuda')
 
 
 @common_utils.skipIfNoCuda
-class TestKaldiFloat64(Kaldi, common_utils.TestCase):
+class TestKaldiFloat64(Kaldi, common_utils.PytorchTestCase):
     dtype = torch.float64
     device = torch.device('cuda')

test/kaldi_compatibility_impl.py

@@ -55,6 +55,8 @@ def _load_params(path):
 
 
 class Kaldi(common_utils.TestBaseMixin):
+    backend = 'sox'
+
     def assert_equal(self, output, *, expected, rtol=None, atol=None):
         expected = expected.to(dtype=self.dtype, device=self.device)
         self.assertEqual(output, expected, rtol=rtol, atol=atol)

test/test_backend.py

@@ -3,8 +3,10 @@
 import torchaudio
 from torchaudio._internal.module_utils import is_module_available
 
+from . import common_utils
 
-class BackendSwitch:
+
+class BackendSwitchMixin:
     """Test set/get_audio_backend works"""
     backend = None
     backend_module = None
@@ -21,20 +23,20 @@ def test_switch(self):
         assert torchaudio.info == self.backend_module.info
 
 
-class TestBackendSwitch_NoBackend(BackendSwitch, unittest.TestCase):
+class TestBackendSwitch_NoBackend(BackendSwitchMixin, common_utils.TorchaudioTestCase):
     backend = None
     backend_module = torchaudio.backend.no_backend
 
 
 @unittest.skipIf(
     not is_module_available('torchaudio._torchaudio'),
     'torchaudio C++ extension not available')
-class TestBackendSwitch_SoX(BackendSwitch, unittest.TestCase):
+class TestBackendSwitch_SoX(BackendSwitchMixin, common_utils.TorchaudioTestCase):
     backend = 'sox'
     backend_module = torchaudio.backend.sox_backend
 
 
 @unittest.skipIf(not is_module_available('soundfile'), '"soundfile" not available')
-class TestBackendSwitch_soundfile(BackendSwitch, unittest.TestCase):
+class TestBackendSwitch_soundfile(BackendSwitchMixin, common_utils.TorchaudioTestCase):
     backend = 'soundfile'
     backend_module = torchaudio.backend.soundfile_backend

test/test_batch_consistency.py

@@ -2,14 +2,14 @@
 import unittest
 
 import torch
-from torch.testing._internal.common_utils import TestCase
 import torchaudio
 import torchaudio.functional as F
 
 from . import common_utils
 
 
-class TestFunctional(TestCase):
+class TestFunctional(common_utils.TorchaudioTestCase):
+    backend = 'default'
     """Test functions defined in `functional` module"""
     def assert_batch_consistency(
             self, functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, seed=42, **kwargs):
@@ -98,12 +98,15 @@ def test_sliding_window_cmn(self):
         self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=False)
 
     def test_vad(self):
+        common_utils.set_audio_backend('default')
         filepath = common_utils.get_asset_path("vad-go-mono-32000.wav")
         waveform, sample_rate = torchaudio.load(filepath)
         self.assert_batch_consistencies(F.vad, waveform, sample_rate=sample_rate)
 
 
-class TestTransforms(TestCase):
+class TestTransforms(common_utils.TorchaudioTestCase):
+    backend = 'default'
+
     """Test suite for classes defined in `transforms` module"""
     def test_batch_AmplitudeToDB(self):
         spec = torch.rand((6, 201))

test/test_compliance_kaldi.py

@@ -1,13 +1,13 @@
-import math
 import os
+import math
+import unittest
+
 import torch
 import torchaudio
 import torchaudio.compliance.kaldi as kaldi
-import unittest
 
 from . import common_utils
 from .compliance import utils as compliance_utils
-from .common_utils import AudioBackendScope, BACKENDS
 
 
 def extract_window(window, wave, f, frame_length, frame_shift, snip_edges):
@@ -46,7 +46,10 @@ def first_sample_of_frame(frame, window_size, window_shift, snip_edges):
             window[f, s] = wave[s_in_wave]
 
 
-class Test_Kaldi(unittest.TestCase):
+@common_utils.skipIfNoSoxBackend
+class Test_Kaldi(common_utils.TorchaudioTestCase):
+    backend = 'sox'
+
     test_filepath = common_utils.get_asset_path('kaldi_file.wav')
     test_8000_filepath = common_utils.get_asset_path('kaldi_file_8000.wav')
     kaldi_output_dir = common_utils.get_asset_path('kaldi')
@@ -162,8 +165,6 @@ def test_mfcc_empty(self):
         # Passing in an empty tensor should result in an error
         self.assertRaises(AssertionError, kaldi.mfcc, torch.empty(0))
 
-    @unittest.skipIf("sox" not in BACKENDS, "sox not available")
-    @AudioBackendScope("sox")
     def test_resample_waveform(self):
         def get_output_fn(sound, args):
             output = kaldi.resample_waveform(sound, args[1], args[2])

test/test_dataloader.py

@@ -4,7 +4,6 @@
 from torch.utils.data import Dataset, DataLoader
 
 from . import common_utils
-from .common_utils import AudioBackendScope, BACKENDS
 
 
 class TORCHAUDIODS(Dataset):
@@ -28,9 +27,10 @@ def __len__(self):
         return len(self.data)
 
 
-class Test_DataLoader(unittest.TestCase):
-    @unittest.skipIf("sox" not in BACKENDS, "sox not available")
-    @AudioBackendScope("sox")
+class Test_DataLoader(common_utils.TorchaudioTestCase):
+    backend = 'sox'
+
+    @common_utils.skipIfNoSoxBackend
     def test_1(self):
         expected_size = (2, 1, 16000)
         ds = TORCHAUDIODS()