Merge pull request #129 from hbredin/chore/prepare_deprecation

feat: add "output_type" argument

tests/test_background_noise.py

@@ -38,11 +38,15 @@ def setUp(self):
 
         self.bg_path = TEST_FIXTURES_DIR / "bg"
         self.bg_short_path = TEST_FIXTURES_DIR / "bg_short"
-        self.bg_noise_transform_guaranteed = AddBackgroundNoise(self.bg_path, 20, p=1.0)
+        self.bg_noise_transform_guaranteed = AddBackgroundNoise(
+            self.bg_path, 20, p=1.0, output_type="dict"
+        )
         self.bg_short_noise_transform_guaranteed = AddBackgroundNoise(
-            self.bg_short_path, 20, p=1.0
+            self.bg_short_path, 20, p=1.0, output_type="dict"
+        )
+        self.bg_noise_transform_no_guarantee = AddBackgroundNoise(
+            self.bg_path, 20, p=0.0, output_type="dict"
         )
-        self.bg_noise_transform_no_guarantee = AddBackgroundNoise(self.bg_path, 20, p=0.0)
 
     def test_background_noise_no_guarantee_with_single_tensor(self):
         mixed_input = self.bg_noise_transform_no_guarantee(
@@ -118,7 +122,11 @@ def test_varying_snr_within_batch(self):
         min_snr_in_db = 3
         max_snr_in_db = 30
         augment = AddBackgroundNoise(
-            self.bg_path, min_snr_in_db=min_snr_in_db, max_snr_in_db=max_snr_in_db, p=1.0
+            self.bg_path,
+            min_snr_in_db=min_snr_in_db,
+            max_snr_in_db=max_snr_in_db,
+            p=1.0,
+            output_type="dict",
         )
         augmented_audios = augment(self.input_audios, self.sample_rate).samples
 
@@ -142,13 +150,17 @@ def test_varying_snr_within_batch(self):
     def test_invalid_params(self):
         with self.assertRaises(ValueError):
             augment = AddBackgroundNoise(
-                self.bg_path, min_snr_in_db=30, max_snr_in_db=3, p=1.0
+                self.bg_path, min_snr_in_db=30, max_snr_in_db=3, p=1.0, output_type="dict"
             )
 
     def test_min_equals_max(self):
         desired_snr = 3.0
         augment = AddBackgroundNoise(
-            self.bg_path, min_snr_in_db=desired_snr, max_snr_in_db=desired_snr, p=1.0
+            self.bg_path,
+            min_snr_in_db=desired_snr,
+            max_snr_in_db=desired_snr,
+            p=1.0,
+            output_type="dict",
         )
         augmented_audios = augment(self.input_audios, self.sample_rate).samples
 
@@ -171,11 +183,9 @@ def test_compatibility_of_resampled_length(self):
             input_sample_rate = random.randint(1000, 5000)
             bg_sample_rate = random.randint(1000, 5000)
 
-            noise = np.random.uniform(
-                low=-0.2,
-                high=0.2,
-                size=(bg_length,),
-            ).astype(np.float32)
+            noise = np.random.uniform(low=-0.2, high=0.2, size=(bg_length,),).astype(
+                np.float32
+            )
             tmp_dir = os.path.join(tempfile.gettempdir(), str(uuid.uuid4()))
             try:
                 os.makedirs(tmp_dir)
@@ -192,6 +202,7 @@ def test_compatibility_of_resampled_length(self):
                     max_snr_in_db=6,
                     p=1.0,
                     sample_rate=input_sample_rate,
+                    output_type="dict",
                 )
                 transform(input_audio)
             except Exception:

tests/test_band_pass_filter.py

@@ -16,7 +16,7 @@ def test_band_pass_filter(self):
         )
         sample_rate = 16000
 
-        augment = BandPassFilter(p=1.0)
+        augment = BandPassFilter(p=1.0, output_type="dict")
         for _ in range(20):
             processed_samples = augment(
                 samples=torch.from_numpy(samples), sample_rate=sample_rate

tests/test_band_stop_filter.py

@@ -16,7 +16,7 @@ def test_band_reject_filter(self):
         )
         sample_rate = 16000
 
-        augment = BandStopFilter(p=1.0)
+        augment = BandStopFilter(p=1.0, output_type="dict")
         processed_samples = augment(
             samples=torch.from_numpy(samples), sample_rate=sample_rate
         ).samples.numpy()

tests/test_base_class.py

@@ -10,12 +10,12 @@
 class TestBaseClass(unittest.TestCase):
     def test_parameters(self):
         # Test that we can access the parameters function of nn.Module
-        augment = PolarityInversion(p=1.0)
+        augment = PolarityInversion(p=1.0, output_type="dict")
         params = augment.parameters()
         assert isinstance(params, types.GeneratorType)
 
     def test_ndim_check(self):
-        augment = PolarityInversion(p=1.0)
+        augment = PolarityInversion(p=1.0, output_type="dict")
         # 1D tensor not allowed
         with pytest.raises(RuntimeError):
             augment(torch.tensor([1.0, 0.5, 0.25, 0.125], dtype=torch.float32))

tests/test_colored_noise.py

@@ -20,8 +20,12 @@ def setUp(self):
         ).unsqueeze(0)
 
         self.input_audios = torch.cat([self.input_audio] * self.batch_size, dim=0)
-        self.cl_noise_transform_guaranteed = AddColoredNoise(20, p=1.0)
-        self.cl_noise_transform_no_guarantee = AddColoredNoise(20, p=0.0)
+        self.cl_noise_transform_guaranteed = AddColoredNoise(
+            20, p=1.0, output_type="dict"
+        )
+        self.cl_noise_transform_no_guarantee = AddColoredNoise(
+            20, p=0.0, output_type="dict"
+        )
 
     def test_colored_noise_no_guarantee_with_single_tensor(self):
         mixed_input = self.cl_noise_transform_no_guarantee(
@@ -76,6 +80,6 @@ def test_colored_noise_guaranteed_with_batched_tensor(self):
 
     def test_invalid_params(self):
         with self.assertRaises(ValueError):
-            AddColoredNoise(min_snr_in_db=30, max_snr_in_db=3, p=1.0)
+            AddColoredNoise(min_snr_in_db=30, max_snr_in_db=3, p=1.0, output_type="dict")
         with self.assertRaises(ValueError):
-            AddColoredNoise(min_f_decay=2, max_f_decay=1, p=1.0)
+            AddColoredNoise(min_f_decay=2, max_f_decay=1, p=1.0, output_type="dict")

tests/test_compose.py

@@ -20,7 +20,8 @@ def test_compose(self):
             [
                 Gain(min_gain_in_db=-6.000001, max_gain_in_db=-6, p=1.0),
                 PolarityInversion(p=1.0),
-            ]
+            ],
+            output_type="dict",
         )
         processed_samples = augment(
             samples=torch.from_numpy(samples), sample_rate=sample_rate
@@ -38,7 +39,9 @@ def test_compose_with_torchaudio_transform(self):
         samples = np.array([[[1.0, 0.5, -0.25, -0.125, 0.0]]], dtype=np.float32)
         sample_rate = 16000
 
-        augment = Compose([Vol(gain=-6, gain_type="db"), PolarityInversion(p=1.0)])
+        augment = Compose(
+            [Vol(gain=-6, gain_type="db"), PolarityInversion(p=1.0),], output_type="dict"
+        )
         processed_samples = augment(
             samples=torch.from_numpy(samples), sample_rate=sample_rate
         ).samples.numpy()
@@ -61,6 +64,7 @@ def test_compose_with_p_zero(self):
                 PolarityInversion(p=1.0),
             ],
             p=0.0,
+            output_type="dict",
         )
         processed_samples = augment(
             samples=torch.from_numpy(samples), sample_rate=sample_rate
@@ -75,9 +79,10 @@ def test_freeze_and_unfreeze_parameters(self):
 
         augment = Compose(
             transforms=[
-                Gain(min_gain_in_db=-16.000001, max_gain_in_db=-2, p=1.0),
+                Gain(min_gain_in_db=-16.000001, max_gain_in_db=-2, p=1.0,),
                 PolarityInversion(p=1.0),
-            ]
+            ],
+            output_type="dict",
         )
 
         processed_samples1 = augment(
@@ -106,6 +111,7 @@ def test_shuffle(self):
                 PeakNormalization(p=1.0),
             ],
             shuffle=True,
+            output_type="dict",
         )
         num_peak_normalization_last = 0
         num_gain_last = 0
@@ -126,14 +132,11 @@ def test_shuffle(self):
         self.assertGreater(num_gain_last, 10)
 
     def test_supported_modes_property(self):
-        augment = Compose(
-            transforms=[
-                PeakNormalization(p=1.0),
-            ],
-        )
+        augment = Compose(transforms=[PeakNormalization(p=1.0),], output_type="dict")
         assert augment.supported_modes == {"per_batch", "per_example", "per_channel"}
 
         augment = Compose(
-            transforms=[PeakNormalization(p=1.0), ShuffleChannels(p=1.0)],
+            transforms=[PeakNormalization(p=1.0,), ShuffleChannels(p=1.0,),],
+            output_type="dict",
         )
         assert augment.supported_modes == {"per_example"}

tests/test_differentiable.py

@@ -25,26 +25,34 @@
     "augment",
     [
         # Differentiable transforms:
-        AddBackgroundNoise(BG_NOISE_PATH, 20, p=1.0),
-        ApplyImpulseResponse(IR_PATH, p=1.0),
+        AddBackgroundNoise(BG_NOISE_PATH, 20, p=1.0, output_type="dict"),
+        ApplyImpulseResponse(IR_PATH, p=1.0, output_type="dict"),
         Compose(
             transforms=[
                 Gain(min_gain_in_db=-15.0, max_gain_in_db=5.0, p=1.0),
                 PolarityInversion(p=1.0),
-            ]
+            ],
+            output_type="dict",
         ),
-        Gain(min_gain_in_db=-6.000001, max_gain_in_db=-6, p=1.0),
-        PolarityInversion(p=1.0),
-        Shift(p=1.0),
+        Gain(min_gain_in_db=-6.000001, max_gain_in_db=-6, p=1.0, output_type="dict"),
+        PolarityInversion(p=1.0, output_type="dict"),
+        Shift(p=1.0, output_type="dict"),
         # Non-differentiable transforms:
         # RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation:
         # [torch.DoubleTensor [1, 1, 5]], which is output 0 of IndexBackward, is at version 1; expected version 0 instead.
         # Hint: enable anomaly detection to find the operation that failed to compute its gradient,
         # with torch.autograd.set_detect_anomaly(True).
-        pytest.param(HighPassFilter(p=1.0), marks=pytest.mark.skip("Not differentiable")),
-        pytest.param(LowPassFilter(p=1.0), marks=pytest.mark.skip("Not differentiable")),
         pytest.param(
-            PeakNormalization(p=1.0), marks=pytest.mark.skip("Not differentiable")
+            HighPassFilter(p=1.0, output_type="dict"),
+            marks=pytest.mark.skip("Not differentiable"),
+        ),
+        pytest.param(
+            LowPassFilter(p=1.0, output_type="dict"),
+            marks=pytest.mark.skip("Not differentiable"),
+        ),
+        pytest.param(
+            PeakNormalization(p=1.0, output_type="dict"),
+            marks=pytest.mark.skip("Not differentiable"),
         ),
     ],
 )