Move lfilter basic test to test_functional (#539)

test/test_functional.py

@@ -10,6 +10,42 @@ import pytest
 import common_utils
 
 
+class _LfilterMixin:
+    device = None
+    dtype = None
+
+    def test_simple(self):
+        """
+        Create a very basic signal,
+        Then make a simple 4th order delay
+        The output should be same as the input but shifted
+        """
+
+        torch.random.manual_seed(42)
+        waveform = torch.rand(2, 44100 * 1, dtype=self.dtype, device=self.device)
+        b_coeffs = torch.tensor([0, 0, 0, 1], dtype=self.dtype, device=self.device)
+        a_coeffs = torch.tensor([1, 0, 0, 0], dtype=self.dtype, device=self.device)
+        output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs)
+
+        torch.testing.assert_allclose(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5)
+
+
+class TestLfilterFloat32CPU(_LfilterMixin, unittest.TestCase):
+    device = torch.device('cpu')
+    dtype = torch.float32
+
+
+class TestLfilterFloat64CPU(_LfilterMixin, unittest.TestCase):
+    device = torch.device('cpu')
+    dtype = torch.float64
+
+
+@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
+class TestLfilterFloat32CUDA(_LfilterMixin, unittest.TestCase):
+    device = torch.device('cuda')
+    dtype = torch.float32
+
+
 class TestComputeDeltas(unittest.TestCase):
     """Test suite for correctness of compute_deltas"""
     def test_one_channel(self):

test/test_sox_compatibility.py

@@ -12,31 +12,6 @@ from common_utils import AudioBackendScope, BACKENDS, create_temp_assets_dir
 class TestFunctionalFiltering(unittest.TestCase):
     test_dirpath, test_dir = create_temp_assets_dir()
 
-    def _test_lfilter_basic(self, dtype, device):
-        """
-        Create a very basic signal,
-        Then make a simple 4th order delay
-        The output should be same as the input but shifted
-        """
-
-        torch.random.manual_seed(42)
-        waveform = torch.rand(2, 44100 * 1, dtype=dtype, device=device)
-        b_coeffs = torch.tensor([0, 0, 0, 1], dtype=dtype, device=device)
-        a_coeffs = torch.tensor([1, 0, 0, 0], dtype=dtype, device=device)
-        output_waveform = F.lfilter(waveform, a_coeffs, b_coeffs)
-
-        torch.testing.assert_allclose(output_waveform[:, 3:], waveform[:, 0:-3], atol=1e-5, rtol=1e-5)
-
-    def test_lfilter_basic(self):
-        self._test_lfilter_basic(torch.float32, torch.device("cpu"))
-
-    def test_lfilter_basic_double(self):
-        self._test_lfilter_basic(torch.float64, torch.device("cpu"))
-
-    @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
-    def test_lfilter_basic_gpu(self):
-        self._test_lfilter_basic(torch.float32, torch.device("cuda:0"))
-
     def _test_lfilter(self, waveform, device):
         """
         Design an IIR lowpass filter using scipy.signal filter design