Apply misc updates to functional/batch_consistency_test.py (#1341)

* Parameterize `test_sliding_window_cmn`

* Extract test naming function

* Pass a spectrogram to `F.sliding_window_cmn`

* Set manual seed for remaining rand calls in suite

test/torchaudio_unittest/functional/batch_consistency_test.py

@@ -9,6 +9,13 @@
 from torchaudio_unittest import common_utils
 
 
+def _name_from_args(func, _, params):
+    """Return a parameterized test name, based on parameter values."""
+    return "{}_{}".format(
+        func.__name__,
+        "_".join(str(arg) for arg in params.args))
+
+
 @parameterized_class([
     # Single-item batch isolates problems that come purely from adding a
     # dimension (rather than processing multiple items)
@@ -58,7 +65,7 @@ def test_griffinlim(self):
     @parameterized.expand(list(itertools.product(
         [8000, 16000, 44100],
         [1, 2],
-    )), name_func=lambda f, _, p: f'{f.__name__}_{"_".join(str(arg) for arg in p.args)}')
+    )), name_func=_name_from_args)
     def test_detect_pitch_frequency(self, sample_rate, n_channels):
         # Use different frequencies to ensure each item in the batch returns a
         # different answer.
@@ -180,16 +187,16 @@ def test_flanger(self):
         sample_rate = 44100
         self.assert_batch_consistency(F.flanger, waveforms, sample_rate)
 
-    def test_sliding_window_cmn(self):
-        waveforms = torch.randn(self.batch_size, 2, 1024) - 0.5
-        self.assert_batch_consistency(
-            F.sliding_window_cmn, waveforms, center=True, norm_vars=True)
-        self.assert_batch_consistency(
-            F.sliding_window_cmn, waveforms, center=True, norm_vars=False)
-        self.assert_batch_consistency(
-            F.sliding_window_cmn, waveforms, center=False, norm_vars=True)
+    @parameterized.expand(list(itertools.product(
+        [True, False],  # center
+        [True, False],  # norm_vars
+    )), name_func=_name_from_args)
+    def test_sliding_window_cmn(self, center, norm_vars):
+        torch.manual_seed(0)
+        spectrogram = torch.rand(self.batch_size, 2, 1024, 1024) * 200
         self.assert_batch_consistency(
-            F.sliding_window_cmn, waveforms, center=False, norm_vars=False)
+            F.sliding_window_cmn, spectrogram, center=center,
+            norm_vars=norm_vars)
 
     def test_vad_from_file(self):
         filepath = common_utils.get_asset_path("vad-go-stereo-44100.wav")
@@ -202,6 +209,7 @@ def test_vad_from_file(self):
     def test_vad_different_items(self):
         """Separate test to ensure VAD consistency with differing items."""
         sample_rate = 44100
+        torch.manual_seed(0)
         waveforms = torch.rand(self.batch_size, 2, 100) - 0.5
         self.assert_batch_consistency(
             F.vad, waveforms, sample_rate=sample_rate)