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Unverified Commit 35d68fdd authored by Chin-Yun Yu's avatar Chin-Yun Yu Committed by GitHub
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Shorten lfilter autograd tests input size (#1443) 

Use shorter input sequences to avoid time out error on CI
parent 0fbfca5c
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Showing with 14 additions and 14 deletions
test/torchaudio_unittest/functional/autograd_impl.py
View file @ 35d68fdd
......@@ -29,7 +29,7 @@ class Autograd(TestBaseMixin):
def test_lfilter_x(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.025, n_channels=2)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2)
a = torch.tensor([0.7, 0.2, 0.6])
b = torch.tensor([0.4, 0.2, 0.9])
x.requires_grad = True
......@@ -37,7 +37,7 @@ class Autograd(TestBaseMixin):
def test_lfilter_a(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2)
a = torch.tensor([0.7, 0.2, 0.6])
b = torch.tensor([0.4, 0.2, 0.9])
a.requires_grad = True
......@@ -45,7 +45,7 @@ class Autograd(TestBaseMixin):
def test_lfilter_b(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2)
a = torch.tensor([0.7, 0.2, 0.6])
b = torch.tensor([0.4, 0.2, 0.9])
b.requires_grad = True
......@@ -53,14 +53,14 @@ class Autograd(TestBaseMixin):
def test_lfilter_all_inputs(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=2)
a = torch.tensor([0.7, 0.2, 0.6])
b = torch.tensor([0.4, 0.2, 0.9])
self.assert_grad(F.lfilter, (x, a, b))
def test_biquad(self):
torch.random.manual_seed(2434)
x = get_whitenoise(sample_rate=22050, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=22050, duration=0.01, n_channels=1)
a = torch.tensor([0.7, 0.2, 0.6])
b = torch.tensor([0.4, 0.2, 0.9])
self.assert_grad(F.biquad, (x, b[0], b[1], b[2], a[0], a[1], a[2]))
......@@ -72,7 +72,7 @@ class Autograd(TestBaseMixin):
def test_band_biquad(self, central_freq, Q, noise):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
central_freq = torch.tensor(central_freq)
Q = torch.tensor(Q)
self.assert_grad(F.band_biquad, (x, sr, central_freq, Q, noise))
......@@ -84,7 +84,7 @@ class Autograd(TestBaseMixin):
def test_bass_biquad(self, central_freq, Q, gain):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
central_freq = torch.tensor(central_freq)
Q = torch.tensor(Q)
gain = torch.tensor(gain)
......@@ -98,7 +98,7 @@ class Autograd(TestBaseMixin):
def test_treble_biquad(self, central_freq, Q, gain):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
central_freq = torch.tensor(central_freq)
Q = torch.tensor(Q)
gain = torch.tensor(gain)
......@@ -110,7 +110,7 @@ class Autograd(TestBaseMixin):
def test_allpass_biquad(self, central_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
central_freq = torch.tensor(central_freq)
Q = torch.tensor(Q)
self.assert_grad(F.allpass_biquad, (x, sr, central_freq, Q))
......@@ -121,7 +121,7 @@ class Autograd(TestBaseMixin):
def test_lowpass_biquad(self, cutoff_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
cutoff_freq = torch.tensor(cutoff_freq)
Q = torch.tensor(Q)
self.assert_grad(F.lowpass_biquad, (x, sr, cutoff_freq, Q))
......@@ -132,7 +132,7 @@ class Autograd(TestBaseMixin):
def test_highpass_biquad(self, cutoff_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
cutoff_freq = torch.tensor(cutoff_freq)
Q = torch.tensor(Q)
self.assert_grad(F.highpass_biquad, (x, sr, cutoff_freq, Q))
......@@ -144,7 +144,7 @@ class Autograd(TestBaseMixin):
def test_bandpass_biquad(self, central_freq, Q, const_skirt_gain):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
central_freq = torch.tensor(central_freq)
Q = torch.tensor(Q)
self.assert_grad(F.bandpass_biquad, (x, sr, central_freq, Q, const_skirt_gain))
......@@ -156,7 +156,7 @@ class Autograd(TestBaseMixin):
def test_equalizer_biquad(self, central_freq, Q, gain):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
central_freq = torch.tensor(central_freq)
Q = torch.tensor(Q)
gain = torch.tensor(gain)
......@@ -168,7 +168,7 @@ class Autograd(TestBaseMixin):
def test_bandreject_biquad(self, central_freq, Q):
torch.random.manual_seed(2434)
sr = 22050
x = get_whitenoise(sample_rate=sr, duration=0.05, n_channels=2)
x = get_whitenoise(sample_rate=sr, duration=0.01, n_channels=1)
central_freq = torch.tensor(central_freq)
Q = torch.tensor(Q)
self.assert_grad(F.bandreject_biquad, (x, sr, central_freq, Q))
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