Skip to content

Shortening lfilter autograd tests input size #1443

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 7 commits into from
Apr 8, 2021
Merged

Shortening lfilter autograd tests input size #1443

Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
fe29555
Merge pull request #1 from pytorch/master
yoyolicoris Feb 19, 2021
f695635
Merge pull request #4 from pytorch/master
yoyolicoris Feb 26, 2021
68f551b
Merge pull request #9 from pytorch/master
yoyolicoris Mar 16, 2021
537a153
Merge pull request #10 from pytorch/master
yoyolicoris Apr 1, 2021
86fcc3d
Merge branch 'master' of https://github.com/yoyololicon/audio
yoyolicoris Apr 3, 2021
01b73e1
Merge branch 'master' of https://github.com/yoyololicon/audio
yoyolicoris Apr 8, 2021
bc66176
test: shorten the size of input
yoyolicoris Apr 8, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 14 additions & 14 deletions test/torchaudio_unittest/functional/autograd_impl.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -29,38 +29,38 @@ def assert_grad(

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
self.assert_grad(F.lfilter, (x, a, b), enable_all_grad=False)

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
self.assert_grad(F.lfilter, (x, a, b), enable_all_grad=False)

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
self.assert_grad(F.lfilter, (x, a, b), enable_all_grad=False)

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]))
Expand All @@ -72,7 +72,7 @@ def test_biquad(self):
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))
Expand All @@ -84,7 +84,7 @@ def test_band_biquad(self, central_freq, Q, noise):
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)
Expand All @@ -98,7 +98,7 @@ def test_bass_biquad(self, central_freq, Q, gain):
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)
Expand All @@ -110,7 +110,7 @@ def test_treble_biquad(self, central_freq, Q, gain):
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))
Expand All @@ -121,7 +121,7 @@ def test_allpass_biquad(self, central_freq, Q):
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))
Expand All @@ -132,7 +132,7 @@ def test_lowpass_biquad(self, cutoff_freq, Q):
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))
Expand All @@ -144,7 +144,7 @@ def test_highpass_biquad(self, cutoff_freq, Q):
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))
Expand All @@ -156,7 +156,7 @@ def test_bandpass_biquad(self, central_freq, Q, const_skirt_gain):
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)
Expand All @@ -168,7 +168,7 @@ def test_equalizer_biquad(self, central_freq, Q, gain):
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))