def testFFTCentralFreqBatch(self):
# Same for batches
for gpu in [True, False]:
x = torch.FloatTensor(4, 10, 10, 2).fill_(0)
x.narrow(3, 0, 1).fill_(1)
if gpu:
x = x.cuda()
a = x.sum()
fft = sl.Fft()
fft(x, inplace=True)
c = x[:, 0, 0, 0].sum()
self.assertEqual(a, c)
def testFFTCentralFreq(self):
# Checked the 0 frequency
for gpu in [True, False]:
x = torch.FloatTensor(10, 10, 2).fill_(0)
x.narrow(2, 0, 1).fill_(1)
if gpu:
x = x.cuda()
a = x.sum()
fft = sl.Fft()
fft(x, inplace=True)
b = x[0, 0, 0]
self.assertAlmostEqual(a, b, places=6)
def testFFTCentralFreq(self):
# Checked the 0 frequency
for gpu in [True, False]:
x = torch.FloatTensor(10, 10, 2).fill_(0)
x.narrow(2, 0, 1).fill_(1)
if gpu:
x = x.cuda()
a = x.sum()
fft = sl.Fft()
fft(x, inplace=True)
b = x[0, 0, 0]
tt.assert_almost_equal(a.cpu(), b.cpu(), decimal=6)
def testFFTUnormalized(self):
# Check for a random tensor:
x = torch.FloatTensor(25, 17, 3, 2).bernoulli_(0.5)
for gpu in [True, False]:
if gpu:
x = x.cuda()
else:
x = x.cpu()
x.narrow(3, 1, 1).fill_(0)
fft = sl.Fft()
y = fft(x)
z = fft(y, direction='C2R')
z /= 17 * 3 # FFTs are unnormalized
self.assertAlmostEqual(linfnorm(x.select(3, 0), z), 0, places=6)
def testFFTUnormalized(self):
# Check for a random tensor:
x = torch.FloatTensor(25, 17, 3, 2).bernoulli_(0.5)
for gpu in [True, False]:
if gpu:
x = x.cuda()
else:
x = x.cpu()
x.narrow(3, 1, 1).fill_(0)
fft = sl.Fft()
y = fft(x)
z = fft(y, direction='C2R')
z /= 17 * 3 # FFTs are unnormalized
tt.assert_allclose(x.select(3, 0).cpu(), z.cpu(), atol=1e-6)