def test_UnitaryTransform(self):
shape = [6]
lamda = 1.0
A = linop.FFT(shape)
P = prox.UnitaryTransform(prox.L2Reg(shape, lamda), A)
x = util.randn(shape)
y = P(0.1, x)
npt.assert_allclose(y, x / (1 + lamda * 0.1))
def test_FFT(self):
for ndim in [1, 2, 3]:
for n in [3, 4, 5, 6]:
ishape = [n] * ndim
A = linop.FFT(ishape)
self.check_linop_linear(A)
self.check_linop_adjoint(A)
self.check_linop_unitary(A)
self.check_linop_pickleable(A)
def test_to_pytorch_function_complex(self):
A = linop.FFT([3])
x = np.array([1 + 1j, 2 + 2j, 3 + 3j], np.complex)
y = np.ones([3], np.complex)
with self.subTest('forward'):
f = pytorch.to_pytorch_function(
A,
input_iscomplex=True,
output_iscomplex=True).apply
x_torch = pytorch.to_pytorch(x)
npt.assert_allclose(f(x_torch).detach().numpy().ravel(),
A(x).view(np.float))
with self.subTest('adjoint'):
y_torch = pytorch.to_pytorch(y)
loss = (f(x_torch) - y_torch).pow(2).sum() / 2
loss.backward()
npt.assert_allclose(x_torch.grad.detach().numpy().ravel(),
A.H(A(x) - y).view(np.float))
