def test_Resize(self):
ishape = [3]
oshape = [5]
A = linop.Resize(oshape, ishape)
self.check_linop_linear(A)
self.check_linop_adjoint(A)
self.check_linop_pickleable(A)
A = linop.Resize(oshape, ishape, oshift=[1])
self.check_linop_linear(A)
self.check_linop_adjoint(A)
self.check_linop_pickleable(A)
ishape = [5]
oshape = [3]
A = linop.Resize(oshape, ishape)
self.check_linop_linear(A)
self.check_linop_adjoint(A)
self.check_linop_pickleable(A)
A = linop.Resize(oshape, ishape, ishift=[1])
self.check_linop_linear(A)
self.check_linop_adjoint(A)
self.check_linop_pickleable(A)
def test_to_pytorch_function(self):
A = linop.Resize([5], [3])
x = np.array([1, 2, 3], np.float)
y = np.ones([5])
with self.subTest('forward'):
f = pytorch.to_pytorch_function(A).apply
x_torch = pytorch.to_pytorch(x)
npt.assert_allclose(f(x_torch).detach().numpy(), A(x))
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(),
A.H(A(x) - y))