def test_raises_domain_exception(self):
shape = [2, 3, 4]
transform = nl.Tanh()
for value in [-2.0, -1.0, 1.0, 2.0]:
with self.assertRaises(InputOutsideDomain):
inputs = torch.full(shape, value)
transform.inverse(inputs)
def test_forward_inverse_are_consistent(self):
batch_size = 10
shape = [5, 10, 15]
inputs = torch.rand(batch_size, *shape)
transforms = [
nl.Tanh(),
nl.LogTanh(),
nl.LeakyReLU(),
nl.Sigmoid(),
nl.Logit(),
nl.CompositeCDFTransform(nl.Sigmoid(), standard.IdentityTransform()),
]
self.eps = 1e-3
for transform in transforms:
with self.subTest(transform=transform):
self.assert_forward_inverse_are_consistent(transform, inputs)
def test_inverse(self):
batch_size = 10
shape = [5, 10, 15]
inputs = torch.rand(batch_size, *shape)
transforms = [
nl.Tanh(),
nl.LogTanh(),
nl.LeakyReLU(),
nl.Sigmoid(),
nl.Logit(),
nl.CompositeCDFTransform(nl.Sigmoid(), standard.IdentityTransform()),
]
for transform in transforms:
with self.subTest(transform=transform):
outputs, logabsdet = transform.inverse(inputs)
self.assert_tensor_is_good(outputs, [batch_size] + shape)
self.assert_tensor_is_good(logabsdet, [batch_size])
