def assert_forward_inverse_are_consistent(self, transform, inputs):
inverse = base.InverseTransform(transform)
identity = base.CompositeTransform([inverse, transform])
outputs, logabsdet = identity(inputs)
self.assert_tensor_is_good(outputs, shape=inputs.shape)
self.assert_tensor_is_good(logabsdet, shape=inputs.shape[:1])
self.assertEqual(outputs, inputs)
self.assertEqual(logabsdet, torch.zeros(inputs.shape[:1]))
def test_inverse(self):
batch_size = 10
shape = [2, 3, 4]
inputs = torch.randn(batch_size, *shape)
transforms = [
standard.AffineScalarTransform(scale=2.0),
standard.IdentityTransform(),
standard.AffineScalarTransform(scale=-0.25),
]
composite = base.CompositeTransform(transforms)
reference = standard.AffineScalarTransform(scale=-0.5)
outputs, logabsdet = composite.inverse(inputs)
outputs_ref, logabsdet_ref = reference.inverse(inputs)
self.assert_tensor_is_good(outputs, [batch_size] + shape)
self.assert_tensor_is_good(logabsdet, [batch_size])
self.assertEqual(outputs, outputs_ref)
self.assertEqual(logabsdet, logabsdet_ref)