def test_GramOperator_call():
torch.manual_seed(0)
target_image = torch.rand(1, 3, 128, 128)
input_image = torch.rand(1, 3, 128, 128)
encoder = enc.SequentialEncoder((nn.Conv2d(3, 3, 1), ))
op = ops.GramOperator(encoder)
op.set_target_image(target_image)
actual = op(input_image)
desired = mse_loss(
pystiche.gram_matrix(encoder(input_image), normalize=True),
pystiche.gram_matrix(encoder(target_image), normalize=True),
)
ptu.assert_allclose(actual, desired)
def test_GramOperator_call(self):
torch.manual_seed(0)
target_image = torch.rand(1, 3, 128, 128)
input_image = torch.rand(1, 3, 128, 128)
encoder = SequentialEncoder((nn.Conv2d(3, 3, 1), ))
op = ops.GramOperator(encoder)
op.set_target_image(target_image)
actual = op(input_image)
desired = mse_loss(
pystiche.batch_gram_matrix(encoder(input_image), normalize=True),
pystiche.batch_gram_matrix(encoder(target_image), normalize=True),
)
self.assertTensorAlmostEqual(actual, desired)
def get_style_op(encoder, layer_weight):
return ops.GramOperator(encoder, score_weight=layer_weight)