def test_batch_gram_matrix_normalize2(self):
torch.manual_seed(0)
tensor_constructors = (torch.ones, torch.rand, torch.randn)
for constructor in tensor_constructors:
x = pystiche.batch_gram_matrix(constructor((1, 3, 128, 128)),
normalize=True)
y = pystiche.batch_gram_matrix(constructor((1, 3, 256, 256)),
normalize=True)
self.assertTensorAlmostEqual(x, y, atol=2e-2)
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 test_batch_gram_matrix_normalize1(self):
num_channels = 3
x = torch.ones((1, num_channels, 128, 128))
y = pystiche.batch_gram_matrix(x, normalize=True)
actual = y.flatten()
desired = torch.ones((num_channels**2, ))
self.assertTensorAlmostEqual(actual, desired)
def test_batch_gram_matrix(self):
size = 100
for dim in (1, 2, 3):
x = torch.ones((1, 1, *[size] * dim))
y = pystiche.batch_gram_matrix(x)
actual = y.item()
desired = float(size**dim)
self.assertAlmostEqual(actual, desired)
def test_batch_gram_matrix_size(self):
batch_size = 1
num_channels = 3
torch.manual_seed(0)
for dim in (1, 2, 3):
size = (batch_size, num_channels, *torch.randint(256, (dim,)).tolist())
x = torch.empty(size)
y = pystiche.batch_gram_matrix(x)
actual = y.size()
desired = (batch_size, num_channels, num_channels)
self.assertTupleEqual(actual, desired)
def enc_to_repr(self, enc: torch.Tensor) -> torch.Tensor:
return pystiche.batch_gram_matrix(enc, normalize=self.normalize)