def test_default_image_optimizer(self):
torch.manual_seed(0)
image = torch.rand(1, 3, 128, 128)
optimizer = optim.default_image_optimizer(image)
self.assertIsInstance(optimizer, Optimizer)
actual = optimizer.param_groups[0]["params"][0]
desired = image
self.assertTensorAlmostEqual(actual, desired)
def test_default_image_optimizer():
torch.manual_seed(0)
image = torch.rand(1, 3, 128, 128)
optimizer = optim.default_image_optimizer(image)
assert isinstance(optimizer, torch.optim.Optimizer)
actual = optimizer.param_groups[0]["params"][0]
desired = image
ptu.assert_allclose(actual, desired)
def test_image_optimization_optimizer_preprocessor():
input_image = torch.empty(1)
criterion = nn.Module()
optimizer = optim.default_image_optimizer(input_image)
preprocessor = nn.Module()
with pytest.raises(RuntimeError):
optim.image_optimization(input_image,
criterion,
optimizer,
preprocessor=preprocessor)