def test_dists_simmilar_to_official_implementation() -> None: # Baseline scores from: https://github.com/dingkeyan93/DISTS loss = DISTS() # Greyscale images goldhill = torch.tensor(imread('tests/assets/goldhill.gif'))[None, None, ...] / 255.0 goldhill_jpeg = torch.tensor( imread('tests/assets/goldhill_jpeg.gif'))[None, None, ...] / 255.0 loss_value = loss(goldhill_jpeg, goldhill) baseline_value = torch.tensor(0.19509) assert torch.isclose(loss_value, baseline_value, atol=1e-3), \ f'Expected PIQ loss to be equal to original. Got {loss_value} and {baseline_value}' # RGB images I01 = torch.tensor(imread('tests/assets/I01.BMP')).permute( 2, 0, 1)[None, ...] / 255.0 i1_01_5 = torch.tensor(imread('tests/assets/i01_01_5.bmp')).permute( 2, 0, 1)[None, ...] / 255.0 loss_value = loss(i1_01_5, I01) baseline_value = torch.tensor(0.17321) assert torch.isclose(loss_value, baseline_value, atol=1e-3), \ f'Expected PIQ loss to be equal to original. Got {loss_value} and {baseline_value}'
def test_dists_loss_forward_for_special_cases(x, y, expectation: Any, value: float) -> None: loss = DISTS() with expectation: loss_value = loss(x, y) assert torch.isclose(loss_value, torch.tensor(value), atol=1e-6), \ f'Expected loss value to be equal to target value. Got {loss_value} and {value}'
def test_dists_computes_grad(x, y, device: str) -> None: x.requires_grad_() loss_value = DISTS()(x.to(device), y.to(device)) loss_value.backward() assert x.grad is not None, NONE_GRAD_ERR_MSG
def test_dists_loss_forward(x, y, device: str) -> None: loss = DISTS() loss(x.to(device), y.to(device))
def test_dists_computes_grad(prediction: torch.Tensor, target: torch.Tensor, device: str) -> None: prediction.requires_grad_() loss_value = DISTS()(prediction.to(device), target.to(device)) loss_value.backward() assert prediction.grad is not None, NONE_GRAD_ERR_MSG
def test_dists_loss_forward(prediction: torch.Tensor, target: torch.Tensor, device: str) -> None: loss = DISTS() loss(prediction.to(device), target.to(device))