def test_mdsi_loss(input_tensors: Tuple[torch.Tensor, torch.Tensor],
device: str) -> None:
x, y = input_tensors
x.requires_grad_()
loss = MDSILoss(data_range=1.)(x=x.to(device), y=y.to(device))
loss.backward()
assert torch.isfinite(
x.grad).all(), f'Expected finite gradient values, got {x.grad}'
def test_mdsi_loss(input_tensors: Tuple[torch.Tensor, torch.Tensor],
device: str) -> None:
prediction, target = input_tensors
prediction.requires_grad_()
loss = MDSILoss(data_range=1.)(prediction=prediction.to(device),
target=target.to(device))
loss.backward()
assert torch.isfinite(prediction.grad).all(
), f'Expected finite gradient values, got {prediction.grad}'
def test_mdsi_loss_compare_with_matlab(input_images_score: Tuple[torch.Tensor, torch.Tensor, torch.Tensor],
combination: str, device: str) -> None:
x, y, y_value = input_images_score
y_value = y_value[combination]
x = x.requires_grad_()
score = MDSILoss(data_range=255, combination=combination)(x=x.to(device), y=y.to(device))
score.backward()
assert torch.isclose(score, 1. - y_value.to(score)), f'The estimated value must be equal to MATLAB ' \
f'provided one, got {score.item():.8f}, ' \
f'while MATLAB equals {1. - y_value}'
assert torch.isfinite(x.grad).all(), f'Expected finite gradient values, got {x.grad}'