73cb3ff00533b03ab64127d2287f9bdfe30c035c,kornia/losses/psnr.py,,psnr_loss,#,47

Before Change

    See :class:`~kornia.losses.PSNRLoss` for details.
    
    if not torch.is_tensor(input) or not torch.is_tensor(target):
        raise TypeError(f"Expected 2 torch tensors but got {type(input)} and {type(target)}")

    if input.shape != target.shape:
        raise TypeError(f"Expected tensors of equal shapes, but got {input.shape} and {target.shape}")
    mse_val = mse_loss(input, target, reduction="mean")
    max_val_tensor: torch.Tensor = torch.tensor(max_val).to(input.device).to(input.dtype)
    return 10 * torch.log10(max_val_tensor * max_val_tensor / mse_val)

After Change

        tensor(-20.0000)
    

    return -1. * psnr(input, target, max_val)


class PSNRLoss(nn.Module):
    rCreates a criterion that calculates the PSNR loss.

Instances