src.losses

Functions

denoising_score_matching_loss(score_net, ...)

Computes denoising score matching (DSM) loss.

Classes

`FDGANLoss`(args, *kwargs)	Combined loss function used in FD-GAN for single-image dehazing.
`PerceptualLoss`(args, *kwargs)	Computes a perceptual similarity metric using early-layer features of VGG16.

class src.losses.FDGANLoss(*args: Any, **kwargs: Any)[source]

Bases: Module

Combined loss function used in FD-GAN for single-image dehazing.

This class implements the generator loss described in the FD-GAN paper, which combines multiple components:

L1 loss
SSIM loss (via Kornia)
Perceptual VGG-based loss
Adversarial BCE loss

The final generator loss is computed as:

L_total = 2 * L1 + 1 * L_ssim + 2 * L_percep + 0.1 * L_adv

Notes

Inputs for SSIM and perceptual loss must be in the range [0, 1].
Inputs are assumed to be in [-1, 1] and internally rescaled.
The perceptual loss uses frozen VGG16 early-layer features.

References

Dong, Y., Liu, Y., Zhang, H., Chen, S., & Qiao, Y. (2020).: FD-GAN: Generative adversarial networks with fusion-discriminator for single image dehazing. AAAI Conference on Artificial Intelligence, 34(07), 10729-10736.

Examples

>>> criterion = FDGANLoss()
>>> fake = torch.rand(1, 3, 256, 256) * 2 - 1
>>> real = torch.rand(1, 3, 256, 256) * 2 - 1
>>> pred = torch.randn(1, 1)
>>> loss, components = criterion(fake, real, pred)
>>> loss.backward()

forward(fake, real, disc_pred)[source]

Compute the combined FD-GAN generator loss.

Parameters:

fake (torch.Tensor) – Generated image tensor in the range [-1, 1].
real (torch.Tensor) – Ground-truth image tensor in the range [-1, 1].
disc_pred (torch.Tensor) – Discriminator predictions on the generated images.

Returns:

A tuple (total_loss, components) where:

total_loss : torch.Tensor Scalar tensor with the final weighted sum.

components : dict Dictionary containing each individual loss term:

{
    "l1":    L1 loss,
    "ssim":  SSIM loss,
    "percep": perceptual loss,
    "adv":   adversarial BCE loss
}

Return type:

tuple

class src.losses.PerceptualLoss(*args: Any, **kwargs: Any)[source]

Bases: Module

Computes a perceptual similarity metric using early-layer features of VGG16.

This loss extracts features from the relu1_2 layer of a pretrained VGG16 network and applies an L1 distance between the feature activations of two images.

Notes

The VGG16 backbone is loaded with ImageNet weights and frozen.
Only the first four layers of vgg.features are used, corresponding to the relu1_2 activation described in the original FD-GAN paper.
Input images are expected to be normalized to the appropriate domain before calling this loss (e.g., [0, 1] range if matching standard VGG preprocessing).

References

Dong, Y., Liu, Y., Zhang, H., Chen, S., & Qiao, Y. (2020).: FD-GAN: Generative adversarial networks with fusion-discriminator for single image dehazing. AAAI Conference on Artificial Intelligence, 34(07), 10729-10736.

Examples

>>> loss_fn = PerceptualLoss()
>>> x = torch.rand(1, 3, 224, 224)
>>> y = torch.rand(1, 3, 224, 224)
>>> loss = loss_fn(x, y)
>>> loss.backward()

forward(x: torch.Tensor, y: torch.Tensor) → torch.Tensor[source]

Computes the perceptual L1 difference between the VGG16 features of two inputs.

Parameters:

x (torch.Tensor) – Input tensor of shape (B, C, H, W).
y (torch.Tensor) – Input tensor of shape (B, C, H, W).

Returns:

Scalar tensor representing the perceptual loss value.

Return type:

torch.Tensor

src.losses.denoising_score_matching_loss(score_net, clean_samples, sigma=0.1)[source]

Computes denoising score matching (DSM) loss.

This loss perturbs samples with Gaussian noise and trains the network to predict the added noise. This approximates score matching without requiring explicit computation of the data score.

Parameters:

score_net (callable) – Neural network that maps noisy samples to predicted noise.
clean_samples (torch.Tensor) – Clean input samples of any shape.
sigma (float, optional) – Standard deviation of the Gaussian perturbation. Default is 0.1.

Returns:

Scalar loss value representing the MSE between predicted and actual noise.

Return type:

torch.Tensor

Notes

This formulation follows standard denoising score matching used in diffusion models and score-based generative modeling.

Examples

>>> net = lambda x: torch.zeros_like(x)
>>> x = torch.randn(8, 3, 32, 32)
>>> loss = denoising_score_matching_loss(net, x)