pyiqa.losses

Submodules

• pyiqa.losses.iqa_losses
• pyiqa.losses.loss_util
• pyiqa.losses.losses

Package Contents

class pyiqa.losses.CharbonnierLoss(loss_weight=1.0, reduction='mean', eps=1e-12)

Bases: torch.nn.Module

Charbonnier loss (one variant of Robust L1Loss, a differentiable variant of L1Loss).

Described in “Deep Laplacian Pyramid Networks for Fast and Accurate

Super-Resolution”.

Parameters:

• loss_weight (float) – Loss weight for L1 loss. Default: 1.0.

• reduction (str) – Specifies the reduction to apply to the output. Supported choices are ‘none’ | ‘mean’ | ‘sum’. Default: ‘mean’.

• eps (float) – A value used to control the curvature near zero. Default: 1e-12.

forward(pred, target, weight=None, **kwargs)

Parameters:

• pred (Tensor) – of shape (N, C, H, W). Predicted tensor.

• target (Tensor) – of shape (N, C, H, W). Ground truth tensor.

• weight (Tensor, optional) – of shape (N, C, H, W). Element-wise weights. Default: None.

class pyiqa.losses.L1Loss(loss_weight=1.0, reduction='mean')

Bases: torch.nn.Module

L1 (mean absolute error, MAE) loss.

Parameters:

• loss_weight (float) – Loss weight for L1 loss. Default: 1.0.

• reduction (str) – Specifies the reduction to apply to the output. Supported choices are ‘none’ | ‘mean’ | ‘sum’. Default: ‘mean’.

forward(pred, target, weight=None, **kwargs)

Parameters:

• pred (Tensor) – of shape (N, C, H, W). Predicted tensor.

• target (Tensor) – of shape (N, C, H, W). Ground truth tensor.

• weight (Tensor, optional) – of shape (N, C, H, W). Element-wise weights. Default: None.

class pyiqa.losses.MSELoss(loss_weight=1.0, reduction='mean')

Bases: torch.nn.Module

MSE (L2) loss.

Parameters:

• loss_weight (float) – Loss weight for MSE loss. Default: 1.0.

• reduction (str) – Specifies the reduction to apply to the output. Supported choices are ‘none’ | ‘mean’ | ‘sum’. Default: ‘mean’.

forward(pred, target, weight=None, **kwargs)

Parameters:

• pred (Tensor) – of shape (N, C, H, W). Predicted tensor.

• target (Tensor) – of shape (N, C, H, W). Ground truth tensor.

• weight (Tensor, optional) – of shape (N, C, H, W). Element-wise weights. Default: None.

class pyiqa.losses.WeightedTVLoss(loss_weight=1.0, reduction='mean')

Bases: L1Loss

Weighted TV loss.

Parameters:

loss_weight (float) – Loss weight. Default: 1.0.

forward(pred, weight=None)

Parameters:

• pred (Tensor) – of shape (N, C, H, W). Predicted tensor.

• target (Tensor) – of shape (N, C, H, W). Ground truth tensor.

• weight (Tensor, optional) – of shape (N, C, H, W). Element-wise weights. Default: None.

class pyiqa.losses.EMDLoss(loss_weight=1.0, r=2, reduction='mean')

Bases: torch.nn.Module

EMD (earth mover distance) loss.

forward(pred, target, weight=None, **kwargs)

class pyiqa.losses.PLCCLoss(loss_weight=1.0)

Bases: torch.nn.Module

PLCC loss, induced from Pearson’s Linear Correlation Coefficient.

forward(pred, target)

class pyiqa.losses.NiNLoss(loss_weight=1.0, p=1, q=2)

Bases: torch.nn.Module

NiN (Norm in Norm) loss

Reference:

• Dingquan Li, Tingting Jiang, and Ming Jiang. Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment. ACMM2020.

• https://arxiv.org/abs/2008.03889

• https://github.com/lidq92/LinearityIQA

This loss can be simply described as: l1_norm(normalize(pred - pred_mean), normalize(target - target_mean))

forward(pred, target)