a  d$@sddlZddlmZddlmZddlmZddlmZddl m Z gdZ e d d Z e d d Z e dddZeGdddejZeGdddejZeGdddejZeGdddeZeGdddejZdS)N)nn) functional)VGGFeatureExtractor) LOSS_REGISTRY) weighted_lossnonemeansumcCstj||ddSNr  reduction)Fl1_losspredtargetrb/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/basicsr/losses/basic_loss.pyr srcCstj||ddSr )rmse_lossrrrrrsr-q=cCst||d|S)N)torchsqrt)rrepsrrrcharbonnier_losssrcs,eZdZdZd fdd Zd ddZZS) L1Lossa L1 (mean absolute error, MAE) loss. Args: loss_weight (float): Loss weight for L1 loss. Default: 1.0. reduction (str): Specifies the reduction to apply to the output. Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. ?r cs:tt||dvr*td|dt||_||_dSNrUnsupported reduction mode: z. Supported ones are: )superr__init__ ValueError_reduction_modes loss_weightrselfr%r __class__rrr"%s zL1Loss.__init__NcKs|jt||||jdS Args: pred (Tensor): of shape (N, C, H, W). Predicted tensor. target (Tensor): of shape (N, C, H, W). Ground truth tensor. weight (Tensor, optional): of shape (N, C, H, W). Element-wise weights. Default: None. r )r%rrr'rrweightkwargsrrrforward-szL1Loss.forward)rr )N__name__ __module__ __qualname____doc__r"r/ __classcell__rrr(rrsrcs,eZdZdZd fdd Zd ddZZS) MSELosszMSE (L2) loss. Args: loss_weight (float): Loss weight for MSE loss. Default: 1.0. reduction (str): Specifies the reduction to apply to the output. Supported choices are 'none' | 'mean' | 'sum'. Default: 'mean'. rr cs:tt||dvr*td|dt||_||_dSr)r!r6r"r#r$r%rr&r(rrr"As zMSELoss.__init__NcKs|jt||||jdSr*)r%rrr,rrrr/IszMSELoss.forward)rr )Nr0rrr(rr67sr6cs,eZdZdZd fdd Zd dd ZZS) CharbonnierLossaCharbonnier loss (one variant of Robust L1Loss, a differentiable variant of L1Loss). Described in "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution". Args: 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. rr rcs@tt||dvr*td|dt||_||_||_dSr)r!r7r"r#r$r%rr)r'r%rrr(rrr"bs zCharbonnierLoss.__init__NcKs|jt||||j|jdS)r+)rr)r%rrrr,rrrr/kszCharbonnierLoss.forward)rr r)Nr0rrr(rr7Ss  r7cs0eZdZdZd fdd Zd fdd ZZS) WeightedTVLosszYWeighted TV loss. Args: loss_weight (float): Loss weight. Default: 1.0. rr cs0|dvrtd|dtt|j||ddS)N)r r r z . Supported ones are: mean | sum)r%r)r#r!r8r"r&r(rrr"}szWeightedTVLoss.__init__Ncs|durd}d}n@|ddddddddf}|ddddddddf}tj|ddddddddf|ddddddddf|d}tj|ddddddddf|ddddddddf|d}||}|S)Nr)r-)r!r/)r'rr-Zy_weightZx_weightZy_diffZx_diffZlossr(rrr/s  JJzWeightedTVLoss.forward)rr )Nr0rrr(rr8usr8cs2eZdZdZdfdd Zd d Zd d ZZS)PerceptualLossa#Perceptual loss with commonly used style loss. Args: layer_weights (dict): The weight for each layer of vgg feature. Here is an example: {'conv5_4': 1.}, which means the conv5_4 feature layer (before relu5_4) will be extracted with weight 1.0 in calculating losses. vgg_type (str): The type of vgg network used as feature extractor. Default: 'vgg19'. use_input_norm (bool): If True, normalize the input image in vgg. Default: True. range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. Default: False. perceptual_weight (float): If `perceptual_weight > 0`, the perceptual loss will be calculated and the loss will multiplied by the weight. Default: 1.0. style_weight (float): If `style_weight > 0`, the style loss will be calculated and the loss will multiplied by the weight. Default: 0. criterion (str): Criterion used for perceptual loss. Default: 'l1'. vgg19TFrl1cstt|||_||_||_tt||||d|_ ||_ |j dkrXt j |_n8|j dkrpt j |_n |j dkrd|_nt|ddS)N)Zlayer_name_listvgg_typeuse_input_norm range_normr=l2froz" criterion has not been supported.)r!r:r"perceptual_weight style_weight layer_weightsrlistkeysvggcriterion_typerrr criterionZL2lossNotImplementedError)r'rEr>r?r@rCrDrJr(rrr"s$    zPerceptualLoss.__init__c Cs>||}||}|jdkrd}|D]Z}|jdkrf|tj||||dd|j|7}q.|||||||j|7}q.||j9}nd}|j dkr2d}|D]r}|jdkr|tj| ||| ||dd|j|7}q||| ||| |||j|7}q||j 9}nd}||fS)zForward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). gt (Tensor): Ground-truth tensor with shape (n, c, h, w). Returns: Tensor: Forward results. rrB)pN) rHdetachrCrGrIrZnormrErJrD _gram_mat)r'xgtZ x_featuresZ gt_featuresZ percep_losskZ style_lossrrrr/s6    *$     zPerceptualLoss.forwardc CsH|\}}}}|||||}|dd}|||||}|S)zCalculate Gram matrix. Args: x (torch.Tensor): Tensor with shape of (n, c, h, w). Returns: torch.Tensor: Gram matrix. rr)sizeviewZ transposeZbmm) r'rOnchwfeaturesZ features_tZgramrrrrNs  zPerceptualLoss._gram_mat)r;TFrr<r=)r1r2r3r4r"r/rNr5rrr(rr:s*r:)r)rrZtorch.nnrrZbasicsr.archs.vgg_archrZbasicsr.utils.registryrZ loss_utilrr$rrrregisterModulerr6r7r8r:rrrrs,        !