():ssim_value=loss_obj(img1,img2).item()print("Initial%s:%f:"%(loss_type,ssim_value))optimizer=Adam(parameters=[img2],learning_rate=0.05)step=0whilessim_value<0.9999:step+=1optimizer.clear_grad()loss=loss_obj(img1,img2)(1-loss).backward()optimizer.step()ssim_value=loss.item()if...
import os import sys import paddle import numpy as np from pil import image from paddle.optimizer import adam from paddle_msssim import ssim, ms_ssimloss_type = 'ssim' assert loss_type in [ 'ssim' , 'msssim' ] if loss_type == 'ssim' : loss_obj = ssim(win_size= 11 , win_sigma...
class MS_SSIM_Loss(MS_SSIM): def forward(self, img1, img2): return 100*(1 - super(MS_SSIM_Loss, self).forward(img1, img2)) class SSIM_Loss(SSIM): def forward(self, img1, img2): return 100*(1 - super(SSIM_Loss, self).forward(img1, img2)) 训练参数 In [ ] def get_arg...
我已经测试过pytorch 1.6没有这个问题。 我研究了piqa库的 ,这使我实现ssim和ms-ssim的速度比以前快了一些。 加速。 仅在GPU上测试。 losser1是 268fc76 losser2是 881d210 losser3是 5caf547 losser4是 1c2f14a losser5是 abaf398 abaf398 在pytorch 1.7....
阅读论文《Loss Functions for Image Restoration With Neural Networks》 ,L1损失函数获得的图像质量会更好。这里论文调研了L1损失,SSIM和MS-SSIM,并将L1损失函数和MS-SSIM结合起来构建新的损失函数。但是目前为止,基于SSIM的指标还没有应用到损失函数中...;xy+C2σx2+σy2+C2(2)=l(p)⋅cs(p)(3)SSIM的...
from MS_SSIM_L1_loss import MS_SSIM_L1_LOSS criterion = MS_SSIM_L1_LOSS() # your pytorch tensor x, y with [B, C, H, W] dimension on cuda device 0 loss = criterion(x, y) Please check demo.py for more details. Requirements: ...
For example, see Loss Functions for Neural Networks for Image Processing. Up to now, I could not find an implementation in TensorFlow. And after trying to do it by myself by porting it from C++ or python code (such as Github: VQMT/SSIM), I got stuck on methods like applying Gaussian ...
introducing a continuous proxy for the discontinuous loss function arising from the quantizer. Under certain conditions, the relaxed loss function may be interpreted as the log likelihood of a generative model, as implemented by a variational autoencoder. Unlike these models, however, the compression ...
PyTorch differentiable Multi-Scale Structural Similarity (MS-SSIM) loss - jorge-pessoa/pytorch-msssim
MS_SSIM.ssim_module = SSIM(win_size=11, win_sigma=1.5, data_range=255, size_average=True, channel=3) ms_ssim_module = MS_SSIM(win_size=11, win_sigma=1.5, data_range=255, size_average=True, channel=3) ssim_loss =1- ssim_module(X, Y) ms_ssim_loss =1- ms_ssim_module(X, ...