device) elif G_lossfn_type == 'l2sum': self.G_lossfn = nn.MSELoss(reduction='sum').to(self.device) elif G_lossfn_type == 'ssim': self.G_lossfn = SSIMLoss().to(self.device) else: raise NotImplementedError('Loss type [{:s}] is not found.'.format(G_lossfn_type)) self.G...
# 需要导入模块: import torch [as 别名]# 或者: from torch importprod[as 别名]defms_ssim(self, img1, img2, levels=5):weight = Variable(torch.Tensor([0.0448,0.2856,0.3001,0.2363,0.1333]).cuda()) msssim = Variable(torch.Tensor(levels,).cuda()) mcs = Variable(torch.Tensor(levels,).cuda...
ssim_weight * SSIM(leftImage, leftImage_fromWarp, leftMask) if rightMask is None: rightMask = torch.ones_like(rightImage > 0) loss += self.rms_weight * self.rms(rightImage[rightMask], rightImage_fromWarp[rightMask]) loss += self.ssim_weight * SSIM(rightImage, rightImage_fromWarp, ...
This is great - the ssim is a huge component in the GS loss. I tested it out and there's a small difference between fused-ssim and StructuralSimilarityIndexMeasure from torchmetrics.image, I can't figure out why - however when training with torchmetrics SSIM I get about ~ 1ish PSNR ...
U2Net + ISNet GT encoder, training base on ssim loss, iou loss and bce loss,experimented on tooth segmentation on panoramic X-ray images. - U2Net-with-multi-loss/torch2onnx.py at main · xuanandsix/U2Net-with-multi-loss
metrics['val.decoder_loss'].append(decoder_loss.item()) metrics['val.decoder_acc'].append(decoder_acc.item()) metrics['val.cover_score'].append(cover_score.item()) metrics['val.generated_score'].append(generated_score.item()) metrics['val.ssim'].append(ssim(cover, generated).item())...
MethodsDis.En.LssimPSNR ↑SSIM ↑LPIPS ↓ EXP-1---26.850.8260.226 EXP-2✓--26.910.8290.232 EXP-3✓✓-26.990.8180.227 Ours✓✓✓27.010.8390.215 Table 4: Ablation study on the size p of the Ray Perception Field. The experiments are conducted based on LLFF. ...
crit = MSELoss().cuda() self.ssim = SSIM().cuda() self.step = 0 self.save_steps = settings.save_steps self.num_workers = settings.num_workers self.batch_size = settings.batch_size self.writers = {} self.dataloaders = {} self.opt = Adam(self.net.parameters(), lr=settings.lr) ...
L1Loss().to(self.device) elif G_lossfn_type == 'l2': self.G_lossfn = nn.MSELoss().to(self.device) elif G_lossfn_type == 'l2sum': self.G_lossfn = nn.MSELoss(reduction='sum').to(self.device) elif G_lossfn_type == 'ssim': self.G_lossfn = SSIMLoss().to(self.device...
Hinge -> HingeLoss Renamed image PSNR metrics (#732) functional.psnr -> functional.peak_signal_noise_ratio PSNR -> PeakSignalNoiseRatio Renamed image PIT metric: (#737) functional.pit -> functional.permutation_invariant_training PIT -> PermutationInvariantTraining Renamed image SSIM metric: (#...