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) else: raise ...
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...
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 ...
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: (#747) functional.ss...
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())...
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, ...
Source File: loss_ssim.py From KAIR with MIT License 6 votes def _ssim(img1, img2, window, window_size, channel, size_average=True): mu1 = F.conv2d(img1, window, padding=window_size//2, groups=channel) mu2 = F.conv2d(img2, window, padding=window_size//2, groups=channel) ...
normalize: When MultiScaleSSIM loss is used for training, it is desirable to use normalizes to improve the training stability. This `normalize` argument is out of scope of the original implementation [1], and it is7 changes: 3 additions & 4 deletions 7 src/torchmetrics/functional/text/__...
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
mean((1.0 - ssim_val) / 2.0) # Classes to re-use window Example #25Source File: model.py From LipReading with MIT License 5 votes def forward(self, input): seq_len = input.size(0) # pad the 0th dimension (T/sequence) with zeroes whose number = context # Once pytorch's ...