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 ...
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, ...
(shift_logits.device) loss = loss_fct(shift_logits, shift_labels) if not return_dict: output = (logits,) + outputs[1:] return (loss,) + output if loss is not None else output return CausalLMOutputWithPast( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_...
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/__...
Renamed image SSIM metric: (#747) functional.ssim->functional.scale_invariant_signal_noise_ratio SSIM->StructuralSimilarityIndexMeasure Renamed detectionMAPtoMeanAveragePrecisionmetric (#754) Renamed Fidelity & LPIPS image metric: (#752) image.FID->image.FrechetInceptionDistance ...
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) ...
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) ...
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
- Loss functions (e.g., SSIM, PSNR, etc.)- Vision-specific optimizers Photometric Functions - Photometric loss functions- Photometric augmentations Filtering - Bilateral filtering- DexiNed- Dissolving- Guided Blur- Laplacian- Gaussian- Non-local means- Sobel- Unsharp masking Color - Color space ...
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 ...