Spaces:
Sleeping
Sleeping
| import torch | |
| import torchvision | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| class VGG19Feats(torch.nn.Module): | |
| def __init__(self, requires_grad=False): | |
| super(VGG19Feats, self).__init__() | |
| vgg = torchvision.models.vgg19(pretrained=True).to(device) #.cuda() | |
| # vgg.eval() | |
| vgg_pretrained_features = vgg.features.eval() | |
| self.requires_grad = requires_grad | |
| self.slice1 = torch.nn.Sequential() | |
| self.slice2 = torch.nn.Sequential() | |
| self.slice3 = torch.nn.Sequential() | |
| self.slice4 = torch.nn.Sequential() | |
| self.slice5 = torch.nn.Sequential() | |
| for x in range(3): | |
| self.slice1.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(3, 8): | |
| self.slice2.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(8, 13): | |
| self.slice3.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(13, 22): | |
| self.slice4.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(22, 31): | |
| self.slice5.add_module(str(x), vgg_pretrained_features[x]) | |
| if not self.requires_grad: | |
| for param in self.parameters(): | |
| param.requires_grad = False | |
| def forward(self, img): | |
| conv1_2 = self.slice1(img) | |
| conv2_2 = self.slice2(conv1_2) | |
| conv3_2 = self.slice3(conv2_2) | |
| conv4_2 = self.slice4(conv3_2) | |
| conv5_2 = self.slice5(conv4_2) | |
| out = [conv1_2, conv2_2, conv3_2, conv4_2, conv5_2] | |
| return out | |
| class VGGPerceptualLoss(torch.nn.Module): | |
| def __init__(self): | |
| super(VGGPerceptualLoss, self).__init__() | |
| self.vgg = VGG19Feats().to(device) | |
| self.criterion = torch.nn.functional.l1_loss | |
| self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) | |
| self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) | |
| self.weights = [1.0/2.6, 1.0/4.8, 1.0/3.7, 1.0/5.6, 1.0*10/1.5] | |
| def forward(self, input_img, target_img): | |
| if input_img.shape[1] != 3: | |
| input_img = input_img.repeat(1, 3, 1, 1) | |
| target_img = target_img.repeat(1, 3, 1, 1) | |
| input_img = (input_img - self.mean) / self.std | |
| target_img = (target_img - self.mean) / self.std | |
| x_vgg, y_vgg = self.vgg(input_img), self.vgg(target_img) | |
| loss = {} | |
| loss['pt_c_loss'] = self.weights[0] * self.criterion(x_vgg[0], y_vgg[0])+\ | |
| self.weights[1] * self.criterion(x_vgg[1], y_vgg[1])+\ | |
| self.weights[2] * self.criterion(x_vgg[2], y_vgg[2])+\ | |
| self.weights[3] * self.criterion(x_vgg[3], y_vgg[3])+\ | |
| self.weights[4] * self.criterion(x_vgg[4], y_vgg[4]) | |
| loss['pt_s_loss'] = 0.0 | |
| return loss |