| import math |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| import torchvision |
|
|
| def default_conv(in_channels, out_channels, kernel_size, bias=True): |
| return nn.Conv2d(in_channels, out_channels, kernel_size, padding=(kernel_size//2), bias=bias) |
|
|
|
|
| class MeanShift(nn.Conv2d): |
| def __init__(self, rgb_range, rgb_mean, rgb_std, sign=-1): |
| super(MeanShift, self).__init__(3, 3, kernel_size=1) |
| std = torch.Tensor(rgb_std) |
| self.weight.data = torch.eye(3).view(3, 3, 1, 1) |
| self.weight.data.div_(std.view(3, 1, 1, 1)) |
| self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) |
| self.bias.data.div_(std) |
| self.weight.requires_grad = False |
| self.bias.requires_grad = False |
|
|
|
|
| class Upsampler(nn.Sequential): |
| def __init__(self, conv, scale, n_feat, act=False, bias=True): |
| m = [] |
| if (scale & (scale - 1)) == 0: |
| for _ in range(int(math.log(scale, 2))): |
| m.append(conv(n_feat, 4 * n_feat, 3, bias)) |
| m.append(nn.PixelShuffle(2)) |
| if act: m.append(act()) |
| elif scale == 3: |
| m.append(conv(n_feat, 9 * n_feat, 3, bias)) |
| m.append(nn.PixelShuffle(3)) |
| if act: m.append(act()) |
| else: |
| raise NotImplementedError |
|
|
| super(Upsampler, self).__init__(*m) |
|
|
|
|
| class VGG19(torch.nn.Module): |
| def __init__(self, requires_grad=False): |
| super().__init__() |
| vgg_pretrained_features = torchvision.models.vgg19(pretrained=True).features |
| 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(2): |
| self.slice1.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(2, 7): |
| self.slice2.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(7, 12): |
| self.slice3.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(12, 21): |
| self.slice4.add_module(str(x), vgg_pretrained_features[x]) |
| for x in range(21, 30): |
| self.slice5.add_module(str(x), vgg_pretrained_features[x]) |
| if not requires_grad: |
| for param in self.parameters(): |
| param.requires_grad = False |
|
|
| def forward(self, X): |
| h_relu1 = self.slice1(X) |
| h_relu2 = self.slice2(h_relu1) |
| h_relu3 = self.slice3(h_relu2) |
| h_relu4 = self.slice4(h_relu3) |
| h_relu5 = self.slice5(h_relu4) |
| out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5] |
| return out |
|
|
| class VGGLoss(nn.Module): |
| def __init__(self): |
| super(VGGLoss, self).__init__() |
| self.vgg = VGG19().cuda() |
| self.criterion = nn.L1Loss() |
| self.weights = [1.0 / 32, 1.0 / 16, 1.0 / 8, 1.0 / 4, 1.0] |
|
|
| def forward(self, x, y): |
| x_vgg, y_vgg = self.vgg(x), self.vgg(y) |
| loss = 0 |
| for i in range(len(x_vgg)): |
| loss += self.weights[i] * self.criterion(x_vgg[i], y_vgg[i].detach()) |
| return loss |
