Create vgg.py

vgg.py

@@ -0,0 +1,60 @@
+import torch
+import torch.nn as nn
+import torchvision
+
+# VGG architecter, used for the perceptual loss using a pretrained VGG network
+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()
+        self.slice6 = 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, 32):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(32, 36):
+            self.slice6.add_module(str(x), vgg_pretrained_features[x])            
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+        
+        self.pool = nn.AdaptiveAvgPool2d(output_size=1)
+                
+        self.mean = torch.tensor([0.485, 0.456, 0.406]).view(1,-1, 1, 1).cuda() * 2 - 1
+        self.std = torch.tensor([0.229, 0.224, 0.225]).view(1,-1, 1, 1).cuda() * 2
+
+    def forward(self, X): # relui_1
+        X = (X-self.mean)/self.std
+        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[:-2](h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+    
+# Perceptual loss that uses a pretrained VGG network
+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