frameworkeval

frameworkeval.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F  
+from torchvision.models import vgg16
+from torchmetrics.functional import structural_similarity_index_measure
+from facenet_pytorch import InceptionResnetV1
+from losseval.denormalize import denormalize_bin, denormalize_tr, denormalize_ar
+    
+class DF(nn.Module):
+    def __init__(self):
+        super(DF, self).__init__()
+        self.mse_weight = 0.25
+        self.perceptual_weight = 0.25
+        self.ssim_weight = 0.25
+        self.idsim_weight = 0.25
+        self.device = "cuda"
+        self.vgg = vgg16(pretrained=True).features[:16].to(device).eval()
+        self.facenet = InceptionResnetV1(pretrained='vggface2').to(device).eval()
+        for param in self.facenet.parameters():
+            param.requires_grad = False  # Freeze the model
+        self.cosloss = nn.CosineEmbeddingLoss()
+   
+    def perceptual_loss(self, real, fake):
+        with torch.no_grad():  # VGG is frozen during training
+            real_features = self.vgg(real)
+            fake_features = self.vgg(fake)
+        return F.mse_loss(real_features, fake_features)
+
+    def idsimilarity(self, real, fake):
+        with torch.no_grad():
+            # Extract embeddings
+            input_embed = self.facenet(real).to(device)
+            generated_embed = self.facenet(fake).to(device)
+        # Compute cosine similarity loss
+        target = torch.ones(input_embed.size(0)).to(real.device)  # Target = 1 (maximize similarity)
+        return self.cosloss(input_embed, generated_embed, target)
+
+    def forward(self, r, f):
+        real = denormalize_bin(r) #[-1,1] to [0,1]
+        fake = denormalize_bin(f)
+        mse_loss = F.mse_loss(real, fake)
+        perceptual_loss = self.perceptual_loss(real, fake)
+        idsim_loss = self.idsimilarity(real, fake)
+        ssim = structural_similarity_index_measure(fake, real)
+        ssim_loss = 1 - ssim
+        id_si = 1 - idsim_loss
+
+        total_loss = (self.mse_weight * mse_loss) + (self.perceptual_weight * perceptual_loss) + (self.idsim_weight * idsim_loss) + (self.ssim_weight * ssim_loss)
+        components = {
+            "MSE Loss": mse_loss.item(),
+            "Perceptual Loss": perceptual_loss.item(),
+            "ID-SIM Loss": idsim_loss.item(),
+            "SSIM Loss": ssim_loss.item()
+        }
+
+        return total_loss, components