Initial commit

architectures/Inception.py

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+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import models
+import time
+
+# Base model
+from architectures.Model import Model
+
+class Inception(Model):
+    def get(self):
+        model = models.inception_v3(weights=models.Inception_V3_Weights.DEFAULT)
+        #for param in model.parameters():
+        #    param.requires_grad = False
+        model.fc = nn.Linear(model.fc.in_features, self.size)
+        model.AuxLogits.fc = nn.Linear(model.AuxLogits.fc.in_features, self.size)
+        return model.to(self.device)
+
+    def train(self, epochs, loader, rate):
+        # Override because Inception returns a tuple (main, aux)
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()), lr=rate)
+
+        print(f"Starting training on {self.device}...")
+        start_time = time.time()
+
+        self.model.train()
+
+        for epoch in range(epochs):
+            total_loss = 0.0
+            for inputs, labels in loader:
+                
+                inputs, labels = inputs.to(self.device), labels.to(self.device)
+                optimizer.zero_grad()
+                
+                outputs, aux_outputs = self.model(inputs)
+                loss = criterion(outputs, labels) + 0.3 * criterion(aux_outputs, labels)
+                
+                loss.backward()
+                optimizer.step()
+                total_loss += loss.item()
+
+            print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss/len(loader):.4f}")
+        
+        if self.device.type == 'cuda': torch.cuda.synchronize()
+        print(f"Training completed in: {time.time() - start_time:.2f}s")