Update pages/19_ResNet.py

pages/19_ResNet.py

@@ -35,25 +35,18 @@ transform = transforms.Compose([
     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
 ])
 
-train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
-val_dataset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
-
-# Using only 1000 samples for simplicity
-subset_indices = list(range(1000))
-train_size = int(0.8 * len(subset_indices))
-val_size = len(subset_indices) - train_size
-
-train_indices = subset_indices[:train_size]
-val_indices = subset_indices[train_size:]
-
-train_dataset = Subset(train_dataset, train_indices)
-val_dataset = Subset(val_dataset, val_indices)
+full_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
+subset_indices = list(range(1000))  # Use only 1000 samples for simplicity
+subset_dataset = Subset(full_dataset, subset_indices)
+train_size = int(0.8 * len(subset_dataset))
+val_size = len(subset_dataset) - train_size
+train_dataset, val_dataset = torch.utils.data.random_split(subset_dataset, [train_size, val_size])
 
 train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
 val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
 
 dataloaders = {'train': train_loader, 'val': val_loader}
-class_names = datasets.CIFAR10(root='./data', download=False).classes
+class_names = full_dataset.classes
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
@@ -78,8 +71,103 @@ imshow(out, title=[class_names[x] for x in classes])
 # Model Preparation Section
 st.markdown("""
 ### Model Preparation
-We will use a pre-trained ResNet-18 model and fine-tune the final fully connected layer to match the number of classes in our dataset.
+We will use a pre-trained ResNet-18 model and fine-tune the final fully connected layer to match the number of classes in our custom dataset.
 """)
 
 # Load Pre-trained ResNet Model
-model_ft = models.resnet
+model_ft = models.resnet18(pretrained=True)
+num_ftrs = model_ft.fc.in_features
+model_ft.fc = nn.Linear(num_ftrs, len(class_names))
+
+model_ft = model_ft.to(device)
+
+# Define Loss Function and Optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=0.9)
+
+# Training Section
+st.markdown("""
+### Training
+We will train the model using stochastic gradient descent (SGD) with a learning rate scheduler. The training and validation loss and accuracy will be plotted to monitor the training process.
+""")
+
+# Train and Evaluate the Model
+def train_model(model, criterion, optimizer, num_epochs=5):
+    best_model_wts = copy.deepcopy(model.state_dict())
+    best_acc = 0.0
+    train_loss_history = []
+    val_loss_history = []
+    train_acc_history = []
+    val_acc_history = []
+
+    for epoch in range(num_epochs):
+        st.write(f'Epoch {epoch+1}/{num_epochs}')
+        st.write('-' * 10)
+
+        for phase in ['train', 'val']:
+            if phase == 'train':
+                model.train()
+            else:
+                model.eval()
+
+            running_loss = 0.0
+            running_corrects = 0
+
+            for inputs, labels in dataloaders[phase]:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+
+                optimizer.zero_grad()
+
+                with torch.set_grad_enabled(phase == 'train'):
+                    outputs = model(inputs)
+                    _, preds = torch.max(outputs, 1)
+                    loss = criterion(outputs, labels)
+
+                    if phase == 'train':
+                        loss.backward()
+                        optimizer.step()
+
+                running_loss += loss.item() * inputs.size(0)
+                running_corrects += torch.sum(preds == labels.data)
+
+            epoch_loss = running_loss / len(dataloaders[phase].dataset)
+            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)
+
+            if phase == 'train':
+                train_loss_history.append(epoch_loss)
+                train_acc_history.append(epoch_acc)
+            else:
+                val_loss_history.append(epoch_loss)
+                val_acc_history.append(epoch_acc)
+
+            st.write(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
+
+            if phase == 'val' and epoch_acc > best_acc:
+                best_acc = epoch_acc
+                best_model_wts = copy.deepcopy(model.state_dict())
+
+    model.load_state_dict(best_model_wts)
+
+    # Plot training history
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 5))
+    ax1.plot(train_loss_history, label='Training Loss')
+    ax1.plot(val_loss_history, label='Validation Loss')
+    ax1.legend(loc='upper right')
+    ax1.set_title('Training and Validation Loss')
+
+    ax2.plot(train_acc_history, label='Training Accuracy')
+    ax2.plot(val_acc_history, label='Validation Accuracy')
+    ax2.legend(loc='lower right')
+    ax2.set_title('Training and Validation Accuracy')
+
+    st.pyplot(fig)
+
+    return model
+
+if st.button('Train Model'):
+    model_ft = train_model(model_ft, criterion, optimizer_ft, num_epochs)
+    # Save the Model
+    torch.save(model_ft.state_dict(), 'fine_tuned_resnet.pth')
+    st.write("Model saved as 'fine_tuned_resnet.pth'")
+