Update pages/19_ResNet.py

pages/19_ResNet.py

@@ -6,10 +6,12 @@ import streamlit as st
 import torch
 import torch.nn as nn
 import torch.optim as optim
+import torchvision  # Add this import
 from torchvision import datasets, models, transforms
 from torch.utils.data import DataLoader, Subset
 import numpy as np
 import time
+import copy  # Add this import
 import matplotlib.pyplot as plt
 
 # Streamlit Interface
@@ -33,21 +35,46 @@ transform = transforms.Compose([
     transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
 ])
 
-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_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)
 
 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 = full_dataset.classes
+class_names = datasets.CIFAR10(root='./data', download=False).classes
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
+# Visualize a few training images
+st.markdown("#### Sample Training Images")
+def imshow(inp, title=None):
+    inp = inp.numpy().transpose((1, 2, 0))
+    mean = np.array([0.485, 0.456, 0.406])
+    std = np.array([0.229, 0.224, 0.225])
+    inp = std * inp + mean
+    inp = np.clip(inp, 0, 1)
+    fig, ax = plt.subplots()
+    ax.imshow(inp)
+    if title is not None:
+        ax.set_title(title)
+    st.pyplot(fig)
+
+inputs, classes = next(iter(dataloaders['train']))
+out = torchvision.utils.make_grid(inputs)
+imshow(out, title=[class_names[x] for x in classes])
+
 # Model Preparation Section
 st.markdown("""
 ### Model Preparation
@@ -55,99 +82,4 @@ We will use a pre-trained ResNet-18 model and fine-tune the final fully connecte
 """)
 
 # Load Pre-trained ResNet Model
-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'")
-
+model_ft = models.resnet