pages/19_ResNet.py

# Install required packages
# !pip install streamlit torch torchvision matplotlib

# Import Libraries
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
st.title("Simple ResNet Fine-Tuning Example")

# User Inputs
st.sidebar.header("Model Parameters")
batch_size = st.sidebar.number_input("Batch Size", value=32)
num_epochs = st.sidebar.number_input("Number of Epochs", value=5)
learning_rate = st.sidebar.number_input("Learning Rate", value=0.001)

# Data Preparation Section
st.markdown("""
### Data Preparation
We will use a small subset of the CIFAR-10 dataset for quick experimentation. The dataset will be split into training and validation sets, and transformations will be applied to normalize the data.
""")

transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    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_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

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
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.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'")