trainmodel.py

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import tqdm

# Define transformations
transform = transforms.Compose([
    transforms.Resize((224, 224)),  # Resize images for ResNet
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# Load CIFAR-10 Dataset
trainset = torchvision.datasets.CIFAR10(root="./data", train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = torchvision.datasets.CIFAR10(root="./data", train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

# Define Model (ResNet-18)
model = torchvision.models.resnet18(pretrained=True)
model.fc = nn.Linear(model.fc.in_features, 10)  # Adjust for 10 CIFAR-10 classes

# Define Loss and Optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Train the Model
num_epochs = 5
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    for images, labels in tqdm.tqdm(trainloader):
        images, labels = images.to(device), labels.to(device)

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
    
    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(trainloader)}")

# Save the Trained Model
torch.save(model.state_dict(), "model.pth")
print("Model training complete and saved as model.pth!")