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import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import torch.nn as nn
import torch.optim as optim
from resnet_model import ResNet50
from tqdm import tqdm
# Define transformations
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2470, 0.2435, 0.2616))
])
# Load CIFAR-10 dataset
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=128, shuffle=True, num_workers=4)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = DataLoader(testset, batch_size=1000, shuffle=False, num_workers=4)
# Initialize model, loss function, and optimizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = ResNet50().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
# Training function
def train(model, device, train_loader, optimizer, criterion, epoch):
model.train()
running_loss = 0.0
correct = 0
total = 0
pbar = tqdm(train_loader)
for batch_idx, (inputs, targets) in enumerate(pbar):
inputs, targets = inputs.to(device), targets.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
running_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
pbar.set_description(desc=f'Epoch {epoch} | Loss: {loss.item():.4f} | Accuracy: {100.*correct/total:.2f}%')
return 100.*correct/total
# Testing function
def test(model, device, test_loader, criterion):
model.eval()
test_loss = 0
correct = 0
total = 0
with torch.no_grad():
for inputs, targets in test_loader:
inputs, targets = inputs.to(device), targets.to(device)
outputs = model(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
test_accuracy = 100.*correct/total
print(f'Test Loss: {test_loss/len(test_loader):.4f}, Accuracy: {test_accuracy:.2f}%')
return test_accuracy
# Main execution
if __name__ == '__main__':
for epoch in range(1, 6): # 20 epochs
train_accuracy = train(model, device, trainloader, optimizer, criterion, epoch)
test_accuracy = test(model, device, testloader, criterion)
print(f'Epoch {epoch} | Train Accuracy: {train_accuracy:.2f}% | Test Accuracy: {test_accuracy:.2f}%') |