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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import os
# Import the DCLR optimizer from the local file
from dclr_optimizer import DCLR
# === Simple CNN Model Definition ===
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(64 * 8 * 8, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 64 * 8 * 8)
x = F.relu(self.fc1(x))
return self.fc2(x)
# === CIFAR-10 Data Loading ===
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
train_set = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_set, batch_size=128, shuffle=True)
test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
test_loader = DataLoader(test_set, batch_size=128, shuffle=False)
# === Training Configuration ===
model = SimpleCNN()
optimizer = DCLR(model.parameters(), lr=0.1, lambda_=0.1, verbose=False)
criterion = nn.CrossEntropyLoss()
epochs = 20
print(f"Starting training with DCLR for {epochs} epochs...")
losses, accs = [], []
# === Training Loop ===
for epoch in range(epochs):
model.train()
running_loss = 0.0
correct = 0
total = 0
for inputs, labels in train_loader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step(output_activations=outputs)
running_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
epoch_loss = running_loss / len(train_loader)
epoch_acc = 100.0 * correct / total
losses.append(epoch_loss)
accs.append(epoch_acc)
print(f"Epoch {epoch+1}/{epochs} - Loss: {epoch_loss:.4f}, Accuracy: {epoch_acc:.2f}%")
print("Training complete.")
# === Evaluate on Test Set ===
model.eval()
correct = 0
total = 0
with torch.no_grad():
for inputs, labels in test_loader:
outputs = model(inputs)
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
test_acc = 100.0 * correct / total
print(f"Final Test Accuracy: {test_acc:.2f}%")
# === Save the Trained Model ===
torch.save(model.state_dict(), 'simple_cnn_dclr_tuned.pth')
print("Model saved to simple_cnn_dclr_tuned.pth")
# === Save Training Performance Plot ===
plt.figure()
plt.plot(range(1, epochs+1), losses, label='Loss')
plt.plot(range(1, epochs+1), accs, label='Accuracy')
plt.xlabel('Epoch')
plt.ylabel('Value')
plt.legend()
plt.title('Training Performance on CIFAR-10 (DCLR)')
plt.savefig('training_performance.png')
print("Training performance plot saved to training_performance.png")
# === Save Final Test Accuracy Plot ===
plt.figure()
plt.bar(['CIFAR-10'], [test_acc])
plt.ylabel('Accuracy (%)')
plt.title('Final Test Accuracy (DCLR)')
plt.savefig('final_test_accuracy.png')
print("Final test accuracy plot saved to final_test_accuracy.png")
# === Save Final Test Accuracy Number ===
with open("final_test_accuracy.txt", "w") as f:
f.write(f"{test_acc:.2f}")
print("Final test accuracy saved to final_test_accuracy.txt")
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