Jeremy Janella
135 lines
4.8 KiB
Python
135 lines
4.8 KiB
Python
#!/usr/bin/env python3
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import os
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import requests
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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from tqdm import tqdm
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from opacus import PrivacyEngine
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from opacus.validators import ModuleValidator
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from safetensors.torch import save_file
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# =============================================================================
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# CONFIGURATION
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# =============================================================================
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BATCH_SIZE = 64
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DP_EPOCHS = 20 # Increased to reach 55% accuracy target
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DP_LR = 1e-3 # Adam LR
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MAX_GRAD_NORM = 1.0
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DELTA = 1e-5
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TARGET_EPSILON = 6.0
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DEVICE = torch.device('cpu')
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MODELS_DIR = "models"
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os.makedirs(MODELS_DIR, exist_ok=True)
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# =============================================================================
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# STABLE MODEL ARCHITECTURE
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# =============================================================================
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class ChallengeSVHN(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
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self.gn1 = nn.GroupNorm(4, 32)
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self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
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self.gn2 = nn.GroupNorm(8, 64)
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self.pool = nn.MaxPool2d(2, 2)
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self.fc1 = nn.Linear(64 * 8 * 8, 64)
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self.fc2 = nn.Linear(64, 10)
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def forward(self, x):
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x = self.pool(F.relu(self.gn1(self.conv1(x))))
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x = self.pool(F.relu(self.gn2(self.conv2(x))))
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x = torch.flatten(x, 1)
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x = F.relu(self.fc1(x))
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return self.fc2(x)
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# =============================================================================
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# MAIN PROCESS
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# =============================================================================
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def run_challenge():
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# 1. Data Setup
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transform = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.4377, 0.4438, 0.4728), (0.1980, 0.2010, 0.1970))
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])
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train_ds = datasets.SVHN("data", split='train', download=True, transform=transform)
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test_ds = datasets.SVHN("data", split='test', download=True, transform=transform)
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train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True)
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test_loader = DataLoader(test_ds, batch_size=BATCH_SIZE, shuffle=False)
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# 2. Model & Optimizer
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model = ChallengeSVHN().to(DEVICE)
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model = ModuleValidator.fix(model)
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optimizer = optim.Adam(model.parameters(), lr=DP_LR)
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# 3. Privacy Engine (Stability Mode)
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privacy_engine = PrivacyEngine()
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model, optimizer, train_loader_dp = privacy_engine.make_private_with_epsilon(
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module=model,
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optimizer=optimizer,
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data_loader=train_loader,
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target_epsilon=TARGET_EPSILON,
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target_delta=DELTA,
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epochs=DP_EPOCHS,
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max_grad_norm=MAX_GRAD_NORM,
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grad_sample_mode="hooks", # CRITICAL: Prevents AMD CPU Segfault
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)
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criterion = nn.CrossEntropyLoss()
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# 4. Training Loop
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print(f"Starting Training for {DP_EPOCHS} epochs...")
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for epoch in range(DP_EPOCHS):
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model.train()
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pbar = tqdm(train_loader_dp, desc=f"Epoch {epoch+1}")
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for images, labels in pbar:
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optimizer.zero_grad()
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outputs = model(images)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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pbar.set_postfix({'eps': f'{privacy_engine.get_epsilon(DELTA):.2f}'})
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# 5. Final Evaluation
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model.eval()
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correct, total = 0, 0
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with torch.no_grad():
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for images, labels in test_loader:
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outputs = model(images.to(DEVICE))
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_, predicted = outputs.max(1)
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total += labels.size(0)
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correct += predicted.eq(labels.to(DEVICE)).sum().item()
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acc = 100.0 * correct / total
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eps = privacy_engine.get_epsilon(DELTA)
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print(f"\nTraining Complete. Final Accuracy: {acc:.2f}% | Final Epsilon: {eps:.2f}")
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# 6. Save Model
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# We save model._module because 'model' is wrapped by Opacus
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model_path = os.path.join(MODELS_DIR, "dp_model.safetensors")
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save_file(model._module.state_dict(), model_path)
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print(f"Model saved to {model_path}")
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# 7. Submit to Server
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if acc >= 55.0:
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print("Accuracy requirement met. Submitting for flag...")
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with open(model_path, "rb") as f:
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response = requests.post(
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"http://154.57.164.77:32346/validate",
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files={"model": ("dp_model.safetensors", f, "application/octet-stream")}
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)
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print("\n--- SERVER RESPONSE ---")
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print(response.text)
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print("-----------------------")
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else:
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print("Accuracy was below 55%. Try running again or adjusting DP_LR.")
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if __name__ == "__main__":
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# Prevent memory collision on CPU
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torch.set_num_threads(1)
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run_challenge()
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