added material

privacy/c1.2.py

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