Jeremy Janella
128 lines
4.4 KiB
Python
128 lines
4.4 KiB
Python
#!/usr/bin/env python3
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import os
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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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# =============================================================================
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# STABLE CONFIGURATION
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# =============================================================================
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BATCH_SIZE = 64 # Smaller batch = more stable memory on CPU
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DP_EPOCHS = 10 # Fewer epochs to start; check stability first
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DP_LR = 5e-4 # Lower LR to prevent 'nan'
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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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# =============================================================================
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# MODEL (MINIMAL & STABLE)
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# =============================================================================
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class SimpleSVHN(nn.Module):
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def __init__(self):
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super().__init__()
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# We use GroupNorm with small groups for maximum stability
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self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
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self.gn1 = nn.GroupNorm(2, 16)
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self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
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self.gn2 = nn.GroupNorm(4, 32)
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self.pool = nn.MaxPool2d(2, 2)
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self.fc = nn.Linear(32 * 8 * 8, 10)
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def forward(self, x):
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x = self.pool(F.relu(self.gn1(self.conv1(x)))) # 16x16
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x = self.pool(F.relu(self.gn2(self.conv2(x)))) # 8x8
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x = torch.flatten(x, 1)
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return self.fc(x)
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# =============================================================================
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# TRAINING SCRIPT
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# =============================================================================
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def run_stable_dp_training():
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# 1. Data Loading
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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 Setup
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model = SimpleSVHN().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 (THE STABILITY FIX)
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# Using grad_sample_mode="hooks" prevents the C++ level SegFaults
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privacy_engine = PrivacyEngine()
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model, optimizer, train_loader = 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",
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)
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criterion = nn.CrossEntropyLoss()
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print(f"Starting Training on {DEVICE}...")
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for epoch in range(DP_EPOCHS):
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model.train()
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total_loss = 0
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pbar = tqdm(train_loader, 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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if torch.isnan(loss):
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print("\n[!] Warning: NaN detected. Skipping batch.")
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continue
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loss.backward()
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optimizer.step()
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total_loss += loss.item()
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pbar.set_postfix({'loss': f'{loss.item():.2f}'})
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epsilon = privacy_engine.get_epsilon(DELTA)
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print(f"Epoch {epoch+1} complete. Current ε: {epsilon:.2f}")
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# 4. Final Evaluation
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model.eval()
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correct = 0
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total = 0
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with torch.no_grad():
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for images, labels in test_loader:
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images, labels = images.to(DEVICE), labels.to(DEVICE)
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outputs = model(images)
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_, predicted = outputs.max(1)
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total += labels.size(0)
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correct += predicted.eq(labels).sum().item()
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print(f"\nFinal Test Accuracy: {100.*correct/total:.2f}%")
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print(f"Final Epsilon: {epsilon:.2f}")
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if __name__ == "__main__":
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# Force single-thread for math if your CPU is still fighting the memory allocation
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torch.set_num_threads(1)
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run_stable_dp_training()
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