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run_dp.py
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126 lines (91 loc) · 4.3 KB
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import warnings
warnings.simplefilter("ignore")
from model import load_model
from dataset import load_data
from utils import parse_args
import numpy as np
import torch
import torch.nn as nn
from opacus import PrivacyEngine
from opacus.utils.batch_memory_manager import BatchMemoryManager
from tqdm import tqdm
from sklearn import metrics
def train(model, criterion, train_loader, optimizer, epoch, privacy_engine, args):
model.train()
losses = []
top1_acc = []
with BatchMemoryManager(
data_loader=train_loader,
max_physical_batch_size=args.MAX_PHYSICAL_BATCH_SIZE,
optimizer=optimizer
) as memory_safe_data_loader:
for i, (images, target) in enumerate(memory_safe_data_loader):
optimizer.zero_grad()
images = images.to(args.DEVICE)
target = target.to(args.DEVICE)
# compute output
output = model(images)
loss = criterion(output, target)
preds = np.argmax(output.detach().cpu().numpy(), axis=1)
labels = target.detach().cpu().numpy()
# measure accuracy and record loss
acc = metrics.accuracy_score(preds, labels)
losses.append(loss.item())
top1_acc.append(acc)
loss.backward()
optimizer.step()
if (i+1) % 200 == 0:
epsilon = privacy_engine.get_epsilon(args.DELTA)
print(
f"\tTrain Epoch: {epoch} \t"
f"Loss: {np.mean(losses):.6f} "
f"Acc@1: {np.mean(top1_acc) * 100:.6f} "
f"(ε = {epsilon:.2f}, δ = {args.DELTA})"
)
def test(model, test_loader, args):
model.eval()
criterion = nn.CrossEntropyLoss()
losses = []
predict_all = np.array([], dtype=int)
labels_all = np.array([], dtype=int)
with torch.no_grad():
for images, target in test_loader:
images = images.to(args.DEVICE)
target = target.to(args.DEVICE)
output = model(images)
loss = criterion(output, target)
preds = np.argmax(output.detach().cpu().numpy(), axis=1)
predict_all = np.append(predict_all, preds)
labels = target.detach().cpu().numpy()
labels_all = np.append(labels_all, labels)
losses.append(loss.item())
acc = metrics.accuracy_score(predict_all, labels_all)
report = metrics.classification_report(labels_all, predict_all, target_names=args.LABEL_NAMES, digits=4)
confusion = metrics.confusion_matrix(labels_all, predict_all)
return acc, np.mean(losses), report, confusion
def main(args):
model = load_model(args.MODEL_NAME, args.NUM_CLASSES, args.DEVICE, args.USE_OPACUS)
criterion = getattr(nn, args.CRITERION)()
optimizer = getattr(torch.optim, args.OPTIMIZER)(model.parameters(), args.LR)
train_loader, test_loader, labelNames = load_data(args)
args.LABEL_NAMES = labelNames
privacy_engine = PrivacyEngine()
model, optimizer, train_loader = privacy_engine.make_private_with_epsilon(module=model,
optimizer=optimizer,
data_loader=train_loader,
epochs=args.EPOCHS,
target_epsilon=args.EPSILON,
target_delta=args.DELTA,
max_grad_norm=args.MAX_GRAD_NORM)
print(f"Using sigma={optimizer.noise_multiplier} and C={args.MAX_GRAD_NORM}")
for epoch in tqdm(range(args.EPOCHS), desc="Epoch", unit="epoch"):
train(model, criterion, train_loader, optimizer, epoch, privacy_engine, args)
test_acc, test_loss, test_report, test_confusion = test(model, test_loader, args)
print('Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}'.format(test_loss, test_acc))
print("Precision, Recall and F1-Score...")
print(test_report)
print("Confusion Matrix...")
print(test_confusion)
if __name__ == '__main__':
args = parse_args()
main(args)