def train_linear_svm(features, labels, epochs=50, lr=0.5, print_time=False):
# Initialize random weights
w = features.new(torch.randn(1, features.size(0)))
b = features.new(torch.randn(1))
if print_time:
pt_time = AverageMeter()
end = time.time()
for epoch in range(epochs):
# Forward
label_predictions = w.matmul(features).add(b).sign()
# Compute accuracy
correct = label_predictions.mul(labels)
accuracy = correct.add(1).div(2).mean()
if crypten.is_encrypted_tensor(accuracy):
accuracy = accuracy.get_plain_text()
# Print Accuracy once
if crypten.communicator.get().get_rank() == 0:
logging.info(f"Epoch {epoch} --- Training Accuracy %.2f%%" %
(accuracy.item() * 100))
# Backward
loss_grad = -labels * (1 - correct) * 0.5 # Hinge loss
b_grad = loss_grad.mean()
w_grad = loss_grad.matmul(features.t()).div(loss_grad.size(1))
# Update
w -= w_grad * lr
b -= b_grad * lr
if print_time:
iter_time = time.time() - end
pt_time.add(iter_time)
logging.info(" Time %.6f (%.6f)" % (iter_time, pt_time.value()))
end = time.time()
return w, b
def validate(val_loader, model, criterion, print_freq=10, flatten=False):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
# compute output
if flatten:
input = input.view(input.size(0), -1)
if isinstance(model, crypten.nn.Module
) and not crypten.is_encrypted_tensor(input):
input = crypten.cryptensor(input)
output = model(input)
if crypten.is_encrypted_tensor(output):
output = output.get_plain_text()
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1, 5))
losses.add(loss.item(), input.size(0))
top1.add(prec1[0], input.size(0))
top5.add(prec5[0], input.size(0))
# measure elapsed time
current_batch_time = time.time() - end
batch_time.add(current_batch_time)
end = time.time()
if (i + 1) % print_freq == 0:
logging.info("\nTest: [{}/{}]\t"
"Time {:.3f} ({:.3f})\t"
"Loss {:.4f} ({:.4f})\t"
"Prec@1 {:.3f} ({:.3f}) \t"
"Prec@5 {:.3f} ({:.3f})".format(
i + 1,
len(val_loader),
current_batch_time,
batch_time.value(),
loss.item(),
losses.value(),
prec1[0],
top1.value(),
prec5[0],
top5.value(),
))
if i > 100:
break
logging.info(" * Prec@1 {:.3f} Prec@5 {:.3f}".format(
top1.value(), top5.value()))
return top1.value()
def train(train_loader,
model,
criterion,
optimizer,
epoch,
print_freq=10,
flatten=False):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# compute output
if flatten:
input = input.view(input.size(0), -1)
output = model(input)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output, target, topk=(1, 5))
losses.add(loss.item(), input.size(0))
top1.add(prec1[0], input.size(0))
top5.add(prec5[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
current_batch_time = time.time() - end
batch_time.add(current_batch_time)
end = time.time()
if i % print_freq == 0:
logging.info("Epoch: [{}][{}/{}]\t"
"Time {:.3f} ({:.3f})\t"
"Loss {:.4f} ({:.4f})\t"
"Prec@1 {:.3f} ({:.3f})\t"
"Prec@5 {:.3f} ({:.3f})".format(
epoch,
i,
len(train_loader),
current_batch_time,
batch_time.value(),
loss.item(),
losses.value(),
prec1[0],
top1.value(),
prec5[0],
top5.value(),
))
def train_linear_svm(features, labels, epochs=50, lr=0.5, print_time=False):
# Initialize random weights
w = features.new(torch.randn(1, features.size(0)))
b = features.new(torch.randn(1))
if print_time:
pt_time = AverageMeter()
end = time.time()
for epoch in range(epochs):
# Forward
#pdb.set_trace()
label_predictions_1 = w.matmul(features)
print(label_predictions_1)
#pdb.set_trace()
#result = recursive_map(label_predictions_1)
#pdb.set_trace()
#label_predictions = w.matmul(features).add(b).sign()
for i in range(len(label_predictions_1[0])):
#pdb.set_trace()
if (label_predictions_1[0][i] < -0.5):
label_predictions_1[0][i] = 0.0
elif label_predictions_1[0][i] > 0.5:
label_predictions_1[0][i] = 1.0
else:
label_predictions_1[0][i] = label_predictions_1[0][i] + 0.5
# f(wx) + b
#label_predictions_1[0][i] = label_predictions_1[0][i].add(b).sign()
label_predictions_1[0][i] = label_predictions_1[0][i].add(b)
print(label_predictions_1)
#pdb.set_trace()
#label_predictions = logistic_regression(w.matmul(features)).add(b).sign()
#print("Iteration: " + epoch)
#print(label_predictions)
# Compute accuracy
label_predictions = label_predictions_1
correct = label_predictions.mul(labels)
accuracy = correct.add(1).div(2).mean()
if crypten.is_encrypted_tensor(accuracy):
accuracy = accuracy.get_plain_text()
# Print Accuracy once
if crypten.communicator.get().get_rank() == 0:
print(f"Epoch {epoch} --- Training Accuracy %.2f%%" %
(accuracy.item() * 100))
# Backward
loss_grad = -labels * (1 - correct) * 0.5 # Hinge loss
b_grad = loss_grad.mean()
w_grad = loss_grad.matmul(features.t()).div(loss_grad.size(1))
# Update
w -= w_grad * lr
b -= b_grad * lr
if print_time:
iter_time = time.time() - end
pt_time.add(iter_time)
logging.info(" Time %.6f (%.6f)" % (iter_time, pt_time.value()))
end = time.time()
return w, b