def train(dataloader, model,criterion, optimizer, scheduler, epoch):
model.train()
print('epoch ' + str(epoch))
train_loss = 0.0
train_acc = 0.0
total = len(dataloader)
start = time.time()
toPilImage = transforms.ToPILImage() # transform tensor into PIL image to save
for batch_num, (x, y) in enumerate(dataloader):
x = x.to(device)
y = y.to(device)
# gauss noise training
gauss_noise = torch.randn_like(x, device=device) * args.noise_sd
# x_noise = x + torch.randn_like(x, device=device) * args.noise_sd
# targeted noise training
tmp_criterion = nn.CrossEntropyLoss()
tmp_optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
classifier = PyTorchClassifier(
model=model,
clip_values=(min_pixel_value, max_pixel_value),
loss=tmp_criterion,
optimizer=tmp_optimizer,
input_shape=(3, 32, 32),
nb_classes=10,
)
# all other classes
targets = []
y_np = y.cpu().numpy()
for i in range(y.shape[0]) :
targets.append( np.expand_dims( np.random.permutation( np.delete(np.arange(get_num_classes()), y_np[i]) ), axis=0 ) )
# print(targets[0].shape)
targets = np.concatenate(targets)
# print(targets.shape)
# exit(0)
mix_noise = torch.zeros_like(x)
for t in range(targets.shape[1]):
# generate random targets
# targets = art.utils.random_targets(y.cpu().numpy(), get_num_classes())
# calculate loss gradient
# print(np.squeeze(targets[:,t]).shape)
# exit()
y_slice = np.squeeze(targets[:,t])
y_oh = np.zeros((y_slice.size, get_num_classes()))
y_oh[np.arange(y_slice.size), y_slice] = 1
grad = classifier.loss_gradient(x=x.cpu().numpy(), y=y_oh) * (-1.0)
scaled_grad = torch.Tensor(grad * args.eps_step).to(device)
mix_noise += scaled_grad
model.zero_grad()
tmp_optimizer.zero_grad()
# print((scaled_grad.shape, gauss_noise.shape, targets.shape))
# combine noise and targeted noise
x_combine = x + (gauss_noise * (1.0 - args.k_value)) + (mix_noise * args.k_value)
model.zero_grad()
output = model(x_combine)
loss = criterion(output, y)
acc = accuracy(output, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_acc += acc
scheduler.step()
end = time.time()
print('trainning time:',end - start,'sec, loss: ', train_loss/total, 'acc: ', train_acc/total)
return train_loss/total, train_acc/total
def train(dataloader, model, criterion, optimizer, scheduler, epoch):
model.train()
print('epoch ' + str(epoch))
train_loss = 0.0
train_acc = 0.0
total = len(dataloader)
start = time.time()
toPilImage = transforms.ToPILImage(
) # transform tensor into PIL image to save
for batch_num, (x, y) in enumerate(dataloader):
x = x.to(device)
y = y.to(device)
# gauss noise training
gauss_noise = torch.randn_like(x, device=device) * args.noise_sd
# x_noise = x + torch.randn_like(x, device=device) * args.noise_sd
# targeted noise training
tmp_criterion = nn.CrossEntropyLoss()
tmp_optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
classifier = PyTorchClassifier(
model=model,
clip_values=(min_pixel_value, max_pixel_value),
loss=tmp_criterion,
optimizer=tmp_optimizer,
input_shape=(3, 32, 32),
nb_classes=10,
)
# generate random targets
targets = art.utils.random_targets(y.cpu().numpy(), get_num_classes())
# calculate loss gradient
grad = classifier.loss_gradient(x=x.cpu().numpy(), y=targets) * (-1.0)
scaled_grad = torch.Tensor(grad * args.eps_step).to(device)
# print((scaled_grad.shape, gauss_noise.shape, targets.shape))
# combine noise and targeted noise
x_combine = x + (gauss_noise *
(1.0 - args.k_value)) + (scaled_grad * args.k_value)
model.zero_grad()
output = model(x_combine)
loss = criterion(output, y)
acc = accuracy(output, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_acc += acc
scheduler.step()
end = time.time()
print('trainning time:', end - start, 'sec, loss: ', train_loss / total,
'acc: ', train_acc / total)
return train_loss / total, train_acc / total
