weight_attention = '/media/unknown/Data/PLP/fast_adv/defenses/weights/cifar10_Attention/cifar10acc0.8729999780654907_120.pth'
weight_025conv_mixatten_ALP = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/0.25Mixed+ALP_cifar10_ep_85_val_acc0.8650.pth'
weight_smooth = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2random_smooth_cifar10_ep_120_val_acc0.8510.pth'
weight_05smooth = '/media/unknown/Data/PLP/fast_adv/defenses/weights/shape_0.5_random/cifar10acc0.6944999784231186_50.pth'
weight_025smooth = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/0.25random_smooth_cifar10_ep_146_val_acc0.8070.pth'
weight_1smooth = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/1random_smooth_cifar10_ep_107_val_acc0.5380.pth'
print('loading weights from : ', weight_AT)
model_dict = torch.load(weight_AT)
model.load_state_dict(model_dict)
model.eval()
model_dict2 = torch.load(weight_025conv_mixatten_ALP)
model2.load_state_dict(model_dict2)
model2.eval()
test_accs = AverageMeter()
test_losses = AverageMeter()
widgets = ['test :', Percentage(), ' ', Bar('#'), ' ', Timer(),
' ', ETA(), ' ', FileTransferSpeed()]
pbar = ProgressBar(widgets=widgets)
with torch.no_grad():
for batch_data in pbar(test_loader):
images, labels = batch_data['image'].to(device), batch_data['label_idx'].to(device)
noise = torch.randn_like(images, device='cuda') * 0.2
image_shape = images + noise
#image_shape = torch.renorm(image_shape - images, p=2, dim=0, maxnorm=1) + images
#logits,_ = model.forward_attention(images.detach(), image_shape.detach())
logits= model(images.detach())
logits2 = model2(images.detach())
if logits.argmax(1) !=labels and logits2.argmax(1) ==labels:
i=0
milestones=[60, 120, 160],
gamma=0.2)
attacker = DDN(steps=args.steps, device=DEVICE)
max_loss = torch.log(torch.tensor(10.)).item() # for callback
best_acc = 0
best_epoch = 0
for epoch in range(args.epochs):
scheduler.step()
cudnn.benchmark = True
model.train()
requires_grad_(m, True)
accs = AverageMeter()
losses = AverageMeter()
attack_norms = AverageMeter()
length = len(train_loader)
for i, (images, labels) in enumerate(tqdm.tqdm(train_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
#原图loss
#logits_clean = model.forward(images)
logits_clean, feature_clean = model.forward(images)
#loss = F.cross_entropy(logits_clean, labels)
if args.adv is not None and epoch >= args.adv:
model.eval()
requires_grad_(m, False)
# Center Loss for Attention Regularization
##############################################
class CenterLoss(nn.Module):
def __init__(self):
super(CenterLoss, self).__init__()
self.l2_loss = nn.MSELoss(reduction='sum')
def forward(self, outputs, targets):
return self.l2_loss(outputs, targets) / outputs.size(0)
cross_entropy_loss = nn.CrossEntropyLoss()
center_loss = CenterLoss()
# loss and metric
loss_container = AverageMeter()
accs = AverageMeter()
raw_metric = AverageMeter()
crop_metric = AverageMeter()
drop_metric = AverageMeter()
attack_norms = AverageMeter()
loss_crop_adv = AverageMeter()
loss_drop = AverageMeter()
valacc_final = 0
# raw_metric = TopKAccuracyMetric(topk=(1, 5))
# crop_metric = TopKAccuracyMetric(topk=(1, 5))
# drop_metric = TopKAccuracyMetric(topk=(1, 5))
import scipy.misc