model.to(DEVICE)
if args.adv is None and val_accs.avg >= best_acc:
best_acc = val_accs.avg
best_epoch = epoch
best_dict = deepcopy(model.state_dict())
if not (epoch + 1) % args.save_freq:
save_checkpoint(model.state_dict(),
os.path.join(
args.save_folder, args.save_name +
'acc{}_{}.pth'.format(val_accs.avg, (epoch + 1))),
cpu=True)
if args.adv is None:
model.load_state_dict(best_dict)
test_accs = AverageMeter()
test_losses = AverageMeter()
with torch.no_grad():
for i, (images, labels) in enumerate(tqdm.tqdm(test_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
logits = model(images)
loss = F.cross_entropy(logits, labels)
test_accs.append((logits.argmax(1) == labels).float().mean().item())
test_losses.append(loss.item())
if args.adv is not None:
pth_file = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2_mixed_attention_cifar10_ep_12_val_acc0.8895.pth'
#pth_file = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/2_2AT_cifar10_ep_29_val_acc0.8870.pth'
model = NormalizedModel(model=m, mean=image_mean, std=image_std).to(device)
#model = model.to(device)
print('loading data for defense using %s ....' % target_model)
test_loader = load_data_for_defense()['dev_data']
# pth_file = os.path.join(weights_path, 'cifar10acc0.9090267625855811_40.pth')#18.24944_17.2555_ep_31__acc0.7470.pthep_36_val_acc0.9844.pthep_36_val_acc0.9844.pth#glob.glob(os.path.join(weights_path, 'cifar10_20_0.73636.pth'))#[0]
print('loading weights from : ', pth_file)
#model_dict = torch.load('jpeg_weight/18.5459_0.9_jpeg_WRN_DDNacc0.9553740539334037_20_0.60.pth')
#model_dict = torch.load('jpeg_weight/ALP_smooth_p_44_val_acc0.9409.pth')
#model_dict = torch.load('jpeg_weight/JPEG_ALP_smooth_acc0.9401_all_0.786.pth')
model_dict = torch.load(pth_file)
model.load_state_dict(model_dict, False)
model.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(
weight_025conv_mixatten='/media/unknown/Data/PLP/fast_adv/defenses/weights/best/0.25MixedAttention_mixed_attention_cifar10_ep_50_val_acc0.8720.pth'
weight_05conv_mixatten = '/media/unknown/Data/PLP/fast_adv/defenses/weights/shape_0.5_cifar10_mixed_Attention/cifar10acc0.8434999763965607_130.pth'
weight_1conv_mixatten = '/media/unknown/Data/PLP/fast_adv/defenses/weights/best/1MixedAttention_mixed_attention_cifar10_ep_25_val_acc0.7080.pth'
weight_shape_alp='/media/unknown/Data/PLP/fast_adv/defenses/weights/best/shape_ALP_cifar10_ep_79_val_acc0.7625.pth'
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
# ########
# model2=NormalizedModel(model=m, mean=image_mean, std=image_std).to(DEVICE) # keep images in the [0, 1] range
# model_dict2 = torch.load('../defenses/weights/AT_cifar10_clean0.879_adv.pth')
# model2.load_state_dict(model_dict2)
# model3=NormalizedModel(model=m, mean=image_mean, std=image_std).to(DEVICE) # keep images in the [0, 1] range
# model_dict3 = torch.load('../defenses/weights/best/ALP_cifar10_ep_39_val_acc0.8592.pth')
# model3.load_state_dict(model_dict3)
# model4=NormalizedModel(model=m, mean=image_mean, std=image_std).to(DEVICE) # keep images in the [0, 1] range
# model_dict4 = torch.load('../defenses/weights/best/PLP1_cifar10_ep_29_val_acc0.8636.pth')
# model4.load_state_dict(model_dict4)
weight_norm = '../defenses/weights/best/2Norm_cifar10_ep_184_val_acc0.9515.pth'
model5 = NormalizedModel(model=m, mean=image_mean, std=image_std).to(
DEVICE) # keep images in the [0, 1] range
model_dict5 = torch.load(weight_norm)
model5.load_state_dict(model_dict5)
#model.eval()
#with torch.no_grad():
for i, (images, labels) in enumerate(tqdm.tqdm(test_loader, ncols=80)):
images, labels = images.to(DEVICE), labels.to(DEVICE)
logits = model5(images)
# loss = F.cross_entropy(logits, labels)
# print(logits)
test_accs = AverageMeter()
test_losses = AverageMeter()
test_accs.append((logits.argmax(1) == labels).float().mean().item())
################ADV########################
attacker = DDN(steps=100, device=DEVICE)
attacker2 = DeepFool(device=DEVICE)