def test(epoch, is_adv=False):
global is_training, best_acc
is_training = False
net.eval()
test_loss = 0
correct = 0
total = 0
# with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
if use_cuda:
inputs, targets = inputs.requires_grad_().cuda(), targets.cuda()
if is_adv:
adversary = PGDAttack(net,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=args.eps,
nb_iter=args.iter,
eps_iter=args.eps / args.iter,
rand_init=True,
clip_min=0.0,
clip_max=1.0,
targeted=False)
with ctx_noparamgrad_and_eval(net):
inputs = adversary.perturb(inputs, targets)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
correct = correct.item()
progress_bar(
batch_idx, len(test_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' %
(test_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
# Save checkpoint.
acc = 100. * correct / total
if acc > best_acc:
best_acc = acc
checkpoint(acc, epoch)
return (test_loss / batch_idx, 100. * correct / total)
def train(epoch):
global is_training
is_training = True
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(train_loader):
if use_cuda:
inputs, targets = inputs.cuda().requires_grad_(), targets.cuda()
# generate adv img
if args.adv_train:
adversary = PGDAttack(net,
loss_fn=nn.CrossEntropyLoss(reduction="sum"),
eps=args.eps,
nb_iter=args.iter,
eps_iter=args.eps / args.iter,
rand_init=True,
clip_min=0.0,
clip_max=1.0,
targeted=False)
with ctx_noparamgrad_and_eval(net):
inputs = adversary.perturb(inputs, targets)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
correct = correct.item()
progress_bar(
batch_idx, len(train_loader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)' %
(train_loss /
(batch_idx + 1), 100. * correct / total, correct, total))
return (train_loss / batch_idx, 100. * correct / total)
acc_adv_image = AverageMeter()
acc_adv_latent = AverageMeter()
progress_bar = tqdm(testloader)
attacker = PGDAttack(predict=net,
eps=8.0 / 255,
eps_iter=2.0 / 255,
nb_iter=nb_iter,
clip_min=-1.0,
clip_max=1.0)
for images, _, labels in progress_bar:
images, labels = images.cuda(), labels.cuda()
images_adv = attacker.perturb(images, labels)
with torch.no_grad():
pred_clean = net(images).argmax(dim=1)
pred_adv = net(images_adv).argmax(dim=1)
acc_clean.update(
(pred_clean == labels).float().mean().item() * 100.0,
images.size(0))
acc_adv_image.update(
(pred_adv == labels).float().mean().item() * 100.0,
images.size(0))
progress_bar.set_description(
'Clean: {acc_clean.avg:.3f} '
'PGD-{nb_iter}-image: {acc_adv_image.avg:.3f}'.format(
acc_clean=acc_clean,
correct_adv = 0
correct_def = 0
for i, (images, labels) in enumerate(progress_bar):
if i < start_ind or i >= end_ind:
continue
images, labels = images.cuda(), labels.cuda()
result_path = os.path.join(result_dir, 'batch_{:04d}.pt'.format(i))
if os.path.isfile(result_path):
result_dict = torch.load(result_path)
images_adv = result_dict['input'].cuda()
images_def = result_dict['rec'].cuda()
else:
# print(images)
images_adv = attacker.perturb(images)
images_def, z_def = proj_fn(images_adv)
# print(images_adv)
print(images_def)
# torch.save({'input': images_adv,
# 'rec': images_def,
# 'z_rec': z_def}, result_path)
l2_dist = per_pixel_l2_dist(images, images_adv)
if i % 1 == 0:
clean_path = os.path.join(vis_dir, 'batch_{:04d}_clean.png'.format(i))
adv_path = os.path.join(vis_dir, 'batch_{:04d}_adv.png'.format(i))
def_path = os.path.join(vis_dir, 'batch_{:04d}_def.png'.format(i))
save_image(images, clean_path, nrow=10, padding=2)
save_image(images_adv, adv_path, nrow=10, padding=2)
def train_Ours(args, train_loader, val_loader, knownclass, Encoder, Decoder,
NorClsfier, SSDClsfier, summary_writer, saver):
seed = init_random_seed(args.manual_seed)
criterionCls = nn.CrossEntropyLoss()
criterionRec = nn.MSELoss()
if args.parallel_train:
Encoder = DataParallel(Encoder)
Decoder = DataParallel(Decoder)
NorClsfier = DataParallel(NorClsfier)
SSDClsfier = DataParallel(SSDClsfier)
optimizer = optim.Adam(
list(Encoder.parameters()) + list(NorClsfier.parameters()) +
list(SSDClsfier.parameters()) + list(Decoder.parameters()),
lr=args.lr)
if args.adv is 'PGDattack':
print("**********Defense PGD Attack**********")
elif args.adv is 'FGSMattack':
print("**********Defense FGSM Attack**********")
if args.adv is 'PGDattack':
from advertorch.attacks import PGDAttack
nor_adversary = PGDAttack(predict1=Encoder,
predict2=NorClsfier,
nb_iter=args.adv_iter)
rot_adversary = PGDAttack(predict1=Encoder,
predict2=SSDClsfier,
nb_iter=args.adv_iter)
elif args.adv is 'FGSMattack':
from advertorch.attacks import GradientSignAttack
nor_adversary = GradientSignAttack(predict1=Encoder,
predict2=NorClsfier)
rot_adversary = GradientSignAttack(predict1=Encoder,
predict2=SSDClsfier)
global_step = 0
# ----------
# Training
# ----------
for epoch in range(args.n_epoch):
Encoder.train()
Decoder.train()
NorClsfier.train()
SSDClsfier.train()
for steps, (orig, label, rot_orig,
rot_label) in enumerate(train_loader):
label = lab_conv(knownclass, label)
orig, label = orig.cuda(), label.long().cuda()
rot_orig, rot_label = rot_orig.cuda(), rot_label.long().cuda()
with ctx_noparamgrad_and_eval(Encoder):
with ctx_noparamgrad_and_eval(NorClsfier):
with ctx_noparamgrad_and_eval(SSDClsfier):
adv = nor_adversary.perturb(orig, label)
rot_adv = rot_adversary.perturb(rot_orig, rot_label)
latent_feat = Encoder(adv)
norpred = NorClsfier(latent_feat)
norlossCls = criterionCls(norpred, label)
recon = Decoder(latent_feat)
lossRec = criterionRec(recon, orig)
ssdpred = SSDClsfier(Encoder(rot_adv))
rotlossCls = criterionCls(ssdpred, rot_label)
loss = args.norClsWgt * norlossCls + args.rotClsWgt * rotlossCls + args.RecWgt * lossRec
optimizer.zero_grad()
loss.backward()
optimizer.step()
#============ tensorboard the log info ============#
lossinfo = {
'loss': loss.item(),
'norlossCls': norlossCls.item(),
'lossRec': lossRec.item(),
'rotlossCls': rotlossCls.item(),
}
global_step += 1
#============ print the log info ============#
if (steps + 1) % args.log_step == 0:
errors = OrderedDict([
('loss', loss.item()),
('norlossCls', norlossCls.item()),
('lossRec', lossRec.item()),
('rotlossCls', rotlossCls.item()),
])
saver.print_current_errors((epoch + 1), (steps + 1), errors)
# evaluate performance on validation set periodically
if ((epoch + 1) % args.val_epoch == 0):
# switch model to evaluation mode
Encoder.eval()
NorClsfier.eval()
running_corrects = 0.0
epoch_size = 0.0
val_loss_list = []
# calculate accuracy on validation set
for steps, (images, label) in enumerate(val_loader):
label = lab_conv(knownclass, label)
images, label = images.cuda(), label.long().cuda()
adv = nor_adversary.perturb(images, label)
with torch.no_grad():
logits = NorClsfier(Encoder(adv))
_, preds = torch.max(logits, 1)
running_corrects += torch.sum(preds == label.data)
epoch_size += images.size(0)
val_loss = criterionCls(logits, label)
val_loss_list.append(val_loss.item())
val_loss_mean = sum(val_loss_list) / len(val_loss_list)
val_acc = running_corrects.double() / epoch_size
print('Val Acc: {:.4f}, Val Loss: {:.4f}'.format(
val_acc, val_loss_mean))
valinfo = {
'Val Acc': val_acc.item(),
'Val Loss': val_loss.item(),
}
for tag, value in valinfo.items():
summary_writer.add_scalar(tag, value, (epoch + 1))
orig_show = vutils.make_grid(orig, normalize=True, scale_each=True)
recon_show = vutils.make_grid(recon,
normalize=True,
scale_each=True)
summary_writer.add_image('Ori_Image', orig_show, (epoch + 1))
summary_writer.add_image('Rec_Image', recon_show, (epoch + 1))
if ((epoch + 1) % args.model_save_epoch == 0):
model_save_path = os.path.join(args.results_path,
args.training_type, 'snapshots',
args.datasetname + '-' + args.split,
args.denoisemean,
args.adv + str(args.adv_iter))
mkdir(model_save_path)
torch.save(
Encoder.state_dict(),
os.path.join(model_save_path,
"Encoder-{}.pt".format(epoch + 1)))
torch.save(
NorClsfier.state_dict(),
os.path.join(model_save_path,
"NorClsfier-{}.pt".format(epoch + 1)))
torch.save(
Decoder.state_dict(),
os.path.join(model_save_path,
"Decoder-{}.pt".format(epoch + 1)))
torch.save(Encoder.state_dict(),
os.path.join(model_save_path, "Encoder-final.pt"))
torch.save(NorClsfier.state_dict(),
os.path.join(model_save_path, "NorClsfier-final.pt"))
torch.save(Decoder.state_dict(),
os.path.join(model_save_path, "Decoder-final.pt"))
out1, out2, out3 = net(inputt)
cal_prob = np.zeros(args.brand_size)
for i in range(args.batch_size):
cal_prob[np.argmax(np.array(out1.data.cpu()), 1)[i]] += 1
Ori_attack_Top1_lable = np.argmax(cal_prob)
if Ori_attack_Top1_lable in B_lable:
Ori_AB1_category = B_name_set[np.argmax(
np.argmax(cal_prob) == B_lable)]
Total = np.sum(cal_prob)
Ori_Top1_Prop = max(cal_prob) / Total
Ori_Top2_Prop, Ori_AB2_category, Ori_attack_Top2_lable = get_Top_prob(
cal_prob, Total)
Ori_Top3_Prop, Ori_AB3_category, Ori_attack_Top3_lable = get_Top_prob(
cal_prob, Total)
adv_inputs = adversary.perturb(inputt, labelt) # inputs
if args.concat:
reconstruct_image(iter, adv_inputs, adv=True)
else:
save_image(iter, adv_inputs, adv=True)
#adv_inputs = inputt
#this section is similar to the above section
outt, outt2, outt3 = net(adv_inputs)
cal_prob = np.zeros(args.brand_size)
for i in range(args.batch_size):
cal_prob[np.argmax(np.array(outt.data.cpu()), 1)[i]] += 1
attack_Top1_lable = np.argmax(cal_prob)
if attack_Top1_lable in B_lable:
OB_category = B_name_set[np.argmax(
np.argmax(np.array(blabelt.cpu()), 1)[0] == B_lable)]
def precalc_weibull(args, dataloader_train, knownclass, Encoder, NorClsfier):
# First generate pre-softmax 'activation vectors' for all training examples
print(
"Weibull: computing features for all correctly-classified training data"
)
activation_vectors = {}
if args.adv is 'PGDattack':
from advertorch.attacks import PGDAttack
adversary = PGDAttack(predict1=Encoder,
predict2=NorClsfier,
nb_iter=args.adv_iter)
elif args.adv is 'FGSMattack':
from advertorch.attacks import FGSM
adversary = FGSM(predict1=Encoder, predict2=NorClsfier)
for _, (images, labels, _, _) in enumerate(dataloader_train):
labels = lab_conv(knownclass, labels)
images, labels = images.cuda(), labels.long().cuda()
print("**********Conduct Attack**********")
advimg = adversary.perturb(images, labels)
with torch.no_grad():
logits = NorClsfier(Encoder(advimg))
correctly_labeled = (logits.data.max(1)[1] == labels)
labels_np = labels.cpu().numpy()
logits_np = logits.data.cpu().numpy()
for i, label in enumerate(labels_np):
if not correctly_labeled[i]:
continue
# If correctly labeled, add this to the list of activation_vectors for this class
if label not in activation_vectors:
activation_vectors[label] = []
activation_vectors[label].append(logits_np[i])
print("Computed activation_vectors for {} known classes".format(
len(activation_vectors)))
for class_idx in activation_vectors:
print("Class {}: {} images".format(class_idx,
len(activation_vectors[class_idx])))
# Compute a mean activation vector for each class
print("Weibull computing mean activation vectors...")
mean_activation_vectors = {}
for class_idx in activation_vectors:
mean_activation_vectors[class_idx] = np.array(
activation_vectors[class_idx]).mean(axis=0)
# Initialize one libMR Wiebull object for each class
print("Fitting Weibull to distance distribution of each class")
weibulls = {}
for class_idx in activation_vectors:
distances = []
mav = mean_activation_vectors[class_idx]
for v in activation_vectors[class_idx]:
distances.append(np.linalg.norm(v - mav))
mr = libmr.MR()
tail_size = min(len(distances), WEIBULL_TAIL_SIZE)
mr.fit_high(distances, tail_size)
weibulls[class_idx] = mr
print("Weibull params for class {}: {}".format(class_idx,
mr.get_params()))
return activation_vectors, mean_activation_vectors, weibulls
def openset_weibull(args,
dataloader_test,
knownclass,
Encoder,
NorClsfier,
activation_vectors,
mean_activation_vectors,
weibulls,
mode='openset'):
# Apply Weibull score to every logit
weibull_scores = []
logits = []
classes = activation_vectors.keys()
running_corrects = 0.0
epoch_size = 0.0
if args.adv is 'PGDattack':
from advertorch.attacks import PGDAttack
adversary = PGDAttack(predict1=Encoder,
predict2=NorClsfier,
nb_iter=args.adv_iter)
elif args.adv is 'FGSMattack':
from advertorch.attacks import FGSM
adversary = FGSM(predict1=Encoder, predict2=NorClsfier)
# reclosslist = []
for steps, (images, labels) in enumerate(dataloader_test):
labels = lab_conv(knownclass, labels)
images, labels = images.cuda(), labels.long().cuda()
print("Calculate weibull_scores in step {}/{}".format(
steps, len(dataloader_test)))
print("**********Conduct Attack**********")
if mode is 'closeset':
advimg = adversary.perturb(images, labels)
else:
advimg = adversary.perturb(images)
with torch.no_grad():
batch_logits_torch = NorClsfier(Encoder(advimg))
batch_logits = batch_logits_torch.data.cpu().numpy()
batch_weibull = np.zeros(shape=batch_logits.shape)
for activation_vector in batch_logits:
weibull_row = np.ones(len(knownclass))
for class_idx in classes:
mav = mean_activation_vectors[class_idx]
dist = np.linalg.norm(activation_vector - mav)
weibull_row[class_idx] = 1 - weibulls[class_idx].w_score(dist)
weibull_scores.append(weibull_row)
logits.append(activation_vector)
if mode is 'closeset':
_, preds = torch.max(batch_logits_torch, 1)
# statistics
running_corrects += torch.sum(preds == labels.data)
epoch_size += images.size(0)
if mode is 'closeset':
running_corrects = running_corrects.double() / epoch_size
print('Test Acc: {:.4f}'.format(running_corrects))
weibull_scores = np.array(weibull_scores)
logits = np.array(logits)
openmax_scores = -np.log(np.sum(np.exp(logits * weibull_scores), axis=1))
if mode is 'closeset':
return running_corrects, np.array(openmax_scores)
else:
return np.array(openmax_scores)