def infer_minibatch(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input).cuda()
target = Variable(target).cuda(async=True)
input = ifgsm(model,
input,
target,
niters=args.niters,
epsilon=args.eps)
input = input.detach()
target = target.detach()
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
del input, target
return top1.avg, objs.avg
def adversarial_workbench(network_list=model_pair):
fool_ratio_dict = []
pert_acc_dict = []
ori_acc_dict = []
for i in range(0, len(model_pair)):
base_model, base_model_name = model_pair[i]
net = base_model.cuda()
net.eval()
adv_examples = ifgsm(net, test_ims, test_lbls, niters=10, epsilon=0.03)
transfer_results = test_adv_examples_across_full_models(
adv_examples,
test_ims,
test_lbls,
network_list=model_pair,
)
fool_ratio_dict.append([])
pert_acc_dict.append([])
ori_acc_dict.append([])
for j in range(0, len(transfer_results)):
fool_ratio_dict[i].append(transfer_results[j][1])
pert_acc_dict[i].append(transfer_results[j][2])
ori_acc_dict[i].append(transfer_results[j][3])
return fool_ratio_dict, pert_acc_dict, ori_acc_dict
def train(train_queue, valid_queue, model, architect, criterion, optimizer,
lr):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
for step, (input, target) in enumerate(train_queue):
model.train()
n = input.size(0)
input = Variable(input, requires_grad=False).cuda()
target = Variable(target, requires_grad=False).cuda(async=True)
# get a random minibatch from the search queue with replacement
input_search, target_search = next(iter(valid_queue))
input_search = Variable(input_search).cuda()
target_search = Variable(target_search).cuda(async=True)
input_search = ifgsm(model,
input_search,
target_search,
niters=args.niters,
epsilon=args.eps)
architect.step(input,
target,
input_search,
target_search,
lr,
optimizer,
unrolled=args.unrolled)
optimizer.zero_grad()
logits = model(input)
loss = criterion(logits, target)
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg,
top5.avg)
return top1.avg, objs.avg
def infer_minibatch(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
input, target = next(iter(valid_queue))
input = Variable(input).cuda()
target = Variable(target).cuda(async=True)
input = ifgsm(model, input, target)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
logging.info('valid %03d %e %f %f', 0, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
transfer_model_names = [x for x in all_model_names if x != opt.modeltype]
transfer_models = [load_model(x) for x in transfer_model_names]
print('Loaded model...')
# pre-process input image
mean, stddev = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
transform_resize = transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224)])
transform_norm = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, stddev)])
img_pil = Image.open(opt.imagepath)
img_pil_resize = transform_resize(img_pil.copy())
img = transform_norm(img_pil_resize.copy()).cuda().unsqueeze(0)
lbl = torch.tensor([opt.imagelabel]).cuda()
# run attack on source model
img_ifgsm = ifgsm(model, img, lbl, niters=opt.niters_baseline, dataset='imagenet')
source_layers = get_source_layers(opt.modeltype, model)
ifgsm_guide = ifgsm(model, img, lbl, learning_rate=0.008, epsilon=opt.epsilon, niters=opt.niters_ila, dataset='imagenet')
img_ila = ILA(model, img, ifgsm_guide, lbl, source_layers[opt.layerindex][1][1], learning_rate=0.01, epsilon=opt.epsilon, niters=opt.niters_ila, dataset='imagenet')
# get labels for source
model.eval()
orig_pred_label, ifgsm_pred_label, ila_pred_label = model(img).max(dim=1)[1].item(), model(img_ifgsm).max(dim=1)[1].item(), model(img_ila).max(dim=1)[1].item()
# get labels for transfer
transfer_labs = []
for mod in transfer_models:
mod.eval()
o, f, i = mod(img).max(dim=1)[1].item(), mod(img_ifgsm).max(dim=1)[1].item(), mod(img_ila).max(dim=1)[1].item()
transfer_labs.append((o, f, i))
