def construct_model(self):
def setup_keys():
if self.key_type != 'random':
if self.arch == 'alexnet':
pretrained_model = AlexNetNormal(self.in_channels,
self.num_classes,
self.norm_type)
else:
pretrained_model = ResNet18(num_classes=self.num_classes,
norm_type=self.norm_type)
pretrained_model.load_state_dict(
torch.load(self.pretrained_path))
pretrained_model = pretrained_model.to(self.device)
self.setup_keys(pretrained_model)
passport_kwargs = construct_passport_kwargs(self)
self.passport_kwargs = passport_kwargs
if self.arch == 'alexnet':
model = AlexNetPassportPrivate(self.in_channels, self.num_classes,
passport_kwargs)
else:
model = ResNet18Private(num_classes=self.num_classes,
passport_kwargs=passport_kwargs)
self.model = model.to(self.device)
setup_keys()
def construct_model(self):
print('Construct Model')
def setup_keys():
if self.key_type != 'random':
pretrained_from_torch = self.pretrained_path is None
if self.arch == 'alexnet':
norm_type = 'none' if pretrained_from_torch else self.norm_type
pretrained_model = AlexNetNormal(
self.in_channels,
self.num_classes,
norm_type=norm_type,
pretrained=pretrained_from_torch)
else:
norm_type = 'bn' if pretrained_from_torch else self.norm_type
pretrained_model = ResNet18(
num_classes=self.num_classes,
norm_type=norm_type,
pretrained=pretrained_from_torch)
if not pretrained_from_torch:
print('Loading pretrained from self-trained model')
pretrained_model.load_state_dict(
torch.load(self.pretrained_path))
else:
print('Loading pretrained from torch-pretrained model')
pretrained_model = pretrained_model.to(self.device)
self.setup_keys(pretrained_model)
passport_kwargs = construct_passport_kwargs(self)
self.passport_kwargs = passport_kwargs
print('Loading arch: ' + self.arch)
if self.arch == 'alexnet':
model = AlexNetPassportPrivate(self.in_channels, self.num_classes,
passport_kwargs)
else:
model = ResNet18Private(num_classes=self.num_classes,
passport_kwargs=passport_kwargs)
self.model = model.to(self.device)
setup_keys()
pprint(self.model)
def main(arch='alexnet', dataset='cifar10', scheme=1, loadpath='',
passport_config='passport_configs/alexnet_passport.json', tagnum=1):
batch_size = 64
nclass = {
'cifar100': 100,
'imagenet1000': 1000
}.get(dataset, 10)
inchan = 3
device = torch.device('cuda')
trainloader, valloader = prepare_dataset({'transfer_learning': False,
'dataset': dataset,
'tl_dataset': '',
'batch_size': batch_size})
passport_kwargs, plkeys = construct_passport_kwargs_from_dict({'passport_config': json.load(open(passport_config)),
'norm_type': 'bn',
'sl_ratio': 0.1,
'key_type': 'shuffle'},
True)
if arch == 'alexnet':
if scheme == 1:
model = AlexNetPassport(inchan, nclass, passport_kwargs)
else:
model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
else:
if scheme == 1:
model = ResNet18Passport(num_classes=nclass, passport_kwargs=passport_kwargs)
else:
model = ResNet18Private(num_classes=nclass, passport_kwargs=passport_kwargs)
sd = torch.load(loadpath)
criterion = nn.CrossEntropyLoss()
prunedf = []
for perc in [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]:
model.load_state_dict(sd)
pruning_resnet(model, perc)
model = model.to(device)
res = detect_signature(model)
res['perc'] = perc
res['tag'] = arch
res['dataset'] = dataset
res.update(test(model, criterion, valloader, device))
prunedf.append(res)
dirname = f'logs/pruning_attack/{loadpath.split("/")[1]}/{loadpath.split("/")[2]}'
os.makedirs(dirname, exist_ok=True)
histdf = pd.DataFrame(prunedf)
histdf.to_csv(f'{dirname}/{arch}-{scheme}-history-{dataset}-{tagnum}.csv')
def run_maximize(rep=1, flipperc=0, arch='alexnet', dataset='cifar10', scheme=1,
loadpath='', passport_config=''):
epochs = 100
batch_size = 64
nclass = 100 if dataset == 'cifar100' else 10
inchan = 3
lr = 0.01
device = torch.device('cuda')
trainloader, valloader = prepare_dataset({'transfer_learning': False,
'dataset': dataset,
'tl_dataset': '',
'batch_size': batch_size})
passport_kwargs = construct_passport_kwargs_from_dict({'passport_config': json.load(open(passport_config)),
'norm_type': 'gn',
'sl_ratio': 0.1,
'key_type': 'random'})
if scheme == 1:
model = AlexNetPassport(inchan, nclass, passport_kwargs)
elif scheme == 2:
model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
else:
model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
task_name = loadpath.split('/')[-2]
loadpath_all = loadpath + '1/models/best.pth'
sd = torch.load(loadpath_all)
model.load_state_dict(sd, strict=False)
logdir = '/data-x/g12/zhangjie/DeepIPR/baseline/passport_attack/' + task_name + '/' + rep
os.makedirs(logdir, exist_ok=True)
best_file = os.path.join(logdir, 'best.txt')
log_file = os.path.join(logdir, 'log.txt')
lf = open(log_file, 'a')
shutil.copy('attack_3.py', str(logdir) + "/attack_3.py")
# #print dict name####
# for param_tensor in model.state_dict():
# print(param_tensor, "\t", model.state_dict()[param_tensor].size())
# print(param_tensor, "\t", model.state_dict()[param_tensor].size(),file=lf)
for param in model.parameters():
param.requires_grad_(False)
passblocks = []
origpassport = []
fakepassport = []
for m in model.modules():
if isinstance(m, PassportBlock) or isinstance(m, PassportPrivateBlock):
passblocks.append(m)
if scheme == 1:
keyname = 'key'
skeyname = 'skey'
else:
keyname = 'key_private'
skeyname = 'skey_private'
key, skey = m.__getattr__(keyname).data.clone(), m.__getattr__(skeyname).data.clone()
origpassport.append(key.to(device))
origpassport.append(skey.to(device))
m.__delattr__(keyname) #删除属性
m.__delattr__(skeyname)
# #fake like ori
# m.register_parameter(keyname, nn.Parameter(key.clone() ))
# m.register_parameter(skeyname, nn.Parameter(skey.clone()))
# fake slightly modify ori
m.register_parameter(keyname, nn.Parameter(key.clone() + torch.randn(*key.size()) * 0.001))
m.register_parameter(skeyname, nn.Parameter(skey.clone() + torch.randn(*skey.size()) * 0.001))
fakepassport.append(m.__getattr__(keyname))
fakepassport.append(m.__getattr__(skeyname))
if flipperc != 0:
print(f'Reverse {flipperc * 100:.2f}% of binary signature')
for m in passblocks:
mflip = flipperc
if scheme == 1:
oldb = m.sign_loss.b
else:
oldb = m.sign_loss_private.b
newb = oldb.clone()
npidx = np.arange(len(oldb)) #bit 长度
randsize = int(oldb.view(-1).size(0) * mflip)
randomidx = np.random.choice(npidx, randsize, replace=False) #随机选择
newb[randomidx] = oldb[randomidx] * -1 # reverse bit 进行翻转
if scheme == 1:
m.sign_loss.set_b(newb)
else:
m.sign_loss_private.set_b(newb)
model.to(device)
optimizer = torch.optim.SGD(fakepassport,
lr=lr,
momentum=0.9,
weight_decay=0.0005)
scheduler = None
criterion = nn.CrossEntropyLoss()
history = []
def run_cs(): #计算余弦相似性
cs = []
for d1, d2 in zip(origpassport, fakepassport):
d1 = d1.view(d1.size(0), -1)
d2 = d2.view(d2.size(0), -1)
cs.append(F.cosine_similarity(d1, d2).item())
return cs
print('Before training')
print('Before training', file = lf)
res = {}
valres = test(model, criterion, valloader, device, scheme)
for key in valres: res[f'valid_{key}'] = valres[key]
# print(res)
# sys.exit(0)
with torch.no_grad():
cs = run_cs()
mseloss = 0
for l, r in zip(origpassport, fakepassport):
mse = F.mse_loss(l, r)
mseloss += mse.item()
mseloss /= len(origpassport)
print(f'MSE of Real and Maximize passport: {mseloss:.4f}')
print(f'MSE of Real and Maximize passport: {mseloss:.4f}', file=lf)
print(f'Cosine Similarity of Real and Maximize passport: {sum(cs) / len(origpassport):.4f}')
print(f'Cosine Similarity of Real and Maximize passport: {sum(cs) / len(origpassport):.4f}', file=lf)
print()
res['epoch'] = 0
res['cosine_similarity'] = cs
res['flipperc'] = flipperc
res['train_mseloss'] = mseloss
history.append(res)
torch.save({'origpassport': origpassport,
'fakepassport': fakepassport,
'state_dict': model.state_dict()},
f'{logdir}/{arch}-{scheme}-last-{dataset}-{rep}-{flipperc:.1f}-e0.pth')
best_acc = 0
best_ep = 0
for ep in range(1, epochs + 1):
if scheduler is not None:
scheduler.step()
print(f'Learning rate: {optimizer.param_groups[0]["lr"]}')
print(f'Epoch {ep:3d}:')
print(f'Epoch {ep:3d}:',file=lf)
print('Training')
trainres = train_maximize(origpassport, fakepassport, model, optimizer, criterion, trainloader, device, scheme)
print('Testing')
print('Testing',file=lf)
valres = test(model, criterion, valloader, device, scheme)
print(valres,file=lf)
print('\n',file=lf)
if best_acc < valres['acc']:
print(f'Found best at epoch {ep}\n')
best_acc = valres['acc']
best_ep = ep
f = open(best_file,'a')
f.write(str(best_acc) + '\n')
f.write("best epoch: %s"%str(best_ep) + '\n')
f.flush()
res = {}
for key in trainres: res[f'train_{key}'] = trainres[key]
for key in valres: res[f'valid_{key}'] = valres[key]
res['epoch'] = ep
res['flipperc'] = flipperc
with torch.no_grad():
cs = run_cs()
res['cosine_similarity'] = cs
print(f'Cosine Similarity of Real and Maximize passport: '
f'{sum(cs) / len(origpassport):.4f}')
print()
print(f'Cosine Similarity of Real and Maximize passport: '
f'{sum(cs) / len(origpassport):.4f}'+'\n', file=lf)
lf.flush()
history.append(res)
torch.save({'origpassport': origpassport,
'fakepassport': fakepassport,
'state_dict': model.state_dict()},
f'{logdir}/{arch}-{scheme}-last-{dataset}-{rep}-{flipperc:.1f}-e{ep}.pth')
histdf = pd.DataFrame(history)
histdf.to_csv(f'{logdir}/{arch}-{scheme}-history-{dataset}-{rep}-{flipperc:.1f}.csv')
def run_attack_1(attack_rep=50, arch='alexnet', dataset='cifar10', scheme=1,
loadpath='', passport_config='passport_configs/alexnet_passport.json'):
batch_size = 64
nclass = 100 if dataset == 'cifar100' else 10
inchan = 3
lr = 0.01
device = torch.device('cuda')
baselinepath = f'logs/alexnet_{dataset}/1/models/best.pth'
passport_kwargs = construct_passport_kwargs_from_dict({'passport_config': json.load(open(passport_config)),
'norm_type': 'bn',
'sl_ratio': 0.1,
'key_type': 'shuffle'})
if scheme == 1:
model = AlexNetPassport(inchan, nclass, passport_kwargs)
elif scheme == 2:
model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
else:
model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
sd = torch.load(loadpath)
model.load_state_dict(sd, strict=False)
for fidx in [0, 2]:
model.features[fidx].bn.weight.data.copy_(sd[f'features.{fidx}.scale'])
model.features[fidx].bn.bias.data.copy_(sd[f'features.{fidx}.bias'])
passblocks = []
for m in model.modules():
if isinstance(m, PassportBlock) or isinstance(m, PassportPrivateBlock):
passblocks.append(m)
trainloader, valloader = prepare_dataset({'transfer_learning': False,
'dataset': dataset,
'tl_dataset': '',
'batch_size': batch_size})
passport_data = valloader
pretrained_model = AlexNetNormal(inchan, nclass)
pretrained_model.load_state_dict(torch.load(baselinepath))
pretrained_model.to(device)
def reset_passport():
print('Reset passport')
x, y = get_passport(passport_data, device)
set_intermediate_keys(model, pretrained_model, x, y)
def run_test():
res = {}
valres = test(model, criterion, valloader, device, 1 if scheme != 1 else 0)
for key in valres: res[f'valid_{key}'] = valres[key]
res['attack_rep'] = 0
return res
criterion = nn.CrossEntropyLoss()
os.makedirs('logs/passport_attack_1', exist_ok=True)
history = []
print('Before training')
res = run_test()
history.append(res)
for r in range(attack_rep):
print(f'Attack count: {r}')
reset_passport()
res = run_test()
history.append(res)
histdf = pd.DataFrame(history)
histdf.to_csv(f'logs/passport_attack_1/{arch}-{scheme}-history-{dataset}-{attack_rep}.csv')
def run_maximize(rep=1,
flipperc=0,
arch='alexnet',
dataset='cifar10',
scheme=1,
loadpath='',
passport_config='',
tagnum=1):
epochs = {'imagenet1000': 30}.get(dataset, 100)
batch_size = 64
nclass = {'cifar100': 100, 'imagenet1000': 1000}.get(dataset, 10)
inchan = 3
lr = 0.01
device = torch.device('cuda')
trainloader, valloader = prepare_dataset({
'transfer_learning': False,
'dataset': dataset,
'tl_dataset': '',
'batch_size': batch_size
})
passport_kwargs = construct_passport_kwargs_from_dict({
'passport_config':
json.load(open(passport_config)),
'norm_type':
'bn',
'sl_ratio':
0.1,
'key_type':
'shuffle'
})
if arch == 'alexnet':
if scheme == 1:
model = AlexNetPassport(inchan, nclass, passport_kwargs)
else:
model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
else:
if scheme == 1:
model = ResNet18Passport(num_classes=nclass,
passport_kwargs=passport_kwargs)
else:
model = ResNet18Private(num_classes=nclass,
passport_kwargs=passport_kwargs)
sd = torch.load(loadpath)
model.load_state_dict(sd)
for param in model.parameters():
param.requires_grad_(False)
passblocks = []
origpassport = []
fakepassport = []
for m in model.modules():
if isinstance(m, PassportBlock) or isinstance(m, PassportPrivateBlock):
passblocks.append(m)
if scheme == 1:
keyname = 'key'
skeyname = 'skey'
else:
keyname = 'key_private'
skeyname = 'skey_private'
key, skey = m.__getattr__(keyname).data.clone(), m.__getattr__(
skeyname).data.clone()
origpassport.append(key.to(device))
origpassport.append(skey.to(device))
m.__delattr__(keyname)
m.__delattr__(skeyname)
# re-initialize the key and skey, but by adding noise on it
m.register_parameter(
keyname,
nn.Parameter(key.clone() + torch.randn(*key.size()) * 0.001))
m.register_parameter(
skeyname,
nn.Parameter(skey.clone() + torch.randn(*skey.size()) * 0.001))
fakepassport.append(m.__getattr__(keyname))
fakepassport.append(m.__getattr__(skeyname))
if flipperc != 0:
print(f'Reverse {flipperc * 100:.2f}% of binary signature')
for m in passblocks:
mflip = flipperc
if scheme == 1:
oldb = m.sign_loss.b
else:
oldb = m.sign_loss_private.b
newb = oldb.clone()
npidx = np.arange(len(oldb))
randsize = int(oldb.view(-1).size(0) * mflip)
randomidx = np.random.choice(npidx, randsize, replace=False)
newb[randomidx] = oldb[randomidx] * -1 # reverse bit
if scheme == 1:
m.sign_loss.set_b(newb)
else:
m.sign_loss_private.set_b(newb)
model.to(device)
optimizer = torch.optim.SGD(fakepassport,
lr=lr,
momentum=0.9,
weight_decay=0.0005)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
# [int(epochs * 0.5), int(epochs * 0.75)],
# 0.1)
scheduler = None
criterion = nn.CrossEntropyLoss()
history = []
dirname = f'logs/passport_attack_3/{loadpath.split("/")[1]}/{loadpath.split("/")[2]}'
os.makedirs(dirname, exist_ok=True)
def run_cs():
cs = []
for d1, d2 in zip(origpassport, fakepassport):
d1 = d1.view(d1.size(0), -1)
d2 = d2.view(d2.size(0), -1)
cs.append(F.cosine_similarity(d1, d2).item())
return cs
print('Before training')
res = {}
valres = test(model, criterion, valloader, device, scheme)
for key in valres:
res[f'valid_{key}'] = valres[key]
with torch.no_grad():
cs = run_cs()
mseloss = 0
for l, r in zip(origpassport, fakepassport):
mse = F.mse_loss(l, r)
mseloss += mse.item()
mseloss /= len(origpassport)
print(f'MSE of Real and Maximize passport: {mseloss:.4f}')
print(
f'Cosine Similarity of Real and Maximize passport: {sum(cs) / len(origpassport):.4f}'
)
print()
res['epoch'] = 0
res['cosine_similarity'] = cs
res['flipperc'] = flipperc
res['train_mseloss'] = mseloss
history.append(res)
torch.save(
{
'origpassport': origpassport,
'fakepassport': fakepassport,
'state_dict': model.state_dict()
},
f'{dirname}/{arch}-{scheme}-last-{dataset}-{rep}-{tagnum}-{flipperc:.1f}-e0.pth'
)
for ep in range(1, epochs + 1):
if scheduler is not None:
scheduler.step()
print(f'Learning rate: {optimizer.param_groups[0]["lr"]}')
print(f'Epoch {ep:3d}:')
print('Training')
trainres = train_maximize(origpassport, fakepassport, model, optimizer,
criterion, trainloader, device, scheme)
print('Testing')
valres = test(model, criterion, valloader, device, scheme)
res = {}
for key in trainres:
res[f'train_{key}'] = trainres[key]
for key in valres:
res[f'valid_{key}'] = valres[key]
res['epoch'] = ep
res['flipperc'] = flipperc
with torch.no_grad():
cs = run_cs()
res['cosine_similarity'] = cs
print(f'Cosine Similarity of Real and Maximize passport: '
f'{sum(cs) / len(origpassport):.4f}')
print()
history.append(res)
torch.save(
{
'origpassport': origpassport,
'fakepassport': fakepassport,
'state_dict': model.state_dict()
},
f'{dirname}/{arch}-{scheme}-{dataset}-{rep}-{tagnum}-{flipperc:.1f}-last.pth'
)
histdf = pd.DataFrame(history)
histdf.to_csv(
f'{dirname}/{arch}-{scheme}-history-{dataset}-{rep}-{tagnum}-{flipperc:.1f}.csv'
)
def run_attack_1(attack_rep=50,
arch='alexnet',
dataset='cifar10',
scheme=1,
loadpath='',
passport_config='passport_configs/alexnet_passport.json',
tagnum=1):
batch_size = 64
nclass = {'cifar100': 100, 'imagenet1000': 1000}.get(dataset, 10)
inchan = 3
lr = 0.01
device = torch.device('cuda')
# baselinepath = f'logs/alexnet_{dataset}/1/models/best.pth'
passport_kwargs, plkeys = construct_passport_kwargs_from_dict(
{
'passport_config': json.load(open(passport_config)),
'norm_type': 'bn',
'sl_ratio': 0.1,
'key_type': 'shuffle'
}, True)
if arch == 'alexnet':
if scheme == 1:
model = AlexNetPassport(inchan, nclass, passport_kwargs)
else:
model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
else:
if scheme == 1:
model = ResNet18Passport(num_classes=nclass,
passport_kwargs=passport_kwargs)
else:
model = ResNet18Private(num_classes=nclass,
passport_kwargs=passport_kwargs)
sd = torch.load(loadpath)
model.load_state_dict(sd)
model = model.to(device)
passblocks = []
for m in model.modules():
if isinstance(m, PassportBlock) or isinstance(m, PassportPrivateBlock):
passblocks.append(m)
trainloader, valloader = prepare_dataset({
'transfer_learning': False,
'dataset': dataset,
'tl_dataset': '',
'batch_size': batch_size,
'shuffle_val': True
})
passport_data = valloader
pretrained_model = load_pretrained(arch, nclass).to(device)
def reset_passport():
print('Reset passport')
x, y = get_passport(passport_data, device)
passport_generator.set_key(pretrained_model, model, x, y)
def run_test():
res = {}
valres = test(model, criterion, valloader, device,
1 if scheme != 1 else 0)
for key in valres:
res[f'valid_{key}'] = valres[key]
res['attack_rep'] = 0
return res
criterion = nn.CrossEntropyLoss()
dirname = f'logs/passport_attack_1/{loadpath.split("/")[1]}/{loadpath.split("/")[2]}'
os.makedirs(dirname, exist_ok=True)
history = []
print('Before training')
res = run_test()
history.append(res)
for r in range(attack_rep):
print(f'Attack count: {r}')
reset_passport()
res = run_test()
res['attack_rep'] = r
history.append(res)
histdf = pd.DataFrame(history)
histdf.to_csv(
f'{dirname}/{arch}-{scheme}-history-{dataset}-{attack_rep}-{tagnum}.csv'
)
def run_attack_2(rep=1, arch='alexnet', dataset='cifar10', scheme=1, loadpath='',
passport_config='passport_configs/alexnet_passport.json', tagnum=1):
epochs = {
'imagenet1000': 30
}.get(dataset, 100)
batch_size = 64
nclass = {
'cifar100': 100,
'imagenet1000': 1000
}.get(dataset, 10)
inchan = 3
lr = 0.01
device = torch.device('cuda')
trainloader, valloader = prepare_dataset({'transfer_learning': False,
'dataset': dataset,
'tl_dataset': '',
'batch_size': batch_size})
passport_kwargs, plkeys = construct_passport_kwargs_from_dict({'passport_config': json.load(open(passport_config)),
'norm_type': 'bn',
'sl_ratio': 0.1,
'key_type': 'shuffle'},
True)
if arch == 'alexnet':
model = AlexNetNormal(inchan, nclass, 'bn' if scheme == 1 else 'gn')
else:
ResNetClass = ResNet18 if arch == 'resnet18' else ResNet9
model = ResNetClass(num_classes=nclass,
norm_type='bn' if scheme == 1 else 'gn')
if arch == 'alexnet':
if scheme == 1:
passport_model = AlexNetPassport(inchan, nclass, passport_kwargs)
else:
passport_model = AlexNetPassportPrivate(inchan, nclass, passport_kwargs)
else:
if scheme == 1:
ResNetClass = ResNet18Passport if arch == 'resnet18' else ResNet9Passport
passport_model = ResNetClass(num_classes=nclass, passport_kwargs=passport_kwargs)
else:
if arch == 'resnet9':
raise NotImplementedError
passport_model = ResNet18Private(num_classes=nclass, passport_kwargs=passport_kwargs)
sd = torch.load(loadpath)
passport_model.load_state_dict(sd)
passport_model = passport_model.to(device)
sd = torch.load(loadpath)
model.load_state_dict(sd, strict=False) # need to load with strict because passport model no scale and bias
model = model.to(device)
for param in model.parameters():
param.requires_grad_(False)
# for fidx in [0, 2]:
# model.features[fidx].bn.weight.data.copy_(sd[f'features.{fidx}.scale'])
# model.features[fidx].bn.bias.data.copy_(sd[f'features.{fidx}.bias'])
if arch == 'alexnet':
for fidx in plkeys:
fidx = int(fidx)
model.features[fidx].bn.weight.data.copy_(passport_model.features[fidx].get_scale().view(-1))
model.features[fidx].bn.bias.data.copy_(passport_model.features[fidx].get_bias().view(-1))
model.features[fidx].bn.weight.requires_grad_(True)
model.features[fidx].bn.bias.requires_grad_(True)
else:
for fidx in plkeys:
layer_key, i, module_key = fidx.split('.')
def get_layer(m):
return m.__getattr__(layer_key)[int(i)].__getattr__(module_key)
convblock = get_layer(model)
passblock = get_layer(passport_model)
convblock.bn.weight.data.copy_(passblock.get_scale().view(-1))
convblock.bn.bias.data.copy_(passblock.get_bias().view(-1))
convblock.bn.weight.requires_grad_(True)
convblock.bn.bias.requires_grad_(True)
optimizer = torch.optim.SGD(model.parameters(),
lr=lr,
momentum=0.9,
weight_decay=0.0005)
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
# [int(epochs * 0.5), int(epochs * 0.75)],
# 0.1)
scheduler = None
criterion = nn.CrossEntropyLoss()
history = []
def evaluate():
print('Before training')
valres = test(model, criterion, valloader, device)
res = {}
for key in valres: res[f'valid_{key}'] = valres[key]
res['epoch'] = 0
history.append(res)
print()
# evaluate()
conv_weights_to_reset = []
total_weight_size = 0
if arch == 'alexnet':
sim = 0
for fidx in plkeys:
fidx = int(fidx)
w = model.features[fidx].bn.weight
size = w.size(0)
conv_weights_to_reset.append(w)
total_weight_size += size
model.features[fidx].bn.bias.data.zero_()
model.features[fidx].bn.weight.requires_grad_(True)
model.features[fidx].bn.bias.requires_grad_(True)
else:
for fidx in plkeys:
layer_key, i, module_key = fidx.split('.')
def get_layer(m):
return m.__getattr__(layer_key)[int(i)].__getattr__(module_key)
convblock = get_layer(model)
passblock = get_layer(passport_model)
w = convblock.bn.weight
size = w.size(0)
conv_weights_to_reset.append(w)
total_weight_size += size
convblock.bn.bias.data.zero_()
convblock.bn.weight.requires_grad_(True)
convblock.bn.bias.requires_grad_(True)
randidxs = torch.randperm(total_weight_size)
idxs = randidxs[:int(total_weight_size * args.flipperc)]
print(total_weight_size, len(idxs))
sim = 0
for w in conv_weights_to_reset:
size = w.size(0)
# wsize of first layer = 64, e.g. 0~63 - 64 = -64~-1, this is the indices within the first layer
print(len(idxs), size)
widxs = idxs[(idxs - size) < 0]
# reset the weights but remains signature sign bit
origsign = w.data.sign()
newsign = origsign.clone()
# reverse the sign on target bit
newsign[widxs] *= -1
# assign new signature
w.data.copy_(newsign)
sim += ((w.data.sign() == origsign).float().mean())
# remove all indices from first layer
idxs = idxs[(idxs - size) >= 0] - size
print('signature similarity', sim / len(conv_weights_to_reset))
evaluate()
dirname = f'logs/passport_attack_2/{loadpath.split("/")[1]}/{loadpath.split("/")[2]}'
os.makedirs(dirname, exist_ok=True)
json.dump(vars(args), open(f'{dirname}/{arch}-{scheme}-last-{dataset}-{rep}-{tagnum}.json', 'w+'))
for ep in range(1, epochs + 1):
if scheduler is not None:
scheduler.step()
print(f'Learning rate: {optimizer.param_groups[0]["lr"]}')
print(f'Epoch {ep:3d}:')
print('Training')
trainres = train(model, optimizer, criterion, trainloader, device)
print('Testing')
valres = test(model, criterion, valloader, device)
print()
res = {}
for key in trainres: res[f'train_{key}'] = trainres[key]
for key in valres: res[f'valid_{key}'] = valres[key]
res['epoch'] = ep
history.append(res)
torch.save(model.state_dict(),
f'{dirname}/{arch}-{scheme}-last-{dataset}-{rep}-{tagnum}.pth')
histdf = pd.DataFrame(history)
histdf.to_csv(f'{dirname}/{arch}-{scheme}-history-{dataset}-{tagnum}.csv')
