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):
def setup_keys():
if self.key_type != 'random':
if self.arch == 'alexnet':
pretrained_model = AlexNetNormal(self.in_channels,
self.num_classes)
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)
def load_pretrained():
if self.pretrained_path is not None:
sd = torch.load(self.pretrained_path)
model.load_state_dict(sd)
if self.train_passport:
passport_kwargs = construct_passport_kwargs(self)
self.passport_kwargs = passport_kwargs
print('Loading arch: ' + self.arch)
if self.arch == 'alexnet':
model = AlexNetPassport(self.in_channels, self.num_classes,
passport_kwargs)
else:
model = ResNet18Passport(num_classes=self.num_classes,
passport_kwargs=passport_kwargs)
self.model = model.to(self.device)
setup_keys()
else: # train normally or train backdoor
print('Loading arch: ' + self.arch)
if self.arch == 'alexnet':
model = AlexNetNormal(self.in_channels, self.num_classes,
self.norm_type)
else:
model = ResNet18(num_classes=self.num_classes,
norm_type=self.norm_type)
load_pretrained()
self.model = model.to(self.device)
pprint(self.model)
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 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:
ResNetClass = ResNet18 if self.arch == 'resnet' else ResNet9
norm_type = 'bn' if pretrained_from_torch else self.norm_type
pretrained_model = ResNetClass(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)
def load_pretrained():
if self.pretrained_path is not None:
sd = torch.load(self.pretrained_path)
model.load_state_dict(sd)
if self.train_passport:
passport_kwargs, plkeys = construct_passport_kwargs(self, True)
self.passport_kwargs = passport_kwargs
self.plkeys = plkeys
self.is_baseline = False
print('Loading arch: ' + self.arch)
if self.arch == 'alexnet':
model = AlexNetPassport(self.in_channels, self.num_classes, passport_kwargs)
else:
ResNetPassportClass = ResNet18Passport if self.arch == 'resnet' else ResNet9Passport
model = ResNetPassportClass(num_classes=self.num_classes,
passport_kwargs=passport_kwargs)
self.model = model.to(self.device)
setup_keys()
else: # train normally or train backdoor
print('Loading arch: ' + self.arch)
self.is_baseline = True
if self.arch == 'alexnet':
model = AlexNetNormal(self.in_channels, self.num_classes, self.norm_type)
else:
ResNetClass = ResNet18 if self.arch == 'resnet' else ResNet9
model = ResNetClass(num_classes=self.num_classes, norm_type=self.norm_type)
load_pretrained()
self.model = model.to(self.device)
pprint(self.model)