def train_models(models_path, device='cpu'):
tasks = ['cifar10', 'cifar100', 'tinyimagenet']
cnns = []
sdns = []
for task in tasks:
af.extend_lists(cnns, sdns,
arcs.create_vgg16bn(models_path, task, save_type='cd'))
af.extend_lists(
cnns, sdns, arcs.create_resnet56(models_path, task,
save_type='cd'))
af.extend_lists(
cnns, sdns,
arcs.create_wideresnet32_4(models_path, task, save_type='cd'))
af.extend_lists(
cnns, sdns, arcs.create_mobilenet(models_path,
task,
save_type='cd'))
train(models_path, cnns, sdn=False, device=device)
train_sdns(models_path, sdns, ic_only=True,
device=device) # train SDNs with IC-only strategy
train_sdns(models_path, sdns, ic_only=False,
device=device) # train SDNs with SDN-training strategy
def sdn_ic_only_backdoored(device):
params = arcs.create_vgg16bn(None, 'cifar10', None, True)
path = 'backdoored_models'
backdoored_cnn_name = 'VGG16_cifar10_backdoored'
save_sdn_name = 'VGG16_cifar10_backdoored_SDN'
# Use the class VGG
backdoored_cnn = VGG(params)
backdoored_cnn.load_state_dict(torch.load('{}/{}'.format(
path, backdoored_cnn_name),
map_location='cpu'),
strict=False)
# convert backdoored cnn into a sdn
backdoored_sdn, sdn_params = af.cnn_to_sdn(
None, backdoored_cnn, params,
preloaded=backdoored_cnn) # load the CNN and convert it to a sdn
arcs.save_model(backdoored_sdn, sdn_params, path, save_sdn_name,
epoch=0) # save the resulting sdn
networks = [save_sdn_name]
train(path, networks, sdn=True, ic_only_sdn=True, device=device)