def load_model(model_config):
modelname = model_config['name']
if modelname == 'DenseNet121':
model = densenet.DenseNet121()
elif modelname == 'spDenseNet121':
sp = model_config['sp']
model = densenet.SparseDenseNet121(sparse_func='vol',
sparsities=[sp, sp, sp, sp])
elif modelname == 'ResNet18':
model = resnet.ResNet18()
elif modelname == 'ResNet34':
model = resnet.ResNet34()
elif modelname == 'ResNet50':
model = resnet.ResNet50()
elif modelname == 'ResNet101':
model = resnet.ResNet101()
elif modelname == 'ResNet152':
model = resnet.ResNet152()
elif modelname == 'spResNet18':
sp = model_config['sp']
model = resnet.SparseResNet18(relu=False,
sparsities=[sp, sp, sp, sp],
sparse_func='vol')
elif modelname == 'spResNet34':
sp = model_config['sp']
model = resnet.SparseResNet34(relu=False,
sparsities=[sp, sp, sp, sp],
sparse_func='vol')
elif modelname == 'spResNet50':
sp = model_config['sp']
model = resnet.SparseResNet50(relu=False,
sparsities=[sp, sp, sp, sp],
sparse_func='vol')
elif modelname == 'spResNet101':
sp = model_config['sp']
model = resnet.SparseResNet101(relu=False,
sparsities=[sp, sp, sp, sp],
sparse_func='vol')
elif modelname == 'spResNet152':
sp = model_config['sp']
model = resnet.SparseResNet152(relu=False,
sparsities=[sp, sp, sp, sp],
sparse_func='vol')
elif modelname == 'spWideResNet':
sp = model_config['sp']
model = wideresnet.SparseWideResNet(depth=model_config['depth'],
num_classes=10,
widen_factor=model_config['width'],
sp=sp,
sp_func='vol',
bias=False)
else:
raise NotImplementedError
return model
def load_model(models_config):
model_list = []
for model_config in models_config['lists']:
modelname = model_config['name']
print(modelname)
if modelname == 'DenseNet121':
model = densenet.DenseNet121()
elif modelname == 'spDenseNet121':
sp = model_config['sp']
model = densenet.SparseDenseNet121(sparse_func='vol',
sparsities=[sp,sp,sp,sp])
elif modelname == 'ResNet18':
model = resnet.ResNet18()
elif modelname == 'ResNet34':
model = resnet.ResNet34()
elif modelname == 'ResNet50':
model = resnet.ResNet50()
elif modelname == 'ResNet101':
model = resnet.ResNet101()
elif modelname == 'ResNet152':
model = resnet.ResNet152()
elif modelname == 'spResNet18':
sp = model_config['sp']
model = resnet.SparseResNet18(relu=False, sparsities=[sp,sp,sp,sp], sparse_func='vol')
elif modelname == 'spResNet34':
sp = model_config['sp']
model = resnet.SparseResNet34(relu=False, sparsities=[sp,sp,sp,sp], sparse_func='vol')
elif modelname == 'spResNet50':
sp = model_config['sp']
model = resnet.SparseResNet50(relu=False, sparsities=[sp,sp,sp,sp], sparse_func='vol')
elif modelname == 'spResNet101':
sp = model_config['sp']
model = resnet.SparseResNet101(relu=False, sparsities=[sp,sp,sp,sp], sparse_func='vol')
elif modelname == 'spResNet152':
sp = model_config['sp']
model = resnet.SparseResNet152(relu=False, sparsities=[sp,sp,sp,sp], sparse_func='vol')
elif modelname == 'spWideResNet':
sp = model_config['sp']
model = wideresnet.SparseWideResNet(depth=model_config['depth'], num_classes=10,
widen_factor=model_config['width'], sp=sp, sp_func='vol', bias=False)
elif modelname == 'WideResNet':
model = wideresnet.WideResNet(depth=model_config['depth'],
widen_factor=model_config['width'], num_classes=10)
else:
raise NotImplementedError
model.load_state_dict(torch.load(model_config['path']))
modelfile = model_config['path'].split('/')[-1][:-4]
model_list.append((modelfile, model))
return model_list
transforms.Normalize((norm_mean, norm_mean, norm_mean),
(norm_var, norm_var, norm_var)),
])
cifar_test = datasets.CIFAR10("./data",
train=False,
download=True,
transform=transform_test)
test_loader = DataLoader(cifar_test, batch_size=400, shuffle=True)
## natural
print("------------nature-----------")
device = torch.device('cuda:0')
model = resnet.ResNet18().to(device)
print("model loading --------Relu")
model.load_state_dict(torch.load('models/resnet18_cifar_80epochs.pth'))
eps = 0.031
model.eval()
test_err, test_loss = training.epoch(test_loader,
model,
device=device,
use_tqdm=True)
print("test", test_err)
adv_err, adv_loss = training.epoch_adversarial(
test_loader,
model,
attack=attack.pgd_linf_untargeted,
device=device,
num_iter=20,
if args.is_kWTA:
load_net = wideresnet.SparseWideResNet(depth=34,
num_classes=10,
widen_factor=10,
sp=0.1,
sp_func='vol').to(device)
else:
load_net = wideresnet.WideResNet(depth=34,
num_classes=10,
widen_factor=10).to(device)
else:
if args.is_kWTA:
load_net = resnet.SparseResNet18(sparsities=[0.1, 0.1, 0.1, 0.1],
sparse_func='vol').to(device)
else:
load_net = resnet.ResNet18().to(device)
if len(args.gpu) > 2:
import torch.backends.cudnn as cudnn
if device == 'cuda':
load_net = torch.nn.DataParallel(load_net)
cudnn.benchmark = True
load_net.load_state_dict(
torch.load('models/resnet18_cifar' + name + '.pth', map_location=device))
if len(args.gpu) > 2:
load_net = list(load_net.children())[0]
# make net don't requires grad
for p in load_net.parameters():