def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
else:
model = pblm.cifar_model().cuda()
return model
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
elif m == 'resnet':
model = pblm.cifar_model_resnet(N=args.resnet_N, factor=args.resnet_factor).cuda()
else:
model = pblm.cifar_model().cuda()
return model
def select_model(m):
if m == 'small':
model = pblm.cifar_model().cuda()
elif m == 'large':
model = pblm.cifar_model_large().cuda()
# elif m == 'resNet':
# model = pblm.cifar_model_resnet().cuda()
else:
raise ValueError('model argument not recognized for imagenet')
return model
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().to(device)
elif m == 'resnet':
model = pblm.cifar_model_resnet(N=args.resnet_N,
factor=args.resnet_factor).to(device)
else:
model = pblm.cifar_model().to(device)
summary(model, (3, 32, 32))
return model
def select_model(m):
if m == 'large':
# raise ValueError
model = pblm.cifar_model_large().cuda()
elif m == 'resnet':
model = pblm.cifar_model_resnet(N=args.resnet_N,
factor=args.resnet_factor).cuda()
elif m == 'm1':
print('using a reduced sized network')
model = pblm.cifar_model_m1().cuda()
elif m == 'm2':
print('using a slightly reduced sized network')
model = pblm.cifar_model_m2().cuda()
else:
model = pblm.cifar_model().cuda()
return model
parser.add_argument('--fashion', action='store_true')
parser.add_argument('--model')
args = parser.parse_args()
if args.mnist:
train_loader, test_loader = pblm.mnist_loaders(args.batch_size)
model = pblm.mnist_model().to(device)
model.load_state_dict(torch.load('icml/mnist_epochs_100_baseline_model.pth'))
elif args.svhn:
train_loader, test_loader = pblm.svhn_loaders(args.batch_size)
model = pblm.svhn_model().to(device)
model.load_state_dict(torch.load('pixel2/svhn_small_batch_size_50_epochs_100_epsilon_0.0078_l1_proj_50_l1_test_median_l1_train_median_lr_0.001_opt_adam_schedule_length_20_seed_0_starting_epsilon_0.001_checkpoint.pth')['state_dict'])
elif args.model == 'cifar':
train_loader, test_loader = pblm.cifar_loaders(args.batch_size)
model = pblm.cifar_model().to(device)
model.load_state_dict(torch.load('pixel2/cifar_small_batch_size_50_epochs_100_epsilon_0.0347_l1_proj_50_l1_test_median_l1_train_median_lr_0.05_momentum_0.9_opt_sgd_schedule_length_20_seed_0_starting_epsilon_0.001_weight_decay_0.0005_checkpoint.pth')['state_dict'])
elif args.har:
pass
elif args.fashion:
pass
else:
raise ValueError("Need to specify which problem.")
for p in model.parameters():
p.requires_grad = False
num_classes = model[-1].out_features
correct = []
incorrect = []
l = []
if args.mnist:
train_loader, test_loader = pblm.mnist_loaders(args.batch_size)
model = pblm.mnist_model().cuda()
model.load_state_dict(
torch.load('icml/mnist_epochs_100_baseline_model.pth'))
elif args.svhn:
train_loader, test_loader = pblm.svhn_loaders(args.batch_size)
model = pblm.svhn_model().cuda()
model.load_state_dict(
torch.load(
'pixel2/svhn_small_batch_size_50_epochs_100_epsilon_0.0078_l1_proj_50_l1_test_median_l1_train_median_lr_0.001_opt_adam_schedule_length_20_seed_0_starting_epsilon_0.001_checkpoint.pth'
)['state_dict'])
elif args.model == 'cifar':
train_loader, test_loader = pblm.cifar_loaders(args.batch_size)
model = pblm.cifar_model().cuda()
model.load_state_dict(
torch.load(
'pixel2/cifar_small_batch_size_50_epochs_100_epsilon_0.0347_l1_proj_50_l1_test_median_l1_train_median_lr_0.05_momentum_0.9_opt_sgd_schedule_length_20_seed_0_starting_epsilon_0.001_weight_decay_0.0005_checkpoint.pth'
)['state_dict'])
elif args.har:
pass
elif args.fashion:
pass
else:
raise ValueError("Need to specify which problem.")
for p in model.parameters():
p.requires_grad = False
num_classes = model[-1].out_features
