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