if args.model_la == 'lstm':
model_la = model_.cnn_lstm()
elif args.model_la == 'resnet':
model_la = model_.ResNet()
elif args.model_la == 'resnet_pca':
model_la = model_.ResNet_pca()
elif args.model_la == 'lcnn_9':
model_la = model_.lcnn_9layers()
elif args.model_la == 'lcnn_29':
model_la = model_.lcnn_29layers_v2()
elif args.model_la == 'lcnn_9_pca':
model_la = model_.lcnn_9layers_pca()
elif args.model_la == 'lcnn_29_pca':
model_la = model_.lcnn_29layers_v2_pca()
elif args.model_la == 'lcnn_9_icqspec':
model_la = model_.lcnn_9layers_icqspec()
elif args.model_la == 'lcnn_9_prodspec':
model_la = model_.lcnn_9layers_prodspec()
elif args.model_la == 'lcnn_9_CC':
model_la = model_.lcnn_9layers_CC(ncoef=args.ncoef_la)
elif args.model_la == 'lcnn_29_CC':
model_la = model_.lcnn_29layers_CC(ncoef=args.ncoef_la)
elif args.model_la == 'resnet_CC':
model_la = model_.ResNet_CC(ncoef=args.ncoef_la)
if args.model_pa == 'lstm':
model_pa = model_.cnn_lstm()
elif args.model_pa == 'resnet':
model_pa = model_.ResNet()
elif args.model_pa == 'resnet_pca':
model_pa = model_.ResNet_pca()
if args.model == 'lstm':
model = model_.cnn_lstm(nclasses=args.n_classes)
elif args.model == 'resnet':
model = model_.ResNet(nclasses=args.n_classes)
elif args.model == 'resnet_pca':
model = model_.ResNet_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_9':
model = model_.lcnn_9layers(nclasses=args.n_classes)
elif args.model == 'lcnn_29':
model = model_.lcnn_29layers_v2(nclasses=args.n_classes)
elif args.model == 'lcnn_9_pca':
model = model_.lcnn_9layers_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_29_pca':
model = model_.lcnn_29layers_v2_pca(nclasses=args.n_classes)
elif args.model == 'lcnn_9_icqspec':
model = model_.lcnn_9layers_icqspec(nclasses=args.n_classes)
elif args.model == 'lcnn_9_prodspec':
model = model_.lcnn_9layers_prodspec(nclasses=args.n_classes)
elif args.model == 'lcnn_9_CC':
model = model_.lcnn_9layers_CC(nclasses=args.n_classes,
ncoef=args.ncoef,
init_coef=args.init_coef)
elif args.model == 'lcnn_29_CC':
model = model_.lcnn_29layers_CC(nclasses=args.n_classes,
ncoef=args.ncoef,
init_coef=args.init_coef)
elif args.model == 'resnet_CC':
model = model_.ResNet_CC(nclasses=args.n_classes,
ncoef=args.ncoef,
init_coef=args.init_coef)
if args.model == 'lstm':
model = model_.cnn_lstm()
elif args.model == 'resnet':
model = model_.ResNet()
elif args.model == 'resnet_pca':
model = model_.ResNet_pca()
elif args.model == 'lcnn_9':
model = model_.lcnn_9layers()
elif args.model == 'lcnn_29':
model = model_.lcnn_29layers_v2()
elif args.model == 'lcnn_9_pca':
model = model_.lcnn_9layers_pca()
elif args.model == 'lcnn_29_pca':
model = model_.lcnn_29layers_v2_pca()
elif args.model == 'lcnn_9_icqspec':
model = model_.lcnn_9layers_icqspec()
elif args.model == 'lcnn_9_prodspec':
model = model_.lcnn_9layers_prodspec()
elif args.model == 'lcnn_9_CC':
model = model_.lcnn_9layers_CC(ncoef=args.ncoef,
init_coef=args.init_coef)
elif args.model == 'lcnn_29_CC':
model = model_.lcnn_29layers_CC(ncoef=args.ncoef,
init_coef=args.init_coef)
elif args.model == 'resnet_34_CC':
model = model_.ResNet_34_CC(ncoef=args.ncoef, init_coef=args.init_coef)
print('Loading model')
ckpt = torch.load(args.cp_path, map_location=lambda storage, loc: storage)
model.load_state_dict(ckpt['model_state'], strict=True)