_, _, loader = iPERLoader(data_root=args.path, batch=args.batch_size, transform=transform).data_load() # model for image model_img = AppVQVAE().to(device) model_img = nn.DataParallel(model_img).to(device) if is_load_model_img is True: print('Loading model_img ...', end='') model_img.load_state_dict(torch.load(args.model_img_path)) model_img.eval() print('Done') else: print('model_img Initialized.') optimizer_img = optim.Adam(model_img.parameters(), lr=args.lr) # model for condition model_cond = VQVAE().to(device) model_cond = nn.DataParallel(model_cond).cuda() if is_load_model_cond is True: print('Loading model_cond ...', end='') model_cond.load_state_dict(torch.load(args.model_cond_path)) model_cond.eval() print('Done') else: print('model_cond Initialized.') optimizer_cond = optim.Adam(model_cond.parameters(), lr=args.lr) # transfer model model_transfer = TransferModel().to(device) model_transfer = nn.DataParallel(model_transfer).to(device) if is_load_model_transfer is True: print('Loading model_transfer ...', end='')
transform=transform).data_load() # model for image model_img = AppVQVAE().to(device) model_img = nn.DataParallel(model_img).to(device) if is_load_model_img is True: print('Loading model_img ...', end='') model_img.load_state_dict(torch.load(args.model_img_path)) model_img.eval() print('Done') else: print('model_img Initialized.') optimizer_img = optim.Adam(model_img.parameters(), lr=args.lr) # model for condition model_cond = VQVAE().to(device) model_cond = nn.DataParallel(model_cond).cuda() if is_load_model_cond is True: print('Loading model_cond ...', end='') model_cond.load_state_dict(torch.load(args.model_cond_path)) model_cond.eval() print('Done') else: print('model_cond Initialized.') optimizer_cond = optim.Adam(model_cond.parameters(), lr=args.lr) # transfer model model_transfer = TransferModel().to(device) model_transfer = nn.DataParallel(model_transfer).to(device) if is_load_model_transfer is True: print('Loading model_transfer ...', end='')