"config": config, "updates": updates, "optim": optim_state_dict, } torch.save(checkpoints, path) if __name__ == "__main__": # Combine command-line arguments and yaml file arguments opt = opts.model_opts() config = yaml.load(open(opt.config, "r")) config = Namespace(**config, **vars(opt)) writer = misc_utils.set_tensorboard(config) device, devices_id = misc_utils.set_cuda(config) misc_utils.set_seed(config.seed) if config.label_dict_file: with open(config.label_dict_file, "r") as f: label_dict = json.load(f) if config.restore: print("loading checkpoint...\n") checkpoints = torch.load(config.restore, map_location=lambda storage, loc: storage) else: checkpoints = None data = load_data(config) print('data: ', data.keys()) # exit()
"config": config, "updates": updates, "optim": optim_state_dict, } torch.save(checkpoints, path) if __name__ == "__main__": # Combine command-line arguments and yaml file arguments opt = opts.model_opts() # opt=options config = yaml.load(open(opt.config, "r")) # yaml对象 config = Namespace(**config, **vars(opt)) # 转换成Namespace对象 writer = misc_utils.set_tensorboard(config) device, devices_id = misc_utils.set_cuda(config) misc_utils.set_seed(config.seed) # 可重复的随机性 if config.label_dict_file: with open(config.label_dict_file, "r") as f: label_dict = json.load(f) # 还不确定label_dict_file是什么文件,是一个json文件 if config.restore: # 模型的位置 print("loading checkpoint...\n") checkpoints = torch.load(config.restore, map_location=lambda storage, loc: storage) else: checkpoints = None data = load_data(config) model, optim = build_model(checkpoints, config, device)