if __name__ == '__main__':
    conf = LinXiaoNetConfig()    conf.set_cuda(True)    conf.set_input_shape(8, 8)    conf.set_train_info(5, 16, 1e-2)    conf.set_checkpoint_config(5, 'checkpoints/v2train')    conf.set_num_worker(0)    conf.set_log('log/v2train.log')    # conf.set_pretrained_path('checkpoints/v2m4000/epoch_15')
    init_logger(conf.log_file)    logger()(conf)
    device = 'cuda' if conf.use_cuda else 'cpu'
    # 创建策略网络    model = LinXiaoNet(3)    model.to(device)
    loss_func = AlphaLoss()    loss_func.to(device)
    optimizer = torch.optim.SGD(model.parameters(), conf.init_lr, 0.9, weight_decay=5e-4)    lr_schedule = torch.optim.lr_scheduler.StepLR(optimizer, 1, 0.95)
    # initial config tree    tree = MonteTree(model, device, chess_size=conf.input_shape[0], simulate_count=500)    data_cache = TrainDataCache(num_worker=conf.num_worker)
    ep_num = 0    chess_num = 0    # config train interval    train_every_chess = 18
    # 加载检查点    if conf.pretrain_path is not None:        model_data, optimizer_data, lr_schedule_data, data_cache, ep_num, chess_num = load_checkpoint(conf.pretrain_path)        model.load_state_dict(model_data)        optimizer.load_state_dict(optimizer_data)        lr_schedule.load_state_dict(lr_schedule_data)        logger()('successfully load pretrained : {}'.format(conf.pretrain_path))
    while True:        logger()(f'self chess game no.{chess_num+1} start.')        # 进行一次自我对弈，获取对弈记录        chess_record = tree.self_game()        logger()(f'self chess game no.{chess_num+1} end.')        # 根据对弈记录生成训练数据        train_data = generate_train_data(tree.chess_size, chess_record)        # 将训练数据存入缓存        for i in range(len(train_data)):            data_cache.push(train_data[i])        if chess_num % train_every_chess == 0:            logger()(f'train start.')            loader = data_cache.get_loader(conf.batch_size)            model.train()            for _ in range(conf.epoch_num):                loss_record = []                for bat_state, bat_dist, bat_winner in loader:                    bat_state, bat_dist, bat_winner = bat_state.to(device), bat_dist.to(device), bat_winner.to(device)                    optimizer.zero_grad()                    prob, value = model(bat_state)                    loss = loss_func(prob, value, bat_dist, bat_winner)                    loss.backward()                    optimizer.step()                    loss_record.append(loss.item())                logger()(f'train epoch {ep_num} loss: {sum(loss_record) / float(len(loss_record))}')                ep_num += 1                if ep_num % conf.checkpoint_save_every_num == 0:                    save_checkpoint(                        os.path.join(conf.checkpoint_save_dir, f'epoch_{ep_num}'),                        ep_num, chess_num, model.state_dict(), optimizer.state_dict(), lr_schedule.state_dict(), data_cache                    )            lr_schedule.step()            logger()(f'train end.')        chess_num += 1        save_chess_record(            os.path.join(conf.checkpoint_save_dir, f'chess_record_{chess_num}.pkl'),            chess_record        )        # break
    pass

# 用户查询机器落子状态@app.route('/state/get/<state_id>', methods=['GET'])def get_state(state_id):    global state_result    state_id = int(state_id)    state = 0    chess_state = None    if state_id in state_result.keys() and state_result[state_id] is not None:        state = 1        chess_state = state_result[state_id]        state_result[state_id] = None    ret = {        'code': 0,        'msg': 'OK',        'data': {            'state': state,            'chess_state': chess_state        }    }    return jsonify(ret)

# 游戏开始，为这场游戏创建蒙特卡洛树@app.route('/game/start', methods=['POST'])def game_start():    global trees    global model, device, chess_size, simulate_count    tree_id = random.randint(1000, 100000)    trees[tree_id] = MonteTree(model, device, chess_size=chess_size, simulate_count=simulate_count)    ret = {        'code': 0,        'msg': 'OK',        'data': {            'tree_id': tree_id        }    }    return jsonify(ret)

# 游戏结束，销毁蒙特卡洛树@app.route('/game/end/<tree_id>', methods=['POST'])def game_end(tree_id):    global trees    tree_id = int(tree_id)    trees[tree_id] = None    ret = {        'code': 0,        'msg': 'OK',        'data': {}    }    return ret

if __name__ == '__main__':    app.run(        '0.0.0.0',        8080    )