import tqdm
import torch


def val_get(args, val_dataloader, model, loss, ema, data_len):
    tqdm_len = (data_len - 1) // (args.batch // args.device_number) + 1
    tqdm_show = tqdm.tqdm(total=tqdm_len)
    with torch.no_grad():
        model = ema.ema if args.ema else model.eval()
        correct = 0
        total = 0
        loss_all = 0
        epoch = 0
        for index, (image_batch, true_batch) in enumerate(val_dataloader):
            image_batch = image_batch.to(args.device, non_blocking=args.latch)
            pred_batch = model(image_batch).detach().cpu()
            loss_batch = loss(pred_batch, true_batch)
            # 获取指标项
            _, predicted = torch.max(pred_batch, 1)
            total += true_batch.size(0)
            correct += (predicted == true_batch).sum().item()
            loss_all += loss_batch.item()
            epoch = epoch + 1
            # 更新进度条数据
            tqdm_show.set_postfix({'val_loss': loss_batch.item()})  # 添加显示
            tqdm_show.update(1)  # 更新进度条
        # tqdm
        tqdm_show.close()
        # 计算指标
        accuracy = correct / total
        print(f'\n| 验证 | val_loss:{loss_all/epoch:.4f} | val_accuracy:{accuracy:.4f} |')
    return loss_all, accuracy