model_watermark/block/val_get.py

import tqdm
import torch
from block.metric_get import metric


# 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()
#         pred_all = []  # 记录所有预测
#         true_all = []  # 记录所有标签
#         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)
#             pred_all.extend(pred_batch)
#             true_all.extend(true_batch)
#             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
#         pred_all = torch.stack(pred_all, dim=0)
#         true_all = torch.stack(true_all, dim=0)
#         loss_all = loss(pred_all, true_all).item()
#         accuracy, precision, recall, m_ap = metric(pred_all, true_all, args.class_threshold)
#         print(f'\n| 验证 | val_loss:{loss_all:.4f} | 阈值:{args.class_threshold:.2f} | val_accuracy:{accuracy:.4f} |'
#               f' val_precision:{precision:.4f} | val_recall:{recall:.4f} | val_m_ap:{m_ap:.4f} |')
#     return loss_all, accuracy, precision, recall, m_ap

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()
        pred_all = []  # 记录所有预测
        true_all = []  # 记录所有标签
        correct = 0
        total = 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)
            pred_all.extend(pred_batch)
            true_all.extend(true_batch)
            # 计算准确率
            _, predicted = torch.max(pred_batch.data, 1)
            labels = torch.argmax(true_batch, dim=1)
            total += true_batch.size(0)
            correct += (predicted == labels).sum().item()
            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
        pred_all = torch.stack(pred_all, dim=0)
        true_all = torch.stack(true_all, dim=0)
        loss_all = loss(pred_all, true_all).item()
        accuracy = correct / (total + 1e-5)
        print(
            f'\n| 验证 | val_loss:{loss_all:.4f} | 阈值:{args.class_threshold:.2f} | val_accuracy:{(100 * correct / (total + 1e-5)):.2f}% |')
    return loss_all, accuracy