PyTorch 模型评估代码：计算 mAP

for idx, (images, target, path) in enumerate(data_loader):
    images = images.cuda(non_blocking=True)
    target = target.cuda(non_blocking=True)
    output = model(images)
    
    # 测量特定准确率
    if config.EVAL_MODE:
        _, predicted = torch.max(output, 1)
        labels.extend(target.cpu().numpy())
        preds.extend(predicted.cpu().numpy())
        paths.extend(path)
    # 测量准确率并记录损失
    loss = criterion(output, target)
    acc1, acc5 = accuracy(output, target, topk=(1, 5))
    
    acc1 = reduce_tensor(acc1)
    acc5 = reduce_tensor(acc5)
    loss = reduce_tensor(loss)

    loss_meter.update(loss.item(), target.size(0))
    acc1_meter.update(acc1.item(), target.size(0))
    acc5_meter.update(acc5.item(), target.size(0))
    
    # 测量经过时间
    batch_time.update(time.time() - end)
    end = time.time()
    
    # 测量 mAP
    if config.EVAL_MODE:
        if idx % config.PRINT_FREQ == 0:
            print('Computing mAP...')
            ap = compute_mAP(labels, preds, paths)
            print('mAP: {:.4f}'.format(ap))

请注意，上述代码中的 compute_mAP 函数是用于计算 mAP 的自定义函数，你需要根据你的实际情况来实现该函数。