yolov5训练后模型的map如何计算代码

在 YOLOv5 中，使用的是 mAP（mean Average Precision）指标来衡量模型的性能。计算 mAP 的代码如下：

from utils.general import plot_results, check_file

def test(cfg, data, batch_size=16, img_size=640, iou_thres=0.65, conf_thres=0.25, save_json=False, single_cls=False, augment=False, verbose=False, model=None):

    # 加载模型
    model = model.to(device).eval()

    # 加载数据集
    dataloader = create_dataloader(data, img_size=img_size, batch_size=batch_size, stride=int(cfg.stride), pad=cfg.pad, rect=cfg.rect, augment=augment, cache=cfg.cache_images, single_cls=single_cls, pad_method=cfg.pad_method, hyp=hyp, rank=rank, world_size=world_size)

    # 初始化变量
    seen = 0
    pred = []
    tbar = tqdm(dataloader, desc=f'Caching images')

    for i, (imgs, targets, paths, shapes) in enumerate(tbar):
        # 将数据传输到 GPU 上
        imgs = imgs.to(device, non_blocking=True)

        # 前向传播
        with torch.no_grad():
            outputs = model(imgs)

        # 后处理
        outputs = non_max_suppression(outputs, conf_thres=conf_thres, iou_thres=iou_thres, multi_label=False)

        # 更新变量
        seen += imgs.shape[0]

        # 处理预测结果
        for si, pred_i in enumerate(outputs):
            # 如果没有预测结果，则跳过
            if pred_i is None:
                continue

            # 反归一化预测框坐标
            pred_i[:, :4] = scale_coords(imgs.shape[2:], pred_i[:, :4], shapes[si]).round()

            # 将预测结果转换为 COCO 格式
            for xi, x in enumerate(pred_i):
                # 如果没有预测结果，则跳过
                if x is None:
                    continue

                # 将预测结果转换为 COCO 格式
                box = x[:4].tolist()
                score = float(x[4])
                label = int(x[5])

                image_id = int(Path(paths[si]).stem.split('_')[-1])

                pred.append({'image_id': image_id,
                             'category_id': label,
                             'bbox': [round(float(x), 3) for x in box],
                             'score': round(score, 5)})

    # 保存预测结果
    if save_json and rank in [-1, 0]:
        pred_path = 'predictions.json'
        with open(pred_path, 'w') as file:
            json.dump(pred, file)

    # 计算 mAP
    if rank in [-1, 0]:
        # 如果是多进程，则需要将预测结果合并
        if world_size > 1:
            pred = comm.gather(pred, dst=0)
            if rank == 0:
                pred = list(itertools.chain(*pred))

        # 计算 mAP
        with open(data) as f:
            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # config dict
        names = ['data/coco.names']
        nc = 80

        if single_cls:
            nc = 1
            names = ['data/single.names']

        if len(pred):
            # 将预测结果转换为 COCO 格式
            pred = {x['image_id']: pred_i for x in pred for pred_i in x['instances']}
            gt = json.load(open(data_dict['val'].get('ann').rstrip('.json') + '_instances.json', 'r'))

            # 计算 AP
            ap, p, r, f1, ap_class = ap_per_class(pred, gt, iou_thres=iou_thres, conf_thres=conf_thres, use_07_metric=False)

            # 输出 mAP
            mp, mr, map50, map = np.mean(p), np.mean(r), ap_class[0], np.mean(ap)
            nt = np.bincount(gt['labels'], minlength=nc)
            if np.bincount(ap_class) > 1:
                print(f"mAP{'@[IoU=0.50:0.95]' * (iou_thres > 0.5)}: {map * 100:.2f}%")
                print(f"mAP{'@[IoU=0.50]' * (iou_thres > 0.5)}: {map50 * 100:.2f}%")
                print(f"mAP{'@[IoU=0.75]' * (iou_thres > 0.75)}: {ap_class.mean() * 100:.2f}%")
                print(f"mAP{'@[IoU=0.50:0.95]' * (iou_thres > 0.5)}, medium: {np.median(ap) * 100:.2f}%")
            print(f"Recall: {mr * 100:.2f}%")
            print(f"Precision: {mp * 100:.2f}%")
            print(f"F1-score: {f1.mean() * 100:.2f}%")
            print(f"Unique classes detected: {len(ap_class)}")
        else:
            print('No detections found by YOLOv5, mAP cannot be calculated.')

其中，`ap_per_class()` 函数用于计算每个类别的 AP 值。