Yolov3中使用的nms函数如何使用

在 YOLOv3 中，nms 函数用于过滤检测结果中的重叠框，保留置信度最高的框。nms 函数的具体实现可以参考以下代码：

def non_max_suppression(prediction, confidence, num_classes, nms_conf=0.4):
    """
    使用非极大值抑制过滤检测结果中的重叠框
    """
    # 获取所有检测框的左上角和右下角坐标
    box_corner = prediction.new(prediction.shape)
    box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
    box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
    box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
    box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
    prediction[:, :, :4] = box_corner[:, :, :4]

    output = [None for _ in range(len(prediction))]
    # 对每个图片的检测结果进行处理
    for image_i, image_pred in enumerate(prediction):
        # 获取该图片中置信度最高的类别
        max_conf, max_conf_idx = torch.max(image_pred[:, 5:5 + num_classes], 1)
        max_conf = max_conf.float().unsqueeze(1)
        max_conf_idx = max_conf_idx.float().unsqueeze(1)
        # 将置信度最高的类别和置信度分数合并到一起
        seq = (image_pred[:, :5], max_conf, max_conf_idx)
        image_pred = torch.cat(seq, 1)
        # 删除置信度小于阈值的框
        non_zero_indices = (torch.nonzero(image_pred[:, 4]))
        try:
            image_pred_ = image_pred[non_zero_indices.squeeze(), :].view(-1, 7)
        except:
            continue
        # 获取所有检测框的面积
        img_classes = unique(image_pred_[:, -1])
        for cls in img_classes:
            # 获取该类别的检测结果
            cls_mask = image_pred_ * (image_pred_[:, -1] == cls).float().unsqueeze(1)
            class_mask_idx = torch.nonzero(cls_mask[:, -2]).squeeze()
            image_pred_class = image_pred_[class_mask_idx].view(-1, 7)
            # 按照置信度排序
            conf_sort_index = torch.sort(image_pred_class[:, 4], descending=True)[1]
            image_pred_class = image_pred_class[conf_sort_index]
            idx = image_pred_class.size(0)  # 检测框的数量
            for i in range(idx):
                # 计算当前检测框与其它检测框的 IoU
                try:
                    ious = bbox_iou(image_pred_class[i].unsqueeze(0), image_pred_class[i + 1:])
                except ValueError:
                    break
                except IndexError:
                    break
                # 将 IoU 大于阈值的检测框删除
                iou_mask = (ious < nms_conf).float().unsqueeze(1)
                image_pred_class[i + 1:] *= iou_mask
                non_zero_indices = torch.nonzero(image_pred_class[:, 4]).squeeze()
                image_pred_class = image_pred_class[non_zero_indices].view(-1, 7)
            batch_index = image_pred_class.new(image_pred_class.size(0), 1).fill_(image_i)
            # 将图片索引加入到检测框中
            seq = batch_index, image_pred_class
            if not output[image_i]:
                output[image_i] = torch.cat(seq, 1)
            else:
                output[image_i] = torch.cat((output[image_i], torch.cat(seq, 1)))
        return output

其中，bbox_iou 函数用于计算两个框的 IoU 值，unique 函数用于获取一个张量中的所有唯一值。使用时，可以将预测结果和对应的置信度输入到 non_max_suppression 函数中进行处理，如下所示：

# 获取预测结果和置信度
prediction = model(x)
conf_mask = (prediction[:, :, 4] > confidence_threshold).float().unsqueeze(2)
prediction = prediction * conf_mask

# 过滤掉重叠的检测框
output = non_max_suppression(prediction, confidence_threshold, num_classes)