maskrcnn_benchmark 代码详解之　inference.py

最新推荐文章于 2024-08-05 01:17:18 发布

leijieZhang

最新推荐文章于 2024-08-05 01:17:18 发布

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分类专栏： maskrcnn benchmark 文章标签： inference.py RPNPostProcessor maskrcnn_benchmark 获取Proposals 目标检测

本文链接：https://blog.youkuaiyun.com/leijieZhang/article/details/91448842

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本文详细解析了maskrcnn_benchmark库中inference.py文件的RPNPostProcessor类，该类用于从FPN不同层提取目标边框。通过参数如pre_nms_top_n、post_nms_top_n、nms_thresh和min_size进行边框筛选和NMS操作。同时介绍了make_rpn_postprocessor函数，用于根据用户参数实例化RPNPostProcessor。

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前言：

　　在maskrcnn_benchmark中，实现从FPN各个层里提取好的边框的功能由inference.py实现。在inference.py中定义了RPNPostProcessor类，该类利用一些提前定义好的参数来在多个FPN层（P2-P5）或者指定的输出特征层（比如C5）选取边框。

常用的参数有：pre_nms_top_n为每一个特征层上选取的边框数；post_nms_top_n为在得到的所有层边框中进一步选择的边框数；

nms_thresh为非极大线性抑制（NMS）的阈值；min_size为所能接受的最小的边框大小；

　　RPNPostProcessor类的代码及其解释如下：

class RPNPostProcessor(torch.nn.Module):
    """
    Performs post-processing on the outputs of the RPN boxes, before feeding the
    proposals to the heads
    """

    def __init__(
        self,
        pre_nms_top_n,
        post_nms_top_n,
        nms_thresh,
        min_size,
        box_coder=None,
        fpn_post_nms_top_n=None,
        fpn_post_nms_per_batch=True,
    ):
        """
        Arguments:
            pre_nms_top_n (int)
            post_nms_top_n (int)
            nms_thresh (float)
            min_size (int)
            box_coder (BoxCoder)
            fpn_post_nms_top_n (int)
        """
        super(RPNPostProcessor, self).__init__()
        # 初始化各种变量
        self.pre_nms_top_n = pre_nms_top_n
        self.post_nms_top_n = post_nms_top_n
        self.nms_thresh = nms_thresh
        self.min_size = min_size

        if box_coder is None:
            box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
        self.box_coder = box_coder

        if fpn_post_nms_top_n is None:
            fpn_post_nms_top_n = post_nms_top_n
        self.fpn_post_nms_top_n = fpn_post_nms_top_n
        self.fpn_post_nms_per_batch = fpn_post_nms_per_batch

    def add_gt_proposals(self, proposals, targets):
        """
        Arguments:
            proposals: list[BoxList]：预测边框列表
            targets: list[BoxList]：基准边框列表
        """
        # Get the device we're operating on，获得运算所在的设备
        device = proposals[0].bbox.device
        # 获得基准边框
        gt_boxes = [target.copy_with_fields([]) for target in targets]

        # later cat of bbox requires al