(self, anchors, objectness, box_regression, targets=None)
return boxlists
Arguments:
            anchors: list[list[BoxList]]
            objectness: list[tensor]
            box_regression: list[tensor]

        Returns:
            boxlists (list[BoxList]): the post-processed anchors, after
                applying box decoding and NMS
Arguments:
            anchors: list[list[BoxList]]
            objectness: list[tensor]
            box_regression: list[tensor]
Arguments : anchors : list [ list [ BoxList ]] objectness : list [ tensor ] box_regression : list [ tensor ]
def forward(self, anchors, objectness, box_regression, targets=None):
        """
        Arguments:
            anchors: list[list[BoxList]]
            objectness: list[tensor]
            box_regression: list[tensor]

        Returns:
            boxlists (list[BoxList]): the post-processed anchors, after
                applying box decoding and NMS
        """
        sampled_boxes = []
        num_levels = len(objectness)
        anchors = list(zip(*anchors))
        for a, o, b in zip(anchors, objectness, box_regression):
            sampled_boxes.append(self.forward_for_single_feature_map(a, o, b))

        boxlists = list(zip(*sampled_boxes))
        boxlists = [cat_boxlist(boxlist) for boxlist in boxlists]

        if num_levels > 1:
            boxlists = self.select_over_all_levels(boxlists)

        # append ground-truth bboxes to proposals
        if self.training and targets is not None:
            boxlists = self.add_gt_proposals(boxlists, targets)

        return boxlists
def forward ( self , anchors , objectness , box_regression , targets = None ) : sampled_boxes = [ ] num_levels = len ( objectness ) anchors = list ( zip ( * anchors ) ) for a , o , b in zip ( anchors , objectness , box_regression ) : sampled_boxes . append ( self . forward_for_single_feature_map ( a , o , b ) ) boxlists = list ( zip ( * sampled_boxes ) ) boxlists = [ cat_boxlist ( boxlist ) for boxlist in boxlists ] if num_levels > 1 : boxlists = self . select_over_all_levels ( boxlists ) # append ground-truth bboxes to proposals if self . training and targets is not None : boxlists = self . add_gt_proposals ( boxlists , targets ) return boxlists