(self, locations, box_cls, box_regression, centerness, image_sizes)
return boxlists
Arguments:
            anchors: list[list[BoxList]]
            box_cls: list[tensor]
            box_regression: list[tensor]
            image_sizes: list[(h, w)]
        Returns:
            boxlists (list[BoxList]): the post-processed anchors, after
                applying box decoding and NMS
Arguments:
            anchors: list[list[BoxList]]
            box_cls: list[tensor]
            box_regression: list[tensor]
            image_sizes: list[(h, w)]
        Returns:
            boxlists (list[BoxList]): the post-processed anchors, after
                applying box decoding and NMS
Arguments : anchors : list [ list [ BoxList ]] box_cls : list [ tensor ] box_regression : list [ tensor ] image_sizes : list [ ( h w ) ] Returns : boxlists ( list [ BoxList ] ) : the post - processed anchors after applying box decoding and NMS
def forward(self, locations, box_cls, box_regression, centerness, image_sizes):
        """
        Arguments:
            anchors: list[list[BoxList]]
            box_cls: list[tensor]
            box_regression: list[tensor]
            image_sizes: list[(h, w)]
        Returns:
            boxlists (list[BoxList]): the post-processed anchors, after
                applying box decoding and NMS
        """
        sampled_boxes = []
        for _, (l, o, b, c) in enumerate(zip(locations, box_cls, box_regression, centerness)):
            sampled_boxes.append(
                self.forward_for_single_feature_map(
                    l, o, b, c, image_sizes
                )
            )

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

        return boxlists
def forward ( self , locations , box_cls , box_regression , centerness , image_sizes ) : sampled_boxes = [ ] for _ , ( l , o , b , c ) in enumerate ( zip ( locations , box_cls , box_regression , centerness ) ) : sampled_boxes . append ( self . forward_for_single_feature_map ( l , o , b , c , image_sizes ) ) boxlists = list ( zip ( * sampled_boxes ) ) boxlists = [ cat_boxlist ( boxlist ) for boxlist in boxlists ] boxlists = self . select_over_all_levels ( boxlists ) return boxlists