def select_over_all_levels()

in maskrcnn_benchmark/modeling/rpn/inference.py [0:0]

• 23 lines of code
• 11 McCabe index (conditional complexity)

    def select_over_all_levels(self, boxlists):
        num_images = len(boxlists)
        # different behavior during training and during testing:
        # during training, post_nms_top_n is over *all* the proposals combined, while
        # during testing, it is over the proposals for each image
        # NOTE: it should be per image, and not per batch. However, to be consistent 
        # with Detectron, the default is per batch (see Issue #672)
        if self.training and self.fpn_post_nms_per_batch:
            objectness = torch.cat(
                [boxlist.get_field("objectness") for boxlist in boxlists], dim=0
            )
            box_sizes = [len(boxlist) for boxlist in boxlists]
            post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
            _, inds_sorted = torch.topk(objectness, post_nms_top_n, dim=0, sorted=True)
            inds_mask = torch.zeros_like(objectness, dtype=torch.bool)
            inds_mask[inds_sorted] = 1
            inds_mask = inds_mask.split(box_sizes)
            for i in range(num_images):
                boxlists[i] = boxlists[i][inds_mask[i]]
        else:
            for i in range(num_images):
                objectness = boxlists[i].get_field("objectness")
                post_nms_top_n = min(self.fpn_post_nms_top_n, len(objectness))
                _, inds_sorted = torch.topk(
                    objectness, post_nms_top_n, dim=0, sorted=True
                )
                boxlists[i] = boxlists[i][inds_sorted]
        return boxlists