def _match_to_lbl()

in seamseg/algos/instance_seg.py [0:0]

• 31 lines of code
• 5 McCabe index (conditional complexity)

    def _match_to_lbl(self, proposals, bbx, cat, ids, msk, match):
        cls_lbl = []
        bbx_lbl = []
        msk_lbl = []
        for i, (proposals_i, bbx_i, cat_i, ids_i, msk_i, match_i) in enumerate(zip(
                proposals, bbx, cat, ids, msk, match)):
            if match_i is not None:
                pos = match_i >= 0

                # Objectness labels
                cls_lbl_i = proposals_i.new_zeros(proposals_i.size(0), dtype=torch.long)
                cls_lbl_i[pos] = cat_i[match_i[pos]] + 1 - self.num_stuff

                # Bounding box regression labels
                if pos.any().item():
                    bbx_lbl_i = calculate_shift(proposals_i[pos], bbx_i[match_i[pos]])
                    bbx_lbl_i *= bbx_lbl_i.new(self.bbx_reg_weights)

                    iis_lbl_i = ids_i[match_i[pos]]
                    # Compute instance segmentation masks
                    msk_i = roi_sampling(
                        msk_i.unsqueeze(0), proposals_i[pos], msk_i.new_zeros(pos.long().sum().item()),
                        self.lbl_roi_size, interpolation="nearest")

                    # Calculate mask segmentation labels
                    msk_lbl_i = (msk_i == iis_lbl_i.view(-1, 1, 1, 1)).any(dim=1).to(torch.long)
                    if not self.void_is_background:
                        msk_lbl_i[(msk_i == 0).all(dim=1)] = -1
                else:
                    bbx_lbl_i = None
                    msk_lbl_i = None

                cls_lbl.append(cls_lbl_i)
                bbx_lbl.append(bbx_lbl_i)
                msk_lbl.append(msk_lbl_i)
            else:
                cls_lbl.append(None)
                bbx_lbl.append(None)
                msk_lbl.append(None)

        return PackedSequence(cls_lbl), PackedSequence(bbx_lbl), PackedSequence(msk_lbl)