def add_fpn_rpn_outputs(model, blobs_in, dim_in, spatial_scales):
    """Add RPN on FPN specific outputs."""
    num_anchors = len(cfg.FPN.RPN_ASPECT_RATIOS)
    dim_out = dim_in

    k_max = cfg.FPN.RPN_MAX_LEVEL  # coarsest level of pyramid
    k_min = cfg.FPN.RPN_MIN_LEVEL  # finest level of pyramid
    assert len(blobs_in) == k_max - k_min + 1
    for lvl in range(k_min, k_max + 1):
        bl_in = blobs_in[k_max - lvl]  # blobs_in is in reversed order
        sc = spatial_scales[k_max - lvl]  # in reversed order
        slvl = str(lvl)

        if lvl == k_min:
            # Create conv ops with randomly initialized weights and
            # zeroed biases for the first FPN level; these will be shared by
            # all other FPN levels
            # RPN hidden representation
            conv_rpn_fpn = model.Conv(
                bl_in,
                'conv_rpn_fpn' + slvl,
                dim_in,
                dim_out,
                kernel=3,
                pad=1,
                stride=1,
                weight_init=gauss_fill(0.01),
                bias_init=const_fill(0.0)
            )
            model.Relu(conv_rpn_fpn, conv_rpn_fpn)
            # Proposal classification scores
            rpn_cls_logits_fpn = model.Conv(
                conv_rpn_fpn,
                'rpn_cls_logits_fpn' + slvl,
                dim_in,
                num_anchors,
                kernel=1,
                pad=0,
                stride=1,
                weight_init=gauss_fill(0.01),
                bias_init=const_fill(0.0)
            )
            # Proposal bbox regression deltas
            rpn_bbox_pred_fpn = model.Conv(
                conv_rpn_fpn,
                'rpn_bbox_pred_fpn' + slvl,
                dim_in,
                4 * num_anchors,
                kernel=1,
                pad=0,
                stride=1,
                weight_init=gauss_fill(0.01),
                bias_init=const_fill(0.0)
            )
        else:
            # Share weights and biases
            sk_min = str(k_min)
            # RPN hidden representation
            conv_rpn_fpn = model.ConvShared(
                bl_in,
                'conv_rpn_fpn' + slvl,
                dim_in,
                dim_out,
                kernel=3,
                pad=1,
                stride=1,
                weight='conv_rpn_fpn' + sk_min + '_w',
                bias='conv_rpn_fpn' + sk_min + '_b'
            )
            model.Relu(conv_rpn_fpn, conv_rpn_fpn)
            # Proposal classification scores
            rpn_cls_logits_fpn = model.ConvShared(
                conv_rpn_fpn,
                'rpn_cls_logits_fpn' + slvl,
                dim_in,
                num_anchors,
                kernel=1,
                pad=0,
                stride=1,
                weight='rpn_cls_logits_fpn' + sk_min + '_w',
                bias='rpn_cls_logits_fpn' + sk_min + '_b'
            )
            # Proposal bbox regression deltas
            rpn_bbox_pred_fpn = model.ConvShared(
                conv_rpn_fpn,
                'rpn_bbox_pred_fpn' + slvl,
                dim_in,
                4 * num_anchors,
                kernel=1,
                pad=0,
                stride=1,
                weight='rpn_bbox_pred_fpn' + sk_min + '_w',
                bias='rpn_bbox_pred_fpn' + sk_min + '_b'
            )

        if not model.train or cfg.MODEL.FASTER_RCNN:
            # Proposals are needed during:
            #  1) inference (== not model.train) for RPN only and Faster R-CNN
            #  OR
            #  2) training for Faster R-CNN
            # Otherwise (== training for RPN only), proposals are not needed
            lvl_anchors = generate_anchors(
                stride=2.**lvl,
                sizes=(cfg.FPN.RPN_ANCHOR_START_SIZE * 2.**(lvl - k_min), ),
                aspect_ratios=cfg.FPN.RPN_ASPECT_RATIOS
            )
            rpn_cls_probs_fpn = model.net.Sigmoid(
                rpn_cls_logits_fpn, 'rpn_cls_probs_fpn' + slvl
            )
            model.GenerateProposals(
                [rpn_cls_probs_fpn, rpn_bbox_pred_fpn, 'im_info'],
                ['rpn_rois_fpn' + slvl, 'rpn_roi_probs_fpn' + slvl],
                anchors=lvl_anchors,
                spatial_scale=sc
            )
Add RPN on FPN specific outputs.
detectron/modeling/FPN.py