def im_proposals(model, im):
    """Generate RPN proposals on a single image."""
    inputs = {}
    inputs['data'], im_scale, inputs['im_info'] = \
        blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
    for k, v in inputs.items():
        workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32, copy=False))
    workspace.RunNet(model.net.Proto().name)

    if cfg.FPN.FPN_ON and cfg.FPN.MULTILEVEL_RPN:
        k_max = cfg.FPN.RPN_MAX_LEVEL
        k_min = cfg.FPN.RPN_MIN_LEVEL
        rois_names = [
            core.ScopedName('rpn_rois_fpn' + str(l))
            for l in range(k_min, k_max + 1)
        ]
        score_names = [
            core.ScopedName('rpn_roi_probs_fpn' + str(l))
            for l in range(k_min, k_max + 1)
        ]
        blobs = workspace.FetchBlobs(rois_names + score_names)
        # Combine predictions across all levels and retain the top scoring
        boxes = np.concatenate(blobs[:len(rois_names)])
        scores = np.concatenate(blobs[len(rois_names):]).squeeze()
        # Discussion: one could do NMS again after combining predictions from
        # the different FPN levels. Conceptually, it's probably the right thing
        # to do. For arbitrary reasons, the original FPN RPN implementation did
        # not do another round of NMS.
        inds = np.argsort(-scores)[:cfg.TEST.RPN_POST_NMS_TOP_N]
        scores = scores[inds]
        boxes = boxes[inds, :]
    else:
        boxes, scores = workspace.FetchBlobs(
            [core.ScopedName('rpn_rois'),
             core.ScopedName('rpn_roi_probs')]
        )
        scores = scores.squeeze()

    # Column 0 is the batch index in the (batch ind, x1, y1, x2, y2) encoding,
    # so we remove it since we just want to return boxes
    # Scale proposals back to the original input image scale
    boxes = boxes[:, 1:] / im_scale
    return boxes, scores
Generate RPN proposals on a single image.
detectron/core/rpn_generator.py