class_docstr: Outputs object detection proposals by applying estimated bounding-box
  transformations to a set of regular boxes (called "anchors").
class_docstr_tok: ["Outputs","object","detection","proposals","by","applying","estimated","bounding-box","transformations","to","a","set","of","regular","boxes","(","called","``","anchors","''",")","."]
code: def forward(self, bottom, top):
      # Algorithm:
      #
      # for each (H, W) location i
      #   generate A anchor boxes centered on cell i
      #   apply predicted bbox deltas at cell i to each of the A anchors
      # clip predicted boxes to image
      # remove predicted boxes with either height or width < threshold
      # sort all (proposal, score) pairs by score from highest to lowest
      # take top pre_nms_topN proposals before NMS
      # apply NMS with threshold 0.7 to remaining proposals
      # take after_nms_topN proposals after NMS
      # return the top proposals (-> RoIs top, scores top)
  
      assert (
          bottom[0].shape[0] == 1
      ), "Only single item batches are supported"
  
      cfg_key = str(self.phase)  # either 'TRAIN' or 'TEST'
      pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
      post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
      nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
      min_size = cfg[cfg_key].RPN_MIN_SIZE
  
      # the first set of _num_anchors channels are bg probs
      # the second set are the fg probs, which we want
      scores = bottom[0][:, self._num_anchors :, :, :]
      bbox_deltas = bottom[1]
      im_info = bottom[2][0, :]
  
      # if DEBUG:
      #    print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
      #    print 'scale: {}'.format(im_info[2])
  
      # 1. Generate proposals from bbox deltas and shifted anchors
      height, width = scores.shape[-2:]
  
      # if DEBUG:
      #    print 'score map size: {}'.format(scores.shape)
  
      # Enumerate all shifts
      shift_x = np.arange(0, width) * self._feat_stride
      shift_y = np.arange(0, height) * self._feat_stride
      shift_x, shift_y = np.meshgrid(shift_x, shift_y)
      shifts = np.vstack(
          (
              shift_x.ravel(),
              shift_y.ravel(),
              shift_x.ravel(),
              shift_y.ravel(),
          )
      ).transpose()
  
      # Enumerate all shifted anchors:
      #
      # add A anchors (1, A, 4) to
      # cell K shifts (K, 1, 4) to get
      # shift anchors (K, A, 4)
      # reshape to (K*A, 4) shifted anchors
      A = self._num_anchors
      K = shifts.shape[0]
      anchors = self._anchors.reshape((1, A, 4)) + shifts.reshape(
          (1, K, 4)
      ).transpose((1, 0, 2))
      anchors = anchors.reshape((K * A, 4))
  
      # Transpose and reshape predicted bbox transformations to get them
      # into the same order as the anchors:
      #
      # bbox deltas will be (1, 4 * A, H, W) format
      # transpose to (1, H, W, 4 * A)
      # reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
      # in slowest to fastest order
      bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
  
      # Same story for the scores:
      #
      # scores are (1, A, H, W) format
      # transpose to (1, H, W, A)
      # reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
      scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
  
      # Convert anchors into proposals via bbox transformations
      proposals = bbox_transform_inv(anchors, bbox_deltas)
  
      # 2. clip predicted boxes to image
      proposals = clip_boxes(proposals, im_info[:2])
  
      # 3. remove predicted boxes with either height or width < threshold
      # (NOTE: convert min_size to input image scale stored in im_info[2])
      keep = _filter_boxes(proposals, min_size * im_info[2])
      proposals = proposals[keep, :]
      scores = scores[keep]
  
      # 4. sort all (proposal, score) pairs by score from highest to lowest
      # 5. take top pre_nms_topN (e.g. 6000)
      order = scores.ravel().argsort(kind="mergesort")[::-1]
      if pre_nms_topN > 0:
          order = order[:pre_nms_topN]
      proposals = proposals[order, :]
      scores = scores[order]
  
      # 6. apply nms (e.g. threshold = 0.7)
      # 7. take after_nms_topN (e.g. 300)
      # 8. return the top proposals (-> RoIs top)
      keep = nms(np.hstack((proposals, scores)), nms_thresh)
      if post_nms_topN > 0:
          keep = keep[:post_nms_topN]
      proposals = proposals[keep, :]
      scores = scores[keep]
  
      # Output rois blob
      # Our RPN implementation only supports a single input image, so all
      # batch inds are 0
      batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
      blob = np.hstack(
          (batch_inds, proposals.astype(np.float32, copy=False))
      )
  
      return blob
  
file_path: CNTK/Examples/Image/Detection/utils/caffe_layers/proposal_layer.py
imports: import numpy
  import yaml
  from utils.caffe_layers.default_config import cfg
  from utils.rpn.generate_anchors import generate_anchors
  from utils.caffe_layers.bbox_transform import bbox_transform_inv, clip_boxes
  from utils.nms_wrapper import nms
label_desc: Use image node_id 3 for calling the ProposalLayer obj's underlying member method code with example usage: obj.forward(bottom, top) and returns: blob