text: # --------------------------------------------------------
  # Deformable Convolutional Networks
  # Copyright (c) 2016 by Contributors
  # Copyright (c) 2017 Microsoft
  # Licensed under The Apache-2.0 License [see LICENSE for details]
  # Modified by Yuwen Xiong
  # --------------------------------------------------------
  
  """
  Proposal Target Operator selects foreground and background roi and assigns label, bbox_transform to them.
  """
  
  import mxnet as mx
  import numpy as np
  from distutils.util import strtobool
  from easydict import EasyDict as edict
  import cPickle
  
  
  from core.rcnn import sample_rois
  
  DEBUG = False
  
  
  class ProposalTargetOperator(mx.operator.CustomOp):
      def __init__(self, num_classes, batch_images, batch_rois, cfg, fg_fraction):
          super(ProposalTargetOperator, self).__init__()
          self._num_classes = num_classes
          self._batch_images = batch_images
          self._batch_rois = batch_rois
          self._cfg = cfg
          self._fg_fraction = fg_fraction
  
          if DEBUG:
              self._count = 0
              self._fg_num = 0
              self._bg_num = 0
  
      def forward(self, is_train, req, in_data, out_data, aux):
          assert self._batch_rois == -1 or self._batch_rois % self._batch_images == 0, \
              'batchimages {} must devide batch_rois {}'.format(self._batch_images, self._batch_rois)
          all_rois = in_data[0].asnumpy()
          gt_boxes = in_data[1].asnumpy()
  
          if self._batch_rois == -1:
              rois_per_image = all_rois.shape[0] + gt_boxes.shape[0]
              fg_rois_per_image = rois_per_image
          else:
              rois_per_image = self._batch_rois / self._batch_images
              fg_rois_per_image = np.round(self._fg_fraction * rois_per_image).astype(int)
  
          # Include ground-truth boxes in the set of candidate rois
          zeros = np.zeros((gt_boxes.shape[0], 1), dtype=gt_boxes.dtype)
          all_rois = np.vstack((all_rois, np.hstack((zeros, gt_boxes[:, :-1]))))
          # Sanity check: single batch only
          assert np.all(all_rois[:, 0] == 0), 'Only single item batches are supported'
  
          rois, labels, bbox_targets, bbox_weights = \
              sample_rois(all_rois, fg_rois_per_image, rois_per_image, self._num_classes, self._cfg, gt_boxes=gt_boxes)
  
          if DEBUG:
              print "labels=", labels
              print 'num fg: {}'.format((labels > 0).sum())
              print 'num bg: {}'.format((labels == 0).sum())
              self._count += 1
              self._fg_num += (labels > 0).sum()
              self._bg_num += (labels == 0).sum()
              print "self._count=", self._count
              print 'num fg avg: {}'.format(self._fg_num / self._count)
              print 'num bg avg: {}'.format(self._bg_num / self._count)
              print 'ratio: {:.3f}'.format(float(self._fg_num) / float(self._bg_num))
  
          for ind, val in enumerate([rois, labels, bbox_targets, bbox_weights]):
              self.assign(out_data[ind], req[ind], val)
  
      def backward(self, req, out_grad, in_data, out_data, in_grad, aux):
          for i in range(len(in_grad)):
              self.assign(in_grad[i], req[i], 0)
  
  
  @mx.operator.register('proposal_target')
  class ProposalTargetProp(mx.operator.CustomOpProp):
      def __init__(self, num_classes, batch_images, batch_rois, cfg, fg_fraction='0.25'):
          super(ProposalTargetProp, self).__init__(need_top_grad=False)
          self._num_classes = int(num_classes)
          self._batch_images = int(batch_images)
          self._batch_rois = int(batch_rois)
          self._cfg = cPickle.loads(cfg)
          self._fg_fraction = float(fg_fraction)
  
      def list_arguments(self):
          return ['rois', 'gt_boxes']
  
      def list_outputs(self):
          return ['rois_output', 'label', 'bbox_target', 'bbox_weight']
  
      def infer_shape(self, in_shape):
          rpn_rois_shape = in_shape[0]
          gt_boxes_shape = in_shape[1]
  
          rois = rpn_rois_shape[0] + gt_boxes_shape[0] if self._batch_rois == -1 else self._batch_rois
  
          output_rois_shape = (rois, 5)
          label_shape = (rois, )
          bbox_target_shape = (rois, self._num_classes * 4)
          bbox_weight_shape = (rois, self._num_classes * 4)
  
          return [rpn_rois_shape, gt_boxes_shape], \
                 [output_rois_shape, label_shape, bbox_target_shape, bbox_weight_shape]
  
      def create_operator(self, ctx, shapes, dtypes):
          return ProposalTargetOperator(self._num_classes, self._batch_images, self._batch_rois, self._cfg, self._fg_fraction)
  
      def declare_backward_dependency(self, out_grad, in_data, out_data):
          return []
  