def _network(self): # Convert scores to probabilities self.rpn_cls_prob = rpn_softmax(self.rpn_cls_score) # Determine best proposals blobs = proposal_layer(rpn_bbox_cls_prob=self.rpn_cls_prob, rpn_bbox_pred=self.rpn_bbox_pred, im_dims=self.im_dims, cfg_key=self.key, _feat_stride=self.rpn_net._feat_stride, anchor_scales=self.flags['anchor_scales']) # Calculate targets for proposals self.rois, self.labels, self.bbox_targets, self.bbox_inside_weights, self.bbox_outside_weights = \ proposal_target_layer(rpn_rois=blobs, gt_boxes=self.gt_boxes,_num_classes=self.flags['num_classes'])
def _network(self): # There shouldn't be any gt_boxes if in evaluation mode if self.eval_mode is True: assert self.gt_boxes is None, \ 'Evaluation mode should not have ground truth boxes (or else what are you detecting for?)' with tf.variable_scope('roi_proposal'): # Convert scores to probabilities 转换得分到概率 self.rpn_cls_prob = rpn_softmax(self.rpn_cls_score) # Determine best proposals key = 'TRAIN' if self.eval_mode is False else 'TEST' self.blobs = proposal_layer(rpn_bbox_cls_prob=self.rpn_cls_prob, rpn_bbox_pred=self.rpn_bbox_pred, im_dims=self.im_dims, cfg_key=key, _feat_stride=self.rpn_net._feat_stride, anchor_scales=self.anchor_scales)
def _network(self): # There shouldn't be any gt_boxes if in evaluation mode if self.eval_mode is True: assert self.gt_boxes is None, \ 'Evaluation mode should not have ground truth boxes (or else what are you detecting for?)' with tf.variable_scope('roi_proposal'): # Convert scores to probabilities self.rpn_cls_prob = rpn_softmax(self.rpn_cls_score) # Determine best proposals key = 'TRAIN' if self.eval_mode is False else 'TEST' self.blobs = proposal_layer(rpn_bbox_cls_prob=self.rpn_cls_prob, rpn_bbox_pred=self.rpn_bbox_pred, im_dims=self.im_dims, cfg_key=key, _feat_stride=self.rpn_net._feat_stride, anchor_scales=self.anchor_scales) if self.eval_mode is False: # Calculate targets for proposals self.rois, self.labels, self.bbox_targets, self.bbox_inside_weights, self.bbox_outside_weights = \ proposal_target_layer(rpn_rois=self.blobs, gt_boxes=self.gt_boxes, _num_classes=self.num_classes)
def _network(self): # There shouldn't be any gt_boxes if in evaluation mode if self.eval_mode == True: assert self.gt_boxes == None, \ 'Evaluation mode should not have ground truth boxes (or else what are you detecting for?)' # Convert scores to probabilities self.rpn_cls_prob = rpn_softmax(self.rpn_cls_score) # Determine best proposals key = 'TRAIN' if self.eval_mode is False else 'TEST' self.blobs = proposal_layer(rpn_bbox_cls_prob=self.rpn_cls_prob, rpn_bbox_pred=self.rpn_bbox_pred, im_dims=self.im_dims, cfg_key=key, _feat_stride=self.rpn_net._feat_stride, anchor_scales=self.flags['anchor_scales']) if self.eval_mode == False: # Calculate targets for proposals self.rois, self.labels, self.bbox_targets, self.bbox_inside_weights, self.bbox_outside_weights = \ proposal_target_layer(rpn_rois=self.blobs, gt_boxes=self.gt_boxes,_num_classes=self.flags['num_classes'])