def _network(self): ''' Define the network outputs ''' with tf.variable_scope('model'): self.cnn = convnet(self.x, [5, 3, 3, 3, 3], [64, 96, 128, 172, 256], strides=[2, 2, 2, 2, 2]) featureMaps = self.cnn.get_output() _feat_stride = self.cnn.get_feat_stride() # Region Proposal Network (RPN) self.rpn_net = rpn(featureMaps, self.gt_boxes, self.im_dims, _feat_stride, flags) rpn_cls_score = self.rpn_net.get_rpn_cls_score() rpn_bbox_pred = self.rpn_net.get_rpn_bbox_pred() # Roi Pooling roi_proposal_net = roi_proposal(rpn_cls_score, rpn_bbox_pred, self.gt_boxes, self.im_dims, flags) # R-CNN Classification self.fast_rcnn_net = fast_rcnn(featureMaps, roi_proposal_net) with tf.variable_scope('model', reuse=True): assert tf.get_variable_scope().reuse is True self.cnn_valid = convnet(self.x_valid, [5, 3, 3, 3, 3], [64, 96, 128, 172, 256], strides=[2, 2, 2, 2, 2]) featureMaps_valid = self.cnn_valid.get_output() _feat_stride_valid = self.cnn_valid.get_feat_stride() # Region Proposal Network (RPN) self.rpn_net_valid = rpn(featureMaps_valid, self.gt_boxes_valid, self.im_dims_valid, _feat_stride_valid, flags) rpn_cls_score_valid = self.rpn_net_valid.get_rpn_cls_score() rpn_bbox_pred_valid = self.rpn_net_valid.get_rpn_bbox_pred() # Roi Pooling self.roi_proposal_net_valid = roi_proposal(rpn_cls_score_valid, rpn_bbox_pred_valid, self.gt_boxes_valid, self.im_dims_valid, flags) # R-CNN Classification self.fast_rcnn_net_valid = fast_rcnn(featureMaps_valid, self.roi_proposal_net_valid)
def _faster_rcnn(self, x, gt_boxes, im_dims, key): self.cnn[key] = convnet(x, [5, 3, 3, 3, 3], [64, 96, 128, 172, 256], strides=[2, 2, 2, 2, 2]) featureMaps = self.cnn[key].get_output() _feat_stride = self.cnn[key].get_feat_stride() # Region Proposal Network (RPN) self.rpn_net[key] = rpn(featureMaps, gt_boxes, im_dims, _feat_stride, flags) # Roi Pooling self.roi_proposal_net[key] = roi_proposal(self.rpn_net[key], gt_boxes, im_dims, key, flags) # R-CNN Classification self.fast_rcnn_net[key] = fast_rcnn(featureMaps, self.roi_proposal_net[key])
def _network(self): ''' Define the network outputs ''' with tf.variable_scope('model'): self.cnn = convnet(self.x, [5, 3, 3, 3, 3], [64, 96, 128, 172, 256], strides=[2, 2, 2, 2, 2]) featureMaps = self.cnn.get_output() _feat_stride = self.cnn.get_feat_stride() # Region Proposal Network (RPN) self.rpn_net = rpn(featureMaps, self.gt_boxes, self.im_dims, _feat_stride, flags) rpn_cls_score = self.rpn_net.get_rpn_cls_score() rpn_bbox_pred = self.rpn_net.get_rpn_bbox_pred() # Roi Pooling roi_proposal_net = roi_proposal(rpn_cls_score, rpn_bbox_pred, self.gt_boxes, self.im_dims, flags) # R-CNN Classification self.fast_rcnn_net = fast_rcnn(featureMaps, roi_proposal_net) with tf.variable_scope('model', reuse=True): assert tf.get_variable_scope().reuse is True self.cnn_valid = convnet(self.x_valid, [5, 3, 3, 3, 3], [64, 96, 128, 172, 256], strides=[2, 2, 2, 2, 2]) featureMaps_valid = self.cnn_valid.get_output() _feat_stride_valid = self.cnn_valid.get_feat_stride() # Region Proposal Network (RPN) self.rpn_net_valid = rpn(featureMaps_valid, self.gt_boxes_valid, self.im_dims_valid, _feat_stride_valid, flags) rpn_cls_score_valid = self.rpn_net_valid.get_rpn_cls_score() rpn_bbox_pred_valid = self.rpn_net_valid.get_rpn_bbox_pred() # Roi Pooling self.roi_proposal_net_valid = roi_proposal(rpn_cls_score_valid, rpn_bbox_pred_valid, self.gt_boxes_valid, self.im_dims_valid, flags) # R-CNN Classification self.fast_rcnn_net_valid = fast_rcnn(featureMaps_valid, self.roi_proposal_net_valid)
def _faster_rcnn(self, x, gt_boxes, im_dims, key): # VALID and TEST are both evaluation mode eval_mode = True if (key == 'EVAL') else False self.cnn[key] = convnet(x, [5, 3, 3, 3, 3], [32, 64, 64, 128, 128], strides=[2, 2, 1, 2, 1]) feature_maps = self.cnn[key].get_output() _feat_stride = self.cnn[key].get_feat_stride() # Region Proposal Network (RPN) self.rpn_net[key] = rpn(feature_maps, gt_boxes, im_dims, _feat_stride, eval_mode) # RoI Proposals self.roi_proposal_net[key] = roi_proposal(self.rpn_net[key], gt_boxes, im_dims, eval_mode) # Fast R-CNN Classification self.fast_rcnn_net[key] = fast_rcnn(feature_maps, self.roi_proposal_net[key], eval_mode)
def _faster_rcnn(self, x, gt_boxes, im_dims, key): # VALID and TEST are both evaluation mode eval_mode = True if (key == 'EVAL') else False self.cnn[key] = convnet(x, [5, 3, 3, 3, 3], [32, 64, 64, 128, 128], strides=[2, 2, 1, 2, 1]) feature_maps = self.cnn[key].get_output() _feat_stride = self.cnn[key].get_feat_stride() # Region Proposal Network (RPN) self.rpn_net[key] = rpn(feature_maps, gt_boxes, im_dims, _feat_stride, eval_mode) # RoI Proposals self.roi_proposal_net[key] = roi_proposal(self.rpn_net[key], gt_boxes, im_dims, eval_mode) # Fast R-CNN Classification self.fast_rcnn_net[key] = fast_rcnn(feature_maps, self.roi_proposal_net[key], eval_mode)
def _faster_rcnn(self, x, gt_boxes, im_dims, key): # VALID and TEST are both evaluation mode eval_mode = True if (key == 'EVAL') else False # CNN Feature extractor feature_maps = resnet50_reduced(x) # CNN downsampling factor _feat_stride = 16 self.print_log(feature_maps) # Region Proposal Network (RPN) self.rpn_net[key] = rpn(feature_maps, gt_boxes, im_dims, _feat_stride, eval_mode) # RoI Proposals self.roi_proposal_net[key] = roi_proposal(self.rpn_net[key], gt_boxes, im_dims, eval_mode) # Fast R-CNN Classification self.fast_rcnn_net[key] = fast_rcnn(feature_maps, self.roi_proposal_net[key], eval_mode)
def _faster_rcnn(self, x, gt_boxes, im_dims, key): # VALID and TEST are both evaluation mode eval_mode = True if (key == 'EVAL') else False # CNN Feature extractor feature_maps = resnet50_reduced(x) # CNN downsampling factor _feat_stride = 16 self.print_log(feature_maps) # Region Proposal Network (RPN) self.rpn_net[key] = rpn(feature_maps, gt_boxes, im_dims, _feat_stride, eval_mode) # RoI Proposals self.roi_proposal_net[key] = roi_proposal(self.rpn_net[key], gt_boxes, im_dims, eval_mode) # Fast R-CNN Classification self.fast_rcnn_net[key] = fast_rcnn(feature_maps, self.roi_proposal_net[key], eval_mode)
def _network(self): ''' Define the network outputs ''' # Convolutional Feature Extractor: ResNet101 # self.cnn = resnet(101, self.x) # Simpler convnet for debugging self.cnn = convnet(self.x, [5, 3, 3, 3, 3], [64, 96, 128, 172, 256], strides=[2, 2, 2, 2, 2]) featureMaps = self.cnn.get_output() _feat_stride = self.cnn.get_feat_stride() # Region Proposal Network (RPN) self.rpn_net = rpn(featureMaps, self.gt_boxes, self.im_dims, _feat_stride, flags) rpn_cls_score = self.rpn_net.get_rpn_cls_score() rpn_bbox_pred = self.rpn_net.get_rpn_bbox_pred() # ROI proposal self.roi_proposal_net = roi_proposal(rpn_cls_score, rpn_bbox_pred, self.gt_boxes, self.im_dims, flags) # R-CNN Classification self.fast_rcnn_net = fast_rcnn(featureMaps, self.roi_proposal_net)