text: import numpy as np
  import sys
  # sys.path.append('/home/yxk/project/aa_demo_graduate/qapNet')
  # sys.path.append('/home/yxk/project/aa_demo_graduate/qapNet/wavedata')
  import tensorflow as tf
  
  from avod.builders import avod_fc_layers_builder
  from avod.builders import avod_loss_builder
  from avod.core import anchor_projector
  from avod.core import anchor_encoder
  from avod.core import box_3d_encoder
  from avod.core import box_8c_encoder
  from avod.core import box_4c_encoder
  
  from avod.core import box_list
  from avod.core import box_list_ops
  
  from avod.core import model
  from avod.core import orientation_encoder
  from Net.rpn_model import RpnModel
  
  
  import argparse
  import avod
  import avod.builders.config_builder_util as config_builder
  import os
  from avod.builders.dataset_builder import DatasetBuilder
  from avod.core import trainer
  
  
  class AvodModel(model.DetectionModel):
      ##############################
      # Keys for Predictions
      ##############################
      # Mini batch (mb) ground truth
      PRED_MB_CLASSIFICATIONS_GT = 'avod_mb_classifications_gt'
      PRED_MB_OFFSETS_GT = 'avod_mb_offsets_gt'
      PRED_MB_ORIENTATIONS_GT = 'avod_mb_orientations_gt'
  
      # Mini batch (mb) predictions
      PRED_MB_CLASSIFICATION_LOGITS = 'avod_mb_classification_logits'
      PRED_MB_CLASSIFICATION_SOFTMAX = 'avod_mb_classification_softmax'
      PRED_MB_OFFSETS = 'avod_mb_offsets'
      PRED_MB_ANGLE_VECTORS = 'avod_mb_angle_vectors'
  
      # Top predictions after BEV NMS
      PRED_TOP_CLASSIFICATION_LOGITS = 'avod_top_classification_logits'
      PRED_TOP_CLASSIFICATION_SOFTMAX = 'avod_top_classification_softmax'
  
      PRED_TOP_PREDICTION_ANCHORS = 'avod_top_prediction_anchors'
      PRED_TOP_PREDICTION_BOXES_3D = 'avod_top_prediction_boxes_3d'
      PRED_TOP_ORIENTATIONS = 'avod_top_orientations'
  
      # Other box representations
      PRED_TOP_BOXES_8C = 'avod_top_regressed_boxes_8c'
      PRED_TOP_BOXES_4C = 'avod_top_prediction_boxes_4c'
  
      # Mini batch (mb) predictions (for debugging)
      PRED_MB_MASK = 'avod_mb_mask'
      PRED_MB_POS_MASK = 'avod_mb_pos_mask'
      PRED_MB_ANCHORS_GT = 'avod_mb_anchors_gt'
      PRED_MB_CLASS_INDICES_GT = 'avod_mb_gt_classes'
  
      # All predictions (for debugging)
      PRED_ALL_CLASSIFICATIONS = 'avod_classifications'
      PRED_ALL_OFFSETS = 'avod_offsets'
      PRED_ALL_ANGLE_VECTORS = 'avod_angle_vectors'
  
      PRED_MAX_IOUS = 'avod_max_ious'
      PRED_ALL_IOUS = 'avod_anchor_ious'
  
      ##############################
      # Keys for Loss
      ##############################
      LOSS_FINAL_CLASSIFICATION = 'avod_classification_loss'
      LOSS_FINAL_REGRESSION = 'avod_regression_loss'
  
      # (for debugging)
      LOSS_FINAL_ORIENTATION = 'avod_orientation_loss'
      LOSS_FINAL_LOCALIZATION = 'avod_localization_loss'
  
      def __init__(self, model_config, train_val_test, dataset):
          """
          Args:
              model_config: configuration for the model
              train_val_test: "train", "val", or "test"
              dataset: the dataset that will provide samples and ground truth
          """
  
          # Sets model configs (_config)
          super(AvodModel, self).__init__(model_config)
  
          self.dataset = dataset
  
          # Dataset config
          self._num_final_classes = self.dataset.num_classes + 1            # 2
  
          # Input config
          input_config = self._config.input_config
          self._bev_pixel_size = np.asarray([input_config.bev_dims_h,
                                             input_config.bev_dims_w])      # [700, 800]
          self._bev_depth = input_config.bev_depth                          # 6
  
          self._img_pixel_size = np.asarray([input_config.img_dims_h,
                                             input_config.img_dims_w])      # [480, 1590]
          self._img_depth = [input_config.img_depth]                        # 3
  
          # print(self._bev_pixel_size,  '\n', self._bev_depth, '\n', self._img_pixel_size, '\n', self._img_depth)
  
          # AVOD config
          avod_config = self._config.avod_config
          self._proposal_roi_crop_size = \
              [avod_config.avod_proposal_roi_crop_size] * 2               # [7, 7]
          self._positive_selection = avod_config.avod_positive_selection  # not_bkg
          self._nms_size = avod_config.avod_nms_size                      # 100
          self._nms_iou_threshold = avod_config.avod_nms_iou_thresh       # 0.01
  
          # self._path_drop_probabilities = self._config.path_drop_probabilities  # [0.9, 0.9]
          self._box_rep = avod_config.avod_box_representation                   # box_4ca
  
          # print(self._proposal_roi_crop_size)
          # print(self._positive_selection)
          # print(self._nms_size)
          # print(self._nms_iou_threshold)
          # print(self._path_drop_probabilities)
          # print(self._box_rep)
  
          if self._box_rep not in ['box_3d', 'box_8c', 'box_8co',
                                   'box_4c', 'box_4ca']:
              raise ValueError('Invalid box representation', self._box_rep)
  
          # Create the RpnModel
          self._rpn_model = RpnModel(model_config, train_val_test, dataset)
  
          if train_val_test not in ["train", "val", "test"]:
              raise ValueError('Invalid train_val_test value,'
                               'should be one of ["train", "val", "test"]')
          self._train_val_test = train_val_test
          self._is_training = (self._train_val_test == 'train')
  
          self.sample_info = {}
  
      def build(self):
          rpn_model = self._rpn_model
  
          # Share the same prediction dict as RPN
          prediction_dict = rpn_model.build()
  
          top_anchors = prediction_dict[RpnModel.PRED_TOP_ANCHORS]
          ground_plane = rpn_model.placeholders[RpnModel.PL_GROUND_PLANE]
  
          class_labels = rpn_model.placeholders[RpnModel.PL_LABEL_CLASSES]
  
          with tf.variable_scope('avod_projection'):
  
              if self._config.expand_proposals_xz > 0.0:
  
                  expand_length = self._config.expand_proposals_xz
  
                  # Expand anchors along x and z
                  with tf.variable_scope('expand_xz'):
                      expanded_dim_x = top_anchors[:, 3] + expand_length
                      expanded_dim_z = top_anchors[:, 5] + expand_length
  
                      expanded_anchors = tf.stack([
                          top_anchors[:, 0],
                          top_anchors[:, 1],
                          top_anchors[:, 2],
                          expanded_dim_x,
                          top_anchors[:, 4],
                          expanded_dim_z
                      ], axis=1)
  
                  avod_projection_in = expanded_anchors
  
              else:
                  avod_projection_in = top_anchors
  
              with tf.variable_scope('bev'):
                  # Project top anchors into bev and image spaces
                  bev_proposal_boxes, bev_proposal_boxes_norm = \
                      anchor_projector.project_to_bev(
                          avod_projection_in,
                          self.dataset.kitti_utils.bev_extents)
  
                  # Reorder projected boxes into [y1, x1, y2, x2]
                  bev_proposal_boxes_tf_order = \
                      anchor_projector.reorder_projected_boxes(
                          bev_proposal_boxes)
                  bev_proposal_boxes_norm_tf_order = \
                      anchor_projector.reorder_projected_boxes(
                          bev_proposal_boxes_norm)
  
              # with tf.variable_scope('img'):
              #     image_shape = tf.cast(tf.shape(
              #         rpn_model.placeholders[RpnModel.PL_IMG_INPUT])[0:2],
              #         tf.float32)
              #     img_proposal_boxes, img_proposal_boxes_norm = \
              #         anchor_projector.tf_project_to_image_space(
              #             avod_projection_in,
              #             rpn_model.placeholders[RpnModel.PL_CALIB_P2],
              #             image_shape)
              #     # Only reorder the normalized img
              #     img_proposal_boxes_norm_tf_order = \
              #         anchor_projector.reorder_projected_boxes(
              #             img_proposal_boxes_norm)
  
          bev_feature_maps = rpn_model.bev_feature_maps
          bev_mask = tf.constant(1.0)
  
          # img_feature_maps = rpn_model.img_feature_maps
  
          # if not (self._path_drop_probabilities[0] ==
          #         self._path_drop_probabilities[1] == 1.0):
          #
          #     with tf.variable_scope('avod_path_drop'):
          #
          #         img_mask = rpn_model.img_path_drop_mask
          #         bev_mask = rpn_model.bev_path_drop_mask
          #
          #         img_feature_maps = tf.multiply(img_feature_maps,
          #                                        img_mask)
          #
          #         bev_feature_maps = tf.multiply(bev_feature_maps,
          #                                        bev_mask)
          # else:
          #     bev_mask = tf.constant(1.0)
          #     img_mask = tf.constant(1.0)
  
          # ROI Pooling
          with tf.variable_scope('avod_roi_pooling'):
              def get_box_indices(boxes):
                  proposals_shape = boxes.get_shape().as_list()
                  if any(dim is None for dim in proposals_shape):
                      proposals_shape = tf.shape(boxes)
                  ones_mat = tf.ones(proposals_shape[:2], dtype=tf.int32)
                  multiplier = tf.expand_dims(
                      tf.range(start=0, limit=proposals_shape[0]), 1)
                  return tf.reshape(ones_mat * multiplier, [-1])
  
              bev_boxes_norm_batches = tf.expand_dims(
                  bev_proposal_boxes_norm, axis=0)
  
              # These should be all 0's since there is only 1 image
              tf_box_indices = get_box_indices(bev_boxes_norm_batches)
  
              # Do ROI Pooling on BEV
              bev_rois = tf.image.crop_and_resize(
                  bev_feature_maps,
                  bev_proposal_boxes_norm_tf_order,
                  tf_box_indices,
                  self._proposal_roi_crop_size,
                  name='bev_rois')
              # Do ROI Pooling on image
              # img_rois = tf.image.crop_and_resize(
              #     img_feature_maps,
              #     img_proposal_boxes_norm_tf_order,
              #     tf_box_indices,
              #     self._proposal_roi_crop_size,
              #     name='img_rois')
  
          # Fully connected layers (Box Predictor)
          avod_layers_config = self.model_config.layers_config.avod_config
  
          fc_output_layers = \
              avod_fc_layers_builder.build(
                  layers_config=avod_layers_config,
                  input_rois=[bev_rois],
                  input_weights=[bev_mask],
                  num_final_classes=self._num_final_classes,
                  box_rep=self._box_rep,
                  top_anchors=top_anchors,
                  ground_plane=ground_plane,
                  is_training=self._is_training)
  
          all_cls_logits = \
              fc_output_layers[avod_fc_layers_builder.KEY_CLS_LOGITS]
          all_offsets = fc_output_layers[avod_fc_layers_builder.KEY_OFFSETS]
  
          # This may be None
          all_angle_vectors = \
              fc_output_layers.get(avod_fc_layers_builder.KEY_ANGLE_VECTORS)
  
          with tf.variable_scope('softmax'):
              all_cls_softmax = tf.nn.softmax(
                  all_cls_logits)
  
          ######################################################
          # Subsample mini_batch for the loss function
          ######################################################
          # Get the ground truth tensors
          anchors_gt = rpn_model.placeholders[RpnModel.PL_LABEL_ANCHORS]
          if self._box_rep in ['box_3d', 'box_4ca']:
              boxes_3d_gt = rpn_model.placeholders[RpnModel.PL_LABEL_BOXES_3D]
              orientations_gt = boxes_3d_gt[:, 6]
          elif self._box_rep in ['box_8c', 'box_8co', 'box_4c']:
              boxes_3d_gt = rpn_model.placeholders[RpnModel.PL_LABEL_BOXES_3D]
          else:
              raise NotImplementedError('Ground truth tensors not implemented')
  
          # Project anchor_gts to 2D bev
          with tf.variable_scope('avod_gt_projection'):
              bev_anchor_boxes_gt, _ = anchor_projector.project_to_bev(
                  anchors_gt, self.dataset.kitti_utils.bev_extents)
  
              bev_anchor_boxes_gt_tf_order = \
                  anchor_projector.reorder_projected_boxes(bev_anchor_boxes_gt)
  
          with tf.variable_scope('avod_box_list'):
              # Convert to box_list format
              anchor_box_list_gt = box_list.BoxList(bev_anchor_boxes_gt_tf_order)
              anchor_box_list = box_list.BoxList(bev_proposal_boxes_tf_order)
  
          mb_mask, mb_class_label_indices, mb_gt_indices = \
              self.sample_mini_batch(
                  anchor_box_list_gt=anchor_box_list_gt,
                  anchor_box_list=anchor_box_list,
                  class_labels=class_labels)
  
          # Create classification one_hot vector
          with tf.variable_scope('avod_one_hot_classes'):
              mb_classification_gt = tf.one_hot(
                  mb_class_label_indices,
                  depth=self._num_final_classes,
                  on_value=1.0 - self._config.label_smoothing_epsilon,
                  off_value=(self._config.label_smoothing_epsilon /
                             self.dataset.num_classes))
  
          # TODO: Don't create a mini batch in test mode
          # Mask predictions
          with tf.variable_scope('avod_apply_mb_mask'):
              # Classification
              mb_classifications_logits = tf.boolean_mask(
                  all_cls_logits, mb_mask)
              mb_classifications_softmax = tf.boolean_mask(
                  all_cls_softmax, mb_mask)
  
              # Offsets
              mb_offsets = tf.boolean_mask(all_offsets, mb_mask)
  
              # Angle Vectors
              if all_angle_vectors is not None:
                  mb_angle_vectors = tf.boolean_mask(all_angle_vectors, mb_mask)
              else:
                  mb_angle_vectors = None
  
          # Encode anchor offsets
          with tf.variable_scope('avod_encode_mb_anchors'):
              mb_anchors = tf.boolean_mask(top_anchors, mb_mask)
  
              if self._box_rep == 'box_3d':
                  # Gather corresponding ground truth anchors for each mb sample
                  mb_anchors_gt = tf.gather(anchors_gt, mb_gt_indices)
                  mb_offsets_gt = anchor_encoder.tf_anchor_to_offset(
                      mb_anchors, mb_anchors_gt)
  
                  # Gather corresponding ground truth orientation for each
                  # mb sample
                  mb_orientations_gt = tf.gather(orientations_gt,
                                                 mb_gt_indices)
              elif self._box_rep in ['box_8c', 'box_8co']:
  
                  # Get boxes_3d ground truth mini-batch and convert to box_8c
                  mb_boxes_3d_gt = tf.gather(boxes_3d_gt, mb_gt_indices)
                  if self._box_rep == 'box_8c':
                      mb_boxes_8c_gt = \
                          box_8c_encoder.tf_box_3d_to_box_8c(mb_boxes_3d_gt)
                  elif self._box_rep == 'box_8co':
                      mb_boxes_8c_gt = \
                          box_8c_encoder.tf_box_3d_to_box_8co(mb_boxes_3d_gt)
  
                  # Convert proposals: anchors -> box_3d -> box8c
                  proposal_boxes_3d = \
                      box_3d_encoder.anchors_to_box_3d(top_anchors, fix_lw=True)
                  proposal_boxes_8c = \
                      box_8c_encoder.tf_box_3d_to_box_8c(proposal_boxes_3d)
  
                  # Get mini batch offsets
                  mb_boxes_8c = tf.boolean_mask(proposal_boxes_8c, mb_mask)
                  mb_offsets_gt = box_8c_encoder.tf_box_8c_to_offsets(
                      mb_boxes_8c, mb_boxes_8c_gt)
  
                  # Flatten the offsets to a (N x 24) vector
                  mb_offsets_gt = tf.reshape(mb_offsets_gt, [-1, 24])
  
              elif self._box_rep in ['box_4c', 'box_4ca']:
  
                  # Get ground plane for box_4c conversion
                  ground_plane = self._rpn_model.placeholders[
                      self._rpn_model.PL_GROUND_PLANE]
  
                  # Convert gt boxes_3d -> box_4c
                  mb_boxes_3d_gt = tf.gather(boxes_3d_gt, mb_gt_indices)
                  mb_boxes_4c_gt = box_4c_encoder.tf_box_3d_to_box_4c(
                      mb_boxes_3d_gt, ground_plane)
  
                  # Convert proposals: anchors -> box_3d -> box_4c
                  proposal_boxes_3d = \
                      box_3d_encoder.anchors_to_box_3d(top_anchors, fix_lw=True)
                  proposal_boxes_4c = \
                      box_4c_encoder.tf_box_3d_to_box_4c(proposal_boxes_3d,
                                                         ground_plane)
  
                  # Get mini batch
                  mb_boxes_4c = tf.boolean_mask(proposal_boxes_4c, mb_mask)
                  mb_offsets_gt = box_4c_encoder.tf_box_4c_to_offsets(
                      mb_boxes_4c, mb_boxes_4c_gt)
  
                  if self._box_rep == 'box_4ca':
                      # Gather corresponding ground truth orientation for each
                      # mb sample
                      mb_orientations_gt = tf.gather(orientations_gt,
                                                     mb_gt_indices)
  
              else:
                  raise NotImplementedError(
                      'Anchor encoding not implemented for', self._box_rep)
  
          ######################################################
          # ROI summary images
          ######################################################
          avod_mini_batch_size = \
              self.dataset.kitti_utils.mini_batch_utils.avod_mini_batch_size
          with tf.variable_scope('bev_avod_rois'):
              mb_bev_anchors_norm = tf.boolean_mask(
                  bev_proposal_boxes_norm_tf_order, mb_mask)
              mb_bev_box_indices = tf.zeros_like(mb_gt_indices, dtype=tf.int32)
  
              # Show the ROIs of the BEV input density map
              # for the mini batch anchors
              bev_input_rois = tf.image.crop_and_resize(
                  self._rpn_model._bev_preprocessed,
                  mb_bev_anchors_norm,
                  mb_bev_box_indices,
                  (32, 32))
  
              bev_input_roi_summary_images = tf.split(
                  bev_input_rois, self._bev_depth, axis=3)
              tf.summary.image('bev_avod_rois',
                               bev_input_roi_summary_images[-1],
                               max_outputs=avod_mini_batch_size)
  
          # with tf.variable_scope('img_avod_rois'):
          #     # ROIs on image input
          #     mb_img_anchors_norm = tf.boolean_mask(
          #         img_proposal_boxes_norm_tf_order, mb_mask)
          #     mb_img_box_indices = tf.zeros_like(mb_gt_indices, dtype=tf.int32)
          #
          #     # Do test ROI pooling on mini batch
          #     img_input_rois = tf.image.crop_and_resize(
          #         self._rpn_model._img_preprocessed,
          #         mb_img_anchors_norm,
          #         mb_img_box_indices,
          #         (32, 32))
          #
          #     tf.summary.image('img_avod_rois',
          #                      img_input_rois,
          #                      max_outputs=avod_mini_batch_size)
  
          ######################################################
          # Final Predictions
          ######################################################
          # Get orientations from angle vectors
          if all_angle_vectors is not None:
              with tf.variable_scope('avod_orientation'):
                  all_orientations = \
                      orientation_encoder.tf_angle_vector_to_orientation(
                          all_angle_vectors)
  
          # Apply offsets to regress proposals
          with tf.variable_scope('avod_regression'):
              if self._box_rep == 'box_3d':
                  prediction_anchors = \
                      anchor_encoder.offset_to_anchor(top_anchors,
                                                      all_offsets)
  
              elif self._box_rep in ['box_8c', 'box_8co']:
                  # Reshape the 24-dim regressed offsets to (N x 3 x 8)
                  reshaped_offsets = tf.reshape(all_offsets,
                                                [-1, 3, 8])
                  # Given the offsets, get the boxes_8c
                  prediction_boxes_8c = \
                      box_8c_encoder.tf_offsets_to_box_8c(proposal_boxes_8c,
                                                          reshaped_offsets)
                  # Convert corners back to box3D
                  prediction_boxes_3d = \
                      box_8c_encoder.box_8c_to_box_3d(prediction_boxes_8c)
  
                  # Convert the box_3d to anchor format for nms
                  prediction_anchors = \
                      box_3d_encoder.tf_box_3d_to_anchor(prediction_boxes_3d)
  
              elif self._box_rep in ['box_4c', 'box_4ca']:
                  # Convert predictions box_4c -> box_3d
                  prediction_boxes_4c = \
                      box_4c_encoder.tf_offsets_to_box_4c(proposal_boxes_4c,
                                                          all_offsets)
  
                  prediction_boxes_3d = \
                      box_4c_encoder.tf_box_4c_to_box_3d(prediction_boxes_4c,
                                                         ground_plane)
  
                  # Convert to anchor format for nms
                  prediction_anchors = \
                      box_3d_encoder.tf_box_3d_to_anchor(prediction_boxes_3d)
  
              else:
                  raise NotImplementedError('Regression not implemented for',
                                            self._box_rep)
  
          # Apply Non-oriented NMS in BEV
          with tf.variable_scope('avod_nms'):
              bev_extents = self.dataset.kitti_utils.bev_extents
  
              with tf.variable_scope('bev_projection'):
                  # Project predictions into BEV
                  avod_bev_boxes, _ = anchor_projector.project_to_bev(
                      prediction_anchors, bev_extents)
                  avod_bev_boxes_tf_order = \
                      anchor_projector.reorder_projected_boxes(
                          avod_bev_boxes)
  
              # Get top score from second column onward
              all_top_scores = tf.reduce_max(all_cls_logits[:, 1:], axis=1)
  
              # Apply NMS in BEV
              nms_indices = tf.image.non_max_suppression(
                  avod_bev_boxes_tf_order,
                  all_top_scores,
                  max_output_size=self._nms_size,
                  iou_threshold=self._nms_iou_threshold)
  
              # Gather predictions from NMS indices
              top_classification_logits = tf.gather(all_cls_logits,
                                                    nms_indices)
              top_classification_softmax = tf.gather(all_cls_softmax,
                                                     nms_indices)
              top_prediction_anchors = tf.gather(prediction_anchors,
                                                 nms_indices)
  
              if self._box_rep == 'box_3d':
                  top_orientations = tf.gather(
                      all_orientations, nms_indices)
  
              elif self._box_rep in ['box_8c', 'box_8co']:
                  top_prediction_boxes_3d = tf.gather(
                      prediction_boxes_3d, nms_indices)
                  top_prediction_boxes_8c = tf.gather(
                      prediction_boxes_8c, nms_indices)
  
              elif self._box_rep == 'box_4c':
                  top_prediction_boxes_3d = tf.gather(
                      prediction_boxes_3d, nms_indices)
                  top_prediction_boxes_4c = tf.gather(
                      prediction_boxes_4c, nms_indices)
  
              elif self._box_rep == 'box_4ca':
                  top_prediction_boxes_3d = tf.gather(
                      prediction_boxes_3d, nms_indices)
                  top_prediction_boxes_4c = tf.gather(
                      prediction_boxes_4c, nms_indices)
                  top_orientations = tf.gather(
                      all_orientations, nms_indices)
  
              else:
                  raise NotImplementedError('NMS gather not implemented for',
                                            self._box_rep)
  
          if self._train_val_test in ['train', 'val']:
              # Additional entries are added to the shared prediction_dict
              # Mini batch predictions
              prediction_dict[self.PRED_MB_CLASSIFICATION_LOGITS] = \
                  mb_classifications_logits
              prediction_dict[self.PRED_MB_CLASSIFICATION_SOFTMAX] = \
                  mb_classifications_softmax
              prediction_dict[self.PRED_MB_OFFSETS] = mb_offsets
  
              # Mini batch ground truth
              prediction_dict[self.PRED_MB_CLASSIFICATIONS_GT] = \
                  mb_classification_gt
              prediction_dict[self.PRED_MB_OFFSETS_GT] = mb_offsets_gt
  
              # Top NMS predictions
              prediction_dict[self.PRED_TOP_CLASSIFICATION_LOGITS] = \
                  top_classification_logits
              prediction_dict[self.PRED_TOP_CLASSIFICATION_SOFTMAX] = \
                  top_classification_softmax
  
              prediction_dict[self.PRED_TOP_PREDICTION_ANCHORS] = \
                  top_prediction_anchors
  
              # Mini batch predictions (for debugging)
              prediction_dict[self.PRED_MB_MASK] = mb_mask
              # prediction_dict[self.PRED_MB_POS_MASK] = mb_pos_mask
              prediction_dict[self.PRED_MB_CLASS_INDICES_GT] = \
                  mb_class_label_indices
  
              # All predictions (for debugging)
              prediction_dict[self.PRED_ALL_CLASSIFICATIONS] = \
                  all_cls_logits
              prediction_dict[self.PRED_ALL_OFFSETS] = all_offsets
  
              # Path drop masks (for debugging)
              prediction_dict['bev_mask'] = bev_mask
              # prediction_dict['img_mask'] = img_mask
  
          else:
              # self._train_val_test == 'test'
              prediction_dict[self.PRED_TOP_CLASSIFICATION_SOFTMAX] = \
                  top_classification_softmax
              prediction_dict[self.PRED_TOP_PREDICTION_ANCHORS] = \
                  top_prediction_anchors
  
          if self._box_rep == 'box_3d':
              prediction_dict[self.PRED_MB_ANCHORS_GT] = mb_anchors_gt
              prediction_dict[self.PRED_MB_ORIENTATIONS_GT] = mb_orientations_gt
              prediction_dict[self.PRED_MB_ANGLE_VECTORS] = mb_angle_vectors
  
              prediction_dict[self.PRED_TOP_ORIENTATIONS] = top_orientations
  
              # For debugging
              prediction_dict[self.PRED_ALL_ANGLE_VECTORS] = all_angle_vectors
  
          elif self._box_rep in ['box_8c', 'box_8co']:
              prediction_dict[self.PRED_TOP_PREDICTION_BOXES_3D] = \
                  top_prediction_boxes_3d
  
              # Store the corners before converting for visualization purposes
              prediction_dict[self.PRED_TOP_BOXES_8C] = top_prediction_boxes_8c
  
          elif self._box_rep == 'box_4c':
              prediction_dict[self.PRED_TOP_PREDICTION_BOXES_3D] = \
                  top_prediction_boxes_3d
              prediction_dict[self.PRED_TOP_BOXES_4C] = top_prediction_boxes_4c
  
          elif self._box_rep == 'box_4ca':
              if self._train_val_test in ['train', 'val']:
                  prediction_dict[self.PRED_MB_ORIENTATIONS_GT] = \
                      mb_orientations_gt
                  prediction_dict[self.PRED_MB_ANGLE_VECTORS] = mb_angle_vectors
  
              prediction_dict[self.PRED_TOP_PREDICTION_BOXES_3D] = \
                  top_prediction_boxes_3d
              prediction_dict[self.PRED_TOP_BOXES_4C] = top_prediction_boxes_4c
              prediction_dict[self.PRED_TOP_ORIENTATIONS] = top_orientations
  
          else:
              raise NotImplementedError('Prediction dict not implemented for',
                                        self._box_rep)
  
          # prediction_dict[self.PRED_MAX_IOUS] = max_ious
          # prediction_dict[self.PRED_ALL_IOUS] = all_ious
  
          return prediction_dict
  
      def sample_mini_batch(self, anchor_box_list_gt, anchor_box_list,
                            class_labels):
  
          with tf.variable_scope('avod_create_mb_mask'):
              # Get IoU for every anchor
              all_ious = box_list_ops.iou(anchor_box_list_gt, anchor_box_list)
              max_ious = tf.reduce_max(all_ious, axis=0)
              max_iou_indices = tf.argmax(all_ious, axis=0)
  
              # Sample a pos/neg mini-batch from anchors with highest IoU match
              mini_batch_utils = self.dataset.kitti_utils.mini_batch_utils
              mb_mask, mb_pos_mask = mini_batch_utils.sample_avod_mini_batch(
                  max_ious)
              mb_class_label_indices = mini_batch_utils.mask_class_label_indices(
                  mb_pos_mask, mb_mask, max_iou_indices, class_labels)
  
              mb_gt_indices = tf.boolean_mask(max_iou_indices, mb_mask)
  
          return mb_mask, mb_class_label_indices, mb_gt_indices
  
      def create_feed_dict(self):
          feed_dict = self._rpn_model.create_feed_dict()
          """
          bev_input/bev_input_pl : (700, 800, 6)
          img_input/img_input_pl : (, , 3)
          pl_labels/label_anchors_pl : (, 6)
          pl_labels/label_boxes_3d_pl: (, 7)
          """
          self.sample_info = self._rpn_model.sample_info
          return feed_dict
  
      def loss(self, prediction_dict):
          # Note: The loss should be using mini-batch values only
          loss_dict, rpn_loss = self._rpn_model.loss(prediction_dict)
          losses_output = avod_loss_builder.build(self, prediction_dict)
  
          classification_loss = \
              losses_output[avod_loss_builder.KEY_CLASSIFICATION_LOSS]
  
          final_reg_loss = losses_output[avod_loss_builder.KEY_REGRESSION_LOSS]
  
          avod_loss = losses_output[avod_loss_builder.KEY_AVOD_LOSS]
  
          offset_loss_norm = \
              losses_output[avod_loss_builder.KEY_OFFSET_LOSS_NORM]
  
          loss_dict.update({self.LOSS_FINAL_CLASSIFICATION: classification_loss})
          loss_dict.update({self.LOSS_FINAL_REGRESSION: final_reg_loss})
  
          # Add localization and orientation losses to loss dict for plotting
          loss_dict.update({self.LOSS_FINAL_LOCALIZATION: offset_loss_norm})
  
          ang_loss_loss_norm = losses_output.get(
              avod_loss_builder.KEY_ANG_LOSS_NORM)
          if ang_loss_loss_norm is not None:
              loss_dict.update({self.LOSS_FINAL_ORIENTATION: ang_loss_loss_norm})
  
          with tf.variable_scope('model_total_loss'):
              total_loss = rpn_loss + avod_loss
  
          return loss_dict, total_loss
  
  
  if __name__=='__main__':
  
      parser = argparse.ArgumentParser()
      # Defaults
      default_pipeline_config_path = avod.root_dir() + \
                                     '/configs/avod_cars_example.config'
      default_data_split = 'train'
      default_device = '0'
  
      parser.add_argument('--pipeline_config',
                          type=str,
                          dest='pipeline_config_path',
                          default=default_pipeline_config_path,
                          help='Path to the pipeline config')
  
      parser.add_argument('--data_split',
                          type=str,
                          dest='data_split',
                          default=default_data_split,
                          help='Data split for training')
  
      parser.add_argument('--device',
                          type=str,
                          dest='device',
                          default=default_device,
                          help='CUDA device id')
  
      args = parser.parse_args()
  
      # Parse pipeline config
      model_config, train_config, _, dataset_config = \
          config_builder.get_configs_from_pipeline_file(
              args.pipeline_config_path, is_training=True)
  
      # print(model_config)
      # print(train_config)
      # print(dataset_config)
  
      # Overwrite data split
      dataset_config.data_split = args.data_split
  
      # Set CUDA device id
      os.environ['CUDA_VISIBLE_DEVICES'] = args.device
  
      dataset = DatasetBuilder.build_kitti_dataset(dataset_config,
                                                   use_defaults=False)
  
      train_val_test = 'train'
      model_name = model_config.model_name
  
      with tf.Graph().as_default():
          if model_name == 'rpn_model':
              model = RpnModel(model_config,
                               train_val_test=train_val_test,
                               dataset=dataset)
          elif model_name == 'avod_model':
              model = AvodModel(model_config,
                                train_val_test=train_val_test,
                                dataset=dataset)
  
              # trainer.train(model, train_config)
  
      trainer.train(model, train_config)
  
  
  
  
  
  
  
  
  
  
  
  