def test_path_drop_weights(self): # Tests the effect of path-drop on network's feature maps. # It sets up a minimal-training process to check the # feature before and after running the 'train_op' while # path-drop is in effect. train_val_test = 'train' # overwrite the training iterations self.train_config.max_iterations = 2 self.train_config.overwrite_checkpoints = True # Overwrite path drop probabilities model_config = config_builder.proto_to_obj(self.model_config) model_config.path_drop_probabilities = [0.0, 0.8] with tf.Graph().as_default(): # Set a graph-level seed tf.set_random_seed(1245) model = RpnModel(model_config, train_val_test=train_val_test, dataset=self.dataset) prediction_dict = model.build() losses_dict, total_loss = model.loss(prediction_dict) global_summaries = set([]) # Optimizer training_optimizer = optimizer_builder.build( self.train_config.optimizer, global_summaries) train_op = slim.learning.create_train_op(total_loss, training_optimizer) init_op = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init_op) for step in range(1, self.train_config.max_iterations): feed_dict = model.create_feed_dict() if step == 1: current_feature_maps = sess.run(model.img_feature_maps, feed_dict=feed_dict) exp_feature_maps = current_feature_maps train_op_loss = sess.run(train_op, feed_dict=feed_dict) print('Step {}, Total Loss {:0.3f} '.format( step, train_op_loss)) updated_feature_maps = sess.run(model.img_feature_maps, feed_dict=feed_dict) # The feature maps should have remained the same since # the image path was dropped np.testing.assert_array_almost_equal(updated_feature_maps, exp_feature_maps, decimal=4)
def test_rpn_loss(self): # Use "val" so that the first sample is loaded each time rpn_model = RpnModel(self.model_config, train_val_test="val", dataset=self.dataset) predictions = rpn_model.build() loss, total_loss = rpn_model.loss(predictions) feed_dict = rpn_model.create_feed_dict() with self.test_session() as sess: init = tf.global_variables_initializer() sess.run(init) loss_dict_out = sess.run(loss, feed_dict=feed_dict) print('Losses ', loss_dict_out)
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 # Input config input_config = self._config.input_config self._bev_pixel_size = np.asarray( [input_config.bev_dims_h, input_config.bev_dims_w]) self._bev_depth = input_config.bev_depth self._img_pixel_size = np.asarray( [input_config.img_dims_h, input_config.img_dims_w]) self._img_depth = [input_config.img_depth] # AVOD config avod_config = self._config.avod_config self._proposal_roi_crop_size = \ [avod_config.avod_proposal_roi_crop_size] * 2 self._positive_selection = avod_config.avod_positive_selection self._nms_size = avod_config.avod_nms_size self._nms_iou_threshold = avod_config.avod_nms_iou_thresh self._path_drop_probabilities = self._config.path_drop_probabilities self._box_rep = avod_config.avod_box_representation 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 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, img_rois], input_weights=[bev_mask, img_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() 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