def test_get_correct_box_encoding_and_class_prediction_shapes(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) proposal_boxes = tf.random_normal([4, 2, 4], dtype=tf.float32) rfcn_box_predictor = box_predictor.RfcnBoxPredictor( is_training=False, num_classes=2, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), num_spatial_bins=[3, 3], depth=4, crop_size=[12, 12], box_code_size=4) box_predictions = rfcn_box_predictor.predict( image_features, num_predictions_per_location=1, scope='BoxPredictor', proposal_boxes=proposal_boxes) box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] class_predictions_with_background = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, class_predictions_shape) = sess.run([ tf.shape(box_encodings), tf.shape(class_predictions_with_background) ]) self.assertAllEqual(box_encodings_shape, [8, 1, 2, 4]) self.assertAllEqual(class_predictions_shape, [8, 1, 3])
def build(argscope_fn, box_predictor_config, is_training, num_classes): """Builds box predictor based on the configuration. Builds box predictor based on the configuration. See box_predictor.proto for configurable options. Also, see box_predictor.py for more details. Args: argscope_fn: A function that takes the following inputs: * hyperparams_pb2.Hyperparams proto * a boolean indicating if the model is in training mode. and returns a tf slim argscope for Conv and FC hyperparameters. box_predictor_config: box_predictor_pb2.BoxPredictor proto containing configuration. is_training: Whether the models is in training mode. num_classes: Number of classes to predict. Returns: box_predictor: box_predictor.BoxPredictor object. Raises: ValueError: On unknown box predictor. """ if not isinstance(box_predictor_config, box_predictor_pb2.BoxPredictor): raise ValueError('box_predictor_config not of type ' 'box_predictor_pb2.BoxPredictor.') box_predictor_oneof = box_predictor_config.WhichOneof( 'box_predictor_oneof') if box_predictor_oneof == 'convolutional_box_predictor': conv_box_predictor = box_predictor_config.convolutional_box_predictor conv_hyperparams = argscope_fn(conv_box_predictor.conv_hyperparams, is_training) box_predictor_object = box_predictor.ConvolutionalBoxPredictor( is_training=is_training, num_classes=num_classes, conv_hyperparams=conv_hyperparams, min_depth=conv_box_predictor.min_depth, max_depth=conv_box_predictor.max_depth, num_layers_before_predictor=( conv_box_predictor.num_layers_before_predictor), use_dropout=conv_box_predictor.use_dropout, dropout_keep_prob=conv_box_predictor.dropout_keep_probability, kernel_size=conv_box_predictor.kernel_size, box_code_size=conv_box_predictor.box_code_size, apply_sigmoid_to_scores=conv_box_predictor.apply_sigmoid_to_scores) return box_predictor_object if box_predictor_oneof == 'oriented_convolutional_box_predictor': conv_box_predictor = box_predictor_config.oriented_convolutional_box_predictor conv_hyperparams = argscope_fn(conv_box_predictor.conv_hyperparams, is_training) box_predictor_object = box_predictor.OrientedConvolutionalBoxPredictor( is_training=is_training, num_classes=num_classes, conv_hyperparams=conv_hyperparams, min_depth=conv_box_predictor.min_depth, max_depth=conv_box_predictor.max_depth, num_layers_before_predictor=( conv_box_predictor.num_layers_before_predictor), use_dropout=conv_box_predictor.use_dropout, dropout_keep_prob=conv_box_predictor.dropout_keep_probability, kernel_size=conv_box_predictor.kernel_size, box_code_size=conv_box_predictor.box_code_size, oriented_box_code_size=conv_box_predictor.oriented_box_code_size, apply_sigmoid_to_scores=conv_box_predictor.apply_sigmoid_to_scores) return box_predictor_object if box_predictor_oneof == 'mask_rcnn_box_predictor': mask_rcnn_box_predictor = box_predictor_config.mask_rcnn_box_predictor fc_hyperparams = argscope_fn(mask_rcnn_box_predictor.fc_hyperparams, is_training) conv_hyperparams = None if mask_rcnn_box_predictor.HasField('conv_hyperparams'): conv_hyperparams = argscope_fn( mask_rcnn_box_predictor.conv_hyperparams, is_training) box_predictor_object = box_predictor.MaskRCNNBoxPredictor( is_training=is_training, num_classes=num_classes, fc_hyperparams=fc_hyperparams, use_dropout=mask_rcnn_box_predictor.use_dropout, dropout_keep_prob=mask_rcnn_box_predictor.dropout_keep_probability, box_code_size=mask_rcnn_box_predictor.box_code_size, conv_hyperparams=conv_hyperparams, predict_instance_masks=mask_rcnn_box_predictor. predict_instance_masks, mask_prediction_conv_depth=( mask_rcnn_box_predictor.mask_prediction_conv_depth), predict_keypoints=mask_rcnn_box_predictor.predict_keypoints) return box_predictor_object if box_predictor_oneof == 'rfcn_box_predictor': rfcn_box_predictor = box_predictor_config.rfcn_box_predictor conv_hyperparams = argscope_fn(rfcn_box_predictor.conv_hyperparams, is_training) box_predictor_object = box_predictor.RfcnBoxPredictor( is_training=is_training, num_classes=num_classes, conv_hyperparams=conv_hyperparams, crop_size=[ rfcn_box_predictor.crop_height, rfcn_box_predictor.crop_width ], num_spatial_bins=[ rfcn_box_predictor.num_spatial_bins_height, rfcn_box_predictor.num_spatial_bins_width ], depth=rfcn_box_predictor.depth, box_code_size=rfcn_box_predictor.box_code_size) return box_predictor_object raise ValueError('Unknown box predictor: {}'.format(box_predictor_oneof))