def test_get_boxes_with_five_classes_share_box_across_classes(self):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
share_box_across_classes=True))
def graph_fn(image_features):
box_predictions = mask_box_predictor([image_features],
prediction_stage=2)
return (box_predictions[box_predictor.BOX_ENCODINGS],
box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND])
image_features = np.random.rand(2, 7, 7, 3).astype(np.float32)
(box_encodings, class_predictions_with_background) = self.execute(
graph_fn, [image_features])
self.assertAllEqual(box_encodings.shape, [2, 1, 1, 4])
self.assertAllEqual(class_predictions_with_background.shape, [2, 1, 6])
def graph_fn(image_features):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
conv_hyperparams=self._build_hyperparams(
op_type=hyperparams_pb2.Hyperparams.CONV),
predict_instance_masks=True))
box_predictions = mask_box_predictor([image_features],
prediction_stage=3)
return (box_predictions[box_predictor.MASK_PREDICTIONS], )
def graph_fn(image_features):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
share_box_across_classes=True))
box_predictions = mask_box_predictor([image_features],
prediction_stage=2)
return (box_predictions[box_predictor.BOX_ENCODINGS],
box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND])
def test_do_not_return_instance_masks_without_request(self):
image_features = tf.random.uniform([2, 7, 7, 3], dtype=tf.float32)
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4))
box_predictions = mask_box_predictor([image_features],
prediction_stage=2)
self.assertEqual(len(box_predictions), 2)
self.assertTrue(box_predictor.BOX_ENCODINGS in box_predictions)
self.assertTrue(
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND in box_predictions)
def _get_mask_rcnn_box_predictor(self):
conv_hyperparams = KerasLayerHyperparamsFromData(
activation=self.activation,
initializer=self.initializer,
batch_norm_params=self.batch_norm_params)
fc_hyperparams = KerasLayerHyperparamsFromData(
activation=self.activation,
initializer=self.initializer,
batch_norm_params=self.batch_norm_params)
mask_rcnn_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=None,
num_classes=self.num_classes,
add_background_class=True,
fc_hyperparams=fc_hyperparams,
freeze_batchnorm=self.freeze_batchnorm,
use_dropout=self._dropout_keep_prob is not None,
dropout_keep_prob=self._dropout_keep_prob,
box_code_size=4,
conv_hyperparams=conv_hyperparams))
return mask_rcnn_box_predictor
def test_get_instance_masks(self):
mask_box_predictor = (
box_predictor_builder.build_mask_rcnn_keras_box_predictor(
is_training=False,
num_classes=5,
fc_hyperparams=self._build_hyperparams(),
freeze_batchnorm=False,
use_dropout=False,
dropout_keep_prob=0.5,
box_code_size=4,
conv_hyperparams=self._build_hyperparams(
op_type=hyperparams_pb2.Hyperparams.CONV),
predict_instance_masks=True))
def graph_fn(image_features):
box_predictions = mask_box_predictor([image_features],
prediction_stage=3)
return (box_predictions[box_predictor.MASK_PREDICTIONS], )
image_features = np.random.rand(2, 7, 7, 3).astype(np.float32)
mask_predictions = self.execute(graph_fn, [image_features])
self.assertAllEqual(mask_predictions.shape, [2, 1, 5, 14, 14])