text: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  """Tests for object_detection.predictors.heads.mask_head."""
  import tensorflow as tf
  
  from google.protobuf import text_format
  from object_detection.builders import hyperparams_builder
  from object_detection.predictors.heads import keras_mask_head
  from object_detection.protos import hyperparams_pb2
  from object_detection.utils import test_case
  
  
  class ConvolutionalMaskPredictorTest(test_case.TestCase):
  
    def _build_conv_hyperparams(self):
      conv_hyperparams = hyperparams_pb2.Hyperparams()
      conv_hyperparams_text_proto = """
      activation: NONE
        regularizer {
          l2_regularizer {
          }
        }
        initializer {
          truncated_normal_initializer {
          }
        }
      """
      text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
      return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
  
    def test_prediction_size_use_depthwise_false(self):
      conv_hyperparams = self._build_conv_hyperparams()
      mask_prediction_head = keras_mask_head.ConvolutionalMaskHead(
          is_training=True,
          num_classes=20,
          use_dropout=True,
          dropout_keep_prob=0.5,
          kernel_size=3,
          conv_hyperparams=conv_hyperparams,
          freeze_batchnorm=False,
          num_predictions_per_location=1,
          use_depthwise=False,
          mask_height=7,
          mask_width=7)
      image_feature = tf.random_uniform(
          [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      mask_predictions = mask_prediction_head(image_feature)
      self.assertAllEqual([64, 323, 20, 7, 7],
                          mask_predictions.get_shape().as_list())
  
    def test_prediction_size_use_depthwise_true(self):
      conv_hyperparams = self._build_conv_hyperparams()
      mask_prediction_head = keras_mask_head.ConvolutionalMaskHead(
          is_training=True,
          num_classes=20,
          use_dropout=True,
          dropout_keep_prob=0.5,
          kernel_size=3,
          conv_hyperparams=conv_hyperparams,
          freeze_batchnorm=False,
          num_predictions_per_location=1,
          use_depthwise=True,
          mask_height=7,
          mask_width=7)
      image_feature = tf.random_uniform(
          [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      mask_predictions = mask_prediction_head(image_feature)
      self.assertAllEqual([64, 323, 20, 7, 7],
                          mask_predictions.get_shape().as_list())
  
    def test_class_agnostic_prediction_size_use_depthwise_false(self):
      conv_hyperparams = self._build_conv_hyperparams()
      mask_prediction_head = keras_mask_head.ConvolutionalMaskHead(
          is_training=True,
          num_classes=20,
          use_dropout=True,
          dropout_keep_prob=0.5,
          kernel_size=3,
          conv_hyperparams=conv_hyperparams,
          freeze_batchnorm=False,
          num_predictions_per_location=1,
          use_depthwise=False,
          mask_height=7,
          mask_width=7,
          masks_are_class_agnostic=True)
      image_feature = tf.random_uniform(
          [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      mask_predictions = mask_prediction_head(image_feature)
      self.assertAllEqual([64, 323, 1, 7, 7],
                          mask_predictions.get_shape().as_list())
  
    def test_class_agnostic_prediction_size_use_depthwise_true(self):
      conv_hyperparams = self._build_conv_hyperparams()
      mask_prediction_head = keras_mask_head.ConvolutionalMaskHead(
          is_training=True,
          num_classes=20,
          use_dropout=True,
          dropout_keep_prob=0.5,
          kernel_size=3,
          conv_hyperparams=conv_hyperparams,
          freeze_batchnorm=False,
          num_predictions_per_location=1,
          use_depthwise=True,
          mask_height=7,
          mask_width=7,
          masks_are_class_agnostic=True)
      image_feature = tf.random_uniform(
          [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      mask_predictions = mask_prediction_head(image_feature)
      self.assertAllEqual([64, 323, 1, 7, 7],
                          mask_predictions.get_shape().as_list())
  
  
  class MaskRCNNMaskHeadTest(test_case.TestCase):
  
    def _build_conv_hyperparams(self,
                                op_type=hyperparams_pb2.Hyperparams.CONV):
      hyperparams = hyperparams_pb2.Hyperparams()
      hyperparams_text_proto = """
        activation: NONE
        regularizer {
          l2_regularizer {
          }
        }
        initializer {
          truncated_normal_initializer {
          }
        }
      """
      text_format.Merge(hyperparams_text_proto, hyperparams)
      hyperparams.op = op_type
      return hyperparams_builder.KerasLayerHyperparams(hyperparams)
  
    def test_prediction_size(self):
      mask_prediction_head = keras_mask_head.MaskRCNNMaskHead(
          is_training=True,
          num_classes=20,
          conv_hyperparams=self._build_conv_hyperparams(),
          freeze_batchnorm=False,
          mask_height=14,
          mask_width=14,
          mask_prediction_num_conv_layers=2,
          mask_prediction_conv_depth=256,
          masks_are_class_agnostic=False)
      roi_pooled_features = tf.random_uniform(
          [64, 7, 7, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      prediction = mask_prediction_head(roi_pooled_features)
      self.assertAllEqual([64, 1, 20, 14, 14], prediction.get_shape().as_list())
  
    def test_prediction_size_with_convolve_then_upsample(self):
      mask_prediction_head = keras_mask_head.MaskRCNNMaskHead(
          is_training=True,
          num_classes=20,
          conv_hyperparams=self._build_conv_hyperparams(),
          freeze_batchnorm=False,
          mask_height=28,
          mask_width=28,
          mask_prediction_num_conv_layers=2,
          mask_prediction_conv_depth=256,
          masks_are_class_agnostic=True,
          convolve_then_upsample=True)
      roi_pooled_features = tf.random_uniform(
          [64, 14, 14, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      prediction = mask_prediction_head(roi_pooled_features)
      self.assertAllEqual([64, 1, 1, 28, 28], prediction.get_shape().as_list())
  
  
  class WeightSharedConvolutionalMaskPredictorTest(test_case.TestCase):
  
    def _build_conv_hyperparams(self):
      conv_hyperparams = hyperparams_pb2.Hyperparams()
      conv_hyperparams_text_proto = """
      activation: NONE
        regularizer {
          l2_regularizer {
          }
        }
        initializer {
          truncated_normal_initializer {
          }
        }
      """
      text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
      return hyperparams_builder.KerasLayerHyperparams(conv_hyperparams)
  
    def test_prediction_size(self):
      mask_prediction_head = (
          keras_mask_head.WeightSharedConvolutionalMaskHead(
              num_classes=20,
              num_predictions_per_location=1,
              conv_hyperparams=self._build_conv_hyperparams(),
              mask_height=7,
              mask_width=7))
      image_feature = tf.random_uniform(
          [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      mask_predictions = mask_prediction_head(image_feature)
      self.assertAllEqual([64, 323, 20, 7, 7],
                          mask_predictions.get_shape().as_list())
  
    def test_class_agnostic_prediction_size(self):
      mask_prediction_head = (
          keras_mask_head.WeightSharedConvolutionalMaskHead(
              num_classes=20,
              num_predictions_per_location=1,
              conv_hyperparams=self._build_conv_hyperparams(),
              mask_height=7,
              mask_width=7,
              masks_are_class_agnostic=True))
      image_feature = tf.random_uniform(
          [64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
      mask_predictions = mask_prediction_head(image_feature)
      self.assertAllEqual([64, 323, 1, 7, 7],
                          mask_predictions.get_shape().as_list())
  
  if __name__ == '__main__':
    tf.test.main()
  