text: # Copyright 2017 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.models.faster_rcnn_resnet_v1_feature_extractor."""
  
  import numpy as np
  import tensorflow as tf
  
  from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as faster_rcnn_resnet_v1
  
  
  class FasterRcnnResnetV1FeatureExtractorTest(tf.test.TestCase):
  
    def _build_feature_extractor(self,
                                 first_stage_features_stride,
                                 architecture='resnet_v1_101'):
      feature_extractor_map = {
          'resnet_v1_50':
              faster_rcnn_resnet_v1.FasterRCNNResnet50FeatureExtractor,
          'resnet_v1_101':
              faster_rcnn_resnet_v1.FasterRCNNResnet101FeatureExtractor,
          'resnet_v1_152':
              faster_rcnn_resnet_v1.FasterRCNNResnet152FeatureExtractor
      }
      return feature_extractor_map[architecture](
          is_training=False,
          first_stage_features_stride=first_stage_features_stride,
          reuse_weights=None,
          weight_decay=0.0)
  
    def test_extract_proposal_features_returns_expected_size(self):
      for architecture in ['resnet_v1_50', 'resnet_v1_101', 'resnet_v1_152']:
        feature_extractor = self._build_feature_extractor(
            first_stage_features_stride=16, architecture=architecture)
        # preprocessed_inputs = tf.random_uniform(
        #     [4, 224, 224, 3], maxval=255, dtype=tf.float32)
        #################################################################################
        preprocessed_inputs = tf.random_uniform(
            [4, 224, 224, 1], maxval=255, dtype=tf.float32)
        rpn_feature_map = feature_extractor.extract_proposal_features(
            preprocessed_inputs, scope='TestScope')
        features_shape = tf.shape(rpn_feature_map)
  
        init_op = tf.global_variables_initializer()
        with self.test_session() as sess:
          sess.run(init_op)
          features_shape_out = sess.run(features_shape)
          self.assertAllEqual(features_shape_out, [4, 14, 14, 1024])
  
    def test_extract_proposal_features_stride_eight(self):
      feature_extractor = self._build_feature_extractor(
          first_stage_features_stride=8)
      # preprocessed_inputs = tf.random_uniform(
      #     [4, 224, 224, 3], maxval=255, dtype=tf.float32)
      ##########################################################################
      preprocessed_inputs = tf.random_uniform(
          [4, 224, 224, 1], maxval=255, dtype=tf.float32)
      rpn_feature_map = feature_extractor.extract_proposal_features(
          preprocessed_inputs, scope='TestScope')
      features_shape = tf.shape(rpn_feature_map)
  
      init_op = tf.global_variables_initializer()
      with self.test_session() as sess:
        sess.run(init_op)
        features_shape_out = sess.run(features_shape)
        self.assertAllEqual(features_shape_out, [4, 28, 28, 1024])
  
    def test_extract_proposal_features_half_size_input(self):
      feature_extractor = self._build_feature_extractor(
          first_stage_features_stride=16)
      # preprocessed_inputs = tf.random_uniform(
      #     [1, 112, 112, 3], maxval=255, dtype=tf.float32)
      ##################################################################
      preprocessed_inputs = tf.random_uniform(
          [1, 112, 112, 1], maxval=255, dtype=tf.float32)
      rpn_feature_map = feature_extractor.extract_proposal_features(
          preprocessed_inputs, scope='TestScope')
      features_shape = tf.shape(rpn_feature_map)
  
      init_op = tf.global_variables_initializer()
      with self.test_session() as sess:
        sess.run(init_op)
        features_shape_out = sess.run(features_shape)
        self.assertAllEqual(features_shape_out, [1, 7, 7, 1024])
  
    def test_extract_proposal_features_dies_on_invalid_stride(self):
      with self.assertRaises(ValueError):
        self._build_feature_extractor(first_stage_features_stride=99)
  
    def test_extract_proposal_features_dies_on_very_small_images(self):
      feature_extractor = self._build_feature_extractor(
          first_stage_features_stride=16)
      # preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 3))
      #############################################################################
      preprocessed_inputs = tf.placeholder(tf.float32, (4, None, None, 1))
      rpn_feature_map = feature_extractor.extract_proposal_features(
          preprocessed_inputs, scope='TestScope')
      features_shape = tf.shape(rpn_feature_map)
  
      init_op = tf.global_variables_initializer()
      with self.test_session() as sess:
        sess.run(init_op)
        with self.assertRaises(tf.errors.InvalidArgumentError):
          sess.run(
              features_shape,
              # feed_dict={preprocessed_inputs: np.random.rand(4, 32, 32, 3)})
              ####################################################################
              feed_dict={preprocessed_inputs: np.random.rand(4, 32, 32, 1)})
  
    def test_extract_proposal_features_dies_with_incorrect_rank_inputs(self):
      feature_extractor = self._build_feature_extractor(
          first_stage_features_stride=16)
      # preprocessed_inputs = tf.random_uniform(
      #     [224, 224, 3], maxval=255, dtype=tf.float32)
      ####################################################################
      preprocessed_inputs = tf.random_uniform(
          [224, 224, 1], maxval=255, dtype=tf.float32)
      with self.assertRaises(ValueError):
        feature_extractor.extract_proposal_features(
            preprocessed_inputs, scope='TestScope')
  
    def test_extract_box_classifier_features_returns_expected_size(self):
      feature_extractor = self._build_feature_extractor(
          first_stage_features_stride=16)
      proposal_feature_maps = tf.random_uniform(
          [3, 7, 7, 1024], maxval=255, dtype=tf.float32)
      proposal_classifier_features = (
          feature_extractor.extract_box_classifier_features(
              proposal_feature_maps, scope='TestScope'))
      features_shape = tf.shape(proposal_classifier_features)
  
      init_op = tf.global_variables_initializer()
      with self.test_session() as sess:
        sess.run(init_op)
        features_shape_out = sess.run(features_shape)
        # self.assertAllEqual(features_shape_out, [3, 7, 7, 2048])
        ####################################################################
        self.assertAllEqual(features_shape_out, [1, 7, 7, 2048])
  
  
  if __name__ == '__main__':
    tf.test.main()
  