Python google.protobuf.text_format.Merge() Examples

def get_configs_from_pipeline_file():
  """Reads evaluation configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads evaluation config from file specified by pipeline_config_path flag.

  Returns:
    model_config: a model_pb2.DetectionModel
    eval_config: a eval_pb2.EvalConfig
    input_config: a input_reader_pb2.InputReader
  """
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
    text_format.Merge(f.read(), pipeline_config)

  model_config = pipeline_config.model
  if FLAGS.eval_training_data:
    eval_config = pipeline_config.train_config
  else:
    eval_config = pipeline_config.eval_config
  input_config = pipeline_config.eval_input_reader

  return model_config, eval_config, input_config 

def test_build_non_max_suppressor_with_correct_parameters(self):
    post_processing_text_proto = """
      batch_non_max_suppression {
        score_threshold: 0.7
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 300
      }
    """
    post_processing_config = post_processing_pb2.PostProcessing()
    text_format.Merge(post_processing_text_proto, post_processing_config)
    non_max_suppressor, _ = post_processing_builder.build(
        post_processing_config)
    self.assertEqual(non_max_suppressor.keywords['max_size_per_class'], 100)
    self.assertEqual(non_max_suppressor.keywords['max_total_size'], 300)
    self.assertAlmostEqual(non_max_suppressor.keywords['score_thresh'], 0.7)
    self.assertAlmostEqual(non_max_suppressor.keywords['iou_thresh'], 0.6) 

def test_build_normalize_image(self):
    preprocessor_text_proto = """
    normalize_image {
      original_minval: 0.0
      original_maxval: 255.0
      target_minval: -1.0
      target_maxval: 1.0
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.normalize_image)
    self.assertEqual(args, {
        'original_minval': 0.0,
        'original_maxval': 255.0,
        'target_minval': -1.0,
        'target_maxval': 1.0,
    }) 

def test_explicit_fc_op_arg_scope_has_fully_connected_op(self):
    conv_hyperparams_text_proto = """
      op: FC
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    self.assertTrue(self._get_scope_key(slim.fully_connected) in scope) 

def test_separable_conv2d_and_conv2d_and_transpose_have_same_parameters(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    kwargs_1, kwargs_2, kwargs_3 = scope.values()
    self.assertDictEqual(kwargs_1, kwargs_2)
    self.assertDictEqual(kwargs_1, kwargs_3) 

def test_return_l2_regularizer_weights(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
          weight: 0.42
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]

    regularizer = conv_scope_arguments['weights_regularizer']
    weights = np.array([1., -1, 4., 2.])
    with self.test_session() as sess:
      result = sess.run(regularizer(tf.constant(weights)))
    self.assertAllClose(np.power(weights, 2).sum() / 2.0 * 0.42, result) 

def test_do_not_use_batch_norm_if_default(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    self.assertEqual(conv_scope_arguments['normalizer_fn'], None)
    self.assertEqual(conv_scope_arguments['normalizer_params'], None) 

def test_use_relu_activation(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    self.assertEqual(conv_scope_arguments['activation_fn'], tf.nn.relu) 

def test_use_relu_6_activation(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    self.assertEqual(conv_scope_arguments['activation_fn'], tf.nn.relu6) 

def test_variance_in_range_with_variance_scaling_initializer_fan_in(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        variance_scaling_initializer {
          factor: 2.0
          mode: FAN_IN
          uniform: false
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    initializer = conv_scope_arguments['weights_initializer']
    self._assert_variance_in_range(initializer, shape=[100, 40],
                                   variance=2. / 100.) 

def test_variance_in_range_with_variance_scaling_initializer_fan_out(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        variance_scaling_initializer {
          factor: 2.0
          mode: FAN_OUT
          uniform: false
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    initializer = conv_scope_arguments['weights_initializer']
    self._assert_variance_in_range(initializer, shape=[100, 40],
                                   variance=2. / 40.) 

def test_variance_in_range_with_variance_scaling_initializer_fan_avg(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        variance_scaling_initializer {
          factor: 2.0
          mode: FAN_AVG
          uniform: false
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    initializer = conv_scope_arguments['weights_initializer']
    self._assert_variance_in_range(initializer, shape=[100, 40],
                                   variance=4. / (100. + 40.)) 

def test_variance_in_range_with_truncated_normal_initializer(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
          mean: 0.0
          stddev: 0.8
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    initializer = conv_scope_arguments['weights_initializer']
    self._assert_variance_in_range(initializer, shape=[100, 40],
                                   variance=0.49, tol=1e-1) 

def testBuildManualStepLearningRate(self):
    learning_rate_text_proto = """
      manual_step_learning_rate {
        schedule {
          step: 0
          learning_rate: 0.006
        }
        schedule {
          step: 90000
          learning_rate: 0.00006
        }
      }
    """
    global_summaries = set([])
    learning_rate_proto = optimizer_pb2.LearningRate()
    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
    learning_rate = optimizer_builder._create_learning_rate(
        learning_rate_proto, global_summaries)
    self.assertTrue(isinstance(learning_rate, tf.Tensor)) 

def testBuildMomentumOptimizer(self):
    optimizer_text_proto = """
      momentum_optimizer: {
        learning_rate: {
          constant_learning_rate {
            learning_rate: 0.001
          }
        }
        momentum_optimizer_value: 0.99
      }
      use_moving_average: false
    """
    global_summaries = set([])
    optimizer_proto = optimizer_pb2.Optimizer()
    text_format.Merge(optimizer_text_proto, optimizer_proto)
    optimizer = optimizer_builder.build(optimizer_proto, global_summaries)
    self.assertTrue(isinstance(optimizer, tf.train.MomentumOptimizer)) 

def testBuildAdamOptimizer(self):
    optimizer_text_proto = """
      adam_optimizer: {
        learning_rate: {
          constant_learning_rate {
            learning_rate: 0.002
          }
        }
      }
      use_moving_average: false
    """
    global_summaries = set([])
    optimizer_proto = optimizer_pb2.Optimizer()
    text_format.Merge(optimizer_text_proto, optimizer_proto)
    optimizer = optimizer_builder.build(optimizer_proto, global_summaries)
    self.assertTrue(isinstance(optimizer, tf.train.AdamOptimizer)) 

def testBuildMovingAverageOptimizer(self):
    optimizer_text_proto = """
      adam_optimizer: {
        learning_rate: {
          constant_learning_rate {
            learning_rate: 0.002
          }
        }
      }
      use_moving_average: True
    """
    global_summaries = set([])
    optimizer_proto = optimizer_pb2.Optimizer()
    text_format.Merge(optimizer_text_proto, optimizer_proto)
    optimizer = optimizer_builder.build(optimizer_proto, global_summaries)
    self.assertTrue(
        isinstance(optimizer, tf.contrib.opt.MovingAverageOptimizer)) 

def testBuildMovingAverageOptimizerWithNonDefaultDecay(self):
    optimizer_text_proto = """
      adam_optimizer: {
        learning_rate: {
          constant_learning_rate {
            learning_rate: 0.002
          }
        }
      }
      use_moving_average: True
      moving_average_decay: 0.2
    """
    global_summaries = set([])
    optimizer_proto = optimizer_pb2.Optimizer()
    text_format.Merge(optimizer_text_proto, optimizer_proto)
    optimizer = optimizer_builder.build(optimizer_proto, global_summaries)
    self.assertTrue(
        isinstance(optimizer, tf.contrib.opt.MovingAverageOptimizer))
    # TODO: Find a way to not depend on the private members.
    self.assertAlmostEqual(optimizer._ema._decay, 0.2) 

def test_build_grid_anchor_generator_with_defaults(self):
    anchor_generator_text_proto = """
      grid_anchor_generator {
      }
     """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               grid_anchor_generator.GridAnchorGenerator))
    self.assertListEqual(anchor_generator_object._scales, [])
    self.assertListEqual(anchor_generator_object._aspect_ratios, [])
    with self.test_session() as sess:
      base_anchor_size, anchor_offset, anchor_stride = sess.run(
          [anchor_generator_object._base_anchor_size,
           anchor_generator_object._anchor_offset,
           anchor_generator_object._anchor_stride])
    self.assertAllEqual(anchor_offset, [0, 0])
    self.assertAllEqual(anchor_stride, [16, 16])
    self.assertAllEqual(base_anchor_size, [256, 256]) 

def read_prototxt(fname):
    """Return a caffe_pb2.NetParameter object that defined in a prototxt file
    """
    proto = caffe_pb2.NetParameter()
    with open(fname, 'r') as f:
        text_format.Merge(str(f.read()), proto)
    return proto 

def RewriteContext():
  context = task_spec_pb2.TaskSpec()
  with gfile.FastGFile(FLAGS.task_context, 'rb') as fin:
    text_format.Merge(fin.read(), context)
  for resource in context.input:
    if resource.creator == StageName():
      del resource.part[:]
      part = resource.part.add()
      part.file_pattern = os.path.join(OutputPath(resource.name))
  with gfile.FastGFile(OutputPath('context'), 'w') as fout:
    fout.write(str(context)) 

def RewriteContext(task_context):
  context = task_spec_pb2.TaskSpec()
  with gfile.FastGFile(task_context, 'rb') as fin:
    text_format.Merge(fin.read(), context)
  for resource in context.input:
    for part in resource.part:
      if part.file_pattern != '-':
        part.file_pattern = os.path.join(FLAGS.resource_dir, part.file_pattern)
  with tempfile.NamedTemporaryFile(delete=False) as fout:
    fout.write(str(context))
    return fout.name 

def load_labelmap(path):
  """Loads label map proto.

  Args:
    path: path to StringIntLabelMap proto text file.
  Returns:
    a StringIntLabelMapProto
  """
  with tf.gfile.GFile(path, 'r') as fid:
    label_map_string = fid.read()
    label_map = string_int_label_map_pb2.StringIntLabelMap()
    try:
      text_format.Merge(label_map_string, label_map)
    except text_format.ParseError:
      label_map.ParseFromString(label_map_string)
  _validate_label_map(label_map)
  return label_map 

def _build_arg_scope_with_hyperparams(self,
                                        hyperparams_text_proto,
                                        is_training):
    hyperparams = hyperparams_pb2.Hyperparams()
    text_format.Merge(hyperparams_text_proto, hyperparams)
    return hyperparams_builder.build(hyperparams, is_training=is_training) 

def _get_second_stage_box_predictor(self, num_classes, is_training):
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(self._get_second_stage_box_predictor_text_proto(),
                      box_predictor_proto)
    return box_predictor_builder.build(
        hyperparams_builder.build,
        box_predictor_proto,
        num_classes=num_classes,
        is_training=is_training) 

def main(_):
  assert FLAGS.pipeline_config_path, 'TrainEvalPipelineConfig missing.'
  assert FLAGS.inference_graph_path, 'Inference graph path missing.'
  assert FLAGS.input_type, 'Input type missing.'
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
    text_format.Merge(f.read(), pipeline_config)
  exporter.export_inference_graph(FLAGS.input_type, pipeline_config,
                                  FLAGS.checkpoint_path,
                                  FLAGS.inference_graph_path,
                                  FLAGS.export_as_saved_model) 

def get_configs_from_multiple_files():
  """Reads evaluation configuration from multiple config files.

  Reads the evaluation config from the following files:
    model_config: Read from --model_config_path
    eval_config: Read from --eval_config_path
    input_config: Read from --input_config_path

  Returns:
    model_config: a model_pb2.DetectionModel
    eval_config: a eval_pb2.EvalConfig
    input_config: a input_reader_pb2.InputReader
  """
  eval_config = eval_pb2.EvalConfig()
  with tf.gfile.GFile(FLAGS.eval_config_path, 'r') as f:
    text_format.Merge(f.read(), eval_config)

  model_config = model_pb2.DetectionModel()
  with tf.gfile.GFile(FLAGS.model_config_path, 'r') as f:
    text_format.Merge(f.read(), model_config)

  input_config = input_reader_pb2.InputReader()
  with tf.gfile.GFile(FLAGS.input_config_path, 'r') as f:
    text_format.Merge(f.read(), input_config)

  return model_config, eval_config, input_config 

def _build_arg_scope_with_conv_hyperparams(self):
    conv_hyperparams = hyperparams_pb2.Hyperparams()
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
    return hyperparams_builder.build(conv_hyperparams, is_training=True) 

def _build_arg_scope_with_conv_hyperparams(self):
    conv_hyperparams = hyperparams_pb2.Hyperparams()
    conv_hyperparams_text_proto = """
      activation: RELU_6
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams)
    return hyperparams_builder.build(conv_hyperparams, is_training=True) 

def _shape_of_resized_random_image_given_text_proto(
      self, input_shape, text_proto):
    image_resizer_config = image_resizer_pb2.ImageResizer()
    text_format.Merge(text_proto, image_resizer_config)
    image_resizer_fn = image_resizer_builder.build(image_resizer_config)
    images = tf.to_float(tf.random_uniform(
        input_shape, minval=0, maxval=255, dtype=tf.int32))
    resized_images = image_resizer_fn(images)
    with self.test_session() as sess:
      return sess.run(resized_images).shape 

def test_build_arg_max_matcher_with_defaults(self):
    matcher_text_proto = """
      argmax_matcher {
      }
    """
    matcher_proto = matcher_pb2.Matcher()
    text_format.Merge(matcher_text_proto, matcher_proto)
    matcher_object = matcher_builder.build(matcher_proto)
    self.assertTrue(isinstance(matcher_object, argmax_matcher.ArgMaxMatcher))
    self.assertAlmostEqual(matcher_object._matched_threshold, 0.5)
    self.assertAlmostEqual(matcher_object._unmatched_threshold, 0.5)
    self.assertTrue(matcher_object._negatives_lower_than_unmatched)
    self.assertFalse(matcher_object._force_match_for_each_row) 

def test_build_arg_max_matcher_without_thresholds(self):
    matcher_text_proto = """
      argmax_matcher {
        ignore_thresholds: true
      }
    """
    matcher_proto = matcher_pb2.Matcher()
    text_format.Merge(matcher_text_proto, matcher_proto)
    matcher_object = matcher_builder.build(matcher_proto)
    self.assertTrue(isinstance(matcher_object, argmax_matcher.ArgMaxMatcher))
    self.assertEqual(matcher_object._matched_threshold, None)
    self.assertEqual(matcher_object._unmatched_threshold, None)
    self.assertTrue(matcher_object._negatives_lower_than_unmatched)
    self.assertFalse(matcher_object._force_match_for_each_row) 

def test_build_bipartite_matcher(self):
    matcher_text_proto = """
      bipartite_matcher {
      }
    """
    matcher_proto = matcher_pb2.Matcher()
    text_format.Merge(matcher_text_proto, matcher_proto)
    matcher_object = matcher_builder.build(matcher_proto)
    self.assertTrue(
        isinstance(matcher_object, bipartite_matcher.GreedyBipartiteMatcher)) 

def test_raise_error_on_empty_matcher(self):
    matcher_text_proto = """
    """
    matcher_proto = matcher_pb2.Matcher()
    text_format.Merge(matcher_text_proto, matcher_proto)
    with self.assertRaises(ValueError):
      matcher_builder.build(matcher_proto) 

def test_build_identity_score_converter(self):
    post_processing_text_proto = """
      score_converter: IDENTITY
    """
    post_processing_config = post_processing_pb2.PostProcessing()
    text_format.Merge(post_processing_text_proto, post_processing_config)
    _, score_converter = post_processing_builder.build(post_processing_config)
    self.assertEqual(score_converter, tf.identity) 

def test_build_sigmoid_score_converter(self):
    post_processing_text_proto = """
      score_converter: SIGMOID
    """
    post_processing_config = post_processing_pb2.PostProcessing()
    text_format.Merge(post_processing_text_proto, post_processing_config)
    _, score_converter = post_processing_builder.build(post_processing_config)
    self.assertEqual(score_converter, tf.sigmoid) 

def test_construct_default_conv_box_predictor(self):
    box_predictor_text_proto = """
      convolutional_box_predictor {
        conv_hyperparams {
          regularizer {
            l1_regularizer {
            }
          }
          initializer {
            truncated_normal_initializer {
            }
          }
        }
      }"""
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=hyperparams_builder.build,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertEqual(box_predictor._min_depth, 0)
    self.assertEqual(box_predictor._max_depth, 0)
    self.assertEqual(box_predictor._num_layers_before_predictor, 0)
    self.assertTrue(box_predictor._use_dropout)
    self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.8)
    self.assertFalse(box_predictor._apply_sigmoid_to_scores)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training) 

def test_box_predictor_builder_calls_fc_argscope_fn(self):
    fc_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
          weight: 0.0003
        }
      }
      initializer {
        truncated_normal_initializer {
          mean: 0.0
          stddev: 0.3
        }
      }
      activation: RELU_6
      op: FC
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
        hyperparams_proto)
    mock_argscope_fn = mock.Mock(return_value='arg_scope')
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_argscope_fn,
        box_predictor_config=box_predictor_proto,
        is_training=False,
        num_classes=10)
    mock_argscope_fn.assert_called_with(hyperparams_proto, False)
    self.assertEqual(box_predictor._fc_hyperparams, 'arg_scope') 

def test_non_default_mask_rcnn_box_predictor(self):
    fc_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
      op: FC
    """
    box_predictor_text_proto = """
      mask_rcnn_box_predictor {
        use_dropout: true
        dropout_keep_probability: 0.8
        box_code_size: 3
      }
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)
    def mock_fc_argscope_builder(fc_hyperparams_arg, is_training):
      return (fc_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_fc_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertTrue(box_predictor._use_dropout)
    self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.8)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 3) 

def test_non_default_rfcn_box_predictor(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
    """
    box_predictor_text_proto = """
      rfcn_box_predictor {
        num_spatial_bins_height: 4
        num_spatial_bins_width: 4
        depth: 4
        box_code_size: 3
        crop_height: 16
        crop_width: 16
      }
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
      return (conv_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_conv_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 3)
    self.assertEqual(box_predictor._num_spatial_bins, [4, 4])
    self.assertEqual(box_predictor._crop_size, [16, 16]) 

def test_default_rfcn_box_predictor(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
      return (conv_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_conv_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 4)
    self.assertEqual(box_predictor._num_spatial_bins, [3, 3])
    self.assertEqual(box_predictor._crop_size, [12, 12]) 

def testBuildIoaSimilarityCalculator(self):
    similarity_calc_text_proto = """
      ioa_similarity {
      }
    """
    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
    similarity_calc = region_similarity_calculator_builder.build(
        similarity_calc_proto)
    self.assertTrue(isinstance(similarity_calc,
                               region_similarity_calculator.IoaSimilarity)) 

def testBuildIouSimilarityCalculator(self):
    similarity_calc_text_proto = """
      iou_similarity {
      }
    """
    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
    similarity_calc = region_similarity_calculator_builder.build(
        similarity_calc_proto)
    self.assertTrue(isinstance(similarity_calc,
                               region_similarity_calculator.IouSimilarity)) 

def test_build_random_horizontal_flip(self):
    preprocessor_text_proto = """
    random_horizontal_flip {
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_horizontal_flip)
    self.assertEqual(args, {}) 

def test_build_random_pixel_value_scale(self):
    preprocessor_text_proto = """
    random_pixel_value_scale {
      minval: 0.8
      maxval: 1.2
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_pixel_value_scale)
    self.assert_dictionary_close(args, {'minval': 0.8, 'maxval': 1.2}) 

def test_build_random_image_scale(self):
    preprocessor_text_proto = """
    random_image_scale {
      min_scale_ratio: 0.8
      max_scale_ratio: 2.2
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_image_scale)
    self.assert_dictionary_close(args, {'min_scale_ratio': 0.8,
                                        'max_scale_ratio': 2.2}) 

def test_build_random_rgb_to_gray(self):
    preprocessor_text_proto = """
    random_rgb_to_gray {
      probability: 0.8
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_rgb_to_gray)
    self.assert_dictionary_close(args, {'probability': 0.8}) 

def test_build_random_adjust_contrast(self):
    preprocessor_text_proto = """
    random_adjust_contrast {
      min_delta: 0.7
      max_delta: 1.1
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_adjust_contrast)
    self.assert_dictionary_close(args, {'min_delta': 0.7, 'max_delta': 1.1}) 

def test_build_random_adjust_hue(self):
    preprocessor_text_proto = """
    random_adjust_hue {
      max_delta: 0.01
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_adjust_hue)
    self.assert_dictionary_close(args, {'max_delta': 0.01}) 

def test_build_random_adjust_saturation(self):
    preprocessor_text_proto = """
    random_adjust_saturation {
      min_delta: 0.75
      max_delta: 1.15
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_adjust_saturation)
    self.assert_dictionary_close(args, {'min_delta': 0.75, 'max_delta': 1.15}) 

def test_build_random_distort_color(self):
    preprocessor_text_proto = """
    random_distort_color {
      color_ordering: 1
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_distort_color)
    self.assertEqual(args, {'color_ordering': 1}) 

def test_build_random_jitter_boxes(self):
    preprocessor_text_proto = """
    random_jitter_boxes {
      ratio: 0.1
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_jitter_boxes)
    self.assert_dictionary_close(args, {'ratio': 0.1}) 

def test_build_random_pad_image(self):
    preprocessor_text_proto = """
    random_pad_image {
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_pad_image)
    self.assertEqual(args, {
        'min_image_size': None,
        'max_image_size': None,
        'pad_color': None,
    }) 

def test_build_random_crop_pad_image(self):
    preprocessor_text_proto = """
    random_crop_pad_image {
      min_object_covered: 0.75
      min_aspect_ratio: 0.75
      max_aspect_ratio: 1.5
      min_area: 0.25
      max_area: 0.875
      overlap_thresh: 0.5
      random_coef: 0.125
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_crop_pad_image)
    self.assertEqual(args, {
        'min_object_covered': 0.75,
        'aspect_ratio_range': (0.75, 1.5),
        'area_range': (0.25, 0.875),
        'overlap_thresh': 0.5,
        'random_coef': 0.125,
        'min_padded_size_ratio': None,
        'max_padded_size_ratio': None,
        'pad_color': None,
    }) 

def test_build_random_crop_to_aspect_ratio(self):
    preprocessor_text_proto = """
    random_crop_to_aspect_ratio {
      aspect_ratio: 0.85
      overlap_thresh: 0.35
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_crop_to_aspect_ratio)
    self.assert_dictionary_close(args, {'aspect_ratio': 0.85,
                                        'overlap_thresh': 0.35}) 

def test_build_random_black_patches(self):
    preprocessor_text_proto = """
    random_black_patches {
      max_black_patches: 20
      probability: 0.95
      size_to_image_ratio: 0.12
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_black_patches)
    self.assert_dictionary_close(args, {'max_black_patches': 20,
                                        'probability': 0.95,
                                        'size_to_image_ratio': 0.12}) 

def test_build_random_resize_method(self):
    preprocessor_text_proto = """
    random_resize_method {
      target_height: 75
      target_width: 100
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.random_resize_method)
    self.assert_dictionary_close(args, {'target_size': [75, 100]}) 

def test_build_resize_image(self):
    preprocessor_text_proto = """
    resize_image {
      new_height: 75
      new_width: 100
      method: BICUBIC
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.resize_image)
    self.assertEqual(args, {'new_height': 75,
                            'new_width': 100,
                            'method': tf.image.ResizeMethod.BICUBIC}) 

def test_build_subtract_channel_mean(self):
    preprocessor_text_proto = """
    subtract_channel_mean {
      means: [1.0, 2.0, 3.0]
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.subtract_channel_mean)
    self.assertEqual(args, {'means': [1.0, 2.0, 3.0]}) 

def test_build_ssd_random_crop(self):
    preprocessor_text_proto = """
    ssd_random_crop {
      operations {
        min_object_covered: 0.0
        min_aspect_ratio: 0.875
        max_aspect_ratio: 1.125
        min_area: 0.5
        max_area: 1.0
        overlap_thresh: 0.0
        random_coef: 0.375
      }
      operations {
        min_object_covered: 0.25
        min_aspect_ratio: 0.75
        max_aspect_ratio: 1.5
        min_area: 0.5
        max_area: 1.0
        overlap_thresh: 0.25
        random_coef: 0.375
      }
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.ssd_random_crop)
    self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
                            'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
                            'area_range': [(0.5, 1.0), (0.5, 1.0)],
                            'overlap_thresh': [0.0, 0.25],
                            'random_coef': [0.375, 0.375]}) 

def test_build_ssd_random_crop_empty_operations(self):
    preprocessor_text_proto = """
    ssd_random_crop {
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.ssd_random_crop)
    self.assertEqual(args, {}) 

def test_default_arg_scope_has_conv2d_op(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    self.assertTrue(self._get_scope_key(slim.conv2d) in scope) 

def test_default_arg_scope_has_separable_conv2d_op(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    self.assertTrue(self._get_scope_key(slim.separable_conv2d) in scope) 

def test_default_arg_scope_has_conv2d_transpose_op(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    self.assertTrue(self._get_scope_key(slim.conv2d_transpose) in scope) 

def test_return_non_default_batch_norm_params_with_train_during_train(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      batch_norm {
        decay: 0.7
        center: false
        scale: true
        epsilon: 0.03
        train: true
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    self.assertEqual(conv_scope_arguments['normalizer_fn'], slim.batch_norm)
    batch_norm_params = conv_scope_arguments['normalizer_params']
    self.assertAlmostEqual(batch_norm_params['decay'], 0.7)
    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
    self.assertFalse(batch_norm_params['center'])
    self.assertTrue(batch_norm_params['scale'])
    self.assertTrue(batch_norm_params['is_training']) 

def test_return_batch_norm_params_with_notrain_during_eval(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      batch_norm {
        decay: 0.7
        center: false
        scale: true
        epsilon: 0.03
        train: true
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=False)
    conv_scope_arguments = scope.values()[0]
    self.assertEqual(conv_scope_arguments['normalizer_fn'], slim.batch_norm)
    batch_norm_params = conv_scope_arguments['normalizer_params']
    self.assertAlmostEqual(batch_norm_params['decay'], 0.7)
    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
    self.assertFalse(batch_norm_params['center'])
    self.assertTrue(batch_norm_params['scale'])
    self.assertFalse(batch_norm_params['is_training']) 

def test_return_batch_norm_params_with_notrain_when_train_is_false(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l2_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      batch_norm {
        decay: 0.7
        center: false
        scale: true
        epsilon: 0.03
        train: false
      }
    """
    conv_hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, conv_hyperparams_proto)
    scope = hyperparams_builder.build(conv_hyperparams_proto, is_training=True)
    conv_scope_arguments = scope.values()[0]
    self.assertEqual(conv_scope_arguments['normalizer_fn'], slim.batch_norm)
    batch_norm_params = conv_scope_arguments['normalizer_params']
    self.assertAlmostEqual(batch_norm_params['decay'], 0.7)
    self.assertAlmostEqual(batch_norm_params['epsilon'], 0.03)
    self.assertFalse(batch_norm_params['center'])
    self.assertTrue(batch_norm_params['scale'])
    self.assertFalse(batch_norm_params['is_training']) 

def testBuildConstantLearningRate(self):
    learning_rate_text_proto = """
      constant_learning_rate {
        learning_rate: 0.004
      }
    """
    global_summaries = set([])
    learning_rate_proto = optimizer_pb2.LearningRate()
    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
    learning_rate = optimizer_builder._create_learning_rate(
        learning_rate_proto, global_summaries)
    self.assertAlmostEqual(learning_rate, 0.004) 

def testBuildExponentialDecayLearningRate(self):
    learning_rate_text_proto = """
      exponential_decay_learning_rate {
        initial_learning_rate: 0.004
        decay_steps: 99999
        decay_factor: 0.85
        staircase: false
      }
    """
    global_summaries = set([])
    learning_rate_proto = optimizer_pb2.LearningRate()
    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
    learning_rate = optimizer_builder._create_learning_rate(
        learning_rate_proto, global_summaries)
    self.assertTrue(isinstance(learning_rate, tf.Tensor)) 

def testRaiseErrorOnEmptyLearningRate(self):
    learning_rate_text_proto = """
    """
    global_summaries = set([])
    learning_rate_proto = optimizer_pb2.LearningRate()
    text_format.Merge(learning_rate_text_proto, learning_rate_proto)
    with self.assertRaises(ValueError):
      optimizer_builder._create_learning_rate(
          learning_rate_proto, global_summaries) 

def testBuildEmptyOptimizer(self):
    optimizer_text_proto = """
    """
    global_summaries = set([])
    optimizer_proto = optimizer_pb2.Optimizer()
    text_format.Merge(optimizer_text_proto, optimizer_proto)
    with self.assertRaises(ValueError):
      optimizer_builder.build(optimizer_proto, global_summaries) 

def test_build_grid_anchor_generator_with_non_default_parameters(self):
    anchor_generator_text_proto = """
      grid_anchor_generator {
        height: 128
        width: 512
        height_stride: 10
        width_stride: 20
        height_offset: 30
        width_offset: 40
        scales: [0.4, 2.2]
        aspect_ratios: [0.3, 4.5]
      }
     """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               grid_anchor_generator.GridAnchorGenerator))
    self.assert_almost_list_equal(anchor_generator_object._scales,
                                  [0.4, 2.2])
    self.assert_almost_list_equal(anchor_generator_object._aspect_ratios,
                                  [0.3, 4.5])
    with self.test_session() as sess:
      base_anchor_size, anchor_offset, anchor_stride = sess.run(
          [anchor_generator_object._base_anchor_size,
           anchor_generator_object._anchor_offset,
           anchor_generator_object._anchor_stride])
    self.assertAllEqual(anchor_offset, [30, 40])
    self.assertAllEqual(anchor_stride, [10, 20])
    self.assertAllEqual(base_anchor_size, [128, 512]) 

def test_build_ssd_anchor_generator_with_defaults(self):
    anchor_generator_text_proto = """
      ssd_anchor_generator {
        aspect_ratios: [1.0]
      }
    """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               multiple_grid_anchor_generator.
                               MultipleGridAnchorGenerator))
    for actual_scales, expected_scales in zip(
        list(anchor_generator_object._scales),
        [(0.1, 0.2, 0.2),
         (0.35, 0.418),
         (0.499, 0.570),
         (0.649, 0.721),
         (0.799, 0.871),
         (0.949, 0.974)]):
      self.assert_almost_list_equal(expected_scales, actual_scales, delta=1e-2)
    for actual_aspect_ratio, expected_aspect_ratio in zip(
        list(anchor_generator_object._aspect_ratios),
        [(1.0, 2.0, 0.5)] + 5 * [(1.0, 1.0)]):
      self.assert_almost_list_equal(expected_aspect_ratio, actual_aspect_ratio)

    with self.test_session() as sess:
      base_anchor_size = sess.run(anchor_generator_object._base_anchor_size)
    self.assertAllClose(base_anchor_size, [1.0, 1.0]) 

def test_build_ssd_anchor_generator_withoud_reduced_boxes(self):
    anchor_generator_text_proto = """
      ssd_anchor_generator {
        aspect_ratios: [1.0]
        reduce_boxes_in_lowest_layer: false
      }
    """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               multiple_grid_anchor_generator.
                               MultipleGridAnchorGenerator))

    for actual_scales, expected_scales in zip(
        list(anchor_generator_object._scales),
        [(0.2, 0.264),
         (0.35, 0.418),
         (0.499, 0.570),
         (0.649, 0.721),
         (0.799, 0.871),
         (0.949, 0.974)]):
      self.assert_almost_list_equal(expected_scales, actual_scales, delta=1e-2)

    for actual_aspect_ratio, expected_aspect_ratio in zip(
        list(anchor_generator_object._aspect_ratios),
        6 * [(1.0, 1.0)]):
      self.assert_almost_list_equal(expected_aspect_ratio, actual_aspect_ratio)

    with self.test_session() as sess:
      base_anchor_size = sess.run(anchor_generator_object._base_anchor_size)
    self.assertAllClose(base_anchor_size, [1.0, 1.0]) 

def main(argv):
  del argv  # unused
  # Constructs lexical resources for SyntaxNet in the given resource path, from
  # the training data.
  lexicon.build_lexicon(
      lexicon_dir,
      training_sentence,
      training_corpus_format='sentence-prototext')

  # Construct the ComponentSpec for tagging. This is a simple left-to-right RNN
  # sequence tagger.
  tagger = spec_builder.ComponentSpecBuilder('tagger')
  tagger.set_network_unit(name='FeedForwardNetwork', hidden_layer_sizes='256')
  tagger.set_transition_system(name='tagger')
  tagger.add_fixed_feature(name='words', fml='input.word', embedding_dim=64)
  tagger.add_rnn_link(embedding_dim=-1)
  tagger.fill_from_resources(lexicon_dir)

  master_spec = spec_pb2.MasterSpec()
  master_spec.component.extend([tagger.spec])

  hyperparam_config = spec_pb2.GridPoint()

  # Build the TensorFlow graph.
  graph = tf.Graph()
  with graph.as_default():
    builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)

    target = spec_pb2.TrainTarget()
    target.name = 'all'
    target.unroll_using_oracle.extend([True])
    dry_run = builder.add_training_from_config(target, trace_only=True)

  # Read in serialized protos from training data.
  sentence = sentence_pb2.Sentence()
  text_format.Merge(open(training_sentence).read(), sentence)
  training_set = [sentence.SerializeToString()]

  with tf.Session(graph=graph) as sess:
    # Make sure to re-initialize all underlying state.
    sess.run(tf.initialize_all_variables())
    traces = sess.run(
        dry_run['traces'], feed_dict={dry_run['input_batch']: training_set})

  with open('dragnn_tutorial_1.html', 'w') as f:
    f.write(visualization.trace_html(traces[0], height='300px').encode('utf-8')) 

def _LoadModel(gd_file, ckpt_file):
  """Load the model from GraphDef and Checkpoint.

  Args:
    gd_file: GraphDef proto text file.
    ckpt_file: TensorFlow Checkpoint file.

  Returns:
    TensorFlow session and tensors dict.
  """
  with tf.Graph().as_default():
    sys.stderr.write('Recovering graph.\n')
    with tf.gfile.FastGFile(gd_file, 'r') as f:
      s = f.read().decode()
      gd = tf.GraphDef()
      text_format.Merge(s, gd)

    tf.logging.info('Recovering Graph %s', gd_file)
    t = {}
    [t['states_init'], t['lstm/lstm_0/control_dependency'],
     t['lstm/lstm_1/control_dependency'], t['softmax_out'], t['class_ids_out'],
     t['class_weights_out'], t['log_perplexity_out'], t['inputs_in'],
     t['targets_in'], t['target_weights_in'], t['char_inputs_in'],
     t['all_embs'], t['softmax_weights'], t['global_step']
    ] = tf.import_graph_def(gd, {}, ['states_init',
                                     'lstm/lstm_0/control_dependency:0',
                                     'lstm/lstm_1/control_dependency:0',
                                     'softmax_out:0',
                                     'class_ids_out:0',
                                     'class_weights_out:0',
                                     'log_perplexity_out:0',
                                     'inputs_in:0',
                                     'targets_in:0',
                                     'target_weights_in:0',
                                     'char_inputs_in:0',
                                     'all_embs_out:0',
                                     'Reshape_3:0',
                                     'global_step:0'], name='')

    sys.stderr.write('Recovering checkpoint %s\n' % ckpt_file)
    sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
    sess.run('save/restore_all', {'save/Const:0': ckpt_file})
    sess.run(t['states_init'])

  return sess, t 

def test_box_predictor_calls_conv_argscope_fn(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
          weight: 0.0003
        }
      }
      initializer {
        truncated_normal_initializer {
          mean: 0.0
          stddev: 0.3
        }
      }
      activation: RELU_6
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
      return (conv_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.convolutional_box_predictor.conv_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_conv_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=False,
        num_classes=10)
    (conv_hyperparams_actual, is_training) = box_predictor._conv_hyperparams
    self.assertAlmostEqual((hyperparams_proto.regularizer.
                            l1_regularizer.weight),
                           (conv_hyperparams_actual.regularizer.l1_regularizer.
                            weight))
    self.assertAlmostEqual((hyperparams_proto.initializer.
                            truncated_normal_initializer.stddev),
                           (conv_hyperparams_actual.initializer.
                            truncated_normal_initializer.stddev))
    self.assertAlmostEqual((hyperparams_proto.initializer.
                            truncated_normal_initializer.mean),
                           (conv_hyperparams_actual.initializer.
                            truncated_normal_initializer.mean))
    self.assertEqual(hyperparams_proto.activation,
                     conv_hyperparams_actual.activation)
    self.assertFalse(is_training) 

def test_construct_non_default_conv_box_predictor(self):
    box_predictor_text_proto = """
      convolutional_box_predictor {
        min_depth: 2
        max_depth: 16
        num_layers_before_predictor: 2
        use_dropout: false
        dropout_keep_probability: 0.4
        kernel_size: 3
        box_code_size: 3
        apply_sigmoid_to_scores: true
      }
    """
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
      return (conv_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor_proto.convolutional_box_predictor.conv_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_conv_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=False,
        num_classes=10)
    self.assertEqual(box_predictor._min_depth, 2)
    self.assertEqual(box_predictor._max_depth, 16)
    self.assertEqual(box_predictor._num_layers_before_predictor, 2)
    self.assertFalse(box_predictor._use_dropout)
    self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.4)
    self.assertTrue(box_predictor._apply_sigmoid_to_scores)
    self.assertEqual(box_predictor.num_classes, 10)
    self.assertFalse(box_predictor._is_training) 

def test_create_faster_rcnn_model_from_config_with_example_miner(self):
    model_text_proto = """
      faster_rcnn {
        num_classes: 3
        feature_extractor {
          type: 'faster_rcnn_inception_resnet_v2'
        }
        image_resizer {
          keep_aspect_ratio_resizer {
            min_dimension: 600
            max_dimension: 1024
          }
        }
        first_stage_anchor_generator {
          grid_anchor_generator {
            scales: [0.25, 0.5, 1.0, 2.0]
            aspect_ratios: [0.5, 1.0, 2.0]
            height_stride: 16
            width_stride: 16
          }
        }
        first_stage_box_predictor_conv_hyperparams {
          regularizer {
            l2_regularizer {
            }
          }
          initializer {
            truncated_normal_initializer {
            }
          }
        }
        second_stage_box_predictor {
          mask_rcnn_box_predictor {
            fc_hyperparams {
              op: FC
              regularizer {
                l2_regularizer {
                }
              }
              initializer {
                truncated_normal_initializer {
                }
              }
            }
          }
        }
        hard_example_miner {
          num_hard_examples: 10
          iou_threshold: 0.99
        }
      }"""
    model_proto = model_pb2.DetectionModel()
    text_format.Merge(model_text_proto, model_proto)
    model = model_builder.build(model_proto, is_training=True)
    self.assertIsNotNone(model._hard_example_miner) 

def test_build_ssd_random_crop_pad(self):
    preprocessor_text_proto = """
    ssd_random_crop_pad {
      operations {
        min_object_covered: 0.0
        min_aspect_ratio: 0.875
        max_aspect_ratio: 1.125
        min_area: 0.5
        max_area: 1.0
        overlap_thresh: 0.0
        random_coef: 0.375
        min_padded_size_ratio: [0.0, 0.0]
        max_padded_size_ratio: [2.0, 2.0]
        pad_color_r: 0.5
        pad_color_g: 0.5
        pad_color_b: 0.5
      }
      operations {
        min_object_covered: 0.25
        min_aspect_ratio: 0.75
        max_aspect_ratio: 1.5
        min_area: 0.5
        max_area: 1.0
        overlap_thresh: 0.25
        random_coef: 0.375
        min_padded_size_ratio: [0.0, 0.0]
        max_padded_size_ratio: [2.0, 2.0]
        pad_color_r: 0.5
        pad_color_g: 0.5
        pad_color_b: 0.5
      }
    }
    """
    preprocessor_proto = preprocessor_pb2.PreprocessingStep()
    text_format.Merge(preprocessor_text_proto, preprocessor_proto)
    function, args = preprocessor_builder.build(preprocessor_proto)
    self.assertEqual(function, preprocessor.ssd_random_crop_pad)
    self.assertEqual(args, {'min_object_covered': [0.0, 0.25],
                            'aspect_ratio_range': [(0.875, 1.125), (0.75, 1.5)],
                            'area_range': [(0.5, 1.0), (0.5, 1.0)],
                            'overlap_thresh': [0.0, 0.25],
                            'random_coef': [0.375, 0.375],
                            'min_padded_size_ratio': [(0.0, 0.0), (0.0, 0.0)],
                            'max_padded_size_ratio': [(2.0, 2.0), (2.0, 2.0)],
                            'pad_color': [(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)]})