Python object_detection.utils.config_util.merge_external_params_with_configs() Examples

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Example #1
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testNewLabelMapPath(self):
    """Tests that label map path can be overwritten in input readers."""
    original_label_map_path = "path/to/original/label_map"
    new_label_map_path = "path//to/new/label_map"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.label_map_path = original_label_map_path
    eval_input_reader = pipeline_config.eval_input_reader
    eval_input_reader.label_map_path = original_label_map_path
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, label_map_path=new_label_map_path)
    self.assertEqual(new_label_map_path,
                     configs["train_input_config"].label_map_path)
    self.assertEqual(new_label_map_path,
                     configs["eval_input_config"].label_map_path)
Example #2
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testNewFocalLossParameters(self):
    """Tests that the loss weight ratio is updated appropriately."""
    original_alpha = 1.0
    original_gamma = 1.0
    new_alpha = 0.3
    new_gamma = 2.0
    hparams = tf.contrib.training.HParams(
        focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    classification_loss = pipeline_config.model.ssd.loss.classification_loss
    classification_loss.weighted_sigmoid_focal.alpha = original_alpha
    classification_loss.weighted_sigmoid_focal.gamma = original_gamma
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    classification_loss = configs["model"].ssd.loss.classification_loss
    self.assertAlmostEqual(new_alpha,
                           classification_loss.weighted_sigmoid_focal.alpha)
    self.assertAlmostEqual(new_gamma,
                           classification_loss.weighted_sigmoid_focal.gamma)
Example #3
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testNewTrainInputPath(self):
    """Tests that train input path can be overwritten with single file."""
    original_train_path = ["path/to/data"]
    new_train_path = "another/path/to/data"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"train_input_path": new_train_path}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual([new_train_path], final_path)
Example #4
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testMergingKeywordArguments(self):
    """Tests that keyword arguments get merged as do hyperparameters."""
    original_num_train_steps = 100
    desired_num_train_steps = 10
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.train_config.num_steps = original_num_train_steps
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"train_steps": desired_num_train_steps}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)
    train_steps = configs["train_config"].num_steps
    self.assertEqual(desired_num_train_steps, train_steps)
Example #5
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def testNewLabelMapPath(self):
    """Tests that label map path can be overwritten in input readers."""
    original_label_map_path = "path/to/original/label_map"
    new_label_map_path = "path//to/new/label_map"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.label_map_path = original_label_map_path
    eval_input_reader = pipeline_config.eval_input_reader.add()
    eval_input_reader.label_map_path = original_label_map_path
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"label_map_path": new_label_map_path}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)
    self.assertEqual(new_label_map_path,
                     configs["train_input_config"].label_map_path)
    for eval_input_config in configs["eval_input_configs"]:
      self.assertEqual(new_label_map_path, eval_input_config.label_map_path)
Example #6
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testMergingKeywordArguments(self):
    """Tests that keyword arguments get merged as do hyperparameters."""
    original_num_train_steps = 100
    original_num_eval_steps = 5
    desired_num_train_steps = 10
    desired_num_eval_steps = 1
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.train_config.num_steps = original_num_train_steps
    pipeline_config.eval_config.num_examples = original_num_eval_steps
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs,
        train_steps=desired_num_train_steps,
        eval_steps=desired_num_eval_steps)
    train_steps = configs["train_config"].num_steps
    eval_steps = configs["eval_config"].num_examples
    self.assertEqual(desired_num_train_steps, train_steps)
    self.assertEqual(desired_num_eval_steps, eval_steps)
Example #7
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testNewTrainInputPath(self):
    """Tests that train input path can be overwritten with single file."""
    original_train_path = ["path/to/data"]
    new_train_path = "another/path/to/data"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, train_input_path=new_train_path)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual([new_train_path], final_path)
Example #8
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testNewTrainInputPathList(self):
    """Tests that train input path can be overwritten with multiple files."""
    original_train_path = ["path/to/data"]
    new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, train_input_path=new_train_path)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual(new_train_path, final_path)
Example #9
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testNewLabelMapPath(self):
    """Tests that label map path can be overwritten in input readers."""
    original_label_map_path = "path/to/original/label_map"
    new_label_map_path = "path//to/new/label_map"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.label_map_path = original_label_map_path
    eval_input_reader = pipeline_config.eval_input_reader
    eval_input_reader.label_map_path = original_label_map_path
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, label_map_path=new_label_map_path)
    self.assertEqual(new_label_map_path,
                     configs["train_input_config"].label_map_path)
    self.assertEqual(new_label_map_path,
                     configs["eval_input_config"].label_map_path)
Example #10
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testDontOverwriteEmptyLabelMapPath(self):
    """Tests that label map path will not by overwritten with empty string."""
    original_label_map_path = "path/to/original/label_map"
    new_label_map_path = ""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.label_map_path = original_label_map_path
    eval_input_reader = pipeline_config.eval_input_reader
    eval_input_reader.label_map_path = original_label_map_path
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, label_map_path=new_label_map_path)
    self.assertEqual(original_label_map_path,
                     configs["train_input_config"].label_map_path)
    self.assertEqual(original_label_map_path,
                     configs["eval_input_config"].label_map_path)
Example #11
Source File: inputs_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def _get_configs_for_model(model_name):
  """Returns configurations for model."""
  fname = os.path.join(
      FLAGS.test_srcdir,
      ('google3/third_party/tensorflow_models/'
       'object_detection/samples/configs/' + model_name + '.config'))
  label_map_path = os.path.join(FLAGS.test_srcdir,
                                ('google3/third_party/tensorflow_models/'
                                 'object_detection/data/pet_label_map.pbtxt'))
  data_path = os.path.join(FLAGS.test_srcdir,
                           ('google3/third_party/tensorflow_models/'
                            'object_detection/test_data/pets_examples.record'))
  configs = config_util.get_configs_from_pipeline_file(fname)
  return config_util.merge_external_params_with_configs(
      configs,
      train_input_path=data_path,
      eval_input_path=data_path,
      label_map_path=label_map_path)
Example #12
Source File: config_util_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def testNewMomentumOptimizerValue(self):
    """Tests that new momentum value is updated appropriately."""
    original_momentum_value = 0.4
    hparams = tf.contrib.training.HParams(momentum_optimizer_value=1.1)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
    optimizer_config.momentum_optimizer_value = original_momentum_value
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
    new_momentum_value = optimizer_config.momentum_optimizer_value
    self.assertAlmostEqual(1.0, new_momentum_value)  # Clipped to 1.0.
Example #13
Source File: config_util_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testNewTrainInputPath(self):
    """Tests that train input path can be overwritten with single file."""
    original_train_path = ["path/to/data"]
    new_train_path = "another/path/to/data"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, train_input_path=new_train_path)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual([new_train_path], final_path)
Example #14
Source File: config_util_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testMergingKeywordArguments(self):
    """Tests that keyword arguments get merged as do hyperparameters."""
    original_num_train_steps = 100
    original_num_eval_steps = 5
    desired_num_train_steps = 10
    desired_num_eval_steps = 1
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.train_config.num_steps = original_num_train_steps
    pipeline_config.eval_config.num_examples = original_num_eval_steps
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs,
        train_steps=desired_num_train_steps,
        eval_steps=desired_num_eval_steps)
    train_steps = configs["train_config"].num_steps
    eval_steps = configs["eval_config"].num_examples
    self.assertEqual(desired_num_train_steps, train_steps)
    self.assertEqual(desired_num_eval_steps, eval_steps)
Example #15
Source File: inputs_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def _get_configs_for_model(model_name):
  """Returns configurations for model."""
  # TODO: Make sure these tests work fine outside google3.
  fname = os.path.join(
      FLAGS.test_srcdir,
      ('google3/third_party/tensorflow_models/'
       'object_detection/samples/configs/' + model_name + '.config'))
  label_map_path = os.path.join(FLAGS.test_srcdir,
                                ('google3/third_party/tensorflow_models/'
                                 'object_detection/data/pet_label_map.pbtxt'))
  data_path = os.path.join(FLAGS.test_srcdir,
                           ('google3/third_party/tensorflow_models/'
                            'object_detection/test_data/pets_examples.record'))
  configs = config_util.get_configs_from_pipeline_file(fname)
  return config_util.merge_external_params_with_configs(
      configs,
      train_input_path=data_path,
      eval_input_path=data_path,
      label_map_path=label_map_path)
Example #16
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def testNewClassificationLocalizationWeightRatio(self):
    """Tests that the loss weight ratio is updated appropriately."""
    original_localization_weight = 0.1
    original_classification_weight = 0.2
    new_weight_ratio = 5.0
    hparams = tf.contrib.training.HParams(
        classification_localization_weight_ratio=new_weight_ratio)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.ssd.loss.localization_weight = (
        original_localization_weight)
    pipeline_config.model.ssd.loss.classification_weight = (
        original_classification_weight)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    loss = configs["model"].ssd.loss
    self.assertAlmostEqual(1.0, loss.localization_weight)
    self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
Example #17
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testNewClassificationLocalizationWeightRatio(self):
    """Tests that the loss weight ratio is updated appropriately."""
    original_localization_weight = 0.1
    original_classification_weight = 0.2
    new_weight_ratio = 5.0
    hparams = tf.contrib.training.HParams(
        classification_localization_weight_ratio=new_weight_ratio)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.ssd.loss.localization_weight = (
        original_localization_weight)
    pipeline_config.model.ssd.loss.classification_weight = (
        original_classification_weight)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    loss = configs["model"].ssd.loss
    self.assertAlmostEqual(1.0, loss.localization_weight)
    self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
Example #18
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testNewFocalLossParameters(self):
    """Tests that the loss weight ratio is updated appropriately."""
    original_alpha = 1.0
    original_gamma = 1.0
    new_alpha = 0.3
    new_gamma = 2.0
    hparams = tf.contrib.training.HParams(
        focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    classification_loss = pipeline_config.model.ssd.loss.classification_loss
    classification_loss.weighted_sigmoid_focal.alpha = original_alpha
    classification_loss.weighted_sigmoid_focal.gamma = original_gamma
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    classification_loss = configs["model"].ssd.loss.classification_loss
    self.assertAlmostEqual(new_alpha,
                           classification_loss.weighted_sigmoid_focal.alpha)
    self.assertAlmostEqual(new_gamma,
                           classification_loss.weighted_sigmoid_focal.gamma)
Example #19
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testMergingKeywordArguments(self):
    """Tests that keyword arguments get merged as do hyperparameters."""
    original_num_train_steps = 100
    original_num_eval_steps = 5
    desired_num_train_steps = 10
    desired_num_eval_steps = 1
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.train_config.num_steps = original_num_train_steps
    pipeline_config.eval_config.num_examples = original_num_eval_steps
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs,
        train_steps=desired_num_train_steps,
        eval_steps=desired_num_eval_steps)
    train_steps = configs["train_config"].num_steps
    eval_steps = configs["eval_config"].num_examples
    self.assertEqual(desired_num_train_steps, train_steps)
    self.assertEqual(desired_num_eval_steps, eval_steps)
Example #20
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testNewTrainInputPath(self):
    """Tests that train input path can be overwritten with single file."""
    original_train_path = ["path/to/data"]
    new_train_path = "another/path/to/data"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, train_input_path=new_train_path)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual([new_train_path], final_path)
Example #21
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testNewTrainInputPathList(self):
    """Tests that train input path can be overwritten with multiple files."""
    original_train_path = ["path/to/data"]
    new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, train_input_path=new_train_path)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual(new_train_path, final_path)
Example #22
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testDontOverwriteEmptyLabelMapPath(self):
    """Tests that label map path will not by overwritten with empty string."""
    original_label_map_path = "path/to/original/label_map"
    new_label_map_path = ""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.label_map_path = original_label_map_path
    eval_input_reader = pipeline_config.eval_input_reader
    eval_input_reader.label_map_path = original_label_map_path
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, label_map_path=new_label_map_path)
    self.assertEqual(original_label_map_path,
                     configs["train_input_config"].label_map_path)
    self.assertEqual(original_label_map_path,
                     configs["eval_input_config"].label_map_path)
Example #23
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def testNewMaskType(self):
    """Tests that mask type can be overwritten in input readers."""
    original_mask_type = input_reader_pb2.NUMERICAL_MASKS
    new_mask_type = input_reader_pb2.PNG_MASKS
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    train_input_reader = pipeline_config.train_input_reader
    train_input_reader.mask_type = original_mask_type
    eval_input_reader = pipeline_config.eval_input_reader
    eval_input_reader.mask_type = original_mask_type
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, mask_type=new_mask_type)
    self.assertEqual(new_mask_type, configs["train_input_config"].mask_type)
    self.assertEqual(new_mask_type, configs["eval_input_config"].mask_type)
Example #24
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testNewTrainInputPathList(self):
    """Tests that train input path can be overwritten with multiple files."""
    original_train_path = ["path/to/data"]
    new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, train_input_path=new_train_path)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual(new_train_path, final_path)
Example #25
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testNewTrainInputPath(self):
    """Tests that train input path can be overwritten with single file."""
    original_train_path = ["path/to/data"]
    new_train_path = "another/path/to/data"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs, train_input_path=new_train_path)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual([new_train_path], final_path)
Example #26
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testMergingKeywordArguments(self):
    """Tests that keyword arguments get merged as do hyperparameters."""
    original_num_train_steps = 100
    original_num_eval_steps = 5
    desired_num_train_steps = 10
    desired_num_eval_steps = 1
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.train_config.num_steps = original_num_train_steps
    pipeline_config.eval_config.num_examples = original_num_eval_steps
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(
        configs,
        train_steps=desired_num_train_steps,
        eval_steps=desired_num_eval_steps)
    train_steps = configs["train_config"].num_steps
    eval_steps = configs["eval_config"].num_examples
    self.assertEqual(desired_num_train_steps, train_steps)
    self.assertEqual(desired_num_eval_steps, eval_steps)
Example #27
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testNewFocalLossParameters(self):
    """Tests that the loss weight ratio is updated appropriately."""
    original_alpha = 1.0
    original_gamma = 1.0
    new_alpha = 0.3
    new_gamma = 2.0
    hparams = tf.contrib.training.HParams(
        focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    classification_loss = pipeline_config.model.ssd.loss.classification_loss
    classification_loss.weighted_sigmoid_focal.alpha = original_alpha
    classification_loss.weighted_sigmoid_focal.gamma = original_gamma
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    classification_loss = configs["model"].ssd.loss.classification_loss
    self.assertAlmostEqual(new_alpha,
                           classification_loss.weighted_sigmoid_focal.alpha)
    self.assertAlmostEqual(new_gamma,
                           classification_loss.weighted_sigmoid_focal.gamma)
Example #28
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def testNewMomentumOptimizerValue(self):
    """Tests that new momentum value is updated appropriately."""
    original_momentum_value = 0.4
    hparams = tf.contrib.training.HParams(momentum_optimizer_value=1.1)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
    optimizer_config.momentum_optimizer_value = original_momentum_value
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
    new_momentum_value = optimizer_config.momentum_optimizer_value
    self.assertAlmostEqual(1.0, new_momentum_value)  # Clipped to 1.0.
Example #29
Source File: config_util_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testNewClassificationLocalizationWeightRatio(self):
    """Tests that the loss weight ratio is updated appropriately."""
    original_localization_weight = 0.1
    original_classification_weight = 0.2
    new_weight_ratio = 5.0
    hparams = tf.HParams(
        classification_localization_weight_ratio=new_weight_ratio)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.ssd.loss.localization_weight = (
        original_localization_weight)
    pipeline_config.model.ssd.loss.classification_weight = (
        original_classification_weight)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    loss = configs["model"].ssd.loss
    self.assertAlmostEqual(1.0, loss.localization_weight)
    self.assertAlmostEqual(new_weight_ratio, loss.classification_weight)
Example #30
Source File: config_util_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def testNewMomentumOptimizerValue(self):
    """Tests that new momentum value is updated appropriately."""
    original_momentum_value = 0.4
    hparams = tf.HParams(momentum_optimizer_value=1.1)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
    optimizer_config.momentum_optimizer_value = original_momentum_value
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
    new_momentum_value = optimizer_config.momentum_optimizer_value
    self.assertAlmostEqual(1.0, new_momentum_value)  # Clipped to 1.0.