Python object_detection.utils.config_util.create_pipeline_proto_from_configs() Examples

The following are 30 code examples of object_detection.utils.config_util.create_pipeline_proto_from_configs(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.utils.config_util , or try the search function .
Example #1
Source File: config_util_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #2
Source File: config_util.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def create_pipeline_proto_from_configs(configs):
  """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.

  This function nearly performs the inverse operation of
  get_configs_from_pipeline_file(). Instead of returning a file path, it returns
  a `TrainEvalPipelineConfig` object.

  Args:
    configs: Dictionary of configs. See get_configs_from_pipeline_file().

  Returns:
    A fully populated pipeline_pb2.TrainEvalPipelineConfig.
  """
  pipeline_config = config_util.create_pipeline_proto_from_configs(configs)
  if "lstm_model" in configs:
    pipeline_config.Extensions[internal_pipeline_pb2.lstm_model].CopyFrom(
        configs["lstm_model"])
  return pipeline_config
Example #3
Source File: config_util_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #4
Source File: config_util_test.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #5
Source File: config_util.py    From models with Apache License 2.0 6 votes vote down vote up 
def create_pipeline_proto_from_configs(configs):
  """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.

  This function nearly performs the inverse operation of
  get_configs_from_pipeline_file(). Instead of returning a file path, it returns
  a `TrainEvalPipelineConfig` object.

  Args:
    configs: Dictionary of configs. See get_configs_from_pipeline_file().

  Returns:
    A fully populated pipeline_pb2.TrainEvalPipelineConfig.
  """
  pipeline_config = config_util.create_pipeline_proto_from_configs(configs)
  if "lstm_model" in configs:
    pipeline_config.Extensions[internal_pipeline_pb2.lstm_model].CopyFrom(
        configs["lstm_model"])
  return pipeline_config
Example #6
Source File: config_util_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #7
Source File: config_util_test.py    From models with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #8
Source File: config_util.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def create_pipeline_proto_from_configs(configs):
  """Creates a pipeline_pb2.TrainEvalPipelineConfig from configs dictionary.

  This function nearly performs the inverse operation of
  get_configs_from_pipeline_file(). Instead of returning a file path, it returns
  a `TrainEvalPipelineConfig` object.

  Args:
    configs: Dictionary of configs. See get_configs_from_pipeline_file().

  Returns:
    A fully populated pipeline_pb2.TrainEvalPipelineConfig.
  """
  pipeline_config = config_util.create_pipeline_proto_from_configs(configs)
  if "lstm_model" in configs:
    pipeline_config.Extensions[internal_pipeline_pb2.lstm_model].CopyFrom(
        configs["lstm_model"])
  return pipeline_config
Example #9
Source File: config_util_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #10
Source File: config_util_test.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #11
Source File: config_util_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #12
Source File: config_util_test.py    From MAX-Object-Detector with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #13
Source File: config_util_test.py    From AniSeg with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #14
Source File: config_util_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #15
Source File: config_util_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #16
Source File: config_util_test.py    From Elphas with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #17
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #18
Source File: config_util_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #19
Source File: config_util_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #20
Source File: config_util_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #21
Source File: config_util_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #22
Source File: config_util_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #23
Source File: config_util_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #24
Source File: config_util_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #25
Source File: config_util_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #26
Source File: config_util_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #27
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #28
Source File: config_util_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #29
Source File: config_util_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed)
Example #30
Source File: config_util_test.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed)