Python object_detection.protos.model_pb2.DetectionModel() Examples

The following are 30 code examples of object_detection.protos.model_pb2.DetectionModel(). 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.protos.model_pb2 , or try the search function .
Example #1
Source File: model_builder.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #2
Source File: model_builder.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #3
Source File: eval.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
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
Example #4
Source File: model_builder.py    From object_detector_app with MIT License 6 votes vote down vote up 
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #5
Source File: config_util.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def get_image_resizer_config(model_config):
  """Returns the image resizer config from a model config.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    An image_resizer_pb2.ImageResizer.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    return model_config.faster_rcnn.image_resizer
  if meta_architecture == "ssd":
    return model_config.ssd.image_resizer

  raise ValueError("Unknown model type: {}".format(meta_architecture))
Example #6
Source File: config_util.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def get_image_resizer_config(model_config):
  """Returns the image resizer config from a model config.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    An image_resizer_pb2.ImageResizer.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    return model_config.faster_rcnn.image_resizer
  if meta_architecture == "ssd":
    return model_config.ssd.image_resizer

  raise ValueError("Unknown model type: {}".format(meta_architecture))
Example #7
Source File: eval.py    From object_detector_app with MIT License 6 votes vote down vote up 
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
Example #8
Source File: model_builder.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #9
Source File: model_builder.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def build(model_config, is_training, add_summaries=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.
  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #10
Source File: model_builder.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #11
Source File: eval.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
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
Example #12
Source File: config_util.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def get_image_resizer_config(model_config):
  """Returns the image resizer config from a model config.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    An image_resizer_pb2.ImageResizer.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    return model_config.faster_rcnn.image_resizer
  if meta_architecture == "ssd":
    return model_config.ssd.image_resizer

  raise ValueError("Unknown model type: {}".format(meta_architecture))
Example #13
Source File: config_util.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def get_image_resizer_config(model_config):
  """Returns the image resizer config from a model config.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    An image_resizer_pb2.ImageResizer.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    return model_config.faster_rcnn.image_resizer
  if meta_architecture == "ssd":
    return model_config.ssd.image_resizer

  raise ValueError("Unknown model type: {}".format(meta_architecture))
Example #14
Source File: model_builder.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def build(model_config, is_training, add_summaries=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #15
Source File: model_builder.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #16
Source File: config_util.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def get_number_of_classes(model_config):
  """Returns the number of classes for a detection model.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    Number of classes.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    return model_config.faster_rcnn.num_classes
  if meta_architecture == "ssd":
    return model_config.ssd.num_classes

  raise ValueError("Expected the model to be one of 'faster_rcnn' or 'ssd'.")
Example #17
Source File: config_util.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def get_image_resizer_config(model_config):
  """Returns the image resizer config from a model config.

  Args:
    model_config: A model_pb2.DetectionModel.

  Returns:
    An image_resizer_pb2.ImageResizer.

  Raises:
    ValueError: If the model type is not recognized.
  """
  meta_architecture = model_config.WhichOneof("model")
  if meta_architecture == "faster_rcnn":
    return model_config.faster_rcnn.image_resizer
  if meta_architecture == "ssd":
    return model_config.ssd.image_resizer

  raise ValueError("Unknown model type: {}".format(meta_architecture))
Example #18
Source File: model_builder.py    From HereIsWally with MIT License 6 votes vote down vote up 
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #19
Source File: eval.py    From HereIsWally with MIT License 6 votes vote down vote up 
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
Example #20
Source File: model_builder.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def build(model_config, is_training, add_summaries=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #21
Source File: eval.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
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
Example #22
Source File: eval.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
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
Example #23
Source File: model_builder.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def build(model_config, is_training, add_summaries=True,
          add_background_class=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.
    add_background_class: Whether to add an implicit background class to one-hot
      encodings of groundtruth labels. Set to false if using groundtruth labels
      with an explicit background class or using multiclass scores instead of
      truth in the case of distillation. Ignored in the case of faster_rcnn.
  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries,
                            add_background_class)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture))
Example #24
Source File: model_builder_test.py    From HereIsWally with MIT License 5 votes vote down vote up 
def create_model(self, model_config):
    """Builds a DetectionModel based on the model config.

    Args:
      model_config: A model.proto object containing the config for the desired
        DetectionModel.

    Returns:
      DetectionModel based on the config.
    """
    return model_builder.build(model_config, is_training=True)
Example #25
Source File: eval.py    From HereIsWally with MIT License 5 votes vote down vote up 
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
Example #26
Source File: model_builder_test.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def create_model(self, model_config):
    """Builds a DetectionModel based on the model config.

    Args:
      model_config: A model.proto object containing the config for the desired
        DetectionModel.

    Returns:
      DetectionModel based on the config.
    """
    return model_builder.build(model_config, is_training=True)
Example #27
Source File: train.py    From garbage-object-detection-tensorflow with MIT License 5 votes vote down vote up 
def get_configs_from_multiple_files():
  """Reads training configuration from multiple config files.

  Reads the training config from the following files:
    model_config: Read from --model_config_path
    train_config: Read from --train_config_path
    input_config: Read from --input_config_path

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: input_reader_pb2.InputReader
  """
  train_config = train_pb2.TrainConfig()
  with tf.gfile.GFile(FLAGS.train_config_path, 'r') as f:
    text_format.Merge(f.read(), train_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, train_config, input_config
Example #28
Source File: train.py    From HereIsWally with MIT License 5 votes vote down vote up 
def get_configs_from_multiple_files():
  """Reads training configuration from multiple config files.

  Reads the training config from the following files:
    model_config: Read from --model_config_path
    train_config: Read from --train_config_path
    input_config: Read from --input_config_path

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: input_reader_pb2.InputReader
  """
  train_config = train_pb2.TrainConfig()
  with tf.gfile.GFile(FLAGS.train_config_path, 'r') as f:
    text_format.Merge(f.read(), train_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, train_config, input_config
Example #29
Source File: model_builder_test.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def create_model(self, model_config):
    """Builds a DetectionModel based on the model config.

    Args:
      model_config: A model.proto object containing the config for the desired
        DetectionModel.

    Returns:
      DetectionModel based on the config.
    """
    return model_builder.build(model_config, is_training=True)
Example #30
Source File: eval.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
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