Python object_detection.protos.train_pb2.TrainConfig() Examples

The following are 30 code examples of object_detection.protos.train_pb2.TrainConfig(). 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.train_pb2 , or try the search function .
Example #1
Source File: train.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #2
Source File: train.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #3
Source File: train.py    From MBMD with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #4
Source File: train.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #5
Source File: train.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #6
Source File: train.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 training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #7
Source File: train.py    From MBMD with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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.ssd
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #8
Source File: train.py    From HereIsWally with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #9
Source File: train_seq.py    From MBMD with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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.ssd
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #10
Source File: train.py    From hands-detection with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #11
Source File: train_video.py    From MBMD with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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.ssd
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #12
Source File: train_imagenet.py    From MBMD with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #13
Source File: train.py    From object_detector_app with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #14
Source File: train.py    From moveo_ros with MIT License 6 votes vote down vote up 
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: 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
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config
Example #15
Source File: config_util.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def get_optimizer_type(train_config):
  """Returns the optimizer type for training.

  Args:
    train_config: A train_pb2.TrainConfig.

  Returns:
    The type of the optimizer
  """
  return train_config.optimizer.WhichOneof("optimizer")
Example #16
Source File: trainer_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 5 votes vote down vote up 
def test_configure_trainer_and_train_two_steps(self):
    train_config_text_proto = """
    optimizer {
      adam_optimizer {
        learning_rate {
          constant_learning_rate {
            learning_rate: 0.01
          }
        }
      }
    }
    data_augmentation_options {
      random_adjust_brightness {
        max_delta: 0.2
      }
    }
    data_augmentation_options {
      random_adjust_contrast {
        min_delta: 0.7
        max_delta: 1.1
      }
    }
    num_steps: 2
    """
    train_config = train_pb2.TrainConfig()
    text_format.Merge(train_config_text_proto, train_config)

    train_dir = self.get_temp_dir()

    trainer.train(create_tensor_dict_fn=get_input_function,
                  create_model_fn=FakeDetectionModel,
                  train_config=train_config,
                  master='',
                  task=0,
                  num_clones=1,
                  worker_replicas=1,
                  clone_on_cpu=True,
                  ps_tasks=0,
                  worker_job_name='worker',
                  is_chief=True,
                  train_dir=train_dir)
Example #17
Source File: config_util.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
def get_optimizer_type(train_config):
  """Returns the optimizer type for training.

  Args:
    train_config: A train_pb2.TrainConfig.

  Returns:
    The type of the optimizer
  """
  return train_config.optimizer.WhichOneof("optimizer")
Example #18
Source File: train.py    From Hands-On-Machine-Learning-with-OpenCV-4 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 #19
Source File: config_util.py    From ros_people_object_detection_tensorflow with Apache License 2.0 5 votes vote down vote up 
def get_optimizer_type(train_config):
  """Returns the optimizer type for training.

  Args:
    train_config: A train_pb2.TrainConfig.

  Returns:
    The type of the optimizer
  """
  return train_config.optimizer.WhichOneof("optimizer")
Example #20
Source File: trainer_test.py    From tensorflow with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def test_configure_trainer_and_train_two_steps(self):
    train_config_text_proto = """
    optimizer {
      adam_optimizer {
        learning_rate {
          constant_learning_rate {
            learning_rate: 0.01
          }
        }
      }
    }
    data_augmentation_options {
      random_adjust_brightness {
        max_delta: 0.2
      }
    }
    data_augmentation_options {
      random_adjust_contrast {
        min_delta: 0.7
        max_delta: 1.1
      }
    }
    num_steps: 2
    """
    train_config = train_pb2.TrainConfig()
    text_format.Merge(train_config_text_proto, train_config)

    train_dir = self.get_temp_dir()

    trainer.train(create_tensor_dict_fn=get_input_function,
                  create_model_fn=FakeDetectionModel,
                  train_config=train_config,
                  master='',
                  task=0,
                  num_clones=1,
                  worker_replicas=1,
                  clone_on_cpu=True,
                  ps_tasks=0,
                  worker_job_name='worker',
                  is_chief=True,
                  train_dir=train_dir)
Example #21
Source File: train.py    From tensorflow with BSD 2-Clause "Simplified" 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 #22
Source File: train.py    From object_detection_kitti with Apache License 2.0 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 #23
Source File: config_util.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def get_optimizer_type(train_config):
  """Returns the optimizer type for training.

  Args:
    train_config: A train_pb2.TrainConfig.

  Returns:
    The type of the optimizer
  """
  return train_config.optimizer.WhichOneof("optimizer")
Example #24
Source File: trainer_test.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def test_configure_trainer_and_train_two_steps(self):
    train_config_text_proto = """
    optimizer {
      adam_optimizer {
        learning_rate {
          constant_learning_rate {
            learning_rate: 0.01
          }
        }
      }
    }
    data_augmentation_options {
      random_adjust_brightness {
        max_delta: 0.2
      }
    }
    data_augmentation_options {
      random_adjust_contrast {
        min_delta: 0.7
        max_delta: 1.1
      }
    }
    num_steps: 2
    """
    train_config = train_pb2.TrainConfig()
    text_format.Merge(train_config_text_proto, train_config)

    train_dir = self.get_temp_dir()

    trainer.train(create_tensor_dict_fn=get_input_function,
                  create_model_fn=FakeDetectionModel,
                  train_config=train_config,
                  master='',
                  task=0,
                  num_clones=1,
                  worker_replicas=1,
                  clone_on_cpu=True,
                  ps_tasks=0,
                  worker_job_name='worker',
                  is_chief=True,
                  train_dir=train_dir)
Example #25
Source File: config_util.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
def get_optimizer_type(train_config):
  """Returns the optimizer type for training.

  Args:
    train_config: A train_pb2.TrainConfig.

  Returns:
    The type of the optimizer
  """
  return train_config.optimizer.WhichOneof("optimizer")
Example #26
Source File: config_util.py    From ros_tensorflow with Apache License 2.0 5 votes vote down vote up 
def get_optimizer_type(train_config):
  """Returns the optimizer type for training.

  Args:
    train_config: A train_pb2.TrainConfig.

  Returns:
    The type of the optimizer
  """
  return train_config.optimizer.WhichOneof("optimizer")
Example #27
Source File: train.py    From hands-detection 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: trainer_test.py    From hands-detection with MIT License 5 votes vote down vote up 
def test_configure_trainer_and_train_two_steps(self):
    train_config_text_proto = """
    optimizer {
      adam_optimizer {
        learning_rate {
          constant_learning_rate {
            learning_rate: 0.01
          }
        }
      }
    }
    data_augmentation_options {
      random_adjust_brightness {
        max_delta: 0.2
      }
    }
    data_augmentation_options {
      random_adjust_contrast {
        min_delta: 0.7
        max_delta: 1.1
      }
    }
    num_steps: 2
    """
    train_config = train_pb2.TrainConfig()
    text_format.Merge(train_config_text_proto, train_config)

    train_dir = self.get_temp_dir()

    trainer.train(create_tensor_dict_fn=get_input_function,
                  create_model_fn=FakeDetectionModel,
                  train_config=train_config,
                  master='',
                  task=0,
                  num_clones=1,
                  worker_replicas=1,
                  clone_on_cpu=True,
                  ps_tasks=0,
                  worker_job_name='worker',
                  is_chief=True,
                  train_dir=train_dir)
Example #29
Source File: train.py    From moveo_ros 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 #30
Source File: trainer_test.py    From moveo_ros with MIT License 5 votes vote down vote up 
def test_configure_trainer_and_train_two_steps(self):
    train_config_text_proto = """
    optimizer {
      adam_optimizer {
        learning_rate {
          constant_learning_rate {
            learning_rate: 0.01
          }
        }
      }
    }
    data_augmentation_options {
      random_adjust_brightness {
        max_delta: 0.2
      }
    }
    data_augmentation_options {
      random_adjust_contrast {
        min_delta: 0.7
        max_delta: 1.1
      }
    }
    num_steps: 2
    """
    train_config = train_pb2.TrainConfig()
    text_format.Merge(train_config_text_proto, train_config)

    train_dir = self.get_temp_dir()

    trainer.train(create_tensor_dict_fn=get_input_function,
                  create_model_fn=FakeDetectionModel,
                  train_config=train_config,
                  master='',
                  task=0,
                  num_clones=1,
                  worker_replicas=1,
                  clone_on_cpu=True,
                  ps_tasks=0,
                  worker_job_name='worker',
                  is_chief=True,
                  train_dir=train_dir)