Python config_util.get_configs_from_multiple_files方法代码示例

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def main(argv):
  del argv
  required_flags = ['input_config_path', 'eval_config_path', 'eval_dir']
  for flag_name in required_flags:
    if not getattr(FLAGS, flag_name):
      raise ValueError('Flag --{} is required'.format(flag_name))

  configs = config_util.get_configs_from_multiple_files(
      eval_input_config_path=FLAGS.input_config_path,
      eval_config_path=FLAGS.eval_config_path)

  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']

  metrics = read_data_and_evaluate(input_config, eval_config)

  # Save metrics
  write_metrics(metrics, FLAGS.eval_dir) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def get_configs_from_multiple_files(model_config_path="",
                                    train_config_path="",
                                    train_input_config_path="",
                                    eval_config_path="",
                                    eval_input_config_path="",
                                    lstm_config_path=""):
  """Reads training configuration from multiple config files.

  Args:
    model_config_path: Path to model_pb2.DetectionModel.
    train_config_path: Path to train_pb2.TrainConfig.
    train_input_config_path: Path to input_reader_pb2.InputReader.
    eval_config_path: Path to eval_pb2.EvalConfig.
    eval_input_config_path: Path to input_reader_pb2.InputReader.
    lstm_config_path: Path to pipeline_pb2.LstmModel.

  Returns:
    Dictionary of configuration objects. Keys are `model`, `train_config`,
      `train_input_config`, `eval_config`, `eval_input_config`, `lstm_model`.
      Key/Values are returned only for valid (non-empty) strings.
  """
  configs = config_util.get_configs_from_multiple_files(
      model_config_path=model_config_path,
      train_config_path=train_config_path,
      train_input_config_path=train_input_config_path,
      eval_config_path=eval_config_path,
      eval_input_config_path=eval_input_config_path)
  if lstm_config_path:
    lstm_config = internal_pipeline_pb2.LstmModel()
    with tf.gfile.GFile(lstm_config_path, "r") as f:
      text_format.Merge(f.read(), lstm_config)
      configs["lstm_model"] = lstm_config
  return configs 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(
        FLAGS.pipeline_config_path,
        os.path.join(FLAGS.eval_dir, 'pipeline.config'),
        overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config, os.path.join(FLAGS.eval_dir, name), overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build, model_config=model_config, is_training=False)

  def get_next(config):
    return dataset_builder.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  categories = label_map_util.create_categories_from_labelmap(
      input_config.label_map_path)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  graph_rewriter_fn = None
  if 'graph_rewriter_config' in configs:
    graph_rewriter_fn = graph_rewriter_builder.build(
        configs['graph_rewriter_config'], is_training=False)

  evaluator.evaluate(
      create_input_dict_fn,
      model_fn,
      eval_config,
      categories,
      FLAGS.checkpoint_dir,
      FLAGS.eval_dir,
      graph_hook_fn=graph_rewriter_fn) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def test_get_configs_from_multiple_files(self):
    """Tests that proto configs can be read from multiple files."""
    temp_dir = self.get_temp_dir()

    # Write model config file.
    model_config_path = os.path.join(temp_dir, "model.config")
    model = model_pb2.DetectionModel()
    model.faster_rcnn.num_classes = 10
    _write_config(model, model_config_path)

    # Write train config file.
    train_config_path = os.path.join(temp_dir, "train.config")
    train_config = train_config = train_pb2.TrainConfig()
    train_config.batch_size = 32
    _write_config(train_config, train_config_path)

    # Write train input config file.
    train_input_config_path = os.path.join(temp_dir, "train_input.config")
    train_input_config = input_reader_pb2.InputReader()
    train_input_config.label_map_path = "path/to/label_map"
    _write_config(train_input_config, train_input_config_path)

    # Write eval config file.
    eval_config_path = os.path.join(temp_dir, "eval.config")
    eval_config = eval_pb2.EvalConfig()
    eval_config.num_examples = 20
    _write_config(eval_config, eval_config_path)

    # Write eval input config file.
    eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
    eval_input_config = input_reader_pb2.InputReader()
    eval_input_config.label_map_path = "path/to/another/label_map"
    _write_config(eval_input_config, eval_input_config_path)

    configs = config_util.get_configs_from_multiple_files(
        model_config_path=model_config_path,
        train_config_path=train_config_path,
        train_input_config_path=train_input_config_path,
        eval_config_path=eval_config_path,
        eval_input_config_path=eval_input_config_path)
    self.assertProtoEquals(model, configs["model"])
    self.assertProtoEquals(train_config, configs["train_config"])
    self.assertProtoEquals(train_input_config,
                           configs["train_input_config"])
    self.assertProtoEquals(eval_config, configs["eval_config"])
    self.assertProtoEquals(eval_input_config, configs["eval_input_configs"][0]) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def test_get_configs_from_multiple_files(self):
    """Tests that proto configs can be read from multiple files."""
    temp_dir = self.get_temp_dir()

    # Write model config file.
    model_config_path = os.path.join(temp_dir, "model.config")
    model = model_pb2.DetectionModel()
    model.faster_rcnn.num_classes = 10
    _write_config(model, model_config_path)

    # Write train config file.
    train_config_path = os.path.join(temp_dir, "train.config")
    train_config = train_config = train_pb2.TrainConfig()
    train_config.batch_size = 32
    _write_config(train_config, train_config_path)

    # Write train input config file.
    train_input_config_path = os.path.join(temp_dir, "train_input.config")
    train_input_config = input_reader_pb2.InputReader()
    train_input_config.label_map_path = "path/to/label_map"
    _write_config(train_input_config, train_input_config_path)

    # Write eval config file.
    eval_config_path = os.path.join(temp_dir, "eval.config")
    eval_config = eval_pb2.EvalConfig()
    eval_config.num_examples = 20
    _write_config(eval_config, eval_config_path)

    # Write eval input config file.
    eval_input_config_path = os.path.join(temp_dir, "eval_input.config")
    eval_input_config = input_reader_pb2.InputReader()
    eval_input_config.label_map_path = "path/to/another/label_map"
    _write_config(eval_input_config, eval_input_config_path)

    configs = config_util.get_configs_from_multiple_files(
        model_config_path=model_config_path,
        train_config_path=train_config_path,
        train_input_config_path=train_input_config_path,
        eval_config_path=eval_config_path,
        eval_input_config_path=eval_input_config_path)
    self.assertProtoEquals(model, configs["model"])
    self.assertProtoEquals(train_config, configs["train_config"])
    self.assertProtoEquals(train_input_config,
                           configs["train_input_config"])
    self.assertProtoEquals(eval_config, configs["eval_config"])
    self.assertProtoEquals(eval_input_config,
                           configs["eval_input_config"]) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  def get_next(config):
    return dataset_util.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  def get_next(config):
    return dataset_util.make_initializable_iterator(
        dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  graph_rewriter_fn = None
  if 'graph_rewriter_config' in configs:
    graph_rewriter_fn = graph_rewriter_builder.build(
        configs['graph_rewriter_config'], is_training=False)

  evaluator.evaluate(
      create_input_dict_fn,
      model_fn,
      eval_config,
      categories,
      FLAGS.checkpoint_dir,
      FLAGS.eval_dir,
      graph_hook_fn=graph_rewriter_fn) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def main(unused_argv):
  if (FLAGS.omp > 0):
    if not os.environ.get("OMP_NUM_THREADS"):
      logging.info('OMP_NUM_THREADS value= %d', FLAGS.omp)
      os.environ["OMP_NUM_THREADS"] = str(FLAGS.omp)
    if not os.environ.get("KMP_BLOCKTIME"):
      logging.info('KMP_BLOCKTIME value= %d', FLAGS.blocktime)
      os.environ["KMP_BLOCKTIME"] = str(FLAGS.blocktime)
    if not os.environ.get("KMP_SETTINGS"):
      os.environ["KMP_SETTINGS"] = "1"
    # os.environ["KMP_AFFINITY"]= "granularity=fine,verbose,compact,1,0"
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.io.gfile.makedirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.io.gfile.copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.io.gfile.copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  input_config = configs['eval_input_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  def get_next(config):
    return tf.compat.v1.data.make_initializable_iterator(
        dataset_util, dataset_builder.build(config)).get_next()

  create_input_dict_fn = functools.partial(get_next, input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir, intra_op=FLAGS.intra_op, inter_op=FLAGS.inter_op) 

# 需要导入模块: from object_detection.utils import config_util [as 别名]
# 或者: from object_detection.utils.config_util import get_configs_from_multiple_files [as 别名]
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  tf.gfile.MakeDirs(FLAGS.eval_dir)
  if FLAGS.pipeline_config_path:
    configs = config_util.get_configs_from_pipeline_file(
        FLAGS.pipeline_config_path)
    tf.gfile.Copy(FLAGS.pipeline_config_path,
                  os.path.join(FLAGS.eval_dir, 'pipeline.config'),
                  overwrite=True)
  else:
    configs = config_util.get_configs_from_multiple_files(
        model_config_path=FLAGS.model_config_path,
        eval_config_path=FLAGS.eval_config_path,
        eval_input_config_path=FLAGS.input_config_path)
    for name, config in [('model.config', FLAGS.model_config_path),
                         ('eval.config', FLAGS.eval_config_path),
                         ('input.config', FLAGS.input_config_path)]:
      tf.gfile.Copy(config,
                    os.path.join(FLAGS.eval_dir, name),
                    overwrite=True)

  model_config = configs['model']
  eval_config = configs['eval_config']
  if FLAGS.eval_training_data:
    input_config = configs['train_input_config']
  else:
    input_config = configs['eval_input_config']

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  if FLAGS.run_once:
    eval_config.max_evals = 1

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)