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Python dataset_utils.write_label_file方法代碼示例

本文整理匯總了Python中slim.datasets.dataset_utils.write_label_file方法的典型用法代碼示例。如果您正苦於以下問題:Python dataset_utils.write_label_file方法的具體用法?Python dataset_utils.write_label_file怎麽用?Python dataset_utils.write_label_file使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在slim.datasets.dataset_utils的用法示例。


在下文中一共展示了dataset_utils.write_label_file方法的1個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: run

# 需要導入模塊: from slim.datasets import dataset_utils [as 別名]
# 或者: from slim.datasets.dataset_utils import write_label_file [as 別名]
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  train_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(train_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  # TODO(konstantinos): Add download and cleanup functionality

  train_validation_filenames = _get_filenames(
      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train'))
  test_filenames = _get_filenames(
      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test'))

  # Divide into train and validation:
  random.seed(_RANDOM_SEED)
  random.shuffle(train_validation_filenames)
  train_filenames = train_validation_filenames[_NUM_VALIDATION:]
  validation_filenames = train_validation_filenames[:_NUM_VALIDATION]

  train_validation_filenames_to_class_ids = _extract_labels(
      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_train_labels.txt'))
  test_filenames_to_class_ids = _extract_labels(
      os.path.join(dataset_dir, 'mnist_m', 'mnist_m_test_labels.txt'))

  # Convert the train, validation, and test sets.
  _convert_dataset('train', train_filenames,
                   train_validation_filenames_to_class_ids, dataset_dir)
  _convert_dataset('valid', validation_filenames,
                   train_validation_filenames_to_class_ids, dataset_dir)
  _convert_dataset('test', test_filenames, test_filenames_to_class_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  print('\nFinished converting the MNIST-M dataset!') 
開發者ID:rky0930,項目名稱:yolo_v2,代碼行數:49,代碼來源:download_and_convert_mnist_m.py


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