本文整理汇总了Python中slim.datasets.dataset_utils.write_label_file方法的典型用法代码示例。如果您正苦于以下问题：Python dataset_utils.write_label_file方法的具体用法？Python dataset_utils.write_label_file怎么用？Python dataset_utils.write_label_file使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类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个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: run
# 需要导入模块: from slim.datasets import dataset_utils [as 别名]
# 或者: from slim.datasets.dataset_utils import write_label_file [as 别名]
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!')