def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = os.path.join(dl_manager.manual_dir, _DATASET)
if not tf.io.gfile.exists(path):
raise AssertionError(
f'You must download the dataset .tar.gz file and place it into {dl_manager.manual_dir}')
return {
'train': self._generate_examples(dl_manager.iter_archive(path), 'train'),
'valid': self._generate_examples(dl_manager.iter_archive(path), 'valid'),
'test': self._generate_examples(dl_manager.iter_archive(path), 'test')
}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
# TODO(blood_quality): Downloads the data and defines the splits
path = os.path.join(dl_manager.manual_dir, self.builder_config.dataset)
if not tf.io.gfile.exists(path):
raise AssertionError(
f'You must download the dataset .zip file and place it into {dl_manager.manual_dir}'
)
path_iter = dl_manager.iter_archive(path)
return {'train': self._generate_examples(path_iter)}
def _split_generators(self, dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
path = os.path.join(dl_manager.manual_dir, self.builder_config.dataset)
if not tf.io.gfile.exists(path):
raise AssertionError(
f'You must download the dataset .tar.gz file and place it into {dl_manager.manual_dir}'
)
if self.builder_config.selection == 'all':
path_iter = dl_manager.iter_archive(path)
return {'train': self._generate_examples(path_iter)}
