def __init__(self, filenames, compression_type=None, buffer_size=None):
"""Creates a `TextLineDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
to buffer. A value of 0 results in the default buffering values chosen
based on the compression type.
"""
self._filenames = filenames
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size",
buffer_size,
argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES)
variant_tensor = gen_dataset_ops.text_line_dataset(self._filenames,
self._compression_type,
self._buffer_size)
super(_TextLineDataset, self).__init__(variant_tensor)
def __init__(self, filenames, compression_type=None, buffer_size=None):
"""Creates a `TextLineDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
to buffer. A value of 0 results in the default buffering values chosen
based on the compression type.
"""
self._filenames = ops.convert_to_tensor(
filenames, dtype=dtypes.string, name="filenames")
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
variant_tensor = gen_dataset_ops.text_line_dataset(
self._filenames, self._compression_type, self._buffer_size)
super(TextLineDatasetV2, self).__init__(variant_tensor)
def __init__(self,
filenames,
compression_type=None,
buffer_size=None,
name=None):
"""Creates a `TextLineDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
buffer_size: (Optional.) A `tf.int64` scalar denoting the number of bytes
to buffer. A value of 0 results in the default buffering values chosen
based on the compression type.
name: (Optional.) A name for the tf.data operation.
"""
self._filenames = filenames
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size",
buffer_size,
argument_default=_DEFAULT_READER_BUFFER_SIZE_BYTES)
self._metadata = dataset_metadata_pb2.Metadata()
if name:
self._metadata.name = dataset_ops._validate_and_encode(name)
kwargs = {}
if name or compat.forward_compatible(2021, 9, 30):
kwargs["metadata"] = self._metadata.SerializeToString()
variant_tensor = gen_dataset_ops.text_line_dataset(
self._filenames, self._compression_type, self._buffer_size,
**kwargs)
super(_TextLineDataset, self).__init__(variant_tensor)
def _as_variant_tensor(self):
return gen_dataset_ops.text_line_dataset(
self._filenames, self._compression_type, self._buffer_size)
def _as_variant_tensor(self):
return gen_dataset_ops.text_line_dataset(
self._filenames, self._compression_type, self._buffer_size)
def _as_variant_tensor(self):
return gen_dataset_ops.text_line_dataset(
self._filenames, self._data_format_type, self._compression_type, self._block_count, self.block_index)
