def _as_variant_tensor(self):
if (self._compression_type is not None or
compat.forward_compatible(2018, 11, 30)):
return gen_dataset_ops.fixed_length_record_dataset_v2(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size, self._compression_type)
else:
return gen_dataset_ops.fixed_length_record_dataset(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size)
def _as_variant_tensor(self):
if (self._compression_type is not None
or compat.forward_compatible(2018, 11, 30)):
return gen_dataset_ops.fixed_length_record_dataset_v2(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size, self._compression_type)
else:
return gen_dataset_ops.fixed_length_record_dataset(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size)
def __init__(self,
filenames,
record_bytes,
header_bytes=None,
footer_bytes=None,
buffer_size=None,
compression_type=None,
name=None):
"""Creates a `FixedLengthRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
record_bytes: A `tf.int64` scalar representing the number of bytes in each
record.
header_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to skip at the start of a file.
footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to ignore at the end of a file.
buffer_size: (Optional.) A `tf.int64` scalar representing the number of
bytes to buffer when reading.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
name: (Optional.) A name for the tf.data operation.
"""
self._filenames = filenames
self._record_bytes = ops.convert_to_tensor(record_bytes,
dtype=dtypes.int64,
name="record_bytes")
self._header_bytes = convert.optional_param_to_tensor(
"header_bytes", header_bytes)
self._footer_bytes = convert.optional_param_to_tensor(
"footer_bytes", footer_bytes)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
self._metadata = dataset_metadata_pb2.Metadata()
if name:
self._metadata.name = dataset_ops._validate_and_encode(name)
variant_tensor = gen_dataset_ops.fixed_length_record_dataset_v2(
self._filenames,
self._header_bytes,
self._record_bytes,
self._footer_bytes,
self._buffer_size,
self._compression_type,
metadata=self._metadata.SerializeToString())
super(_FixedLengthRecordDataset, self).__init__(variant_tensor)
def __init__(self,
filenames,
record_bytes,
header_bytes=None,
footer_bytes=None,
buffer_size=None,
compression_type=None):
"""Creates a `FixedLengthRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
record_bytes: A `tf.int64` scalar representing the number of bytes in
each record.
header_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to skip at the start of a file.
footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to ignore at the end of a file.
buffer_size: (Optional.) A `tf.int64` scalar representing the number of
bytes to buffer when reading.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
"""
self._filenames = ops.convert_to_tensor(filenames,
dtype=dtypes.string,
name="filenames")
self._record_bytes = ops.convert_to_tensor(record_bytes,
dtype=dtypes.int64,
name="record_bytes")
self._header_bytes = convert.optional_param_to_tensor(
"header_bytes", header_bytes)
self._footer_bytes = convert.optional_param_to_tensor(
"footer_bytes", footer_bytes)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
if (self._compression_type is not None
or compat.forward_compatible(2018, 11, 30)):
variant_tensor = gen_dataset_ops.fixed_length_record_dataset_v2(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size, self._compression_type)
else:
variant_tensor = gen_dataset_ops.fixed_length_record_dataset(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size)
super(FixedLengthRecordDatasetV2, self).__init__(variant_tensor)
def __init__(self,
filenames,
record_bytes,
header_bytes=None,
footer_bytes=None,
buffer_size=None,
compression_type=None):
"""Creates a `FixedLengthRecordDataset`.
Args:
filenames: A `tf.string` tensor containing one or more filenames.
record_bytes: A `tf.int64` scalar representing the number of bytes in
each record.
header_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to skip at the start of a file.
footer_bytes: (Optional.) A `tf.int64` scalar representing the number of
bytes to ignore at the end of a file.
buffer_size: (Optional.) A `tf.int64` scalar representing the number of
bytes to buffer when reading.
compression_type: (Optional.) A `tf.string` scalar evaluating to one of
`""` (no compression), `"ZLIB"`, or `"GZIP"`.
"""
self._filenames = ops.convert_to_tensor(
filenames, dtype=dtypes.string, name="filenames")
self._record_bytes = ops.convert_to_tensor(
record_bytes, dtype=dtypes.int64, name="record_bytes")
self._header_bytes = convert.optional_param_to_tensor(
"header_bytes", header_bytes)
self._footer_bytes = convert.optional_param_to_tensor(
"footer_bytes", footer_bytes)
self._buffer_size = convert.optional_param_to_tensor(
"buffer_size", buffer_size, _DEFAULT_READER_BUFFER_SIZE_BYTES)
self._compression_type = convert.optional_param_to_tensor(
"compression_type",
compression_type,
argument_default="",
argument_dtype=dtypes.string)
if (self._compression_type is not None or
compat.forward_compatible(2018, 11, 30)):
variant_tensor = gen_dataset_ops.fixed_length_record_dataset_v2(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size, self._compression_type)
else:
variant_tensor = gen_dataset_ops.fixed_length_record_dataset(
self._filenames, self._header_bytes, self._record_bytes,
self._footer_bytes, self._buffer_size)
super(FixedLengthRecordDatasetV2, self).__init__(variant_tensor)
