def __init__(self,
filenames,
compression_type=None,
buffer_size=None,
name=None):
"""Creates a `TFRecordDataset`.
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 representing the number of
bytes in the read buffer. 0 means no buffering.
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)
variant_tensor = gen_dataset_ops.tf_record_dataset(
self._filenames,
self._compression_type,
self._buffer_size,
metadata=self._metadata.SerializeToString())
super(_TFRecordDataset, self).__init__(variant_tensor)
def __init__(self,
input_dataset,
map_func,
cycle_length,
block_length,
sloppy,
buffer_output_elements,
prefetch_input_elements,
name=None):
"""See `tf.data.experimental.parallel_interleave()` for details."""
self._input_dataset = input_dataset
self._map_func = structured_function.StructuredFunctionWrapper(
map_func, self._transformation_name(), dataset=input_dataset)
if not isinstance(self._map_func.output_structure,
dataset_ops.DatasetSpec):
raise TypeError(
"The `map_func` argument must return a `Dataset` object. Got "
f"{_get_type(self._map_func.output_structure)!r}.")
self._element_spec = self._map_func.output_structure._element_spec # pylint: disable=protected-access
self._cycle_length = ops.convert_to_tensor(cycle_length,
dtype=dtypes.int64,
name="cycle_length")
self._block_length = ops.convert_to_tensor(block_length,
dtype=dtypes.int64,
name="block_length")
self._buffer_output_elements = convert.optional_param_to_tensor(
"buffer_output_elements",
buffer_output_elements,
argument_default=2 * block_length)
self._prefetch_input_elements = convert.optional_param_to_tensor(
"prefetch_input_elements",
prefetch_input_elements,
argument_default=2 * cycle_length)
if sloppy is None:
self._deterministic = "default"
elif sloppy:
self._deterministic = "false"
else:
self._deterministic = "true"
self._metadata = dataset_metadata_pb2.Metadata()
if name:
self._metadata.name = dataset_ops._validate_and_encode(name)
kwargs = self._flat_structure
if name or compat.forward_compatible(2021, 9, 30):
kwargs["metadata"] = self._metadata.SerializeToString()
variant_tensor = ged_ops.legacy_parallel_interleave_dataset_v2(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
self._map_func.function.captured_inputs,
self._cycle_length,
self._block_length,
self._buffer_output_elements,
self._prefetch_input_elements,
f=self._map_func.function,
deterministic=self._deterministic,
**kwargs)
super(ParallelInterleaveDataset,
self).__init__(input_dataset, variant_tensor)
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,
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)
