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._data_format_type, self._compression_type, self._block_count, self.block_index)