def _as_variant_tensor(self):
# pylint: disable=protected-access
return ged_ops.experimental_parallel_interleave_dataset(
self._input_dataset._as_variant_tensor(),
self._map_func.function.captured_inputs,
self._cycle_length,
self._block_length,
self._sloppy,
self._buffer_output_elements,
self._prefetch_input_elements,
f=self._map_func.function,
**dataset_ops.flat_structure(structure=self._output_structure))
def _as_variant_tensor(self):
# pylint: disable=protected-access
return ged_ops.experimental_parallel_interleave_dataset(
self._input_dataset._as_variant_tensor(),
self._map_func.function.captured_inputs,
self._cycle_length,
self._block_length,
self._sloppy,
self._buffer_output_elements,
self._prefetch_input_elements,
f=self._map_func.function,
**dataset_ops.flat_structure(structure=self._output_structure))
def __init__(self, input_dataset, map_func, cycle_length, block_length,
sloppy, buffer_output_elements, prefetch_input_elements):
"""See `tf.data.experimental.parallel_interleave()` for details."""
self._input_dataset = input_dataset
self._map_func = dataset_ops.StructuredFunctionWrapper(
map_func, self._transformation_name(), dataset=input_dataset)
if not isinstance(self._map_func.output_structure,
dataset_ops.DatasetSpec):
raise TypeError("`map_func` must return a `Dataset` object.")
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._sloppy = ops.convert_to_tensor(sloppy,
dtype=dtypes.bool,
name="sloppy")
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 compat.forward_compatible(2019, 8, 3):
variant_tensor = ged_ops.parallel_interleave_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
self._map_func.function.captured_inputs,
self._cycle_length,
self._block_length,
self._sloppy,
self._buffer_output_elements,
self._prefetch_input_elements,
f=self._map_func.function,
**self._flat_structure)
else:
variant_tensor = ged_ops.experimental_parallel_interleave_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
self._map_func.function.captured_inputs,
self._cycle_length,
self._block_length,
self._sloppy,
self._buffer_output_elements,
self._prefetch_input_elements,
f=self._map_func.function,
**self._flat_structure)
super(ParallelInterleaveDataset,
self).__init__(input_dataset, variant_tensor)
def __init__(self, input_dataset, map_func, cycle_length, block_length,
sloppy, buffer_output_elements, prefetch_input_elements):
"""See `tf.data.experimental.parallel_interleave()` for details."""
self._input_dataset = input_dataset
self._map_func = dataset_ops.StructuredFunctionWrapper(
map_func, self._transformation_name(), dataset=input_dataset)
if not isinstance(self._map_func.output_structure,
dataset_ops.DatasetStructure):
raise TypeError("`map_func` must return a `Dataset` object.")
self._structure = self._map_func.output_structure._element_structure # 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._sloppy = ops.convert_to_tensor(
sloppy, dtype=dtypes.bool, name="sloppy")
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)
variant_tensor = ged_ops.experimental_parallel_interleave_dataset(
self._input_dataset._variant_tensor, # pylint: disable=protected-access
self._map_func.function.captured_inputs,
self._cycle_length,
self._block_length,
self._sloppy,
self._buffer_output_elements,
self._prefetch_input_elements,
f=self._map_func.function,
**dataset_ops.flat_structure(self))
super(ParallelInterleaveDataset, self).__init__(input_dataset,
variant_tensor)