171 lines
6.3 KiB
Python
171 lines
6.3 KiB
Python
# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""The implementation of `tf.data.Dataset.interleave`."""
|
|
|
|
import warnings
|
|
|
|
from tensorflow.python.data.ops import dataset_ops
|
|
from tensorflow.python.data.ops import debug_mode
|
|
from tensorflow.python.data.ops import structured_function
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.ops import gen_dataset_ops
|
|
|
|
|
|
def _interleave( # pylint: disable=unused-private-name
|
|
input_dataset,
|
|
map_func,
|
|
cycle_length=None,
|
|
block_length=None,
|
|
num_parallel_calls=None,
|
|
deterministic=None,
|
|
name=None):
|
|
"""See `Dataset.interleave()` for details."""
|
|
if block_length is None:
|
|
block_length = 1
|
|
|
|
if cycle_length is None:
|
|
cycle_length = dataset_ops.AUTOTUNE
|
|
|
|
if num_parallel_calls is None or debug_mode.DEBUG_MODE:
|
|
if deterministic is not None and not debug_mode.DEBUG_MODE:
|
|
warnings.warn("The `deterministic` argument has no effect unless the "
|
|
"`num_parallel_calls` argument is specified.")
|
|
return _InterleaveDataset(
|
|
input_dataset, map_func, cycle_length, block_length, name=name)
|
|
else:
|
|
return _ParallelInterleaveDataset(
|
|
input_dataset,
|
|
map_func,
|
|
cycle_length,
|
|
block_length,
|
|
num_parallel_calls,
|
|
deterministic=deterministic,
|
|
name=name)
|
|
|
|
|
|
class _InterleaveDataset(dataset_ops.UnaryDataset):
|
|
"""A `Dataset` that interleaves the result of transformed inputs."""
|
|
|
|
def __init__(self,
|
|
input_dataset,
|
|
map_func,
|
|
cycle_length,
|
|
block_length,
|
|
name=None):
|
|
"""See `Dataset.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"{dataset_ops.get_type(self._map_func.output_structure)!r}.")
|
|
self._structure = 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._name = name
|
|
variant_tensor = gen_dataset_ops.interleave_dataset(
|
|
input_dataset._variant_tensor, # pylint: disable=protected-access
|
|
self._map_func.function.captured_inputs, # pylint: disable=protected-access
|
|
self._cycle_length,
|
|
self._block_length,
|
|
f=self._map_func.function,
|
|
**self._common_args)
|
|
super().__init__(input_dataset, variant_tensor)
|
|
|
|
def _functions(self):
|
|
return [self._map_func]
|
|
|
|
@property
|
|
def element_spec(self):
|
|
return self._structure
|
|
|
|
def _transformation_name(self):
|
|
return "Dataset.interleave()"
|
|
|
|
|
|
class _ParallelInterleaveDataset(dataset_ops.UnaryDataset):
|
|
"""A `Dataset` that maps a function over its input and interleaves the result.
|
|
"""
|
|
|
|
def __init__(self,
|
|
input_dataset,
|
|
map_func,
|
|
cycle_length,
|
|
block_length,
|
|
num_parallel_calls,
|
|
buffer_output_elements=dataset_ops.AUTOTUNE,
|
|
prefetch_input_elements=dataset_ops.AUTOTUNE,
|
|
deterministic=None,
|
|
name=None):
|
|
"""See `Dataset.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"{dataset_ops.get_type(self._map_func.output_structure)!r}.")
|
|
self._structure = 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 = ops.convert_to_tensor(
|
|
buffer_output_elements,
|
|
dtype=dtypes.int64,
|
|
name="buffer_output_elements")
|
|
self._prefetch_input_elements = ops.convert_to_tensor(
|
|
prefetch_input_elements,
|
|
dtype=dtypes.int64,
|
|
name="prefetch_input_elements")
|
|
|
|
self._num_parallel_calls = ops.convert_to_tensor(
|
|
num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls")
|
|
if deterministic is None:
|
|
deterministic_string = "default"
|
|
elif deterministic:
|
|
deterministic_string = "true"
|
|
else:
|
|
deterministic_string = "false"
|
|
|
|
self._name = name
|
|
variant_tensor = gen_dataset_ops.parallel_interleave_dataset_v4(
|
|
input_dataset._variant_tensor, # pylint: disable=protected-access
|
|
self._map_func.function.captured_inputs, # pylint: disable=protected-access
|
|
self._cycle_length,
|
|
self._block_length,
|
|
self._buffer_output_elements,
|
|
self._prefetch_input_elements,
|
|
self._num_parallel_calls,
|
|
f=self._map_func.function,
|
|
deterministic=deterministic_string,
|
|
**self._common_args)
|
|
super().__init__(input_dataset, variant_tensor)
|
|
|
|
def _functions(self):
|
|
return [self._map_func]
|
|
|
|
@property
|
|
def element_spec(self):
|
|
return self._structure
|
|
|
|
def _transformation_name(self):
|
|
return "Dataset.interleave()"
|