143 lines
5.4 KiB
Python
143 lines
5.4 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.batch`."""
|
|
|
|
import warnings
|
|
|
|
from tensorflow.python.data.ops import dataset_ops
|
|
from tensorflow.python.data.ops import debug_mode
|
|
from tensorflow.python.data.util import nest
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.framework import tensor_util
|
|
from tensorflow.python.ops import gen_dataset_ops
|
|
|
|
|
|
def _batch(input_dataset,
|
|
batch_size,
|
|
drop_remainder=False,
|
|
num_parallel_calls=None,
|
|
deterministic=None,
|
|
name=None):
|
|
"""See `Dataset.batch` for details."""
|
|
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 _BatchDataset(input_dataset, batch_size, drop_remainder, name=name)
|
|
else:
|
|
return _ParallelBatchDataset(
|
|
input_dataset,
|
|
batch_size,
|
|
drop_remainder,
|
|
num_parallel_calls,
|
|
deterministic,
|
|
name=name)
|
|
|
|
|
|
class _BatchDataset(dataset_ops.UnaryDataset):
|
|
"""A `Dataset` that batches contiguous elements from its input."""
|
|
|
|
def __init__(self, input_dataset, batch_size, drop_remainder, name=None):
|
|
"""See `Dataset.batch()` for details."""
|
|
self._input_dataset = input_dataset
|
|
self._batch_size = ops.convert_to_tensor(
|
|
batch_size, dtype=dtypes.int64, name="batch_size")
|
|
self._drop_remainder = ops.convert_to_tensor(
|
|
drop_remainder, dtype=dtypes.bool, name="drop_remainder")
|
|
|
|
constant_drop_remainder = tensor_util.constant_value(self._drop_remainder)
|
|
# pylint: disable=protected-access
|
|
if constant_drop_remainder:
|
|
# NOTE(mrry): `constant_drop_remainder` may be `None` (unknown statically)
|
|
# or `False` (explicitly retaining the remainder).
|
|
# pylint: disable=g-long-lambda
|
|
constant_batch_size = tensor_util.constant_value(self._batch_size)
|
|
self._structure = nest.map_structure(
|
|
lambda component_spec: component_spec._batch(constant_batch_size),
|
|
input_dataset.element_spec)
|
|
else:
|
|
self._structure = nest.map_structure(
|
|
lambda component_spec: component_spec._batch(None),
|
|
input_dataset.element_spec)
|
|
|
|
self._name = name
|
|
variant_tensor = gen_dataset_ops.batch_dataset_v2(
|
|
input_dataset._variant_tensor,
|
|
batch_size=self._batch_size,
|
|
drop_remainder=self._drop_remainder,
|
|
**self._common_args)
|
|
super().__init__(input_dataset, variant_tensor)
|
|
|
|
@property
|
|
def element_spec(self):
|
|
return self._structure
|
|
|
|
|
|
class _ParallelBatchDataset(dataset_ops.UnaryDataset):
|
|
"""A `Dataset` that batches contiguous elements from its input in parallel."""
|
|
|
|
def __init__(self,
|
|
input_dataset,
|
|
batch_size,
|
|
drop_remainder,
|
|
num_parallel_calls,
|
|
deterministic,
|
|
name=None):
|
|
"""See `Dataset.batch()` for details."""
|
|
self._input_dataset = input_dataset
|
|
self._batch_size = ops.convert_to_tensor(
|
|
batch_size, dtype=dtypes.int64, name="batch_size")
|
|
self._drop_remainder = ops.convert_to_tensor(
|
|
drop_remainder, dtype=dtypes.bool, name="drop_remainder")
|
|
self._num_parallel_calls = ops.convert_to_tensor(
|
|
num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls")
|
|
if deterministic is None:
|
|
self._deterministic = "default"
|
|
elif deterministic:
|
|
self._deterministic = "true"
|
|
else:
|
|
self._deterministic = "false"
|
|
|
|
constant_drop_remainder = tensor_util.constant_value(self._drop_remainder)
|
|
# pylint: disable=protected-access
|
|
if constant_drop_remainder:
|
|
# NOTE(mrry): `constant_drop_remainder` may be `None` (unknown statically)
|
|
# or `False` (explicitly retaining the remainder).
|
|
# pylint: disable=g-long-lambda
|
|
constant_batch_size = tensor_util.constant_value(self._batch_size)
|
|
self._structure = nest.map_structure(
|
|
lambda component_spec: component_spec._batch(constant_batch_size),
|
|
input_dataset.element_spec)
|
|
else:
|
|
self._structure = nest.map_structure(
|
|
lambda component_spec: component_spec._batch(None),
|
|
input_dataset.element_spec)
|
|
|
|
self._name = name
|
|
variant_tensor = gen_dataset_ops.parallel_batch_dataset(
|
|
input_dataset._variant_tensor,
|
|
batch_size=self._batch_size,
|
|
num_parallel_calls=self._num_parallel_calls,
|
|
drop_remainder=self._drop_remainder,
|
|
deterministic=self._deterministic,
|
|
**self._common_args)
|
|
|
|
super().__init__(input_dataset, variant_tensor)
|
|
|
|
@property
|
|
def element_spec(self):
|
|
return self._structure
|