183 lines
6.4 KiB
Python
183 lines
6.4 KiB
Python
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# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""The implementation of `tf.data.Dataset.map`."""
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import warnings
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.ops import debug_mode
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from tensorflow.python.data.ops import structured_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import gen_dataset_ops
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def _map_v2(input_dataset, # pylint: disable=unused-private-name
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map_func,
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num_parallel_calls=None,
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deterministic=None,
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name=None):
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"""See `Dataset.map()` for details."""
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if num_parallel_calls is None or debug_mode.DEBUG_MODE:
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if deterministic is not None and not debug_mode.DEBUG_MODE:
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warnings.warn("The `deterministic` argument has no effect unless the "
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"`num_parallel_calls` argument is specified.")
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return _MapDataset(
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input_dataset, map_func, preserve_cardinality=True, name=name)
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else:
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return _ParallelMapDataset(
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input_dataset,
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map_func,
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num_parallel_calls=num_parallel_calls,
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deterministic=deterministic,
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preserve_cardinality=True,
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name=name)
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def _map_v1(input_dataset, # pylint: disable=unused-private-name
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map_func,
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num_parallel_calls=None,
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deterministic=None):
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"""See `Dataset.map()` for details."""
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if num_parallel_calls is None or debug_mode.DEBUG_MODE:
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return dataset_ops.DatasetV1Adapter(
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_MapDataset(input_dataset, map_func, preserve_cardinality=False))
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else:
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return dataset_ops.DatasetV1Adapter(
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_ParallelMapDataset(
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input_dataset,
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map_func,
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num_parallel_calls,
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deterministic,
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preserve_cardinality=False))
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def _map_v1_with_legacy_function( # pylint: disable=unused-private-name
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input_dataset,
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map_func,
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num_parallel_calls=None,
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deterministic=None):
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"""See `Dataset.map()` for details."""
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if num_parallel_calls is None:
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if deterministic is not None:
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warnings.warn("The `deterministic` argument has no effect unless the "
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"`num_parallel_calls` argument is specified.")
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return dataset_ops.DatasetV1Adapter(
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_MapDataset(
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input_dataset,
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map_func,
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preserve_cardinality=False,
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use_legacy_function=True))
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else:
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return dataset_ops.DatasetV1Adapter(
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_ParallelMapDataset(
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input_dataset,
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map_func,
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num_parallel_calls,
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deterministic,
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preserve_cardinality=False,
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use_legacy_function=True))
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class _MapDataset(dataset_ops.UnaryDataset):
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"""A `Dataset` that maps a function over elements in its input."""
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def __init__(self,
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input_dataset,
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map_func,
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use_inter_op_parallelism=True,
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preserve_cardinality=True,
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use_legacy_function=False,
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name=None):
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self._input_dataset = input_dataset
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self._use_inter_op_parallelism = use_inter_op_parallelism
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self._preserve_cardinality = preserve_cardinality
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self._map_func = structured_function.StructuredFunctionWrapper(
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map_func,
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self._transformation_name(),
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dataset=input_dataset,
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use_legacy_function=use_legacy_function)
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self._name = name
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variant_tensor = gen_dataset_ops.map_dataset(
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input_dataset._variant_tensor, # pylint: disable=protected-access
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self._map_func.function.captured_inputs,
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f=self._map_func.function,
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use_inter_op_parallelism=self._use_inter_op_parallelism,
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preserve_cardinality=self._preserve_cardinality,
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**self._common_args)
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super().__init__(input_dataset, variant_tensor)
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def _functions(self):
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return [self._map_func]
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@property
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def element_spec(self):
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return self._map_func.output_structure
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def _transformation_name(self):
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return "Dataset.map()"
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class _ParallelMapDataset(dataset_ops.UnaryDataset):
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"""A `Dataset` that maps a function over elements in its input in parallel."""
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def __init__(self,
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input_dataset,
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map_func,
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num_parallel_calls,
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deterministic,
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use_inter_op_parallelism=True,
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preserve_cardinality=False,
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use_legacy_function=False,
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name=None):
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"""See `Dataset.map()` for details."""
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self._input_dataset = input_dataset
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self._use_inter_op_parallelism = use_inter_op_parallelism
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self._map_func = structured_function.StructuredFunctionWrapper(
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map_func,
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self._transformation_name(),
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dataset=input_dataset,
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use_legacy_function=use_legacy_function)
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if deterministic is None:
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self._deterministic = "default"
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elif deterministic:
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self._deterministic = "true"
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else:
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self._deterministic = "false"
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self._preserve_cardinality = preserve_cardinality
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self._num_parallel_calls = ops.convert_to_tensor(
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num_parallel_calls, dtype=dtypes.int64, name="num_parallel_calls")
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self._name = name
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variant_tensor = gen_dataset_ops.parallel_map_dataset_v2(
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input_dataset._variant_tensor, # pylint: disable=protected-access
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self._map_func.function.captured_inputs,
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f=self._map_func.function,
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num_parallel_calls=self._num_parallel_calls,
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deterministic=self._deterministic,
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use_inter_op_parallelism=self._use_inter_op_parallelism,
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preserve_cardinality=self._preserve_cardinality,
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**self._common_args)
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super().__init__(input_dataset, variant_tensor)
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def _functions(self):
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return [self._map_func]
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@property
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def element_spec(self):
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return self._map_func.output_structure
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def _transformation_name(self):
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return "Dataset.map()"
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