65 lines
2.4 KiB
Python
65 lines
2.4 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.random`."""
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import warnings
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from tensorflow.python import tf2
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.data.util import random_seed
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import tensor_spec
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from tensorflow.python.ops import gen_dataset_ops
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from tensorflow.python.ops import gen_experimental_dataset_ops as ged_ops
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def _random( # pylint: disable=unused-private-name
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seed=None,
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rerandomize_each_iteration=None,
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name=None):
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"""See `Dataset.random()` for details."""
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return _RandomDataset(
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seed=seed,
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rerandomize_each_iteration=rerandomize_each_iteration,
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name=name)
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class _RandomDataset(dataset_ops.DatasetSource):
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"""A `Dataset` of pseudorandom values."""
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def __init__(self, seed=None, rerandomize_each_iteration=None, name=None):
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"""A `Dataset` of pseudorandom values."""
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self._seed, self._seed2 = random_seed.get_seed(seed)
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self._rerandomize = rerandomize_each_iteration
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self._name = name
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if rerandomize_each_iteration:
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if not tf2.enabled():
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warnings.warn("In TF 1, the `rerandomize_each_iteration=True` option "
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"is only supported for repeat-based epochs.")
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variant_tensor = ged_ops.random_dataset_v2(
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seed=self._seed,
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seed2=self._seed2,
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seed_generator=gen_dataset_ops.dummy_seed_generator(),
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rerandomize_each_iteration=self._rerandomize,
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**self._common_args)
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else:
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variant_tensor = ged_ops.random_dataset(
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seed=self._seed, seed2=self._seed2, **self._common_args)
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super().__init__(variant_tensor)
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@property
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def element_spec(self):
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return tensor_spec.TensorSpec([], dtypes.int64)
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