73 lines
3.0 KiB
Python
73 lines
3.0 KiB
Python
# Copyright 2018 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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from absl.testing import parameterized
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from tensorflow.python.distribute import combinations
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from tensorflow.python.distribute import strategy_combinations
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from tensorflow.python.eager import test
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import variables as variables_lib
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from tensorflow.python.training.tracking import util as trackable_utils
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class TrainingCheckpointTests(test.TestCase, parameterized.TestCase):
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@combinations.generate(
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combinations.combine(
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distribution=[
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strategy_combinations.mirrored_strategy_with_one_cpu,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.tpu_strategy,
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strategy_combinations.tpu_strategy_packed_var,
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strategy_combinations.central_storage_strategy_with_two_gpus,
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],
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mode=["eager"]))
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def testInitializeFromCheckpoint(self, distribution):
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variable_shape = [5]
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save_checkpoint = trackable_utils.Checkpoint(v=variables_lib.Variable(
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array_ops.ones(variable_shape)))
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save_path = save_checkpoint.save(
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os.path.join(self.get_temp_dir(), "checkpoint"))
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with distribution.scope():
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restore_checkpoint = trackable_utils.Checkpoint()
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restore_checkpoint.restore(save_path)
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initial_value = restore_checkpoint._preload_simple_restoration(
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"v")
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v = variables_lib.Variable(initial_value)
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# Check that the variable is now tagged as restored. `Checkpoint` then
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# knows it doesn't have to restore `v`'s value when it's assigned to an
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# object.
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self.assertGreater(v._update_uid, 0)
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self.assertAllClose(array_ops.ones(variable_shape), v)
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v.assign(array_ops.zeros(variable_shape))
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# Assignment to an object should not trigger restoration, since we already
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# restored the object through an initializer. This wouldn't be a
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# correctness issue, but it would mean that models would use twice as much
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# memory when loading (the buffer already assigned to the variable, and
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# the new restoration).
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restore_checkpoint.v = v
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self.assertAllClose(array_ops.zeros(variable_shape), v)
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if __name__ == "__main__":
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test.main()
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