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# Copyright (c) 2018 PaddlePaddle 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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import paddle.fluid as fluid
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import unittest
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import os
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import tempfile
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class TestCheckpoint(unittest.TestCase):
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def setUp(self):
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self.dirname = tempfile.mktemp()
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self.max_num_checkpoints = 3
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self.epoch_interval = 1
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self.step_interval = 1
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self.trainer_id = 0
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self.chief = self.trainer_id == 0
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self.place = fluid.CPUPlace()
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self.epoch_id = 100
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self.step_id = 20
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def test_checkpoint(self):
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self.save_checkpoint()
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serial = fluid.io.get_latest_checkpoint_serial(self.dirname)
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self.assertTrue(serial >= 0)
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trainer_args = ["epoch_id", "step_id"]
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epoch_id, step_id = fluid.io.load_trainer_args(
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self.dirname, serial, self.trainer_id, trainer_args)
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self.assertEqual(self.step_id, int(step_id))
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self.assertEqual(self.epoch_id, int(epoch_id))
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program = fluid.Program()
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with fluid.program_guard(program):
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exe = fluid.Executor(self.place)
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fluid.io.load_checkpoint(exe, self.dirname, serial, program)
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fluid.io.clean_checkpoint(self.dirname, delete_dir=True)
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self.assertFalse(os.path.isdir(self.dirname))
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def save_checkpoint(self):
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config = fluid.CheckpointConfig(self.dirname, self.max_num_checkpoints,
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self.epoch_interval, self.step_interval)
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trainer_args = {}
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trainer_args["epoch_id"] = self.epoch_id
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trainer_args["step_id"] = self.step_id
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program = fluid.Program()
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with fluid.program_guard(program):
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program.global_block().create_var(
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name="scale_0",
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psersistable=True,
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dtype="float32",
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shape=[32, 32])
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exe = fluid.Executor(self.place)
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for i in xrange(10):
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fluid.io.save_checkpoint(exe, config.checkpoint_dir,
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self.trainer_id, trainer_args, program,
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config.max_num_checkpoints)
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if __name__ == '__main__':
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unittest.main()
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