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86 lines
3.3 KiB
86 lines
3.3 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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import paddle.fluid as fluid
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class TestExponentialMovingAverage(unittest.TestCase):
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def setUp(self):
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self._places = [fluid.CPUPlace()]
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if fluid.core.is_compiled_with_cuda():
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self._places.append(fluid.CUDAPlace(0))
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self._ema_decay = 0.999
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self._param_name = "fc.weight"
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self._train_program = fluid.Program()
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self._startup_prog = fluid.Program()
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with fluid.program_guard(self._train_program, self._startup_prog):
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with fluid.unique_name.guard():
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data = fluid.data(name='x', shape=[-1, 5], dtype='float32')
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hidden = fluid.layers.fc(input=data,
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size=10,
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param_attr=self._param_name)
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cost = fluid.layers.mean(hidden)
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self._test_program = fluid.default_main_program().clone(
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for_test=True)
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optimizer = fluid.optimizer.Adam(learning_rate=0.001)
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optimizer.minimize(cost)
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self._ema = fluid.optimizer.ExponentialMovingAverage(
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self._ema_decay)
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self._ema.update()
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def train(self, place):
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exe = fluid.Executor(place)
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exe.run(self._startup_prog)
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params = []
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for pass_id in range(2):
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for batch_id in range(3):
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data = np.random.random(size=(10, 5)).astype('float32')
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tmp_param = np.array(fluid.global_scope().find_var(
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self._param_name).get_tensor())
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exe.run(program=self._train_program, feed={'x': data})
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tmp_param = np.array(fluid.global_scope().find_var(
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self._param_name).get_tensor())
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params.append(tmp_param)
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with self._ema.apply(exe):
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final_ema = np.array(fluid.global_scope().find_var(self._param_name)
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.get_tensor())
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data = np.random.random(size=(10, 5)).astype('float32')
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exe.run(program=self._test_program, feed={'x': data})
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return params, final_ema
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def test_check_ema(self):
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for place in self._places:
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params, final_ema = self.train(place)
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manu_ema = np.zeros_like(final_ema)
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if len(params) > 0:
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for param in params:
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manu_ema = self._ema_decay * manu_ema + (1 - self._ema_decay
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) * param
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manu_ema = manu_ema / (1.0 - self._ema_decay**len(params))
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self.assertTrue(np.allclose(manu_ema, final_ema))
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if __name__ == '__main__':
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unittest.main()
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