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70 lines
2.3 KiB
70 lines
2.3 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 paddle
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import paddle.fluid as fluid
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import contextlib
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import unittest
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def train_simulator(test_batch_size=10):
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if test_batch_size <= 0:
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raise ValueError("batch_size should be a positive integeral value, "
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"but got batch_size={}".format(test_batch_size))
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None)
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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cost = fluid.layers.square_error_cost(input=y_predict, label=y)
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avg_cost = fluid.layers.mean(cost)
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost)
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# Calculate memory usage in current network config
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lower_usage, upper_usage, unit = fluid.contrib.memory_usage(
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fluid.default_main_program(), batch_size=test_batch_size)
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print("memory usage is about %.3f - %.3f %s" %
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(lower_usage, upper_usage, unit))
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class TestMemoryUsage(unittest.TestCase):
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def test_with_unit_B(self):
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with self.program_scope_guard():
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train_simulator()
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def test_with_unit_KB(self):
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with self.program_scope_guard():
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train_simulator(test_batch_size=1000)
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def test_with_unit_MB(self):
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with self.program_scope_guard():
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train_simulator(test_batch_size=100000)
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@contextlib.contextmanager
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def program_scope_guard(self):
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prog = fluid.Program()
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startup_prog = fluid.Program()
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scope = fluid.core.Scope()
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with fluid.scope_guard(scope):
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with fluid.program_guard(prog, startup_prog):
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yield
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if __name__ == '__main__':
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unittest.main()
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