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96 lines
4.0 KiB
96 lines
4.0 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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import unittest
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import os
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import numpy as np
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import paddle.fluid as fluid
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import paddle.fluid.profiler as profiler
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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class TestProfiler(unittest.TestCase):
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def net_profiler(self, state, profile_path='/tmp/profile'):
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enable_if_gpu = state == 'GPU' or state == "All"
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if enable_if_gpu and not core.is_compiled_with_cuda():
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return
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startup_program = fluid.Program()
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main_program = fluid.Program()
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with fluid.program_guard(main_program, startup_program):
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image = fluid.layers.data(name='x', shape=[784], dtype='float32')
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hidden1 = fluid.layers.fc(input=image, size=64, act='relu')
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i = layers.zeros(shape=[1], dtype='int64')
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counter = fluid.layers.zeros(
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shape=[1], dtype='int64', force_cpu=True)
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until = layers.fill_constant([1], dtype='int64', value=10)
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data_arr = layers.array_write(hidden1, i)
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cond = fluid.layers.less_than(x=counter, y=until)
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while_op = fluid.layers.While(cond=cond)
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with while_op.block():
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hidden_n = fluid.layers.fc(input=hidden1, size=64, act='relu')
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layers.array_write(hidden_n, i, data_arr)
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fluid.layers.increment(x=counter, value=1, in_place=True)
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layers.less_than(x=counter, y=until, cond=cond)
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hidden_n = layers.array_read(data_arr, i)
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hidden2 = fluid.layers.fc(input=hidden_n, size=64, act='relu')
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predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
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label = fluid.layers.data(name='y', shape=[1], dtype='int64')
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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batch_size = fluid.layers.create_tensor(dtype='int64')
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batch_acc = fluid.layers.accuracy(
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input=predict, label=label, total=batch_size)
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optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
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opts = optimizer.minimize(avg_cost, startup_program=startup_program)
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place = fluid.CPUPlace() if state == 'CPU' else fluid.CUDAPlace(0)
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exe = fluid.Executor(place)
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exe.run(startup_program)
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pass_acc_calculator = fluid.average.WeightedAverage()
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with profiler.profiler(state, 'total', profile_path) as prof:
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for iter in range(10):
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if iter == 2:
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profiler.reset_profiler()
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x = np.random.random((32, 784)).astype("float32")
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y = np.random.randint(0, 10, (32, 1)).astype("int64")
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outs = exe.run(main_program,
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feed={'x': x,
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'y': y},
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fetch_list=[avg_cost, batch_acc, batch_size])
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acc = np.array(outs[1])
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b_size = np.array(outs[2])
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pass_acc_calculator.add(value=acc, weight=b_size)
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pass_acc = pass_acc_calculator.eval()
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def test_cpu_profiler(self):
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self.net_profiler('CPU')
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def test_cuda_profiler(self):
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self.net_profiler('GPU')
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def test_all_profiler(self):
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self.net_profiler('All', '/tmp/profile_out')
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with open('/tmp/profile_out', 'r') as f:
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self.assertGreater(len(f.read()), 0)
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if __name__ == '__main__':
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unittest.main()
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