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95 lines
3.3 KiB
95 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 os
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os.environ['CPU_NUM'] = str(1)
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import paddle.fluid as fluid
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from paddle.fluid import compiler
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import paddle
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import numpy as np
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import unittest
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class TestReaderReset(unittest.TestCase):
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def prepare_data(self):
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def fake_data_generator():
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for n in range(self.total_ins_num):
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yield np.ones(self.ins_shape) * n, n
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return fake_data_generator
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def setUp(self):
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self.use_cuda = fluid.core.is_compiled_with_cuda()
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self.ins_shape = [3]
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self.batch_size = 5
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self.batch_num = 20
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self.total_ins_num = self.batch_size * self.batch_num
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self.test_pass_num = 100
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self.prepare_data()
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def main(self, with_double_buffer):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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image = fluid.layers.data(
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name='image', shape=self.ins_shape, dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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data_reader_handle = fluid.io.PyReader(
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feed_list=[image, label],
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capacity=16,
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iterable=False,
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use_double_buffer=with_double_buffer)
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fetch_list = [image.name, label.name]
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place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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data_reader_handle.decorate_sample_list_generator(
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paddle.batch(
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self.prepare_data(), batch_size=self.batch_size))
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train_cp = compiler.CompiledProgram(main_prog).with_data_parallel()
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batch_id = 0
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pass_count = 0
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while pass_count < self.test_pass_num:
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data_reader_handle.start()
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try:
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while True:
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data_val, label_val = exe.run(train_cp,
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fetch_list=fetch_list,
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return_numpy=True)
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ins_num = data_val.shape[0]
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broadcasted_label = np.ones((ins_num, ) + tuple(
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self.ins_shape)) * label_val.reshape((ins_num, 1))
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self.assertEqual(data_val.all(), broadcasted_label.all())
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batch_id += 1
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except fluid.core.EOFException:
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data_reader_handle.reset()
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pass_count += 1
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self.assertEqual(pass_count * self.batch_num, batch_id)
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self.assertEqual(pass_count, self.test_pass_num)
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def test_all(self):
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self.main(with_double_buffer=False)
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self.main(with_double_buffer=True)
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if __name__ == '__main__':
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unittest.main()
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