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119 lines
4.5 KiB
119 lines
4.5 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.fluid as fluid
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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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# Prepare data
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(fake_data_generator, batch_size=1)
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feeder = fluid.DataFeeder(
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feed_list=[
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fluid.layers.data(
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name='data', shape=[3], dtype='float32'),
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fluid.layers.data(
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name='label', shape=[1], dtype='int64'),
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],
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place=fluid.CPUPlace())
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fluid.recordio_writer.convert_reader_to_recordio_file(
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self.data_file_name, reader, feeder)
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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.data_file_name = './reader_reset_test.recordio'
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self.ins_shape = [3]
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self.batch_size = 5
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self.total_ins_num = self.batch_size * 20
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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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data_reader_handle = fluid.layers.io.open_files(
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filenames=[self.data_file_name],
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shapes=[[-1] + self.ins_shape, [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'],
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thread_num=1,
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pass_num=1)
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data_reader = fluid.layers.io.batch(data_reader_handle,
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self.batch_size)
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if with_double_buffer:
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data_reader = fluid.layers.double_buffer(data_reader)
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image, label = fluid.layers.read_file(data_reader)
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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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build_strategy = fluid.BuildStrategy()
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if with_double_buffer:
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build_strategy.enable_data_balance = True
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exec_strategy = fluid.ExecutionStrategy()
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parallel_exe = fluid.ParallelExecutor(
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use_cuda=self.use_cuda,
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main_program=main_prog,
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build_strategy=build_strategy,
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exec_strategy=exec_strategy)
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data_appeared = [False] * self.total_ins_num
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pass_count = 0
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while (True):
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try:
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data_val, label_val = parallel_exe.run(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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for l in label_val:
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self.assertFalse(data_appeared[l[0]])
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data_appeared[l[0]] = True
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except fluid.core.EOFException:
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pass_count += 1
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if with_double_buffer:
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data_appeared = data_appeared[:-parallel_exe.device_count *
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self.batch_size]
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for i in data_appeared:
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self.assertTrue(i)
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if pass_count < self.test_pass_num:
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data_appeared = [False] * self.total_ins_num
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data_reader_handle.reset()
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else:
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break
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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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