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95 lines
3.3 KiB
95 lines
3.3 KiB
# Copyright (c) 2020 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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"""
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TestCases for Monitor
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"""
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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 paddle.fluid.core as core
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import numpy as np
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import os
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import unittest
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class TestDatasetWithStat(unittest.TestCase):
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""" TestCases for Dataset. """
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def setUp(self):
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self.use_data_loader = False
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self.epoch_num = 10
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self.drop_last = False
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def test_dataset_run_with_stat(self):
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with open("test_in_memory_dataset_run_a.txt", "w") as f:
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data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
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data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
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data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
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f.write(data)
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with open("test_in_memory_dataset_run_b.txt", "w") as f:
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data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
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data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
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data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
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data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
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f.write(data)
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slots = ["slot1", "slot2", "slot3", "slot4"]
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slots_vars = []
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for slot in slots:
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var = fluid.layers.data(
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name=slot, shape=[1], dtype="int64", lod_level=1)
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slots_vars.append(var)
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dataset = paddle.distributed.InMemoryDataset()
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dataset._set_batch_size(32)
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dataset._set_thread(3)
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dataset.set_filelist([
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"test_in_memory_dataset_run_a.txt",
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"test_in_memory_dataset_run_b.txt"
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])
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dataset._set_pipe_command("cat")
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dataset._set_use_var(slots_vars)
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dataset.load_into_memory()
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dataset._set_fea_eval(1, True)
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dataset.slots_shuffle(["slot1"])
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exe = fluid.Executor(fluid.CPUPlace())
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exe.run(fluid.default_startup_program())
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if self.use_data_loader:
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data_loader = fluid.io.DataLoader.from_dataset(dataset,
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fluid.cpu_places(),
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self.drop_last)
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for i in range(self.epoch_num):
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for data in data_loader():
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exe.run(fluid.default_main_program(), feed=data)
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else:
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for i in range(self.epoch_num):
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try:
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exe.train_from_dataset(fluid.default_main_program(),
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dataset)
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except Exception as e:
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self.assertTrue(False)
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int_stat = core.get_int_stats()
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# total 56 keys
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print(int_stat["STAT_total_feasign_num_in_mem"])
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os.remove("./test_in_memory_dataset_run_a.txt")
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os.remove("./test_in_memory_dataset_run_b.txt")
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if __name__ == '__main__':
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unittest.main()
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