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Paddle/python/paddle/fluid/tests/unittests/test_monitor.py

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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
TestCases for Monitor
"""
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import numpy as np
import os
import unittest
class TestDatasetWithStat(unittest.TestCase):
""" TestCases for Dataset. """
def setUp(self):
self.use_data_loader = False
self.epoch_num = 10
self.drop_last = False
def test_dataset_run_with_stat(self):
with open("test_in_memory_dataset_run_a.txt", "w") as f:
data = "1 1 2 3 3 4 5 5 5 5 1 1\n"
data += "1 2 2 3 4 4 6 6 6 6 1 2\n"
data += "1 3 2 3 5 4 7 7 7 7 1 3\n"
f.write(data)
with open("test_in_memory_dataset_run_b.txt", "w") as f:
data = "1 4 2 3 3 4 5 5 5 5 1 4\n"
data += "1 5 2 3 4 4 6 6 6 6 1 5\n"
data += "1 6 2 3 5 4 7 7 7 7 1 6\n"
data += "1 7 2 3 6 4 8 8 8 8 1 7\n"
f.write(data)
slots = ["slot1", "slot2", "slot3", "slot4"]
slots_vars = []
for slot in slots:
var = fluid.layers.data(
name=slot, shape=[1], dtype="int64", lod_level=1)
slots_vars.append(var)
dataset = paddle.distributed.InMemoryDataset()
dataset._set_batch_size(32)
dataset._set_thread(3)
dataset.set_filelist([
"test_in_memory_dataset_run_a.txt",
"test_in_memory_dataset_run_b.txt"
])
dataset._set_pipe_command("cat")
dataset._set_use_var(slots_vars)
dataset.load_into_memory()
dataset._set_fea_eval(1, True)
dataset.slots_shuffle(["slot1"])
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
if self.use_data_loader:
data_loader = fluid.io.DataLoader.from_dataset(dataset,
fluid.cpu_places(),
self.drop_last)
for i in range(self.epoch_num):
for data in data_loader():
exe.run(fluid.default_main_program(), feed=data)
else:
for i in range(self.epoch_num):
try:
exe.train_from_dataset(fluid.default_main_program(),
dataset)
except Exception as e:
self.assertTrue(False)
int_stat = core.get_int_stats()
# total 56 keys
print(int_stat["STAT_total_feasign_num_in_mem"])
os.remove("./test_in_memory_dataset_run_a.txt")
os.remove("./test_in_memory_dataset_run_b.txt")
if __name__ == '__main__':
unittest.main()