forked from mindspore-Ecosystem/mindspore
modify longtime python ut
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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"""
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@File : test_summary.py
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@Author:
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@Date : 2019-07-4
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@Desc : test summary function
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"""
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import os
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import logging
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import time
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import numpy as np
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from mindspore.train.summary.summary_record import SummaryRecord, _cache_summary_tensor_data
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from mindspore.common.tensor import Tensor
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CUR_DIR = os.getcwd()
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SUMMARY_DIR = CUR_DIR + "/test_temp_summary_event_file/"
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log = logging.getLogger("test")
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log.setLevel(level=logging.ERROR)
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def get_now_time_ns():
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"""get the time of second"""
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time_second = int(time.time_ns())
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return time_second
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def get_test_data(step):
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""" get_test_data """
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# pylint: disable=unused-argument
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test_data_list = []
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tag1 = "xt1[:Tensor]"
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tag2 = "xt2[:Tensor]"
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tag3 = "xt3[:Tensor]"
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np1 = np.random.random((5, 4, 3, 5))
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np2 = np.random.random((5, 5, 3, 5))
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np3 = np.random.random((4, 5, 3, 5))
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dict1 = {}
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dict1["name"] = tag1
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dict1["data"] = Tensor(np1)
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dict2 = {}
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dict2["name"] = tag2
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dict2["data"] = Tensor(np2)
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dict3 = {}
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dict3["name"] = tag3
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dict3["data"] = Tensor(np3)
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test_data_list.append(dict1)
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test_data_list.append(dict2)
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return test_data_list
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# Test 1: summary sample of scalar
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def test_summary_performance():
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""" test_summary_performance """
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log.debug("begin test_scalar_summary_sample")
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current_time = time.time()
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print("time = ", current_time)
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# step 0: create the thread
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test_writer = SummaryRecord(SUMMARY_DIR, flush_time=120)
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# step 1: create the test data for summary
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old_time = get_now_time_ns()
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# step 2: create the Event
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for i in range(1, 10):
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test_data = get_test_data(i)
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_cache_summary_tensor_data(test_data)
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test_writer.record(i)
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now_time = get_now_time_ns()
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consume_time = (now_time - old_time)/1000/1000
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old_time = now_time
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print("step test_summary_performance conusmer time is:", consume_time)
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# step 3: send the event to mq
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# step 4: accept the event and write the file
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test_writer.flush()
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test_writer.close()
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current_time = time.time() - current_time
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print("consume time = ", current_time)
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log.debug("finished test_scalar_summary_sample")
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