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111 lines
3.6 KiB
111 lines
3.6 KiB
# Copyright 2019 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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import numpy as np
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import os
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import pytest
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import random
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import shutil
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import time
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore.common.tensor import Tensor
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from mindspore.ops import operations as P
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from mindspore.train.summary.summary_record import SummaryRecord
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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CUR_DIR = os.getcwd()
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SUMMARY_DIR_ME = CUR_DIR + "/test_me_summary_event_file/"
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SUMMARY_DIR_ME_TEMP = CUR_DIR + "/test_me_temp_summary_event_file/"
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def clean_environment_file(srcDir):
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if os.path.exists(srcDir):
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ls = os.listdir(srcDir)
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for line in ls:
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filePath = os.path.join(srcDir, line)
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os.remove(filePath)
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os.removedirs(srcDir)
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def save_summary_events_file(srcDir, desDir):
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if not os.path.exists(desDir):
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print("-- create desDir")
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os.makedirs(desDir)
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ls = os.listdir(srcDir)
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for line in ls:
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filePath = os.path.join(srcDir, line)
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if os.path.isfile(filePath):
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print("-- move events file : {}".format(filePath))
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shutil.copy(filePath, desDir)
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os.remove(filePath)
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os.removedirs(srcDir)
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class SummaryNet(nn.Cell):
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def __init__(self, tag_tuple=None, scalar=1):
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super(SummaryNet, self).__init__()
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self.summary_s = P.ScalarSummary()
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self.summary_i = P.ImageSummary()
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self.summary_t = P.TensorSummary()
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self.histogram_summary = P.HistogramSummary()
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self.add = P.TensorAdd()
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self.tag_tuple = tag_tuple
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self.scalar = scalar
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def construct(self, x, y):
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self.summary_i("image", x)
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self.summary_s("x1", x)
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z = self.add(x, y)
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self.summary_t("z1", z)
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self.histogram_summary("histogram", z)
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return z
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def train_summary_record_scalar_for_1(test_writer, steps, fwd_x, fwd_y):
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net = SummaryNet()
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out_me_dict = {}
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for i in range(0, steps):
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x = Tensor(np.array([1.1 + random.uniform(1, 10)]).astype(np.float32))
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y = Tensor(np.array([1.2 + random.uniform(1, 10)]).astype(np.float32))
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out_put = net(x, y)
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test_writer.record(i)
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print("-----------------output: %s-------------\n", out_put.asnumpy())
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out_me_dict[i] = out_put.asnumpy()
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return out_me_dict
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def me_scalar_summary(steps, tag=None, value=None):
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with SummaryRecord(SUMMARY_DIR_ME_TEMP) as test_writer:
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x = Tensor(np.array([1.1]).astype(np.float32))
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y = Tensor(np.array([1.2]).astype(np.float32))
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out_me_dict = train_summary_record_scalar_for_1(test_writer, steps, x, y)
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return out_me_dict
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_scalarsummary_scalar1_step10_summaryrecord1():
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clean_environment_file(SUMMARY_DIR_ME_TEMP)
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output_dict = me_scalar_summary(10)
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print("test_scalarsummary_scalar1_step10_summaryrecord1 \n", output_dict)
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save_summary_events_file(SUMMARY_DIR_ME_TEMP, SUMMARY_DIR_ME)
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clean_environment_file(SUMMARY_DIR_ME)
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