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141 lines
4.0 KiB
141 lines
4.0 KiB
# Copyright 2020-2021 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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"""Test summary."""
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import os
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import random
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import numpy as np
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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, _cache_summary_tensor_data
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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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def get_test_data(step):
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""" get_test_data """
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test_data_list = []
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tag1 = "x1[:Scalar]"
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tag2 = "x2[:Scalar]"
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np1 = np.array(step + 1).astype(np.float32)
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np2 = np.array(step + 2).astype(np.float32)
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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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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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def test_scalar_summary_sample():
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""" test_scalar_summary_sample """
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with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
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for i in range(1, 5):
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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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def get_test_data_shape_1(step):
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""" get_test_data_shape_1 """
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test_data_list = []
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tag1 = "x1[:Scalar]"
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tag2 = "x2[:Scalar]"
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np1 = np.array([step + 1]).astype(np.float32)
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np2 = np.array([step + 2]).astype(np.float32)
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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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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: shape = (1,)
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def test_scalar_summary_sample_with_shape_1():
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""" test_scalar_summary_sample_with_shape_1 """
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with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
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for i in range(1, 100):
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test_data = get_test_data_shape_1(i)
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_cache_summary_tensor_data(test_data)
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test_writer.record(i)
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# Test: test with ge
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class SummaryDemo(nn.Cell):
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""" SummaryDemo definition """
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def __init__(self,):
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super(SummaryDemo, self).__init__()
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self.s = P.ScalarSummary()
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self.histogram_summary = P.HistogramSummary()
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self.add = P.Add()
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def construct(self, x, y):
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self.s("x1", x)
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z = self.add(x, y)
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self.s("z1", z)
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self.s("y1", y)
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self.histogram_summary("histogram", z)
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return z
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def test_scalar_summary_with_ge():
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""" test_scalar_summary_with_ge """
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with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
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net = SummaryDemo()
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net.set_train()
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# step 2: create the Event
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steps = 100
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for i in range(1, 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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net(x, y)
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test_writer.record(i)
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# test the problem of two consecutive use cases going wrong
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def test_scalar_summary_with_ge_2():
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""" test_scalar_summary_with_ge_2 """
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with SummaryRecord(SUMMARY_DIR, file_suffix="_MS_SCALAR") as test_writer:
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net = SummaryDemo()
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net.set_train()
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steps = 100
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for i in range(1, steps):
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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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net(x, y)
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test_writer.record(i)
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