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# 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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"""Summary cpu st."""
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import os
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import tempfile
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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from tests.summary_utils import SummaryReader
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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='CPU')
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class SummaryNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.scalar_summary = P.ScalarSummary()
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self.image_summary = P.ImageSummary()
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self.tensor_summary = P.TensorSummary()
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self.histogram_summary = P.HistogramSummary()
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def construct(self, image_tensor):
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self.image_summary("image", image_tensor)
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self.tensor_summary("tensor", image_tensor)
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self.histogram_summary("histogram", image_tensor)
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scalar = image_tensor[0][0][0][0]
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self.scalar_summary("scalar", scalar)
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return scalar
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def train_summary_record(test_writer, steps):
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"""Train and record summary."""
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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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image_tensor = Tensor(np.array([[[[i]]]]).astype(np.float32))
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out_put = net(image_tensor)
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test_writer.record(i)
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out_me_dict[i] = out_put.asnumpy()
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return out_me_dict
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class TestCpuSummary:
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"""Test cpu summary."""
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu_training
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@pytest.mark.env_onecard
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def test_summary_step2_summary_record1(self):
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"""Test record 10 step summary."""
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with tempfile.TemporaryDirectory() as tmp_dir:
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steps = 2
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with SummaryRecord(tmp_dir) as test_writer:
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train_summary_record(test_writer, steps=steps)
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file_name = os.path.realpath(test_writer.full_file_name)
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with SummaryReader(file_name) as summary_writer:
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for _ in range(steps):
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event = summary_writer.read_event()
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tags = set(value.tag for value in event.summary.value)
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assert tags == {'tensor', 'histogram', 'scalar', 'image'}
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