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# Copyright 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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"""Export net test.""" |
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import os |
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import numpy as np |
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import pytest |
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import mindspore as ms |
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import mindspore.nn as nn |
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from mindspore import context |
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from mindspore.common.tensor import Tensor |
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from mindspore.train.serialization import export |
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class SliceNet(nn.Cell): |
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def __init__(self): |
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super().__init__() |
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self.relu = nn.ReLU() |
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def construct(self, x, y): |
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x = self.relu(x) |
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x[2,] = y |
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return x |
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@pytest.mark.level0 |
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@pytest.mark.platform_x86_ascend_training |
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@pytest.mark.platform_arm_ascend_training |
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@pytest.mark.env_onecard |
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def test_export_slice_net(): |
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") |
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input_x = Tensor(np.random.rand(4, 4, 4), ms.float32) |
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input_y = Tensor(np.array([1]), ms.float32) |
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net = SliceNet() |
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file_name = "slice_net" |
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export(net, input_x, input_y, file_name=file_name, file_format='AIR') |
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verify_name = file_name + ".air" |
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assert os.path.exists(verify_name) |
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os.remove(verify_name) |
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export(net, input_x, input_y, file_name=file_name, file_format='MINDIR') |
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verify_name = file_name + ".mindir" |
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assert os.path.exists(verify_name) |
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os.remove(verify_name) |