44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
# Copyright 2022 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 pytest
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import mindspore.nn as nn
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from mindspore import Tensor
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.softmax2d = nn.Softmax2d()
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def construct(self, x):
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return self.softmax2d(x)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_softmax2d_normal():
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"""
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Feature: Softmax2d
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Description: Verify the result of Softmax2d
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Expectation: success
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"""
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net = Net()
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a = Tensor(np.array([[[[0.1, 0.2]], [[0.3, 0.4]], [[0.6, 0.5]]]]).astype(np.float32))
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output = net(a)
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expected_output = np.array([[[[0.258, 0.28]], [[0.316, 0.342]], [[0.426, 0.378]]]]).astype(np.float32)
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assert np.allclose(output.asnumpy(), expected_output, 1e-3, 1e-3)
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