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62 lines
1.9 KiB
62 lines
1.9 KiB
# Copyright 2020 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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from cus_square import CusSquare
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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 composite as C
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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grad_with_sens = C.GradOperation(sens_param=True)
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class Net(nn.Cell):
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"""Net definition"""
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def __init__(self):
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super(Net, self).__init__()
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self.square = CusSquare()
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def construct(self, data):
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return self.square(data)
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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_net():
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x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
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square = Net()
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output = square(Tensor(x))
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expect = np.array([1.0, 16.0, 81.0]).astype(np.float32)
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assert (output.asnumpy() == expect).all()
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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_grad_net():
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x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
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sens = np.array([1.0, 1.0, 1.0]).astype(np.float32)
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square = Net()
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dx = grad_with_sens(square)(Tensor(x), Tensor(sens))
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expect = np.array([2.0, 8.0, 18.0]).astype(np.float32)
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assert (dx.asnumpy() == expect).all()
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