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63 lines
2.0 KiB
63 lines
2.0 KiB
# 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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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.common.api import ms_function
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from mindspore.ops import operations as P
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from mindspore.ops.composite import GradOperation
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = GradOperation(get_all=True, sens_param=True)
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self.network = network
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@ms_function
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def construct(self, input_, output_grad):
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return self.grad(self.network)(input_, output_grad)
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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.HSigmoid = P.HSigmoid()
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def construct(self, x):
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return self.HSigmoid(x)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_net():
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x = np.array([-1, -2, 0, 2, 1]).astype(np.float32)
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hswish = Net()
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y = hswish(Tensor(x))
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expect = np.array([0.33333334, 0.16666667, 0.5, 0.8333333, 0.6666667]).astype(np.float32)
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assert np.all(y.asnumpy() == expect)
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sens = np.random.randn(5).astype(np.float32)
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backword_net = Grad(Net())
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output = backword_net(Tensor(x), Tensor(sens))
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print(len(output))
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print(output[0].asnumpy())
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