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64 lines
2.2 KiB
64 lines
2.2 KiB
# Copyright 2019-2021 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 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.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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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="Ascend")
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context.set_context(device_target="Ascend")
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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.bn = P.BatchNorm()
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self.scale = Parameter(initializer('ones', [64]), name='scale')
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self.b = Parameter(initializer('zeros', [64]), name='b')
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self.mean = Parameter(initializer('ones', [64]), name='mean')
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self.variance = Parameter(initializer('zeros', [64]), name='variance')
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def construct(self, x):
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return self.bn(x, self.scale, self.b, self.mean, self.variance)[0]
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def test_net():
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x = np.random.randn(1, 64, 112, 112).astype(np.float32)
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sens = np.random.randn(1, 64, 112, 112).astype(np.float32)
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net = Grad(Net())
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output = net(Tensor(x), Tensor(sens))
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print("***********x*********")
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print(x)
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print("***********output y*********")
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print(output.asnumpy())
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