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- # Copyright 2019 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- from mindspore import Tensor
- from mindspore.ops import operations as P
- from mindspore.ops.operations import _grad_ops as G
- import mindspore.nn as nn
- from mindspore.common.api import ms_function
- import numpy as np
- import mindspore.context as context
- from mindspore.common.initializer import initializer
- from mindspore.common.parameter import Parameter
- context.set_context(device_target="Ascend")
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.bias_add_grad = G.BiasAddGrad()
- #self.dout = Parameter(initializer(
- #'normal', [2, 3, 3, 4]), name='dout')
-
-
- @ms_function
- def construct(self, dout):
- return self.bias_add_grad(dout)
-
- dout = np.ones([2,3,4,4]).astype(np.float32)
- bias_add_grad = Net()
- output = bias_add_grad(Tensor(dout))
- expect_output = np.array([32.,32.,32.]).astype(np.float32)
- assert np.all(output.asnumpy()==expect_output), "bias_add_grad execute failed, please check current code commit"
- print(output.asnumpy())
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