forked from huawei/mindspore2022
37 lines
1.5 KiB
Python
37 lines
1.5 KiB
Python
# 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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from mindspore import context
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from mindspore import Tensor, Parameter
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from mindspore.nn import Cell
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from mindspore.ops import operations as P
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import mindspore.ops.composite as C
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from mindspore.common.api import _executor
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context.set_context(mode=context.GRAPH_MODE)
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def test_net_vargs_expand():
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class AddNet(Cell):
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def __init__(self):
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super(AddNet, self).__init__()
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self.w = Parameter(Tensor(np.ones((3, 4, 5), np.float32)), "w2", requires_grad=True)
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def construct(self, x, y):
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return x + y
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x = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
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y = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
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sens = Tensor(np.random.normal(0, 1, [3, 4, 5]).astype(np.float32))
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net = AddNet()
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out = C.grad_all_with_sens(net, net.trainable_params())(x, y, sens)
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