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63 lines
1.8 KiB
63 lines
1.8 KiB
# Copyright 2019 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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import mindspore.ops.composite as C
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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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.add = P.AddN()
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def construct(self, x, y):
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return self.add((x, y))
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def test_net():
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x = np.random.randn(1, 3, 3, 4).astype(np.float32)
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y = np.random.randn(1, 3, 3, 4).astype(np.float32)
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add = Net()
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output = add(Tensor(x), Tensor(y))
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print(x)
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print(y)
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print(output.asnumpy())
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x = 1.0
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y = 2.0
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expect = 3.0
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add = Net()
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output = add(x, y)
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assert output == expect
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def test_grad_addn_with_list():
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grad_op = C.GradOperation(get_all=True)
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class AddN(nn.Cell):
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def __init__(self):
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super().__init__()
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self.add_n = P.AddN()
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def construct(self, a, b):
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return self.add_n([a, b])
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inp = Tensor(np.ones([128, 96]).astype(np.float32))
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grad_op(AddN())(inp, inp)
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