forked from mindspore-Ecosystem/mindspore
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
# Copyright 2022 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.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.ops import operations as P
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from mindspore.nn import Cell
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context.set_context(mode=context.GRAPH_MODE)
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class SeqNet(Cell):
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def __init__(self):
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super().__init__()
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self.op_seq = (P.Sqrt(), P.Reciprocal(), P.Square())
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def construct(self, x):
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t = x
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for op in self.op_seq:
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t = op(t)
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return t
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def test_op_seq_net_ge():
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"""
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Feature: unify ge and vm backend
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Description: test op seq with ge backend
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Expectation: success
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"""
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net = SeqNet()
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input_np = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me = Tensor(input_np)
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net(input_me)
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class TriuNet(nn.Cell):
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def __init__(self):
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super(TriuNet, self).__init__()
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self.value = Tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
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def construct(self):
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triu = nn.Triu()
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return triu(self.value, 0)
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def test_triu_ge():
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"""
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Feature: unify ge and vm backend
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Description: test TriuNet with ge backend
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Expectation: success
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"""
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net = TriuNet()
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out = net()
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assert np.sum(out.asnumpy()) == 26
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if __name__ == "__main__":
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test_op_seq_net_ge()
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test_triu_ge()
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