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60 lines
2.1 KiB
60 lines
2.1 KiB
# 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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""" test_hypermap_partial """
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import numpy as np
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import mindspore.common.dtype as mstype
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore.common.api import ms_function
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from mindspore.ops import composite as C
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE)
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def test_hypermap_specialize_param():
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.mul = P.Mul()
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def construct(self, x, y):
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ret = self.mul(x, y)
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return ret
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factor1 = Tensor(5, dtype=mstype.int32)
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x = Tensor(np.ones([1]).astype(np.int32))
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y = Tensor(np.ones([2]).astype(np.int32))
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net = Net()
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hypermap = C.HyperMap()
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@ms_function
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def hypermap_specialize_param():
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ret1 = hypermap(F.partial(net, factor1), (x, y))
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# List will be converted to Tuple in SimlifyDataStructurePass.
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ret2 = hypermap(F.partial(net, factor1), [x, y])
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return ret1, ret2
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expected_ret = (Tensor(np.full(1, 5).astype(np.int32)), Tensor(np.full(2, 5).astype(np.int32)))
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ret = hypermap_specialize_param()
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assert ret[0][0].asnumpy() == expected_ret[0].asnumpy()
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assert np.all(ret[0][1].asnumpy() == expected_ret[1].asnumpy())
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assert ret[1][0].asnumpy() == list(expected_ret[0].asnumpy())
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assert np.all(ret[1][1].asnumpy() == list(expected_ret[1].asnumpy()))
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