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50 lines
1.8 KiB
50 lines
1.8 KiB
# Copyright 2021 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 pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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import mindspore.common.dtype as mstype
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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, axis=0):
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super(Net, self).__init__()
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self.unique = P.Unique()
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self.reshape = P.Reshape()
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self.concat = P.Concat(axis=axis)
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def construct(self, x1, x2):
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out1_unique, _ = self.unique(x1)
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out2_unique, _ = self.unique(x2)
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out1_shape = self.reshape(out1_unique, (1, -1, 2))
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out2_shape = self.reshape(out2_unique, (1, -1, 2))
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return self.concat((out1_shape, out2_shape))
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_dynamic_concat():
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x1 = Tensor(np.array([1, 2, 3, 1, 4, 2]), mstype.int32)
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x2 = Tensor(np.array([1, 2, 3, 4, 5, 6]), mstype.int32)
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net = Net(axis=1)
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output = net(x1, x2)
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expect = np.array([[[1, 2], [3, 4], [1, 2], [3, 4], [5, 6]]])
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assert (output.asnumpy() == expect).all()
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