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116 lines
3.6 KiB
116 lines
3.6 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 pytest
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from mindspore import Tensor
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from mindspore.ops import operations as P
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import mindspore.nn as nn
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from mindspore.common.api import ms_function
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import numpy as np
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import mindspore.context as context
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from mindspore.common.initializer import initializer
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from mindspore.common.parameter import Parameter
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context.set_context(device_target='GPU')
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class ConcatV32(nn.Cell):
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def __init__(self):
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super(ConcatV32, self).__init__()
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self.cat = P.Concat(axis=2)
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self.x1 = Parameter(initializer(
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Tensor(np.arange(2 * 2 * 1).reshape(2, 2, 1).astype(np.float32)), [2, 2, 1]), name='x1')
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self.x2 = Parameter(initializer(
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Tensor(np.arange(2 * 2 * 2).reshape(2, 2, 2).astype(np.float32)), [2, 2, 2]), name='x2')
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@ms_function
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def construct(self):
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return self.cat((self.x1, self.x2))
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_axis32():
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cat = ConcatV32()
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output = cat()
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expect = [[[0., 0., 1.],
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[1., 2., 3.]],
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[[2., 4., 5.],
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[3., 6., 7.]]]
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print(output)
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assert (output.asnumpy() == expect).all()
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class ConcatV43(nn.Cell):
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def __init__(self):
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super(ConcatV43, self).__init__()
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self.cat = P.Concat(axis=3)
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self.x1 = Parameter(initializer(
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Tensor(np.arange(2 * 2 * 2 * 2).reshape(2, 2, 2, 2).astype(np.float32)), [2, 2, 2, 2]), name='x1')
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self.x2 = Parameter(initializer(
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Tensor(np.arange(2 * 2 * 2 * 3).reshape(2, 2, 2, 3).astype(np.float32)), [2, 2, 2, 3]), name='x2')
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@ms_function
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def construct(self):
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return self.cat((self.x1, self.x2))
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_axis43():
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cat = ConcatV43()
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output = cat()
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expect = [[[[0., 1., 0., 1., 2.],
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[2., 3., 3., 4., 5.]],
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[[4., 5., 6., 7., 8.],
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[6., 7., 9., 10., 11.]]],
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[[[8., 9., 12., 13., 14.],
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[10., 11., 15., 16., 17.]],
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[[12., 13., 18., 19., 20.],
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[14., 15., 21., 22., 23.]]]]
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assert (output.asnumpy() == expect).all()
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print(output)
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class ConcatV21(nn.Cell):
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def __init__(self):
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super(ConcatV21, self).__init__()
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self.cat = P.Concat(axis=1)
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self.x1 = Parameter(initializer(
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Tensor(np.arange(2 * 2).reshape(2, 2).astype(np.float32)), [2, 2]), name='x1')
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self.x2 = Parameter(initializer(
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Tensor(np.arange(2 * 3).reshape(2, 3).astype(np.float32)), [2, 3]), name='x2')
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@ms_function
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def construct(self):
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return self.cat((self.x1, self.x2))
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_axis21():
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cat = ConcatV21()
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output = cat()
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expect = [[0., 1., 0., 1., 2.],
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[2., 3., 3., 4., 5.]]
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assert (output.asnumpy() == expect).all()
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print(output)
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