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87 lines
2.5 KiB
87 lines
2.5 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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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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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class OpNetWrapper(nn.Cell):
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def __init__(self, op):
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super(OpNetWrapper, self).__init__()
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self.op = op
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def construct(self, *inputs):
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return self.op(*inputs)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_out1_axis0():
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op = P.Split(0, 1)
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op_wrapper = OpNetWrapper(op)
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input_x = Tensor(np.arange(24).astype(np.int32).reshape((2, 2, 6)))
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outputs = op_wrapper(input_x)
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print(outputs)
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assert outputs[0].shape == (2, 2, 6)
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assert np.allclose(outputs[0].asnumpy()[0, 0, :], [0, 1, 2, 3, 4, 5])
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_out2_axis2():
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op = P.Split(2, 2)
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op_wrapper = OpNetWrapper(op)
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input_x = Tensor(np.arange(24).astype(np.int32).reshape((2, 2, 6)))
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outputs = op_wrapper(input_x)
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print(outputs)
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assert outputs[0].shape == (2, 2, 3)
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assert outputs[1].shape == (2, 2, 3)
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assert np.allclose(outputs[0].asnumpy()[0, 0, :], [0, 1, 2])
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assert np.allclose(outputs[1].asnumpy()[0, 0, :], [3, 4, 5])
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_out2_axis1neg():
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op = P.Split(-1, 2)
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op_wrapper = OpNetWrapper(op)
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input_x = Tensor(np.arange(24).astype(np.float32).reshape((2, 2, 6)))
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outputs = op_wrapper(input_x)
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print(outputs)
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assert np.allclose(outputs[0].asnumpy()[0, :, :], [[0., 1., 2.], [6., 7., 8.]])
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assert np.allclose(outputs[1].asnumpy()[0, :, :], [[3., 4., 5.], [9., 10., 11.]])
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if __name__ == '__main__':
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test_out1_axis0()
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test_out2_axis2()
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test_out2_axis1neg()
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