forked from mindspore-Ecosystem/mindspore
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
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# 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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from mindspore import Tensor
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self, axis=0, out_nums=1):
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super(Net, self).__init__()
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self.split = P.Split(axis, out_nums)
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def construct(self, x):
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return self.split(x)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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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_split():
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x = np.array([[[1, -1, 1], [2, -2, 2]],
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[[3, -3, 3], [4, -4, 4]],
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[[5, -5, 5], [6, -6, 6]]]).astype(np.float32)
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split_op = Net(0, 3)
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outputs = split_op(Tensor(x))
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for i, out in enumerate(outputs):
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assert (out.asnumpy() == x[i]).all()
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def test_split_4d():
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x_np = np.random.randn(2, 6, 4, 4).astype(np.float32)
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y = np.split(x_np, 3, axis=1)
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split_op = Net(1, 3)
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outputs = split_op(Tensor(x_np))
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for i, out in enumerate(outputs):
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assert (out.asnumpy() == y[i]).all()
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