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133 lines
3.9 KiB
133 lines
3.9 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.common.api import ms_function
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from mindspore.ops import operations as P
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x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis0 = 3
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x1 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis1 = 3
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x2 = np.random.rand(2, 3, 1, 4).astype(np.float32)
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axis2 = 2
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x3 = np.random.rand(2, 3, 1, 4).astype(np.float32)
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axis3 = 2
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x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis4 = 1
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x5 = np.random.rand(2, 3).astype(np.float32)
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axis5 = 1
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x6 = np.random.rand(1, 1, 1, 1).astype(np.float32)
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axis6 = 0
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context.set_context(device_target='GPU')
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class CumSum(nn.Cell):
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def __init__(self):
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super(CumSum, self).__init__()
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self.x0 = Tensor(x0)
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self.axis0 = axis0
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self.x1 = Tensor(x1)
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self.axis1 = axis1
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self.x2 = Tensor(x2)
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self.axis2 = axis2
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self.x3 = Tensor(x3)
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self.axis3 = axis3
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self.x4 = Tensor(x4)
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self.axis4 = axis4
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self.x5 = Tensor(x5)
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self.axis5 = axis5
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self.x6 = Tensor(x6)
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self.axis6 = axis6
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@ms_function
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def construct(self):
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return (P.CumSum()(self.x0, self.axis0),
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P.CumSum()(self.x1, self.axis1),
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P.CumSum()(self.x2, self.axis2),
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P.CumSum()(self.x3, self.axis3),
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P.CumSum()(self.x4, self.axis4),
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P.CumSum()(self.x5, self.axis5),
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P.CumSum()(self.x6, self.axis6))
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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_CumSum():
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cumsum = CumSum()
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output = cumsum()
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expect0 = np.cumsum(x0, axis=axis0)
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diff0 = abs(output[0].asnumpy() - expect0)
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error0 = np.ones(shape=expect0.shape) * 1.0e-5
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assert np.all(diff0 < error0)
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assert output[0].shape == expect0.shape
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expect1 = np.cumsum(x1, axis=axis1)
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diff1 = abs(output[1].asnumpy() - expect1)
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error1 = np.ones(shape=expect1.shape) * 1.0e-5
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assert np.all(diff1 < error1)
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assert output[1].shape == expect1.shape
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expect2 = np.cumsum(x2, axis=axis2)
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diff2 = abs(output[2].asnumpy() - expect2)
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error2 = np.ones(shape=expect2.shape) * 1.0e-5
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assert np.all(diff2 < error2)
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assert output[2].shape == expect2.shape
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expect3 = np.cumsum(x3, axis=axis3)
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diff3 = abs(output[3].asnumpy() - expect3)
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error3 = np.ones(shape=expect3.shape) * 1.0e-5
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assert np.all(diff3 < error3)
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assert output[3].shape == expect3.shape
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expect4 = np.cumsum(x4, axis=axis4)
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diff4 = abs(output[4].asnumpy() - expect4)
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error4 = np.ones(shape=expect4.shape) * 1.0e-5
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assert np.all(diff4 < error4)
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assert output[4].shape == expect4.shape
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expect5 = np.cumsum(x5, axis=axis5)
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diff5 = abs(output[5].asnumpy() - expect5)
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error5 = np.ones(shape=expect5.shape) * 1.0e-5
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assert np.all(diff5 < error5)
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assert output[5].shape == expect5.shape
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expect6 = np.cumsum(x6, axis=axis6)
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diff6 = abs(output[6].asnumpy() - expect6)
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error6 = np.ones(shape=expect6.shape) * 1.0e-5
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assert np.all(diff6 < error6)
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assert output[6].shape == expect6.shape
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