forked from mindspore-Ecosystem/mindspore
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
# 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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from mindspore.ops import operations as P
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class ReduceProd(nn.Cell):
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def __init__(self, keep_dims):
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super(ReduceProd, self).__init__()
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self.reduce_prod = P.ReduceProd(keep_dims=keep_dims)
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def construct(self, x, axis):
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return self.reduce_prod(x, axis)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('decimal, dtype',
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[(1e-10, np.int8), (1e-3, np.float16), (1e-5, np.float32), (1e-8, np.float64)])
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@pytest.mark.parametrize('shape, axis, keep_dims',
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[((2, 3, 4, 4), 3, True), ((2, 3, 4, 4), 3, False), ((2, 3, 1, 4), 2, True),
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((2, 3, 1, 4), 2, False), ((2, 3, 4, 4), None, True), ((2, 3, 4, 4), None, False),
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((2, 3, 4, 4), -2, False), ((2, 3, 4, 4), (-2, -1), False), ((1, 1, 1, 1), None, True)])
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def test_reduce_prod(decimal, dtype, shape, axis, keep_dims):
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"""
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Feature: ALL To ALL
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Description: test cases for ReduceProd
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
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x = np.random.rand(*shape).astype(dtype)
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tensor_x = Tensor(x)
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reduce_prod = ReduceProd(keep_dims)
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ms_axis = axis if axis is not None else ()
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output = reduce_prod(tensor_x, ms_axis)
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expect = np.prod(x, axis=axis, keepdims=keep_dims)
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diff = abs(output.asnumpy() - expect)
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error = np.ones(shape=expect.shape) * decimal
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assert np.all(diff < error)
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assert output.shape == expect.shape
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