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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- import numpy as np
- import pytest
-
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.ops import operations as P
-
-
- class ReduceProd(nn.Cell):
- def __init__(self, keep_dims):
- super(ReduceProd, self).__init__()
- self.reduce_prod = P.ReduceProd(keep_dims=keep_dims)
-
- def construct(self, x, axis):
- return self.reduce_prod(x, axis)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- @pytest.mark.parametrize('decimal, dtype',
- [(1e-10, np.int8), (1e-3, np.float16), (1e-5, np.float32), (1e-8, np.float64)])
- @pytest.mark.parametrize('shape, axis, keep_dims',
- [((2, 3, 4, 4), 3, True), ((2, 3, 4, 4), 3, False), ((2, 3, 1, 4), 2, True),
- ((2, 3, 1, 4), 2, False), ((2, 3, 4, 4), None, True), ((2, 3, 4, 4), None, False),
- ((2, 3, 4, 4), -2, False), ((2, 3, 4, 4), (-2, -1), False), ((1, 1, 1, 1), None, True)])
- def test_reduce_prod(decimal, dtype, shape, axis, keep_dims):
- """
- Feature: ALL To ALL
- Description: test cases for ReduceProd
- Expectation: the result match to numpy
- """
- context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
- x = np.random.rand(*shape).astype(dtype)
- tensor_x = Tensor(x)
-
- reduce_prod = ReduceProd(keep_dims)
- ms_axis = axis if axis is not None else ()
- output = reduce_prod(tensor_x, ms_axis)
-
- expect = np.prod(x, axis=axis, keepdims=keep_dims)
- diff = abs(output.asnumpy() - expect)
- error = np.ones(shape=expect.shape) * decimal
- assert np.all(diff < error)
- assert output.shape == expect.shape
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