forked from mindspore-Ecosystem/mindspore
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
# Copyright 2022 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 pytest
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import numpy as np
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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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from mindspore.ops import functional as F
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from mindspore.common.api import jit
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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class NetReduceStd(nn.Cell):
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def __init__(self, axis, keep_dims):
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super(NetReduceStd, self).__init__()
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self._axis = axis
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self._keep_dims = keep_dims
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@jit
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def construct(self, indice):
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if self._axis is None:
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return F.std(indice, unbiased=False, keep_dims=self._keep_dims)
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return F.std(indice, axis=self._axis, unbiased=False, keep_dims=self._keep_dims)
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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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@pytest.mark.parametrize('axis', [None, 0, 1, -1, (-1, 0, 1), (0, 1, 2)])
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@pytest.mark.parametrize('keep_dims', [True, False])
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@pytest.mark.parametrize('dtype', [np.float16, np.float32])
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def test_reduce_std_op(axis, keep_dims, dtype):
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"""
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Feature: Reduce std CPU operation
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Description: input the unbiased and keep_dims, test the output value
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Expectation: the reduce std result match to numpy
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"""
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reduce_std = NetReduceStd(axis, keep_dims)
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tensor_x = Tensor(np.array([
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[[0., 2., 1., 4., 0., 2.], [3., 1., 2., 2., 4., 0.]],
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[[2., 0., 1., 5., 0., 1.], [1., 0., 0., 4., 4., 3.]],
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[[4., 1., 4., 0., 0., 0.], [2., 5., 1., 0., 1., 3.]]
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]).astype(dtype))
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output = reduce_std(tensor_x)
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expect_std0 = np.std(tensor_x.asnumpy(), axis=axis, keepdims=keep_dims, dtype=dtype)
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expect_mean0 = np.mean(tensor_x.asnumpy(), axis=axis, keepdims=keep_dims, dtype=dtype)
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np.allclose(output[0].asnumpy(), expect_std0, 0.0001, 0.0001)
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np.allclose(output[1].asnumpy(), expect_mean0, 0.0001, 0.0001)
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class ReduceStdDynamicShapeNet(nn.Cell):
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def __init__(self):
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super(ReduceStdDynamicShapeNet, self).__init__()
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self.unique = P.Unique()
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self.reshape = P.Reshape()
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def construct(self, x):
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x_unique, _ = self.unique(x)
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x_unique = self.reshape(x_unique, (2, 5))
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return F.std(x_unique, unbiased=False, keep_dims=False)
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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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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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def test_reduce_std_dynamic_shape(mode):
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"""
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Feature: test ReduceStd dynamic_shape feature.
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Description: test ReduceStd dynamic_shape feature.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target="CPU")
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x = Tensor(np.array([8., -3., 0., 0., 10., 1., 21., -3., 10., 8., 3, 4, 5, 6]).astype(np.float32))
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output = ReduceStdDynamicShapeNet()(x)
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np_x = np.array([8., -3., 0., 10, 1., 21., 3, 4, 5, 6], dtype=np.float32)
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expect_output_std = np.std(np_x, keepdims=False)
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expect_output_mean = np.mean(np_x, keepdims=False)
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assert (output[0].asnumpy() == expect_output_std).all()
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assert (output[1].asnumpy() == expect_output_mean).all()
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