forked from mindspore-Ecosystem/mindspore
131 lines
4.6 KiB
Python
131 lines
4.6 KiB
Python
# Copyright 2019 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 import dtype as mstype
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from mindspore.ops import operations as P
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from mindspore.ops.operations import _inner_ops as inner
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class ReduceMean(nn.Cell):
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def __init__(self, keep_dims):
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super(ReduceMean, self).__init__()
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self.reduce_mean = P.ReduceMean(keep_dims=keep_dims)
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def construct(self, x, axis):
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return self.reduce_mean(x, axis)
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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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@pytest.mark.parametrize('dtype', [np.float16, np.float32, 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, 1), 3, True), ((2, 3, 4, 1), 3, False),
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((2, 3, 4, 4), (1, 2), False), ((2, 3, 4, 4), (1, 2), True), ((2, 1, 1, 4), (1, 2), True),
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((2, 1, 1, 4), (1, 2), False), ((2, 3, 4, 4), (0, 1, 2, 3), False),
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((1, 1, 1, 1), (0, 1, 2, 3), False), ((2, 3, 4, 4, 5, 6), -2, False),
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((2, 3, 4, 4), (-2, -1), True), ((1, 1, 1, 1), (), True)])
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def test_reduce_mean(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 ReduceMean
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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_mean = ReduceMean(keep_dims)
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output = reduce_mean(tensor_x, axis)
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expect = np.mean(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) * 1.0e-5
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assert np.all(diff < error)
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assert output.shape == expect.shape
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class ReduceMeanDynamic(nn.Cell):
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def __init__(self, x, axis, keepdims=False):
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super(ReduceMeanDynamic, self).__init__()
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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self.reduce_mean = P.ReduceMean(keep_dims=keepdims)
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self.x = x
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self.axis = axis
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def construct(self):
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dynamic_x = self.test_dynamic(self.x)
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output = self.reduce_mean(dynamic_x, self.axis)
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return output
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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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@pytest.mark.parametrize('dtype', [np.float32])
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@pytest.mark.parametrize('shape, axis, keep_dims',
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[((2, 3, 4, 4), 3, True), ((1, 1, 1, 1), (), True), ((2, 3, 4, 4, 5, 6), -2, False)])
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def test_dynamic_reduce_mean(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 ReduceMean with dynamic shape
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Expectation: the result match to numpy
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"""
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context.set_context(mode=context.GRAPH_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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net = ReduceMeanDynamic(tensor_x, axis, keepdims=keep_dims)
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output = net()
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expect = np.mean(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) * 1.0e-5
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assert np.all(diff < error)
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assert output.shape == expect.shape
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class ReduceMeanNegativeNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.mean0 = P.ReduceMean(True)
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self.mean1 = P.ReduceMean(False)
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def construct(self, x):
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t = self.mean0(x, ())
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return self.mean1(t, (-1,))
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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_reduce_mean_negative():
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"""
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Feature: ALL To ALL
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Description: test cases for ReduceMean with negative axis.
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Expectation: the result match expectation
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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x = Tensor([[[1, 2, 3,], [3, 2, 1]]], mstype.float32)
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net = ReduceMeanNegativeNet()
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out = net(x)
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assert out.shape == (1, 1)
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