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341 lines
11 KiB
341 lines
11 KiB
# Copyright 2019-2021 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.common.api import ms_function
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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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x0 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis0 = 3
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keep_dims0 = True
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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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keep_dims1 = False
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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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keep_dims2 = True
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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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keep_dims3 = False
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x4 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis4 = ()
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np_axis4 = None
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keep_dims4 = True
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x5 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis5 = ()
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np_axis5 = None
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keep_dims5 = False
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x6 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis6 = (1, 2)
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keep_dims6 = False
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x7 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis7 = (1, 2)
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keep_dims7 = True
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x8 = np.random.rand(2, 1, 1, 4).astype(np.float32)
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axis8 = (1, 2)
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keep_dims8 = True
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x9 = np.random.rand(2, 1, 1, 4).astype(np.float32)
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axis9 = (1, 2)
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keep_dims9 = False
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x10 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis10 = (0, 1, 2, 3)
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keep_dims10 = False
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x11 = np.random.rand(1, 1, 1, 1).astype(np.float32)
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axis11 = (0, 1, 2, 3)
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keep_dims11 = False
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x12 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis12 = -2
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keep_dims12 = False
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x13 = np.random.rand(2, 3, 4, 4).astype(np.float32)
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axis13 = (-2, -1)
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keep_dims13 = True
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x14 = np.random.rand(1, 1, 1, 1).astype(np.float32)
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axis14 = ()
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np_axis14 = None
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keep_dims14 = True
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context.set_context(device_target='GPU')
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class ReduceSum(nn.Cell):
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def __init__(self):
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super(ReduceSum, self).__init__()
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self.x0 = Tensor(x0)
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self.axis0 = axis0
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self.keep_dims0 = keep_dims0
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self.x1 = Tensor(x1)
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self.axis1 = axis1
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self.keep_dims1 = keep_dims1
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self.x2 = Tensor(x2)
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self.axis2 = axis2
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self.keep_dims2 = keep_dims2
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self.x3 = Tensor(x3)
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self.axis3 = axis3
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self.keep_dims3 = keep_dims3
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self.x4 = Tensor(x4)
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self.axis4 = axis4
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self.keep_dims4 = keep_dims4
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self.x5 = Tensor(x5)
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self.axis5 = axis5
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self.keep_dims5 = keep_dims5
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self.x6 = Tensor(x6)
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self.axis6 = axis6
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self.keep_dims6 = keep_dims6
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self.x7 = Tensor(x7)
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self.axis7 = axis7
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self.keep_dims7 = keep_dims7
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self.x8 = Tensor(x8)
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self.axis8 = axis8
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self.keep_dims8 = keep_dims8
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self.x9 = Tensor(x9)
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self.axis9 = axis9
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self.keep_dims9 = keep_dims9
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self.x10 = Tensor(x10)
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self.axis10 = axis10
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self.keep_dims10 = keep_dims10
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self.x11 = Tensor(x11)
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self.axis11 = axis11
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self.keep_dims11 = keep_dims11
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self.x12 = Tensor(x12)
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self.axis12 = axis12
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self.keep_dims12 = keep_dims12
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self.x13 = Tensor(x13)
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self.axis13 = axis13
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self.keep_dims13 = keep_dims13
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self.x14 = Tensor(x14)
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self.axis14 = axis14
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self.keep_dims14 = keep_dims14
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@ms_function
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def construct(self):
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return (P.ReduceSum(self.keep_dims0)(self.x0, self.axis0),
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P.ReduceSum(self.keep_dims1)(self.x1, self.axis1),
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P.ReduceSum(self.keep_dims2)(self.x2, self.axis2),
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P.ReduceSum(self.keep_dims3)(self.x3, self.axis3),
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P.ReduceSum(self.keep_dims4)(self.x4, self.axis4),
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P.ReduceSum(self.keep_dims5)(self.x5, self.axis5),
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P.ReduceSum(self.keep_dims6)(self.x6, self.axis6),
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P.ReduceSum(self.keep_dims7)(self.x7, self.axis7),
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P.ReduceSum(self.keep_dims8)(self.x8, self.axis8),
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P.ReduceSum(self.keep_dims9)(self.x9, self.axis9),
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P.ReduceSum(self.keep_dims10)(self.x10, self.axis10),
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P.ReduceSum(self.keep_dims11)(self.x11, self.axis11),
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P.ReduceSum(self.keep_dims12)(self.x12, self.axis12),
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P.ReduceSum(self.keep_dims13)(self.x13, self.axis13),
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P.ReduceSum(self.keep_dims14)(self.x14, self.axis14))
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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_ReduceSum():
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reduce_sum = ReduceSum()
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output = reduce_sum()
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expect0 = np.sum(x0, axis=axis0, keepdims=keep_dims0)
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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.sum(x1, axis=axis1, keepdims=keep_dims1)
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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.sum(x2, axis=axis2, keepdims=keep_dims2)
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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.sum(x3, axis=axis3, keepdims=keep_dims3)
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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.sum(x4, axis=np_axis4, keepdims=keep_dims4)
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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.sum(x5, axis=np_axis5, keepdims=keep_dims5)
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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.sum(x6, axis=axis6, keepdims=keep_dims6)
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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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expect7 = np.sum(x7, axis=axis7, keepdims=keep_dims7)
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diff7 = abs(output[7].asnumpy() - expect7)
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error7 = np.ones(shape=expect7.shape) * 1.0e-5
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assert np.all(diff7 < error7)
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assert output[7].shape == expect7.shape
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expect8 = np.sum(x8, axis=axis8, keepdims=keep_dims8)
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diff8 = abs(output[8].asnumpy() - expect8)
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error8 = np.ones(shape=expect8.shape) * 1.0e-5
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assert np.all(diff8 < error8)
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assert output[8].shape == expect8.shape
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expect9 = np.sum(x9, axis=axis9, keepdims=keep_dims9)
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diff9 = abs(output[9].asnumpy() - expect9)
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error9 = np.ones(shape=expect9.shape) * 1.0e-5
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assert np.all(diff9 < error9)
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assert output[9].shape == expect9.shape
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expect10 = np.sum(x10, axis=axis10, keepdims=keep_dims10)
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diff10 = abs(output[10].asnumpy() - expect10)
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error10 = np.ones(shape=expect10.shape) * 1.0e-5
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assert np.all(diff10 < error10)
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assert output[10].shape == expect10.shape
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expect11 = np.sum(x11, axis=axis11, keepdims=keep_dims11)
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diff11 = abs(output[11].asnumpy() - expect11)
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error11 = np.ones(shape=expect11.shape) * 1.0e-5
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assert np.all(diff11 < error11)
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assert output[11].shape == expect11.shape
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expect12 = np.sum(x12, axis=axis12, keepdims=keep_dims12)
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diff12 = abs(output[12].asnumpy() - expect12)
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error12 = np.ones(shape=expect12.shape) * 1.0e-5
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assert np.all(diff12 < error12)
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assert output[12].shape == expect12.shape
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expect13 = np.sum(x13, axis=axis13, keepdims=keep_dims13)
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diff13 = abs(output[13].asnumpy() - expect13)
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error13 = np.ones(shape=expect13.shape) * 1.0e-5
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assert np.all(diff13 < error13)
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assert output[13].shape == expect13.shape
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expect14 = np.sum(x14, axis=np_axis14, keepdims=keep_dims14)
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diff14 = abs(output[14].asnumpy() - expect14)
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error14 = np.ones(shape=expect14.shape) * 1.0e-5
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assert np.all(diff14 < error14)
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assert output[14].shape == expect14.shape
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x_1 = x8
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axis_1 = 0
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x_2 = x1
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axis_2 = 0
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class ReduceSumDynamic(nn.Cell):
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def __init__(self, x, axis):
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super(ReduceSumDynamic, self).__init__()
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self.reducesum = P.ReduceSum(True)
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self.test_dynamic = inner.GpuConvertToDynamicShape()
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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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return self.reducesum(dynamic_x, self.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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def test_reduce_sum_dynamic():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net1 = ReduceSumDynamic(Tensor(x_1), axis_1)
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net2 = ReduceSumDynamic(Tensor(x_2), axis_2)
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expect_1 = np.sum(x_1, axis=axis_1, keepdims=True)
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expect_2 = np.sum(x_2, axis=axis_2, keepdims=True)
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output1 = net1()
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output2 = net2()
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np.testing.assert_almost_equal(output1.asnumpy(), expect_1)
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np.testing.assert_almost_equal(output2.asnumpy(), expect_2)
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class ReduceSumTypeNet(nn.Cell):
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def __init__(self, nptype):
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super(ReduceSumTypeNet, self).__init__()
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self.x0 = Tensor(x0.astype(nptype))
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self.axis0 = axis0
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self.keep_dims0 = keep_dims0
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def construct(self):
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return P.ReduceSum(self.keep_dims0)(self.x0, self.axis0)
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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_sum_float64():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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net = ReduceSumTypeNet(np.float64)
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output = net()
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expect = np.sum(x0, axis=axis0, keepdims=keep_dims0).astype(np.float64)
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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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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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net = ReduceSumTypeNet(np.float64)
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output = net()
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expect = np.sum(x0, axis=axis0, keepdims=keep_dims0).astype(np.float64)
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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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