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- # Copyright 2019 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.common.api import ms_function
- from mindspore.common.initializer import initializer
- from mindspore.common.parameter import Parameter
- from mindspore.ops import operations as P
-
- context.set_context(device_target='GPU')
-
-
- class TensroAdd(nn.Cell):
- def __init__(self):
- super(TensroAdd, self).__init__()
-
- self.add = P.TensorAdd()
-
- self.x = Parameter(initializer(
- Tensor(np.random.randn(2, 0).astype(np.float32)), [2, 0]), name='x')
- self.y = Parameter(initializer(
- Tensor(np.random.randn(2, 1).astype(np.float32)), [2, 1]), name='y')
-
- self.x1 = Parameter(initializer(
- Tensor(np.arange(3).reshape(3).astype(np.float32)), [3]), name='x1')
- self.y1 = Parameter(initializer(
- Tensor(np.array([2]).astype(np.float32)), [1]), name='y1')
-
- self.x2 = Parameter(initializer(
- Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(np.float32)), [3, 3, 3, 3]), name='x2')
- self.y2 = Parameter(initializer(
- Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(np.float32)), [3, 3, 3, 3]), name='y2')
-
- self.x3 = Parameter(initializer(
- Tensor(np.arange(1 * 1 * 3 * 3).reshape(1, 1, 3, 3).astype(np.float32)), [1, 1, 3, 3]), name='x3')
- self.y3 = Parameter(initializer(
- Tensor(np.arange(3 * 3 * 3 * 3).reshape(3, 3, 3, 3).astype(np.float32)), [3, 3, 3, 3]), name='y3')
-
- @ms_function
- def construct(self):
- return (
- self.add(self.x, self.y), self.add(self.x1, self.y1), self.add(self.x2, self.y2),
- self.add(self.x3, self.y3))
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_TensroAdd():
- add = TensroAdd()
- output = add()
- expect0 = np.array([])
- expect1 = np.array([2, 3, 4])
- expect2 = np.array(
- [[[[0., 2., 4.],
- [6., 8., 10.],
- [12., 14., 16.]],
- [[18., 20., 22.],
- [24., 26., 28.],
- [30., 32., 34.]],
- [[36., 38., 40.],
- [42., 44., 46.],
- [48., 50., 52.]]],
- [[[54., 56., 58.],
- [60., 62., 64.],
- [66., 68., 70.]],
- [[72., 74., 76.],
- [78., 80., 82.],
- [84., 86., 88.]],
- [[90., 92., 94.],
- [96., 98., 100.],
- [102., 104., 106.]]],
- [[[108., 110., 112.],
- [114., 116., 118.],
- [120., 122., 124.]],
- [[126., 128., 130.],
- [132., 134., 136.],
- [138., 140., 142.]],
- [[144., 146., 148.],
- [150., 152., 154.],
- [156., 158., 160.]]]])
- expect3 = np.array(
- [[[[0., 2., 4.],
- [6., 8., 10.],
- [12., 14., 16.]],
- [[9., 11., 13.],
- [15., 17., 19.],
- [21., 23., 25.]],
- [[18., 20., 22.],
- [24., 26., 28.],
- [30., 32., 34.]]],
- [[[27., 29., 31.],
- [33., 35., 37.],
- [39., 41., 43.]],
- [[36., 38., 40.],
- [42., 44., 46.],
- [48., 50., 52.]],
- [[45., 47., 49.],
- [51., 53., 55.],
- [57., 59., 61.]]],
- [[[54., 56., 58.],
- [60., 62., 64.],
- [66., 68., 70.]],
- [[63., 65., 67.],
- [69., 71., 73.],
- [75., 77., 79.]],
- [[72., 74., 76.],
- [78., 80., 82.],
- [84., 86., 88.]]]]
- )
- assert (output[0].asnumpy() == expect0).all()
- assert (output[1].asnumpy() == expect1).all()
- assert (output[2].asnumpy() == expect2).all()
- assert (output[3].asnumpy() == expect3).all()
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