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1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import numpy as np
17import pytest
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor
22from mindspore.common import dtype as mstype
23from mindspore.common.api import ms_function
24from mindspore.ops.operations import _grad_ops as G
25from mindspore.ops import operations as P
26
27context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
28
29
30class SliceGrad(nn.Cell):
31    def __init__(self):
32        super(SliceGrad, self).__init__()
33
34        self.slicegrad = G.SliceGrad()
35
36    @ms_function
37    def construct(self, dy, x):
38        return self.slicegrad(dy, x, (0, 1, 0), (2, 1, 3))
39
40
41@pytest.mark.level0
42@pytest.mark.platform_x86_cpu
43@pytest.mark.env_onecard
44def test_slice_grad():
45    x = Tensor(np.array([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]]), mstype.float32)
46    dy = Tensor(np.array([[[3., 1., 2.]], [[4., 1., 4.]]]), mstype.float32)
47    slicegrad = SliceGrad()
48    output = slicegrad(dy, x)
49    expect = [[[0., 0., 0.],
50               [3., 1., 2.]],
51              [[0., 0., 0.],
52               [4., 1., 4.]],
53              [[0., 0., 0.],
54               [0., 0., 0.]]]
55    print("output:\n", output)
56    assert (output.asnumpy() == expect).all()
57
58
59class SliceGrad2(nn.Cell):
60    def __init__(self):
61        super(SliceGrad2, self).__init__()
62        self.slicegrad = G.SliceGrad()
63
64    def construct(self, dy, x):
65        return self.slicegrad(dy, x, (0, 1, 0), (2, 2, 2))
66
67
68@pytest.mark.level0
69@pytest.mark.platform_x86_cpu
70@pytest.mark.env_onecard
71def test_slice_grad2():
72    dy = Tensor(np.array([[[2., 3.], [4., 5.]], [[8., 9.], [10., 11.]]]), mstype.float32)
73    x = Tensor(np.arange(2 * 3 * 2).reshape(2, 3, 2), mstype.float32)
74    grad = SliceGrad2()
75    output = grad(dy, x)
76    print("output:\n", output)
77    expect = [[[0., 0.], [2., 3.], [4., 5.]],
78              [[0., 0.], [8., 9.], [10., 11.]]]
79    assert (output.asnumpy() == expect).all()
80
81def test_slice_grad3():
82    x = Tensor(np.array([[[1.0, 3.5, 5.8], [2.5, 4, 1]], [[3.5, 15.3, 3.1], [2.2, 4.0, 1.1]],
83                         [[43.4, 1.1, 12.1], [2.4, 6.5, 6.3]]]), mstype.float64)
84    dy = Tensor(np.array([[[3.1, 1.1, 2.2]], [[4.4, 1.2, 4.2]]]), mstype.float64)
85    slicegrad = SliceGrad()
86    output = slicegrad(dy, x)
87    expect = [[[0., 0., 0.],
88               [3.1, 1.1, 2.2]],
89              [[0., 0., 0.],
90               [4.4, 1.2, 4.2]],
91              [[0., 0., 0.],
92               [0., 0., 0.]]]
93    print("output:\n", output)
94    assert (output.asnumpy() == expect).all()
95
96class StridedSliceGrad(nn.Cell):
97    def __init__(self, x, begin, end, stride):
98        super(StridedSliceGrad, self).__init__()
99        self.shape_op = P.Shape()
100        self.shapex = self.shape_op(x)
101        self.begin = begin
102        self.end = end
103        self.stride = stride
104        self.stride_slice = G.StridedSliceGrad()
105
106    def construct(self, dy):
107        return self.stride_slice(dy, self.shapex, self.begin, self.end, self.stride)
108
109@pytest.mark.level0
110@pytest.mark.platform_x86_cpu
111@pytest.mark.env_onecard
112def test_strided_slice_grad_bool_type():
113    x = Tensor([[[False, False, True], [False, True, False]], [[False, True, False], [True, False, False]],
114                [[False, True, True], [True, False, True]]], mstype.bool_)
115    dy = Tensor([False, True, False], mstype.bool_)
116    begin = (1, 0, 0)
117    end = (2, 1, 3)
118    stride = (1, 1, 1)
119    slice_op = StridedSliceGrad(x, begin, end, stride)
120    output = slice_op(dy)
121    expected_output = np.array([[[False, False, False], [False, False, False]],
122                                [[False, True, False], [False, False, False]],
123                                [[False, False, False], [False, False, False]]])
124    assert (output.asnumpy() == expected_output).all()
125
126@pytest.mark.level0
127@pytest.mark.platform_x86_cpu
128@pytest.mark.env_onecard
129def test_strided_slice_grad_float32_type():
130    x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]], [[5, 5, 5], [6, 6, 6]]], mstype.float32)
131    dy = Tensor([3, 3, 3], mstype.float32)
132    begin = (1, 0, 0)
133    end = (2, 1, 3)
134    stride = (1, 1, 1)
135    slice_op = StridedSliceGrad(x, begin, end, stride)
136    output = slice_op(dy)
137    expected_output = np.array([[[0, 0, 0], [0, 0, 0]], [[3, 3, 3], [0, 0, 0]], [[0, 0, 0], [0, 0, 0]]])
138    assert (output.asnumpy() == expected_output).all()
139
140if __name__ == '__main__':
141    test_slice_grad()
142    test_slice_grad2()
143    test_strided_slice_grad_bool_type()
144    test_strided_slice_grad_float32_type()
145