• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
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
21import mindspore.ops.operations.array_ops as P
22from mindspore import Tensor
23from mindspore.common.api import ms_function
24from mindspore.common.initializer import initializer
25from mindspore.common.parameter import Parameter
26
27
28class UnstackNet(nn.Cell):
29    def __init__(self, nptype):
30        super(UnstackNet, self).__init__()
31
32        self.unstack = P.Unstack(axis=3)
33        self.data_np = np.array([[[[[0, 0],
34                                    [0, 1]],
35                                   [[0, 0],
36                                    [2, 3]]],
37                                  [[[0, 0],
38                                    [4, 5]],
39                                   [[0, 0],
40                                    [6, 7]]]],
41                                 [[[[0, 0],
42                                    [8, 9]],
43                                   [[0, 0],
44                                    [10, 11]]],
45                                  [[[0, 0],
46                                    [12, 13]],
47                                   [[0, 0],
48                                    [14, 15]]]]]).astype(nptype)
49        self.x1 = Parameter(initializer(Tensor(self.data_np), [2, 2, 2, 2, 2]), name='x1')
50
51    @ms_function
52    def construct(self):
53        return self.unstack(self.x1)
54
55
56def unstack(nptype):
57    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
58    unstack_ = UnstackNet(nptype)
59    output = unstack_()
60    expect = (np.reshape(np.array([0] * 16).astype(nptype), (2, 2, 2, 2)),
61              np.arange(2 * 2 * 2 * 2).reshape(2, 2, 2, 2).astype(nptype))
62
63    for i, exp in enumerate(expect):
64        assert (output[i].asnumpy() == exp).all()
65
66
67def unstack_pynative(nptype):
68    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
69    x1 = np.array([[[[[0, 0],
70                      [0, 1]],
71                     [[0, 0],
72                      [2, 3]]],
73                    [[[0, 0],
74                      [4, 5]],
75                     [[0, 0],
76                      [6, 7]]]],
77                   [[[[0, 0],
78                      [8, 9]],
79                     [[0, 0],
80                      [10, 11]]],
81                    [[[0, 0],
82                      [12, 13]],
83                     [[0, 0],
84                      [14, 15]]]]]).astype(nptype)
85    x1 = Tensor(x1)
86    expect = (np.reshape(np.array([0] * 16).astype(nptype), (2, 2, 2, 2)),
87              np.arange(2 * 2 * 2 * 2).reshape(2, 2, 2, 2).astype(nptype))
88    output = P.Unstack(axis=3)(x1)
89
90    for i, exp in enumerate(expect):
91        assert (output[i].asnumpy() == exp).all()
92
93
94@pytest.mark.level0
95@pytest.mark.platform_x86_gpu_training
96@pytest.mark.env_onecard
97def test_unstack_graph_float32():
98    unstack(np.float32)
99
100
101@pytest.mark.level0
102@pytest.mark.platform_x86_gpu_training
103@pytest.mark.env_onecard
104def test_unstack_graph_float16():
105    unstack(np.float16)
106
107
108@pytest.mark.level1
109@pytest.mark.platform_x86_gpu_training
110@pytest.mark.env_onecard
111def test_unstack_graph_int32():
112    unstack(np.int32)
113
114
115@pytest.mark.level1
116@pytest.mark.platform_x86_gpu_training
117@pytest.mark.env_onecard
118def test_unstack_graph_int16():
119    unstack(np.int16)
120
121
122@pytest.mark.level1
123@pytest.mark.platform_x86_gpu_training
124@pytest.mark.env_onecard
125def test_unstack_graph_uint8():
126    unstack(np.uint8)
127
128
129@pytest.mark.level1
130@pytest.mark.platform_x86_gpu_training
131@pytest.mark.env_onecard
132def test_unstack_graph_bool():
133    unstack(np.bool)
134
135
136@pytest.mark.level0
137@pytest.mark.platform_x86_gpu_training
138@pytest.mark.env_onecard
139def test_unstack_pynative_float32():
140    unstack_pynative(np.float32)
141
142
143@pytest.mark.level0
144@pytest.mark.platform_x86_gpu_training
145@pytest.mark.env_onecard
146def test_unstack_pynative_float16():
147    unstack_pynative(np.float16)
148
149
150@pytest.mark.level1
151@pytest.mark.platform_x86_gpu_training
152@pytest.mark.env_onecard
153def test_unstack_pynative_int32():
154    unstack_pynative(np.int32)
155
156
157@pytest.mark.level1
158@pytest.mark.platform_x86_gpu_training
159@pytest.mark.env_onecard
160def test_unstack_pynative_int16():
161    unstack_pynative(np.int16)
162
163
164@pytest.mark.level1
165@pytest.mark.platform_x86_gpu_training
166@pytest.mark.env_onecard
167def test_unstack_pynative_uint8():
168    unstack_pynative(np.uint8)
169
170
171@pytest.mark.level1
172@pytest.mark.platform_x86_gpu_training
173@pytest.mark.env_onecard
174def test_unstack_pynative_bool():
175    unstack_pynative(np.bool)
176