昇思基础课程打卡（SPONGE课前学习）9.使用静态图加速

作者：空白诗007 | 2024-08-07 10:42:49

踩

https://gitee.com/mindspore/docs/blob/master/tutorials/source_zh_cn/beginner/accelerate_with_static_graph.ipynb

基本介绍 || 快速入门 || 张量 Tensor || 数据集 Dataset || 数据变换 Transforms || 网络构建 || 函数式自动微分 || 模型训练 || 保存与加载 || 使用静态图加速

使用静态图加速

背景介绍

AI编译框架分为两种运行模式，分别是动态图模式以及静态图模式。MindSpore默认情况下是以动态图模式运行，但也支持手工切换为静态图模式。两种运行模式的详细介绍如下：

动态图模式

动态图的特点是计算图的构建和计算同时发生（Define by run），其符合Python的解释执行方式，在计算图中定义一个Tensor时，其值就已经被计算且确定，因此在调试模型时较为方便，能够实时得到中间结果的值，但由于所有节点都需要被保存，导致难以对整个计算图进行优化。

在MindSpore中，动态图模式又被称为PyNative模式。由于动态图的解释执行特性，在脚本开发和网络流程调试过程中，推荐使用动态图模式进行调试。
如需要手动控制框架采用PyNative模式，可以通过以下代码进行网络构建：

import numpy as np
import mindspore as ms
from mindspore import nn, Tensor
ms.set_context(mode=ms.PYNATIVE_MODE)  # 使用set_context进行动态图模式的配置

class Network(nn.Cell):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.dense_relu_sequential = nn.SequentialCell(
            nn.Dense(28*28, 512),
            nn.ReLU(),
            nn.Dense(512, 512),
            nn.ReLU(),
            nn.Dense(512, 10)
        )

    def construct(self, x):
        x = self.flatten(x)
        logits = self.dense_relu_sequential(x)
        return logits

model = Network()
input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))
output = model(input)
print(output)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

[WARNING] ME(4706:281473506197520,MainProcess):2024-08-06-03:55:44.901.390 [mindspore/hal/device.py:155] Backend Ascend is not created yet.
[WARNING] ME(4706,ffffa859c010,python):2024-08-06-03:55:44.901.152 [mindspore/ccsrc/runtime/hardware/device_context_manager.cc:466] GetDeviceContext] Device context of device Ascend is not created yet.


[[-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]
 [-0.02150986  0.14259806 -0.09672696  0.06892041 -0.07039133  0.05796466
   0.08224779 -0.0833988  -0.09188233 -0.03166037]]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132

静态图模式

相较于动态图而言，静态图的特点是将计算图的构建和实际计算分开（Define and run）。有关静态图模式的运行原理，可以参考静态图语法支持。

在MindSpore中，静态图模式又被称为Graph模式，在Graph模式下，基于图优化、计算图整图下沉等技术，编译器可以针对图进行全局的优化，获得较好的性能，因此比较适合网络固定且需要高性能的场景。

如需要手动控制框架采用静态图模式，可以通过以下代码进行网络构建：

import numpy as np
import mindspore as ms
from mindspore import nn, Tensor
ms.set_context(mode=ms.GRAPH_MODE)  # 使用set_context进行运行静态图模式的配置

class Network(nn.Cell):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.dense_relu_sequential = nn.SequentialCell(
            nn.Dense(28*28, 512),
            nn.ReLU(),
            nn.Dense(512, 512),
            nn.ReLU(),
            nn.Dense(512, 10)
        )

    def construct(self, x):
        x = self.flatten(x)
        logits = self.dense_relu_sequential(x)
        return logits

model = Network()
input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))
output = model(input)
print(output)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

[WARNING] ME(4706:281473506197520,MainProcess):2024-08-06-03:55:54.732.739 [mindspore/context.py:1104] For 'context.set_context' in Ascend backend, the backend is already initialized, please set it before the definition of any Tensor and Parameter, and the instantiation and execution of any operation and net, otherwise the settings may not take effect.
[ERROR] CORE(4706,ffffa859c010,python):2024-08-06-03:55:55.090.857 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_4706/4016835682.py]


[[-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]
 [-0.04193115 -0.09265137 -0.12658691 -0.0970459  -0.12939453  0.21777344
   0.12585449 -0.03903198  0.06396484  0.03710938]]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132

静态图模式的使用场景

MindSpore编译器重点面向Tensor数据的计算以及其微分处理。因此使用MindSpore API以及基于Tensor对象的操作更适合使用静态图编译优化。其他操作虽然可以部分入图编译，但实际优化作用有限。另外，静态图模式先编译后执行的模式导致其存在编译耗时。因此，如果函数无需反复执行，那么使用静态图加速也可能没有价值。

有关使用静态图来进行网络编译的示例，请参考网络构建。

静态图模式开启方式

通常情况下，由于动态图的灵活性，我们会选择使用PyNative模式来进行自由的神经网络构建，以实现模型的创新和优化。但是当需要进行性能加速时，我们需要对神经网络部分或整体进行加速。MindSpore提供了两种切换为图模式的方式，分别是基于装饰器的开启方式以及基于全局context的开启方式。

基于装饰器的开启方式

MindSpore提供了jit装饰器，可以通过修饰Python函数或者Python类的成员函数使其被编译成计算图，通过图优化等技术提高运行速度。此时我们可以简单的对想要进行性能优化的模块进行图编译加速，而模型其他部分，仍旧使用解释执行方式，不丢失动态图的灵活性。无论全局context是设置成静态图模式还是动态图模式，被jit修饰的部分始终会以静态图模式进行运行。

在需要对Tensor的某些运算进行编译加速时，可以在其定义的函数上使用jit修饰器，在调用该函数时，该模块自动被编译为静态图。需要注意的是，jit装饰器只能用来修饰函数，无法对类进行修饰。jit的使用示例如下：

import numpy as np
import mindspore as ms
from mindspore import nn, Tensor

class Network(nn.Cell):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.dense_relu_sequential = nn.SequentialCell(
            nn.Dense(28*28, 512),
            nn.ReLU(),
            nn.Dense(512, 512),
            nn.ReLU(),
            nn.Dense(512, 10)
        )

    def construct(self, x):
        x = self.flatten(x)
        logits = self.dense_relu_sequential(x)
        return logits

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

@ms.jit  # 使用ms.jit装饰器，使被装饰的函数以静态图模式运行
def run(x):
    model = Network()
    return model(x)

output = run(input)
print(output)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

[ERROR] CORE(4706,ffffa859c010,python):2024-08-06-03:55:59.637.602 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_4706/4227391393.py]


[[ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]
 [ 0.38671875  0.17675781 -0.1050415  -0.0670166  -0.22253418  0.15893555
  -0.11834717 -0.07342529 -0.13439941  0.25048828]]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

除使用修饰器外，也可使用函数变换方式调用jit方法，示例如下：

import numpy as np
import mindspore as ms
from mindspore import nn, Tensor

class Network(nn.Cell):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.dense_relu_sequential = nn.SequentialCell(
            nn.Dense(28*28, 512),
            nn.ReLU(),
            nn.Dense(512, 512),
            nn.ReLU(),
            nn.Dense(512, 10)
        )

    def construct(self, x):
        x = self.flatten(x)
        logits = self.dense_relu_sequential(x)
        return logits

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))

def run(x):
    model = Network()
    return model(x)

run_with_jit = ms.jit(run)  # 通过调用jit将函数转换为以静态图方式执行
output = run_with_jit(input)
print(output)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30

[ERROR] CORE(4706,ffffa859c010,python):2024-08-06-03:56:00.218.220 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_4706/1035776523.py]


[[ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]
 [ 0.00085497  0.11248779 -0.00818634 -0.20715332  0.05758667 -0.14367676
   0.16821289  0.29223633  0.03942871  0.07867432]]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

当我们需要对神经网络的某部分进行加速时，可以直接在construct方法上使用jit修饰器，在调用实例化对象时，该模块自动被编译为静态图。示例如下：

import numpy as np
import mindspore as ms
from mindspore import nn, Tensor

class Network(nn.Cell):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.dense_relu_sequential = nn.SequentialCell(
            nn.Dense(28*28, 512),
            nn.ReLU(),
            nn.Dense(512, 512),
            nn.ReLU(),
            nn.Dense(512, 10)
        )

    @ms.jit  # 使用ms.jit装饰器，使被装饰的函数以静态图模式运行
    def construct(self, x):
        x = self.flatten(x)
        logits = self.dense_relu_sequential(x)
        return logits

input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))
model = Network()
output = model(input)
print(output)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

[ERROR] CORE(4706,ffffa859c010,python):2024-08-06-03:56:01.117.912 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_4706/2029473088.py]


[[-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]
 [-0.15197754  0.12036133 -0.14147949 -0.08544922 -0.06933594 -0.05297852
  -0.0256958  -0.05389404  0.00824738  0.0171051 ]]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

基于context的开启方式

context模式是一种全局的设置模式。代码示例如下：

import numpy as np
import mindspore as ms
from mindspore import nn, Tensor
ms.set_context(mode=ms.GRAPH_MODE)  # 使用set_context进行运行静态图模式的配置

class Network(nn.Cell):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.dense_relu_sequential = nn.SequentialCell(
            nn.Dense(28*28, 512),
            nn.ReLU(),
            nn.Dense(512, 512),
            nn.ReLU(),
            nn.Dense(512, 10)
        )

    def construct(self, x):
        x = self.flatten(x)
        logits = self.dense_relu_sequential(x)
        return logits

model = Network()
input = Tensor(np.ones([64, 1, 28, 28]).astype(np.float32))
output = model(input)
print(output)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

[WARNING] ME(4706:281473506197520,MainProcess):2024-08-06-03:56:01.512.676 [mindspore/context.py:1104] For 'context.set_context' in Ascend backend, the backend is already initialized, please set it before the definition of any Tensor and Parameter, and the instantiation and execution of any operation and net, otherwise the settings may not take effect.
[ERROR] CORE(4706,ffffa859c010,python):2024-08-06-03:56:01.755.075 [mindspore/core/utils/file_utils.cc:253] GetRealPath] Get realpath failed, path[/tmp/ipykernel_4706/4016835682.py]


[[ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]
 [ 0.08453369  0.04693604 -0.13366699  0.09106445  0.15905762 -0.04476929
   0.13061523 -0.06280518 -0.02482605  0.0479126 ]]
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132

静态图的语法约束

在Graph模式下，Python代码并不是由Python解释器去执行，而是将代码编译成静态计算图，然后执行静态计算图。因此，编译器无法支持全量的Python语法。MindSpore的静态图编译器维护了Python常用语法子集，以支持神经网络的构建及训练。详情可参考静态图语法支持。

静态图高级编程技巧

使用静态图高级编程技巧可以有效地提高编译效率以及执行效率，并可以使程序运行的更加稳定。详情可参考静态图高级编程技巧。

声明：本文内容由网友自发贡献，不代表【wpsshop博客】立场，版权归原作者所有，本站不承担相应法律责任。如您发现有侵权的内容，请联系我们。转载请注明出处：https://www.wpsshop.cn/w/空白诗007/article/detail/942158