[TOC]

模型参数量和计算量是什么

计算量是指网络模型需要计算的运算次数，参数量是指网络模型自带的参数数量多少
计算量对应时间复杂度，参数量对应于空间复杂度
计算量决定了网络执行时间的长短，参数量决定了占用显存的量

为什么要统计模型参数量和计算量

好的网络模型不仅要求精度准，还要要求模型的参数量和计算量不大，才能有利于部署
统计模型的参数量和计算量可以用于不同网络模型之间的对比分析
有的模型虽然参数量相同，但是可能因为连接方式和结构等不同而导致计算量不同

常见的模型参数量和计算量的计算方法

卷积层

其中，K表示核尺寸，$C_l$表示数量第l层通道数

全连接层
$$
计算量=参数量=weight_{in}×weight_{out}
$$
推荐方法3

方法1 torchsummary.summary

from torchsummary import summary

summary(net,input_size=(3,224,224))

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1         [-1, 64, 224, 224]           1,792
              ReLU-2         [-1, 64, 224, 224]               0
            Conv2d-3         [-1, 64, 224, 224]          36,928
              ReLU-4         [-1, 64, 224, 224]               0
         MaxPool2d-5         [-1, 64, 112, 112]               0
            Conv2d-6        [-1, 128, 112, 112]          73,856
              ReLU-7        [-1, 128, 112, 112]               0
            Conv2d-8        [-1, 128, 112, 112]         147,584
              ReLU-9        [-1, 128, 112, 112]               0
        MaxPool2d-10          [-1, 128, 56, 56]               0
           Conv2d-11          [-1, 256, 56, 56]         295,168
             ReLU-12          [-1, 256, 56, 56]               0
           Conv2d-13          [-1, 256, 56, 56]         590,080
             ReLU-14          [-1, 256, 56, 56]               0
           Conv2d-15          [-1, 256, 56, 56]         590,080
             ReLU-16          [-1, 256, 56, 56]               0
        MaxPool2d-17          [-1, 256, 28, 28]               0
           Conv2d-18          [-1, 512, 28, 28]       1,180,160
             ReLU-19          [-1, 512, 28, 28]               0
           Conv2d-20          [-1, 512, 28, 28]       2,359,808
             ReLU-21          [-1, 512, 28, 28]               0
           Conv2d-22          [-1, 512, 28, 28]       2,359,808
             ReLU-23          [-1, 512, 28, 28]               0
        MaxPool2d-24          [-1, 512, 14, 14]               0
           Conv2d-25          [-1, 512, 14, 14]       2,359,808
             ReLU-26          [-1, 512, 14, 14]               0
           Conv2d-27          [-1, 512, 14, 14]       2,359,808
             ReLU-28          [-1, 512, 14, 14]               0
           Conv2d-29          [-1, 512, 14, 14]       2,359,808
             ReLU-30          [-1, 512, 14, 14]               0
        MaxPool2d-31            [-1, 512, 7, 7]               0
AdaptiveAvgPool2d-32            [-1, 512, 7, 7]               0
           Linear-33                 [-1, 4096]     102,764,544
             ReLU-34                 [-1, 4096]               0
          Dropout-35                 [-1, 4096]               0
           Linear-36                 [-1, 4096]      16,781,312
             ReLU-37                 [-1, 4096]               0
          Dropout-38                 [-1, 4096]               0
           Linear-39                 [-1, 1000]       4,097,000
================================================================
Total params: 138,357,544
Trainable params: 138,357,544
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.57
Forward/backward pass size (MB): 218.78
Params size (MB): 527.79
Estimated Total Size (MB): 747.15
----------------------------------------------------------------

方法2 torchstat.stat

from torchstat import stat

stat(net,(3,224,224))

[MAdd]: AdaptiveAvgPool2d is not supported!
[Flops]: AdaptiveAvgPool2d is not supported!
[Memory]: AdaptiveAvgPool2d is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
[MAdd]: Dropout is not supported!
[Flops]: Dropout is not supported!
[Memory]: Dropout is not supported!
        module name  input shape output shape       params memory(MB)              MAdd             Flops   MemRead(B)  MemWrite(B) duration[%]    MemR+W(B)
0        features.0    3 224 224   64 224 224       1792.0      12.25     173,408,256.0      89,915,392.0     609280.0   12845056.0       4.91%   13454336.0
1        features.1   64 224 224   64 224 224          0.0      12.25       3,211,264.0       3,211,264.0   12845056.0   12845056.0       1.23%   25690112.0
2        features.2   64 224 224   64 224 224      36928.0      12.25   3,699,376,128.0   1,852,899,328.0   12992768.0   12845056.0      10.66%   25837824.0
3        features.3   64 224 224   64 224 224          0.0      12.25       3,211,264.0       3,211,264.0   12845056.0   12845056.0       1.23%   25690112.0
4        features.4   64 224 224   64 112 112          0.0       3.06       2,408,448.0       3,211,264.0   12845056.0    3211264.0       6.97%   16056320.0
5        features.5   64 112 112  128 112 112      73856.0       6.12   1,849,688,064.0     926,449,664.0    3506688.0    6422528.0       4.92%    9929216.0
6        features.6  128 112 112  128 112 112          0.0       6.12       1,605,632.0       1,605,632.0    6422528.0    6422528.0       0.41%   12845056.0
7        features.7  128 112 112  128 112 112     147584.0       6.12   3,699,376,128.0   1,851,293,696.0    7012864.0    6422528.0       7.79%   13435392.0
8        features.8  128 112 112  128 112 112          0.0       6.12       1,605,632.0       1,605,632.0    6422528.0    6422528.0       0.41%   12845056.0
9        features.9  128 112 112  128  56  56          0.0       1.53       1,204,224.0       1,605,632.0    6422528.0    1605632.0       3.28%    8028160.0
10      features.10  128  56  56  256  56  56     295168.0       3.06   1,849,688,064.0     925,646,848.0    2786304.0    3211264.0       3.69%    5997568.0
11      features.11  256  56  56  256  56  56          0.0       3.06         802,816.0         802,816.0    3211264.0    3211264.0       0.00%    6422528.0
12      features.12  256  56  56  256  56  56     590080.0       3.06   3,699,376,128.0   1,850,490,880.0    5571584.0    3211264.0       6.56%    8782848.0
13      features.13  256  56  56  256  56  56          0.0       3.06         802,816.0         802,816.0    3211264.0    3211264.0       0.41%    6422528.0
14      features.14  256  56  56  256  56  56     590080.0       3.06   3,699,376,128.0   1,850,490,880.0    5571584.0    3211264.0       6.56%    8782848.0
15      features.15  256  56  56  256  56  56          0.0       3.06         802,816.0         802,816.0    3211264.0    3211264.0       0.00%    6422528.0
16      features.16  256  56  56  256  28  28          0.0       0.77         602,112.0         802,816.0    3211264.0     802816.0       2.46%    4014080.0
17      features.17  256  28  28  512  28  28    1180160.0       1.53   1,849,688,064.0     925,245,440.0    5523456.0    1605632.0       4.51%    7129088.0
18      features.18  512  28  28  512  28  28          0.0       1.53         401,408.0         401,408.0    1605632.0    1605632.0       0.00%    3211264.0
19      features.19  512  28  28  512  28  28    2359808.0       1.53   3,699,376,128.0   1,850,089,472.0   11044864.0    1605632.0       7.38%   12650496.0
20      features.20  512  28  28  512  28  28          0.0       1.53         401,408.0         401,408.0    1605632.0    1605632.0       0.00%    3211264.0
21      features.21  512  28  28  512  28  28    2359808.0       1.53   3,699,376,128.0   1,850,089,472.0   11044864.0    1605632.0       7.38%   12650496.0
22      features.22  512  28  28  512  28  28          0.0       1.53         401,408.0         401,408.0    1605632.0    1605632.0       0.00%    3211264.0
23      features.23  512  28  28  512  14  14          0.0       0.38         301,056.0         401,408.0    1605632.0     401408.0       0.82%    2007040.0
24      features.24  512  14  14  512  14  14    2359808.0       0.38     924,844,032.0     462,522,368.0    9840640.0     401408.0       3.28%   10242048.0
25      features.25  512  14  14  512  14  14          0.0       0.38         100,352.0         100,352.0     401408.0     401408.0       0.00%     802816.0
26      features.26  512  14  14  512  14  14    2359808.0       0.38     924,844,032.0     462,522,368.0    9840640.0     401408.0       2.87%   10242048.0
27      features.27  512  14  14  512  14  14          0.0       0.38         100,352.0         100,352.0     401408.0     401408.0       0.00%     802816.0
28      features.28  512  14  14  512  14  14    2359808.0       0.38     924,844,032.0     462,522,368.0    9840640.0     401408.0       2.46%   10242048.0
29      features.29  512  14  14  512  14  14          0.0       0.38         100,352.0         100,352.0     401408.0     401408.0       0.00%     802816.0
30      features.30  512  14  14  512   7   7          0.0       0.10          75,264.0         100,352.0     401408.0     100352.0       0.00%     501760.0
31          avgpool  512   7   7  512   7   7          0.0       0.10               0.0               0.0          0.0          0.0       0.41%          0.0
32     classifier.0        25088         4096  102764544.0       0.02     205,516,800.0     102,760,448.0  411158528.0      16384.0       7.79%  411174912.0
33     classifier.1         4096         4096          0.0       0.02           4,096.0           4,096.0      16384.0      16384.0       0.00%      32768.0
34     classifier.2         4096         4096          0.0       0.02               0.0               0.0          0.0          0.0       0.00%          0.0
35     classifier.3         4096         4096   16781312.0       0.02      33,550,336.0      16,777,216.0   67141632.0      16384.0       1.23%   67158016.0
36     classifier.4         4096         4096          0.0       0.02           4,096.0           4,096.0      16384.0      16384.0       0.00%      32768.0
37     classifier.5         4096         4096          0.0       0.02               0.0               0.0          0.0          0.0       0.00%          0.0
38     classifier.6         4096         1000    4097000.0       0.00       8,191,000.0       4,096,000.0   16404384.0       4000.0       0.41%   16408384.0
total                                          138357544.0     109.39  30,958,666,264.0  15,503,489,024.0   16404384.0       4000.0     100.00%  783170624.0
============================================================================================================================================================
Total params: 138,357,544
------------------------------------------------------------------------------------------------------------------------------------------------------------
Total memory: 109.39MB
Total MAdd: 30.96GMAdd
Total Flops: 15.5GFlops
Total MemR+W: 746.89MB

方法3 thop.profile

安装pip install thop

from thop import profile
from thop import clever_format

net = Model() 
x = torch.randn(1, 3, 112, 112)
flops, params = profile(net, inputs=(x, ))
print('flops: ', flops, 'params: ', params)
# macs, params = clever_format([flops, params], "%.3f")
# print(macs,params)

注意：

输入input的第一维度是批量(batch size)，批量的大小不回影响参数量，计算量是batch_size=1的倍数
profile(net, (inputs,))的 (inputs,)中必须加上逗号，否者会报错