深度学习中模型计算量(FLOPs)和参数量(Params)的理解以及四种计算方法总结

接下来要分别概述以下内容:

1 首先什么是参数量,什么是计算量

2 如何计算 参数量,如何统计 计算量

3 换算参数量,把他换算成我们常用的单位,比如:mb

4 对于各个经典网络,论述他们是计算量大还是参数量,有什么好处

5 计算量,参数量分别对显存,芯片提出什么要求,我们又是怎么权衡
  

深度学习中模型参数量和计算量的理解与计算

1 首先什么是计算量,什么是参数量

计算量对应我们之前的时间复杂度,参数量对应于我们之前的空间复杂度,这么说就很明显了

也就是计算量要看网络执行时间的长短,参数量要看占用显存的量

2 如何计算:参数量,计算量

(1)针对于卷积层的
 《深度学习中模型计算量(FLOPs)和参数量(Params)的理解以及四种计算方法总结》
其中上面的公式是计算时间复杂度(计算量),而下面的公式是计算空间复杂度(参数量)

对于卷积层:

参数量就是

(kernel*kernel) *channel_input*channel_output

kernel*kernel 就是 weight * weight

其中kernel*kernel = 1个feature的参数量

计算量就是

(kernel*kernel*map*map) *channel_input*channel_output

kernel*kernel 就是weight*weight

map*map是下个featuremap的大小,也就是上个weight*weight到底做了多少次运算

其中kernel*kernel*map*map= 1个feature的计算量

(2)针对于池化层:

无参数

(3)针对于全连接层:

参数量=计算量=weight_in*weight_out

3 对于换算计算量

  • 一般一个参数是值一个float,也就是4个字节

  • 1kb=1024字节

4 对于各个经典网络:

《深度学习中模型计算量(FLOPs)和参数量(Params)的理解以及四种计算方法总结》
(1)换算

以alexnet为例:

参数量:6000万

设每个参数都是float,也就是一个参数是4字节,

总的字节数是24000万字节

24000万字节= 24000万/1024/1024=228mb

(2)为什么模型之间差距这么大

这个关乎于模型的设计了,其中模型里面最费参数的就是全连接层,这个可以看alex和vgg,

alex,vgg有很多fc(全连接层)

resnet就一个fc

inceptionv1(googlenet)也是就一个fc

(3)计算量

densenet其实这个模型不大,也就是参数量不大,因为就1个fc

但是他的计算量确实很大,因为每一次都把上一个feature加进来,所以计算量真的很大

5 计算量与参数量对于硬件要求

计算量,参数量对于硬件的要求是不同的

计算量的要求是在于芯片的floaps(指的是gpu的运算能力)

参数量取决于显存大小

6 计算量(FLOPs)和参数量(Params)

6.1 第一种方法:thop

计算量:
FLOPs,FLOP时指浮点运算次数,s是指秒,即每秒浮点运算次数的意思,考量一个网络模型的计算量的标准。

参数量:
Params,是指网络模型中需要训练的参数总数。

第一步:安装模块

pip install thop

第二步:计算

# -- coding: utf-8 --
import torch
import torchvision
from thop import profile

# Model
print('==> Building model..')
model = torchvision.models.alexnet(pretrained=False)

dummy_input = torch.randn(1, 3, 224, 224)
flops, params = profile(model, (dummy_input,))
print('flops: ', flops, 'params: ', params)
print('flops: %.2f M, params: %.2f M' % (flops / 1000000.0, params / 1000000.0))

结果

==> Building model..
[INFO] Register count_convNd() for <class 'torch.nn.modules.conv.Conv2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.activation.ReLU'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.pooling.MaxPool2d'>.
[WARN] Cannot find rule for <class 'torch.nn.modules.container.Sequential'>. Treat it as zero Macs and zero Params.
[INFO] Register count_adap_avgpool() for <class 'torch.nn.modules.pooling.AdaptiveAvgPool2d'>.
[INFO] Register zero_ops() for <class 'torch.nn.modules.dropout.Dropout'>.
[INFO] Register count_linear() for <class 'torch.nn.modules.linear.Linear'>.
[WARN] Cannot find rule for <class 'torchvision.models.alexnet.AlexNet'>. Treat it as zero Macs and zero Params.
flops:  714691904.0 params:  61100840.0
flops: 714.69 M, params: 61.10 M

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

6.2 第二种方法:ptflops

# -- coding: utf-8 --
import torchvision
from ptflops import get_model_complexity_info

model = torchvision.models.alexnet(pretrained=False)
flops, params = get_model_complexity_info(model, (3, 224, 224), as_strings=True, print_per_layer_stat=True)
print('flops: ', flops, 'params: ', params)

结果

AlexNet(
  61.101 M, 100.000% Params, 0.716 GMac, 100.000% MACs, 
  (features): Sequential(
    2.47 M, 4.042% Params, 0.657 GMac, 91.804% MACs, 
    (0): Conv2d(0.023 M, 0.038% Params, 0.07 GMac, 9.848% MACs, 3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
    (1): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.027% MACs, inplace=True)
    (2): MaxPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.027% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (3): Conv2d(0.307 M, 0.503% Params, 0.224 GMac, 31.316% MACs, 64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
    (4): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.020% MACs, inplace=True)
    (5): MaxPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.020% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
    (6): Conv2d(0.664 M, 1.087% Params, 0.112 GMac, 15.681% MACs, 192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (7): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.009% MACs, inplace=True)
    (8): Conv2d(0.885 M, 1.448% Params, 0.15 GMac, 20.902% MACs, 384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (9): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.006% MACs, inplace=True)
    (10): Conv2d(0.59 M, 0.966% Params, 0.1 GMac, 13.936% MACs, 256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (11): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.006% MACs, inplace=True)
    (12): MaxPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.006% MACs, kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
  )
  (avgpool): AdaptiveAvgPool2d(0.0 M, 0.000% Params, 0.0 GMac, 0.001% MACs, output_size=(6, 6))
  (classifier): Sequential(
    58.631 M, 95.958% Params, 0.059 GMac, 8.195% MACs, 
    (0): Dropout(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, p=0.5, inplace=False)
    (1): Linear(37.753 M, 61.788% Params, 0.038 GMac, 5.276% MACs, in_features=9216, out_features=4096, bias=True)
    (2): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.001% MACs, inplace=True)
    (3): Dropout(0.0 M, 0.000% Params, 0.0 GMac, 0.000% MACs, p=0.5, inplace=False)
    (4): Linear(16.781 M, 27.465% Params, 0.017 GMac, 2.345% MACs, in_features=4096, out_features=4096, bias=True)
    (5): ReLU(0.0 M, 0.000% Params, 0.0 GMac, 0.001% MACs, inplace=True)
    (6): Linear(4.097 M, 6.705% Params, 0.004 GMac, 0.573% MACs, in_features=4096, out_features=1000, bias=True)
  )
)
flops:  0.72 GMac params:  61.1 M

6.3 第三种方法:pytorch_model_summary

import torch
import torchvision
from pytorch_model_summary import summary

# Model
print('==> Building model..')
model = torchvision.models.alexnet(pretrained=False)

dummy_input = torch.randn(1, 3, 224, 224)
print(summary(model, dummy_input, show_input=False, show_hierarchical=False))

结果

==> Building model..
-----------------------------------------------------------------------------
           Layer (type)         Output Shape         Param # Tr. Param #
=============================================================================
               Conv2d-1      [1, 64, 55, 55]          23,296          23,296
                 ReLU-2      [1, 64, 55, 55]               0               0
            MaxPool2d-3      [1, 64, 27, 27]               0               0
               Conv2d-4     [1, 192, 27, 27]         307,392         307,392
                 ReLU-5     [1, 192, 27, 27]               0               0
            MaxPool2d-6     [1, 192, 13, 13]               0               0
               Conv2d-7     [1, 384, 13, 13]         663,936         663,936
                 ReLU-8     [1, 384, 13, 13]               0               0
               Conv2d-9     [1, 256, 13, 13]         884,992         884,992
                ReLU-10     [1, 256, 13, 13]               0               0
              Conv2d-11     [1, 256, 13, 13]         590,080         590,080
                ReLU-12     [1, 256, 13, 13]               0               0
           MaxPool2d-13       [1, 256, 6, 6]               0               0
   AdaptiveAvgPool2d-14       [1, 256, 6, 6]               0               0
             Dropout-15            [1, 9216]               0               0
              Linear-16            [1, 4096]      37,752,832      37,752,832
                ReLU-17            [1, 4096]               0               0
             Dropout-18            [1, 4096]               0               0
              Linear-19            [1, 4096]      16,781,312      16,781,312
                ReLU-20            [1, 4096]               0               0
              Linear-21            [1, 1000]       4,097,000       4,097,000
=============================================================================
Total params: 61,100,840
Trainable params: 61,100,840
Non-trainable params: 0
-----------------------------------------------------------------------------

6.4 第四种方法:参数总量和可训练参数总量

import torch
import torchvision
from pytorch_model_summary import summary

# Model
print('==> Building model..')
model = torchvision.models.alexnet(pretrained=False)

pytorch_total_params = sum(p.numel() for p in model.parameters())
trainable_pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)

print('Total - ', pytorch_total_params)
print('Trainable - ', trainable_pytorch_total_params)

结果

==> Building model..
Total -  61100840
Trainable -  61100840

7 输入数据对模型的参数量和计算量的影响

# -- coding: utf-8 --
import torch
import torchvision
from thop import profile

# Model
print('==> Building model..')
model = torchvision.models.alexnet(pretrained=False)

dummy_input = torch.randn(1, 3, 224, 224)
flops, params = profile(model, (dummy_input,))
print('flops: ', flops, 'params: ', params)
print('flops: %.2f M, params: %.2f M' % (flops / 1000000.0, params / 1000000.0))
  • 输入数据:(1, 3, 224, 224),一张224*224的RGB图像
flops:  714691904.0 params:  61100840.0
flops: 714.69 M, params: 61.10 M
  • 输入数据:(1, 3, 512, 512),一张512*512的RGB图像
flops:  3710034752.0 params:  61100840.0
flops: 3710.03 M params: 61.10 M
  • 输入数据:(8, 3, 224, 224),八张224*224的RGB图像
flops:  5717535232.0 params:  61100840.0
flops: 5717.54 M params: 61.10 M
输入数据计算量(flops)参数量(params)
(1, 3, 224, 224)714.69 M61.10 M
(1, 3, 512, 512)3710.03 M61.10 M
(8, 3, 224, 224)5717.54 M61.10 M

参考资料

  1. https://www.cnblogs.com/lllcccddd/p/10671879.html
  2. https://blog.csdn.net/Caesar6666/article/details/109842379
    原文作者:紫芝
    原文地址: https://blog.csdn.net/qq_40507857/article/details/118764782
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