方法1 自带
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total = sum ([param.nelement() for param in model.parameters()])
print ( "Number of parameter: %.2fM" % (total / 1e6 ))
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或者
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total = sum (p.numel() for p in model.parameters())
print ( "Total params: %.2fM" % (total / 1e6 ))
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方法2 编写代码
计算模型参数总量和模型计算量
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def count_params(model, input_size = 224 ):
# param_sum = 0
with open ( 'models.txt' , 'w' ) as fm:
fm.write( str (model))
# 计算模型的计算量
calc_flops(model, input_size)
# 计算模型的参数总量
model_parameters = filter ( lambda p: p.requires_grad, model.parameters())
params = sum ([np.prod(p.size()) for p in model_parameters])
print ( 'The network has {} params.' . format (params))
# 计算模型的计算量
def calc_flops(model, input_size):
def conv_hook( self , input , output):
batch_size, input_channels, input_height, input_width = input [ 0 ].size()
output_channels, output_height, output_width = output[ 0 ].size()
kernel_ops = self .kernel_size[ 0 ] * self .kernel_size[ 1 ] * ( self .in_channels / self .groups) * (
2 if multiply_adds else 1 )
bias_ops = 1 if self .bias is not None else 0
params = output_channels * (kernel_ops + bias_ops)
flops = batch_size * params * output_height * output_width
list_conv.append(flops)
def linear_hook( self , input , output):
batch_size = input [ 0 ].size( 0 ) if input [ 0 ].dim() = = 2 else 1
weight_ops = self .weight.nelement() * ( 2 if multiply_adds else 1 )
bias_ops = self .bias.nelement()
flops = batch_size * (weight_ops + bias_ops)
list_linear.append(flops)
def bn_hook( self , input , output):
list_bn.append( input [ 0 ].nelement())
def relu_hook( self , input , output):
list_relu.append( input [ 0 ].nelement())
def pooling_hook( self , input , output):
batch_size, input_channels, input_height, input_width = input [ 0 ].size()
output_channels, output_height, output_width = output[ 0 ].size()
kernel_ops = self .kernel_size * self .kernel_size
bias_ops = 0
params = output_channels * (kernel_ops + bias_ops)
flops = batch_size * params * output_height * output_width
list_pooling.append(flops)
def foo(net):
childrens = list (net.children())
if not childrens:
if isinstance (net, torch.nn.Conv2d):
net.register_forward_hook(conv_hook)
if isinstance (net, torch.nn.Linear):
net.register_forward_hook(linear_hook)
if isinstance (net, torch.nn.BatchNorm2d):
net.register_forward_hook(bn_hook)
if isinstance (net, torch.nn.ReLU):
net.register_forward_hook(relu_hook)
if isinstance (net, torch.nn.MaxPool2d) or isinstance (net, torch.nn.AvgPool2d):
net.register_forward_hook(pooling_hook)
return
for c in childrens:
foo(c)
multiply_adds = False
list_conv, list_bn, list_relu, list_linear, list_pooling = [], [], [], [], []
foo(model)
if '0.4.' in torch.__version__:
if assets.USE_GPU:
input = torch.cuda.FloatTensor(torch.rand( 2 , 3 , input_size, input_size).cuda())
else :
input = torch.FloatTensor(torch.rand( 2 , 3 , input_size, input_size))
else :
input = Variable(torch.rand( 2 , 3 , input_size, input_size), requires_grad = True )
_ = model( input )
total_flops = ( sum (list_conv) + sum (list_linear) + sum (list_bn) + sum (list_relu) + sum (list_pooling))
print ( ' + Number of FLOPs: %.2fM' % (total_flops / 1e6 / 2 ))
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方法3 thop
需要安装thop
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pip install thop
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调用方法:计算模型参数总量和模型计算量,而且会打印每一层网络的具体信息
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from thop import profile
input = torch.randn( 1 , 3 , 224 , 224 )
flops, params = profile(model, inputs = ( input ,))
print (flops)
print (params)
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或者
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from torchvision.models import resnet50
from thop import profile
# model = resnet50()
checkpoints = '模型path'
model = torch.load(checkpoints)
model_name = 'yolov3 cut asff'
input = torch.randn( 1 , 3 , 224 , 224 )
flops, params = profile(model, inputs = ( input , ),verbose = True )
print ( "%s | %.2f | %.2f" % (model_name, params / ( 1000 * * 2 ), flops / ( 1000 * * 3 ))) #这里除以1000的平方,是为了化成M的单位,
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注意:输入必须是四维的
提高输出可读性, 加入一下代码。
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from thop import clever_format
macs, params = clever_format([flops, params], "%.3f" )
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方法4 torchstat
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from torchstat import stat
from torchvision.models import resnet50, resnet101, resnet152, resnext101_32x8d
model = resnet50()
stat(model, ( 3 , 224 , 224 )) # (3,224,224)表示输入图片的尺寸
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使用torchstat这个库来查看网络模型的一些信息,包括总的参数量params、MAdd、显卡内存占用量和FLOPs等。需要安装torchstat:
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pip install torchstat
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方法5 ptflops
作用:计算模型参数总量和模型计算量
安装方法:pip install ptflops
或者
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pip install - - upgrade git + https: / / github.com / sovrasov / flops - counter.pytorch.git
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使用方法
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import torchvision.models as models
import torch
from ptflops import get_model_complexity_info
with torch.cuda.device( 0 ):
net = models.resnet18()
flops, params = get_model_complexity_info(net, ( 3 , 224 , 224 ), as_strings = True , print_per_layer_stat = True ) #不用写batch_size大小,默认batch_size=1
print ( 'Flops: ' + flops)
print ( 'Params: ' + params)
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或者
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from torchvision.models import resnet50
import torch
import torchvision.models as models
# import torch
from ptflops import get_model_complexity_info
# model = models.resnet50() #调用官方的模型,
checkpoints = '自己模型的path'
model = torch.load(checkpoints)
model_name = 'yolov3 cut'
flops, params = get_model_complexity_info(model, ( 3 , 320 , 320 ),as_strings = True ,print_per_layer_stat = True )
print ( "%s |%s |%s" % (model_name,flops,params))
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注意,这里输入一定是要tuple类型,且不需要输入batch,直接输入输入通道数量与尺寸,如(3,320,320) 320为网络输入尺寸。
输出为网络模型的总参数量(单位M,即百万)与计算量(单位G,即十亿)
方法6 torchsummary
安装:pip install torchsummary
使用方法:
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from torchsummary import summary
...
summary(your_model, input_size = (channels, H, W))
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作用:
1、每一层的类型、shape 和 参数量
2、模型整体的参数量
3、模型大小,和 fp/bp 一次需要的内存大小,可以用来估计最佳 batch_size
补充:pytorch计算模型算力与参数大小
ptflops介绍
这个脚本设计用于计算卷积神经网络中乘法-加法操作的理论数量。它还可以计算参数的数量和打印给定网络的每层计算成本。
支持layer:Conv1d/2d/3d,ConvTranspose2d,BatchNorm1d/2d/3d,激活(ReLU, PReLU, ELU, ReLU6, LeakyReLU),Linear,Upsample,Poolings (AvgPool1d/2d/3d、MaxPool1d/2d/3d、adaptive ones)
安装要求:Pytorch >= 0.4.1, torchvision >= 0.2.1
get_model_complexity_info()
get_model_complexity_info是ptflops下的一个方法,可以计算出网络的算力与模型参数大小,并且可以输出每层的算力消耗。
栗子
以输出Mobilenet_v2算力信息为例:
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from ptflops import get_model_complexity_info
from torchvision import models
net = models.mobilenet_v2()
ops, params = get_model_complexity_info(net, ( 3 , 224 , 224 ), as_strings = True , print_per_layer_stat = True , verbose = True )
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从图中可以看到,MobileNetV2在输入图像尺寸为(3, 224, 224)的情况下将会产生3.505MB的参数,算力消耗为0.32G,同时还打印出了每个层所占用的算力,权重参数数量。当然,整个模型的算力大小与模型大小也被存到了变量ops与params中。
以上为个人经验,希望能给大家一个参考,也希望大家多多支持服务器之家。
原文链接:https://blog.csdn.net/qq_35407318/article/details/109359006