pytorch 计算Parameter和FLOP的操作

pip install thop

pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git # 下载源码安装

from torchvision.models import resnet50 # 引入ResNet50模型
from thop import profile
model = resnet50()
flops, params = profile(model, input_size=(1, 3, 224,224)) # profile（模型，输入数据）

  from thop import profile
  from utils.ShuffleNetV2 import shufflenetv2 # 导入shufflenet2 模块
  import torch 
  
  model_shuffle = shufflenetv2(width_mult=0.5)
  model = torch.nn.DataParallel(model_shuffle)  # 调用shufflenet2 模型，该模型为自己定义的
  flop, para = profile(model, input_size=(1, 3, 224, 224),) 
  print("%.2fM" % (flop/1e6), "%.2fM" % (para/1e6))

  from thop import profile
  from utils.ShuffleNetV2 import shufflenetv2 # 导入shufflenet2 模块
  import torch 
  
  model_shuffle = shufflenetv2(width_mult=0.5)
  model = torch.nn.DataParallel(model_shuffle)
  total = sum([param.nelement() for param in model.parameters()])
  print("Number of parameter: %.2fM" % (total / 1e6))

pip install thop

import torch
from thop import profile
net = Model() # 定义好的网络模型
input = torch.randn(1, 3, 112, 112)
flops, params = profile(net, (inputs,))
print('flops: ', flops, 'params: ', params)