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深度学习中,模型训练完后,查看模型的参数量和浮点计算量,在此记录下:
1 THOP
在pytorch中有现成的包thop用于计算参数数量和FLOP,首先安装thop:
pip install thop
注意安装thop时可能出现如下错误:
解决方法:
pip install --upgrade git+https://github.com/Lyken17/pytorch-OpCounter.git # 下载源码安装
使用方法如下:
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)
对于自己构建的函数也一样,例如shuffleNetV2
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))
更多细节,可参考thop GitHub链接: https://github.com/Lyken17/pytorch-OpCounter
2 计算参数
pytorch本身带有计算参数的方法
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))