model.parameters(),model.state_dict(),model .load_state_dict()以及torch.load()

一.model.parameters()与model.state_dict()

model.parameters()model.state_dict()都是Pytorch中用于查看网络参数的方法

一般来说,前者多见于优化器的初始化,例如:

后者多见于模型的保存,如:

当我们对网络调参或者查看网络的参数是否具有可复现性时,可能会查看网络的参数

pretrained_dict = torch.load(yolov4conv137weight)

model_dict = _model.state_dict()  #查看模型的权重和biass系数
          
pretrained_dict = {k1: v for (k, v), k1 in zip(pretrained_dict.items(), model_dict)}

model_dict.update(pretrained_dict) #更新model网络模型的参数的权值和biass,这相当于是一个浅拷贝,对这个更新改变会更改模型的权重和biass

model.state_dict()其实返回的是一个OrderDict,存储了网络结构的名字和对应的参数。

例子:

#encoding:utf-8
 
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import numpy as mp
import matplotlib.pyplot as plt
import torch.nn.functional as F
 
#define model
class TheModelClass(nn.Module):
    def __init__(self):
        super(TheModelClass,self).__init__()
        self.conv1=nn.Conv2d(3,6,5)
        self.pool=nn.MaxPool2d(2,2)
        self.conv2=nn.Conv2d(6,16,5)
        self.fc1=nn.Linear(16*5*5,120)
        self.fc2=nn.Linear(120,84)
        self.fc3=nn.Linear(84,10)
 
    def forward(self,x):
        x=self.pool(F.relu(self.conv1(x)))
        x=self.pool(F.relu(self.conv2(x)))
        x=x.view(-1,16*5*5)
        x=F.relu(self.fc1(x))
        x=F.relu(self.fc2(x))
        x=self.fc3(x)
        return x
 
def main():
    # Initialize model
    model = TheModelClass()
 
    #Initialize optimizer
    optimizer=optim.SGD(model.parameters(),lr=0.001,momentum=0.9)
 
    #print model's state_dict
    print('Model.state_dict:')
    for param_tensor in model.state_dict():
        #打印 key value字典
        print(param_tensor,'\t',model.state_dict()[param_tensor].size())
 
    #print optimizer's state_dict
    print('Optimizer,s state_dict:')
    for var_name in optimizer.state_dict():
        print(var_name,'\t',optimizer.state_dict()[var_name])
 
 
 
if __name__=='__main__':
    main()
 

具体的输出结果如下:可以很清晰的观测到state_dict中存放的key和value的值

Model.state_dict:
conv1.weight 	 torch.Size([6, 3, 5, 5])
conv1.bias 	 torch.Size([6])
conv2.weight 	 torch.Size([16, 6, 5, 5])
conv2.bias 	 torch.Size([16])
fc1.weight 	 torch.Size([120, 400])
fc1.bias 	 torch.Size([120])
fc2.weight 	 torch.Size([84, 120])
fc2.bias 	 torch.Size([84])
fc3.weight 	 torch.Size([10, 84])
fc3.bias 	 torch.Size([10])
Optimizer,s state_dict:
state 	 {}
param_groups 	 [{'lr': 0.001, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'params': [367949288, 367949432, 376459056, 381121808, 381121952, 381122024, 381121880, 381122168, 381122096, 381122312]}]

二.torch.load()和load_state_dict()

load_state_dict(state_dict, strict=True)

从 state_dict 中复制参数和缓冲区到 Module 及其子类中 

state_dict:包含参数和缓冲区的 Module 状态字典

strict:默认 True,是否严格匹配 state_dict 的键值和 Module.state_dict()的键值
 

 model = nn.Sequential(self.down1, self.down2, self.down3, self.down4, self.down5, self.neek)

pretrained_dict = torch.load(yolov4conv137weight)  #加载已经训练好的模型参数

model_dict = model.state_dict()  #查看权重和偏重

# 1. filter out unnecessary keys
pretrained_dict = {k1: v for (k, v), k1 in zip(pretrained_dict.items(), model_dict)}

# 2. overwrite entries in the existing state dict
 model_dict.update(pretrained_dict)  #更新已有的模型的权重和偏重
  
model.load_state_dict(model_dict)   #将更新后的参数重新加载至网络模型中

官方推荐的方法,只保存和恢复模型中的参数

# save
torch.save(model.state_dict(), PATH)
 
# load
model = MyModel(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()

torch.load("path路径")表示加载已经训练好的模型

而model.load_state_dict(torch.load(PATH))表示将训练好的模型参数重新加载至网络模型中

来源:无尽的沉默

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