state_dict詳解

在pytorch中，torch.nn.Module模塊中的state_dict變量存放訓練過程中需要學習的權重和偏執系數，state_dict作為python的字典對象將每一層的參數映射成tensor張量，需要注意的是torch.nn.Module模塊中的state_dict只包含卷積層和全連接層的參數，當網絡中存在batchnorm時，例如vgg網絡結構，torch.nn.Module模塊中的state_dict也會存放batchnorm's running_mean，關於batchnorm詳解可見https://blog.csdn.net/wzy_zju/article/details/81262453

torch.optim模塊中的Optimizer優化器對象也存在一個state_dict對象，此處的state_dict字典對象包含state和param_groups的字典對象，而param_groups key對應的value也是一個由學習率，動量等參數組成的一個字典對象。

因為state_dict本質上Python字典對象，所以可以很好地進行保存、更新、修改和恢復操作（python字典結構的特性），從而為PyTorch模型和優化器增加了大量的模塊化。

通過一個簡單的案例來輸出state_dict字典對象中存放的變量

#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()
 

具體的輸出結果如下：可以很清晰的觀測到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]}]

轉載自：https://blog.csdn.net/bigFatCat_Tom/article/details/90722261