在pytorch中，torch.nn.Module模块中的state_dict变量存放训练过程中需要学习的权重和偏执系数，state_dict作为python的字典对象将每一层的参数映射成tensor张量，需要注意的是torch.nn.Module模块中的state_dict只包含卷积层和全连接层的参数，当网络中存在batchnorm时，例如vgg网络结构，torch.nn.Module模块中的state_dict也会存放batchnorm's running_mean。
torch.optim模块中的Optimizer优化器对象也存在一个state_dict对象，此处的state_dict字典对象包含state和param_groups的字典对象，而param_groups key对应的value也是一个由学习率，动量等参数组成的一个字典对象。因为state_dict本质上Python字典对象，所以可以很好地进行保存、更新、修改和恢复操作（python字典结构的特性），从而为PyTorch模型和优化器增加了大量的模块化。
Sample
通过一个简单的案例来输出state_dict字典对象中存放的变量。
代码语言：javascript复制#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()
Output:
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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]}]
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