Pytorch中实现只导入部分模型参数的方式

import torch as t
from torch.nn import Module
from torch import nn
from torch.nn import functional as F
class Net(Module):
  def __init__(self):
    super(Net,self).__init__()
    self.conv1 = nn.Conv2d(3,32,3,1)
    self.conv2 = nn.Conv2d(32,3,3,1)
    self.w = nn.Parameter(t.randn(3,10))
    for p in self.children():
      nn.init.xavier_normal_(p.weight.data)
      nn.init.constant_(p.bias.data, 0)
  def forward(self, x):
    out = self.conv1(x)
    out = self.conv2(x)
 
    out = F.avg_pool2d(out,(out.shape[2],out.shape[3]))
    out = F.linear(out,weight=self.w)
    return out

model = Net()
t.save(model.state_dict(),'xxx.pth')

import torch as t
from torch.nn import Module
from torch import nn
from torch.nn import functional as F
 
 
class Net(Module):
  def __init__(self):
    super(Net, self).__init__()
    self.conv1 = nn.Conv2d(3, 32, 3, 1)
    self.conv2 = nn.Conv2d(32, 3, 3, 1)
    self.conv3 = nn.Conv2d(3,64,3,1)
    self.conv4 = nn.Conv2d(64,32,3,1)
    for p in self.children():
      nn.init.xavier_normal_(p.weight.data)
      nn.init.constant_(p.bias.data, 0)
 
    self.w = nn.Parameter(t.randn(3, 10))
  def forward(self, x):
    out = self.conv1(x)
    out = self.conv2(x)
 
    out = F.avg_pool2d(out, (out.shape[2], out.shape[3]))
    out = F.linear(out, weight=self.w)
    return out

path = 'xxx.pth'
model = Net()
model.load_state_dict(t.load(path))
 
'''
RuntimeError: Error(s) in loading state_dict for Net:
 Missing key(s) in state_dict: "conv3.weight", "conv3.bias", "conv4.weight", "conv4.bias". 
'''

path = 'xxx.pth'
model = Net()
save_model = t.load(path)
model_dict = model.state_dict()
state_dict = {k:v for k,v in save_model.items() if k in model_dict.keys()}
print(state_dict.keys()) # dict_keys(['w', 'conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias'])
model_dict.update(state_dict)
model.load_state_dict(model_dict)

for k in state_dict.keys():
  print(k)
 
'''
w
conv1.weight
conv1.bias
conv2.weight
conv2.bias
'''
for k in model_dict.keys():
  print(k)
 
'''
w
conv1.weight
conv1.bias
conv2.weight
conv2.bias
conv3.weight
conv3.bias
conv4.weight
conv4.bias
'''