"Hands-on Deep Learning Pytorch Edition" 5.5 Reading and Writing Files

5.5.1 Loading and saving

import torch
from torch import nn
from torch.nn import functional as F

x = torch.arange(4)
torch.save(x, 'x-file')  # 使用 save 保存

x2 = torch.load('x-file')  # 使用 load 读回内存
x2

tensor([0, 1, 2, 3])

y = torch.zeros(4)
torch.save([x, y],'x-files')  # 也可以存储张量列表
x2, y2 = torch.load('x-files')
(x2, y2)

(tensor([0, 1, 2, 3]), tensor([0., 0., 0., 0.]))

mydict = {
    
    'x': x, 'y': y}  # 存储从字符串映射到张量的字典
torch.save(mydict, 'mydict')
mydict2 = torch.load('mydict')
mydict2

{'x': tensor([0, 1, 2, 3]), 'y': tensor([0., 0., 0., 0.])}

5.5.2 Loading and saving model parameters

class MLP(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden = nn.Linear(20, 256)
        self.output = nn.Linear(256, 10)

    def forward(self, x):
        return self.output(F.relu(self.hidden(x)))

net = MLP()
X = torch.randn(size=(2, 20))
Y = net(X)

torch.save(net.state_dict(), 'mlp.params')  # 保存模型参数

clone = MLP()
clone.load_state_dict(torch.load('mlp.params'))  # 加载文件中存储的参数

Y_clone = clone(X)  # 参数一致则计算结果也应相同

clone.eval(), Y_clone == Y

(MLP(
   (hidden): Linear(in_features=20, out_features=256, bias=True)
   (output): Linear(in_features=256, out_features=10, bias=True)
 ),
 tensor([[True, True, True, True, True, True, True, True, True, True],
         [True, True, True, True, True, True, True, True, True, True]]))

practise

(1) Even if there is no need to deploy the trained model to different devices, what practical benefit does the saved model parameters have?

It can be used as a backup or for further processing.

---

(2) Suppose we only want to reuse a part of the network and have merged it into a different network architecture. For example, if you want to use the first two layers of the previous network in a new network, what should you do?

torch.save(net.hidden.state_dict(), 'mlp.hidden.params')  # 需要哪里存哪里
clone = MLP()
clone.hidden.load_state_dict(torch.load('mlp.hidden.params'))  # 需要哪里加载哪里

clone.eval(), clone.hidden.weight == net.hidden.weight

(MLP(
   (hidden): Linear(in_features=20, out_features=256, bias=True)
   (output): Linear(in_features=256, out_features=10, bias=True)
 ),
 tensor([[True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         ...,
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True],
         [True, True, True,  ..., True, True, True]]))

---

(3) How to save network architecture and parameters at the same time? What constraints need to be placed on the architecture?

net = nn.Sequential(nn.Linear(20, 256), nn.ReLU(), nn.Linear(256, 10))
torch.save(net, 'net')  # pytorch 本身就支持保存模型
net_new = torch.load('net')
net_new

Sequential(
  (0): Linear(in_features=20, out_features=256, bias=True)
  (1): ReLU()
  (2): Linear(in_features=256, out_features=10, bias=True)
)