如何将 model.state_dict() 存储在临时变量中以备后用？

Question

I tried to store the state dict of my model in a variable temporarily and wanted to restore it to my model later, but the content of this variable changed automatically as the model updated.

有一个最小的例子：

import torch as t
import torch.nn as nn
from torch.optim import Adam


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc = nn.Linear(3, 2)

    def forward(self, x):
        return self.fc(x)


net = Net()
loss_fc = nn.MSELoss()
optimizer = Adam(net.parameters())

weights = net.state_dict()
print(weights)

x = t.rand((5, 3))
y = t.rand((5, 2))
loss = loss_fc(net(x), y)

optimizer.zero_grad()
loss.backward()
optimizer.step()

print(weights)

我认为这两个输出是相同的，但我得到了（输出可能会因随机初始化而改变）

OrderedDict([('fc.weight', tensor([[-0.5557,  0.0544, -0.2277],
        [-0.0793,  0.4334, -0.1548]])), ('fc.bias', tensor([-0.2204,  0.2846]))])
OrderedDict([('fc.weight', tensor([[-0.5547,  0.0554, -0.2267],
        [-0.0783,  0.4344, -0.1538]])), ('fc.bias', tensor([-0.2194,  0.2856]))])

weights的内容发生了变化，这太奇怪了。

我还尝试.copy()和t.no_grad()如下，但它们没有帮助。

with t.no_grad():
    weights = net.state_dict().copy()

是的，我知道我可以使用t.save()保存 state 字典，但我只想弄清楚前面的示例中发生了什么。

我正在使用Python 3.8.5和Pytorch 1.8.1

谢谢你的帮助。

Answer 1

这就是OrderedDict的工作原理。 这是一个更简单的例子：

from collections import OrderedDict

# a mutable variable
l = [1,2,3]

# an OrderedDict with an entry pointing to that mutable variable
x = OrderedDict([("a", l)])

# if you change the list
l[1] = 20

# the change is reflected in the OrderedDict
print(x)
# >> OrderedDict([('a', [1, 20, 3])])

如果你想避免这种情况，你必须做一个深deepcopy而不是浅copy ：

from copy import deepcopy
x2 = deepcopy(x)

print(x2)
# >> OrderedDict([('a', [1, 20, 3])])

# now, if you change the list
l[2] = 30

# you do not change your copy
print(x2)
# >> OrderedDict([('a', [1, 20, 3])])

# but you keep changing the original dict
print(x)
# >> OrderedDict([('a', [1, 20, 30])])

由于Tensor也是可变的，因此在您的情况下预期会有相同的行为。 因此，您可以使用：

from copy import deepcopy

weights = deepcopy(net.state_dict())