问题描述

我想将我的一些模型设置为冻结状态.按照官方文档:

I want to set some of my model frozen. Following the official docs:

with torch.no_grad():
    linear = nn.Linear(1, 1)
    linear.eval()
    print(linear.weight.requires_grad)

但它打印 True 而不是 False.如果我想将模型设置为 eval 模式，我该怎么做?

But it prints True instead of False. If I want to set the model in eval mode, what should I do?

推荐答案

requires_grad=False

如果您想冻结部分模型并训练其余部分，您可以将要冻结的参数的requires_grad设置为False.

例如，如果您只想保持 VGG16 的卷积部分固定:

For example, if you only want to keep the convolutional part of VGG16 fixed:

model = torchvision.models.vgg16(pretrained=True)
for param in model.features.parameters():
    param.requires_grad = False

通过将 requires_grad 标志切换为 False，不会保存中间缓冲区，直到计算达到某个点，其中操作的输入之一需要梯度.

By switching the requires_grad flags to False, no intermediate buffers will be saved, until the computation gets to some point where one of the inputs of the operation requires the gradient.

使用上下文管理器 torch.no_grad 是实现该目标的不同方式:在 no_grad 上下文中，所有计算结果都将具有 requires_grad=False，即使输入有 requires_grad=True.请注意，您将无法将梯度反向传播到 no_grad 之前的层.例如:

Using the context manager torch.no_grad is a different way to achieve that goal: in the no_grad context, all the results of the computations will have requires_grad=False, even if the inputs have requires_grad=True. Notice that you won't be able to backpropagate the gradient to layers before the no_grad. For example:

x = torch.randn(2, 2)
x.requires_grad = True

lin0 = nn.Linear(2, 2)
lin1 = nn.Linear(2, 2)
lin2 = nn.Linear(2, 2)
x1 = lin0(x)
with torch.no_grad():    
    x2 = lin1(x1)
x3 = lin2(x2)
x3.sum().backward()
print(lin0.weight.grad, lin1.weight.grad, lin2.weight.grad)

输出:

(None, None, tensor([[-1.4481, -1.1789],
         [-1.4481, -1.1789]]))

这里 lin1.weight.requires_grad 为 True，但没有计算梯度，因为操作是在 no_grad 上下文中完成的.

Here lin1.weight.requires_grad was True, but the gradient wasn't computed because the oepration was done in the no_grad context.

如果您的目标不是微调，而是将模型设置为推理模式，最方便的方法是使用 torch.no_grad 上下文管理器.在这种情况下，您还必须将模型设置为 evaluation 模式，这是通过在 nn.Module 上调用 eval() 来实现的，例如例子:

If your goal is not to finetune, but to set your model in inference mode, the most convenient way is to use the torch.no_grad context manager. In this case you also have to set your model to evaluation mode, this is achieved by calling eval() on the nn.Module, for example:

model = torchvision.models.vgg16(pretrained=True)
model.eval()

此操作将层的属性self.training 设置为False，实际上这将改变Dropout 或BatchNorm 必须在训练和测试时表现不同.

This operation sets the attribute self.training of the layers to False, in practice this will change the behavior of operations like Dropout or BatchNorm that must behave differently at training and test time.

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