Difference between state_dict and parameters()

I saw two methods of accessing a model’s parameters:

  1. using state_dict

  2. using parameters()

Which is more correct?

What are the differences?



model.parameters() contains all the learnable parameters of the model. state_dict() is a python dict mapping from layer to their parameters. Optimizers have their own state_dict. As state_dict are python dicts they are easy to save and load. docs

Additionally to @Kushaj’s description, the state_dict holds all buffers besides the learnable parameters, e.g. BatchNorm's running estimates, so that you can recreate your model properly.


I think should be added to docs as well.
EDIT: It is in the docs my bad.

Well, in the docs you’ve posted this sentence might be misleading:

Note that only layers with learnable parameters (convolutional layers, linear layers, etc.) have entries in the model’s state_dict.

So I see your point here.
Have a look at this simple model:

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 3, 1, 1)
        self.bn1 = nn.BatchNorm2d(6)
        self.register_buffer('buf1', torch.randn(1))
    def forward(self, x):
        x = self.bn1(self.conv1(x))
        return x

model = MyModel()

As you can see buf1 is also in the state_dict.
Could you create an issue on GitHub and propose a change?
If not, let me know and I’ll do it. :wink:

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I will post the issue.

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The issue is posted on Github. link

Another difference appears to be that for all learnable parameters which appear in both model.state_dict() and model.parameters(), requires_grad is True in the model.parameters() output and False in the model.state_dict() output.

Why is this?


That’s true, not sure why exactly but after reloading my model using load_state_dict as -


It does everything as expected. Sets all the parameters respectively with requires_grad True.
So my guess is in the state_dict the requires_grad is False in order to avoid any unnecessary autograd graph creation if the user does some computation to the tensors directly from state_dict.

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I asked on Stack Overflow and got this answer.

If something should be added or subtracted from it, please let me know.
Otherwise, I will accept it here as well.