Hello,
I am in a situation were I initialize and train a model using some datasets. I then save the model state dict. I would like to be able to load the model without having to load the data again. However because of the dimensions mismatch when I call load_state_dict I don’t seem to be able to do that.
example :
class Model(nn.Module):
    def __init__(
            self,
            layer_list,
            x_mean: Optional[np.array] = None,
            x_std: Optional[np.array] = None,
            **_
    ):
        super(Model, self).__init__()

        self.register_buffer('x_mean', None if x_mean is None else torch.FloatTensor(x_mean))
        self.register_buffer('x_std', None if x_std is None else torch.FloatTensor(x_std))
        self.sequential = get_sequential(layer_list)

After training the x_mean, x_std, … are filled with tensor of certain shapes. However when I load the model, I ‘don’t know’ the shape of those, and I would like to avoid to search in the data what are the shape of x_mean and x_std.
In this case I would like to load that model, just using the layer_list and the function load_state_dict
Thanks!