How to linearly interpolate between two models?

Hi I’m trying to linearly interpolate between two models but it doesn’t seem to be working. Here is my function to do the interpolation. I have the output attached below.

`def interpolate_models(model, model1):
    alphas = np.linspace(0,1,10)
    acc_array = np.zeros(alphas.shape)
    iter = 0
    tic = time.perf_counter()
    for alpha in alphas:   
        new_model = copy.deepcopy(model)
        new_model_1 = copy.deepcopy(model1)
        for n, p in new_model.named_parameters():
   = (alpha) * + (1 - alpha) * {n:p for n, p in new_model_1.named_parameters()}[n].data

        _, inter_acc = evaluate_pgd(new_model, val_loader, 20,1,None)
        acc_array[iter] = 100 - inter_acc
        iter = iter + 1
        del new_model
        del new_model_1
    toc = time.perf_counter()
    print(f"Eval took {(toc - tic)/60:0.4f} mins")
    return alphas, acc_array`

Clearly, the accuracy for model is about what I would expect but not for model1. Is my interpolation function totally wrong or is pytorch doing something weird to the weights under the hood?

Thanks in advance.

The manipulation of the .data attribute is not recommended as it might yield unwanted side effects.
Could you try to “interpolate” the models by using their state_dicts and load the new one into a model instead?

Yes, thank you. As soon as I tried your suggestion, it worked. In my case, I forgot to include the batch norm parameters in my interpolation so of course my results will be wrong. state_dicts helped me catch that and resolve my bug. Thanks again!