I guess the non-trainable parameters should come from the buffers, e.g. running stats in batchnorm layers, but I’m not sure how model.summary() calculates them.
Anyway, the trainable parameters match, which is great.
Sure, please share the code and more importantly your setup, i.e. which PyTorch, TF, CUDA and cudnn versions are you using in both use cases.