[FFmpeg-devel] [GSOC] [PATCH] TensorFlow backend introduction for DNN module

[Sergey Lavrushkin]

>
> My concern is when we add more models, currently we have to store 2
> models, one for the "native" implementation and one for the TF
> backend.
> There is also the case were one wants to update the weights for a
> model, it will be necessary to update both the native and TF data.
> Having duplicated data is much easier to get inconsistencies between
> implementations.
>

I understand the problem, but I am afraid that manual graph construction can
take a lot of time, especially if we add something more complicated than
srcnn,
and the second approach passing weights in placeholders will require to
add some logic for it in other parts of API besides model loading.
I am thinking of another way, that is to get weights for native model from
this binary
tf model, if they are stored there consistently, and not specify them as
float arrays.
But then for each new model we need to find offsets for each weights array.