2018-06-03 15:25 GMT-03:00 Sergey Lavrushkin <dual...@gmail.com>: > 2018-06-03 19:57 GMT+03:00 Pedro Arthur <bygran...@gmail.com>: >> >> 2018-05-31 12:01 GMT-03:00 Sergey Lavrushkin <dual...@gmail.com>: >> > Hello, >> > >> > This patch introduces TensorFlow backend for DNN inference module. >> > This backend uses TensorFlow binary models and requires from model >> > to have the operation named 'x' as an input operation and the operation >> > named 'y' as an output operation. Models are executed using >> > libtensorflow. >> >> Hi, >> >> You added the tf model in dnn_srcnn.h, it seems the data is being >> duplicated as it already contains the weights as C float arrays. >> Is it possible to construct the model graph via C api and set the >> weights using the ones we already have, eliminating the need for >> storing the whole tf model? > > > Hi, > > I think, it is possible, but it will require to manually create every > operation > and specify each of their attributes and inputs in a certain order specified > by > operations declaration. Here is that model: > https://drive.google.com/file/d/1s7bW7QnUfmTaYoMLPdYYTOLujqNgRq0J/view?usp=sharing > It is just a lot easier to store the whole model and not construct it > manually. > Another way, I think of, is to pass weights in placeholders and not save > them in > model, but it has to be done when session is already created and not during > model > loading. Maybe some init operation can be specified with variables > assignment to values > passed through placeholders during model loading, if it is possible. But is > it really crucial > to not store the whole tf model? It is not that big.
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. _______________________________________________ ffmpeg-devel mailing list ffmpeg-devel@ffmpeg.org http://ffmpeg.org/mailman/listinfo/ffmpeg-devel
