2018-05-29 4:08 GMT+03:00 Pedro Arthur <bygran...@gmail.com>:
> 2018-05-28 19:52 GMT-03:00 Sergey Lavrushkin <dual...@gmail.com>:
> > 2018-05-28 9:32 GMT+03:00 Guo, Yejun <yejun....@intel.com>:
> >
> >> looks that no tensorflow dependency is introduced, a new model format is
> >> created together with some CPU implementation for inference. With this
> >> idea, Android Neural Network would be a very good reference, see
> >> https://developer.android.google.cn/ndk/guides/neuralnetworks/. It
> >> defines how the model is organized, and also provided a CPU optimized
> >> inference implementation (within the NNAPI runtime, it is open source).
> It
> >> is still under development but mature enough to run some popular dnn
> models
> >> with proper performance. We can absorb some basic design. Anyway, just a
> >> reference fyi. (btw, I'm not sure about any IP issue)
> >>
> >
> > The idea was to first introduce something to use when tensorflow is not
> > available. Here is another patch, that introduces tensorflow backend.
> I think it would be better for reviewing if you send the second patch
> in a new email.
Then we need to push the first patch, I think.
> >
> >
> >> For this patch, I have two comments.
> >>
> >> 1. change from "DNNModel* (*load_default_model)(DNNDefaultModel
> >> model_type);" to " DNNModel* (*load_builtin_model)(DNNBuiltinModel
> >> model_type);"
> >> The DNNModule can be invoked by many filters, default model is a good
> >> name at the filter level, while built-in model is better within the DNN
> >> scope.
> >>
> >> typedef struct DNNModule{
> >> // Loads model and parameters from given file. Returns NULL if it is
> >> not possible.
> >> DNNModel* (*load_model)(const char* model_filename);
> >> // Loads one of the default models
> >> DNNModel* (*load_default_model)(DNNDefaultModel model_type);
> >> // Executes model with specified input and output. Returns DNN_ERROR
> >> otherwise.
> >> DNNReturnType (*execute_model)(const DNNModel* model);
> >> // Frees memory allocated for model.
> >> void (*free_model)(DNNModel** model);
> >> } DNNModule;
> >>
> >>
> >> 2. add a new variable 'number' for DNNData/InputParams
> >> As a typical DNN concept, the data shape usually is: <number, height,
> >> width, channel> or <number, channel, height, width>, the last component
> >> denotes its index changes the fastest in the memory. We can add this
> >> concept into the API, and decide to support <NHWC> or <NCHW> or both.
> >
> >
> > I did not add number of elements in batch because I thought, that we
> would
> > not feed more than one element at once to a network in a ffmpeg filter.
> > But it can be easily added if necessary.
> >
> > So here is the patch that adds tensorflow backend with the previous
> patch.
> > I forgot to change include guards from AVUTIL_* to AVFILTER_* in it.
> You moved the files from libavutil to libavfilter while it was
> proposed to move them to libavformat.
Not only, it was also proposed to move it to libavfilter if it is going to
be used only
in filters. I do not know if this module is useful anywhere else besides
libavfilter.
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