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.
> > >> 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. > > _______________________________________________ > ffmpeg-devel mailing list > ffmpeg-devel@ffmpeg.org > http://ffmpeg.org/mailman/listinfo/ffmpeg-devel > _______________________________________________ ffmpeg-devel mailing list ffmpeg-devel@ffmpeg.org http://ffmpeg.org/mailman/listinfo/ffmpeg-devel