[FFmpeg-devel] [GSOC] [PATCH] DNN module introduction and SRCNN filter update

Pedro Arthur bygrandao at gmail.com
Tue May 29 04:08:56 EEST 2018

2018-05-28 19:52 GMT-03:00 Sergey Lavrushkin <dualfal at gmail.com>:
> 2018-05-28 9:32 GMT+03:00 Guo, Yejun <yejun.guo at 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.

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