2018-05-28 3:32 GMT-03:00 Guo, Yejun <[email protected]>:
> 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)
>
> 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.
I have no problem with either name, both seems reasonable.
>
> 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.
You mean how often the layer filter data changes?
If yes, I don't see much use for it, atm we are performing only inference.
If there are no more concerns about the patch I may push it by tomorrow.
Thanks.
>
>
> Thanks
> Yejun (郭叶军)
>
> -----Original Message-----
> From: ffmpeg-devel [mailto:[email protected]] On Behalf Of
> Sergey Lavrushkin
> Sent: Saturday, May 26, 2018 2:02 AM
> To: FFmpeg development discussions and patches <[email protected]>
> Subject: Re: [FFmpeg-devel] [GSOC] [PATCH] DNN module introduction and SRCNN
> filter update
>
>>
>> You should use the ff_ prefix for internal, non-static functions only.
>> You don't need to use any prefix for internal structs.
>> The AV* prefix is only for public structs, and the av_ prefix is for
>> public functions.
>>
>> >
>> >
>> > And you need to indent and prettify the tables a bit.
>> >
>> >
>> > Do you mean kernels and biases in dnn_srcnn.h?
>> > Their formatting represents their 4D structure a little bit and it
>> > is similar to one that I used for the first srcnn filter version
>> > that was successfully pushed before.
>>
>> Yes, and i suppose they were pushed like that without the reviewer
>> noticing they had no indentation or vertical alignment.
>>
>
> Here is the patch with DNN module moved to libavfilter and proper formatting
> of tables in dnn_srcnn.h.
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