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Common class for Linear, Conv1d, Conv2d,…, LSTM,


When should you use a class vs a struct in C++?*.h or *.hpp for your class definitionsUse 'class' or 'typename' for template parameters?How to call a parent class function from derived class function?Meaning of 'const' last in a function declaration of a class?error: request for member '..' in '..' which is of non-class typeConstexpr class: Inheritance?Checking template parameter inheritance with SFINAEHow to correctly give inputs to Embedding, LSTM and Linear layers in PyTorch?pytorch - Where is “conv1d” implemented?






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2















Is there any class that all torch::nn::Linear, torch::nn::Conv1d, torch::nn::Conv2d, ... torch::nn::GRU, .... all inherit from that? torch::nn::Module seems be a good option, though there is a middle class, called torch::nn::Cloneable, so that torch::nn::Module does not work. Also, torch::nn::Cloneable itself is a template so that needs type in the declaration.
I want to create a general class model, which has std::vector<the common class> layers, so that later I can fill layers with any type of layer that I want, e.g., Linear, LSTM, etc. Is there such a capability in the current API? This can be done easily in python, though here we need declaration and this hinders the python's easiness.



Thanks,
Afshin










share|improve this question






























    2















    Is there any class that all torch::nn::Linear, torch::nn::Conv1d, torch::nn::Conv2d, ... torch::nn::GRU, .... all inherit from that? torch::nn::Module seems be a good option, though there is a middle class, called torch::nn::Cloneable, so that torch::nn::Module does not work. Also, torch::nn::Cloneable itself is a template so that needs type in the declaration.
    I want to create a general class model, which has std::vector<the common class> layers, so that later I can fill layers with any type of layer that I want, e.g., Linear, LSTM, etc. Is there such a capability in the current API? This can be done easily in python, though here we need declaration and this hinders the python's easiness.



    Thanks,
    Afshin










    share|improve this question


























      2












      2








      2


      1






      Is there any class that all torch::nn::Linear, torch::nn::Conv1d, torch::nn::Conv2d, ... torch::nn::GRU, .... all inherit from that? torch::nn::Module seems be a good option, though there is a middle class, called torch::nn::Cloneable, so that torch::nn::Module does not work. Also, torch::nn::Cloneable itself is a template so that needs type in the declaration.
      I want to create a general class model, which has std::vector<the common class> layers, so that later I can fill layers with any type of layer that I want, e.g., Linear, LSTM, etc. Is there such a capability in the current API? This can be done easily in python, though here we need declaration and this hinders the python's easiness.



      Thanks,
      Afshin










      share|improve this question














      Is there any class that all torch::nn::Linear, torch::nn::Conv1d, torch::nn::Conv2d, ... torch::nn::GRU, .... all inherit from that? torch::nn::Module seems be a good option, though there is a middle class, called torch::nn::Cloneable, so that torch::nn::Module does not work. Also, torch::nn::Cloneable itself is a template so that needs type in the declaration.
      I want to create a general class model, which has std::vector<the common class> layers, so that later I can fill layers with any type of layer that I want, e.g., Linear, LSTM, etc. Is there such a capability in the current API? This can be done easily in python, though here we need declaration and this hinders the python's easiness.



      Thanks,
      Afshin







      c++ pytorch libtorch






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      asked Mar 18 at 14:31









      Afshin OroojlooyAfshin Oroojlooy

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          I found that nn::sequential can be used for a this purpose, and it does not need a forward implementation, which can be a positive point and at a same time a negative point. nn::sequential already requires each module to have a forward implementation, and calls the forward functions in a sequence that they have added in. So, one cannot create an ad-hock non-usual forward pass like Dense-Net with that, though it is good enough for general usages.



          In addition, it seems that nn::sequential just uses a std::vector<nn::AnyModule> as its underlying module list. So, std::vector<nn::AnyModule> also might be used.






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            I found that nn::sequential can be used for a this purpose, and it does not need a forward implementation, which can be a positive point and at a same time a negative point. nn::sequential already requires each module to have a forward implementation, and calls the forward functions in a sequence that they have added in. So, one cannot create an ad-hock non-usual forward pass like Dense-Net with that, though it is good enough for general usages.



            In addition, it seems that nn::sequential just uses a std::vector<nn::AnyModule> as its underlying module list. So, std::vector<nn::AnyModule> also might be used.






            share|improve this answer





























              0















              I found that nn::sequential can be used for a this purpose, and it does not need a forward implementation, which can be a positive point and at a same time a negative point. nn::sequential already requires each module to have a forward implementation, and calls the forward functions in a sequence that they have added in. So, one cannot create an ad-hock non-usual forward pass like Dense-Net with that, though it is good enough for general usages.



              In addition, it seems that nn::sequential just uses a std::vector<nn::AnyModule> as its underlying module list. So, std::vector<nn::AnyModule> also might be used.






              share|improve this answer



























                0














                0










                0









                I found that nn::sequential can be used for a this purpose, and it does not need a forward implementation, which can be a positive point and at a same time a negative point. nn::sequential already requires each module to have a forward implementation, and calls the forward functions in a sequence that they have added in. So, one cannot create an ad-hock non-usual forward pass like Dense-Net with that, though it is good enough for general usages.



                In addition, it seems that nn::sequential just uses a std::vector<nn::AnyModule> as its underlying module list. So, std::vector<nn::AnyModule> also might be used.






                share|improve this answer













                I found that nn::sequential can be used for a this purpose, and it does not need a forward implementation, which can be a positive point and at a same time a negative point. nn::sequential already requires each module to have a forward implementation, and calls the forward functions in a sequence that they have added in. So, one cannot create an ad-hock non-usual forward pass like Dense-Net with that, though it is good enough for general usages.



                In addition, it seems that nn::sequential just uses a std::vector<nn::AnyModule> as its underlying module list. So, std::vector<nn::AnyModule> also might be used.







                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Mar 27 at 20:01









                Afshin OroojlooyAfshin Oroojlooy

                5479 silver badges21 bronze badges




                5479 silver badges21 bronze badges





















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