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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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?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
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
add a comment |
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
add a comment |
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
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
c++ pytorch libtorch
asked Mar 18 at 14:31
Afshin OroojlooyAfshin Oroojlooy
5479 silver badges21 bronze badges
5479 silver badges21 bronze badges
add a comment |
add a comment |
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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.
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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active
oldest
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active
oldest
votes
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.
add a comment |
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.
add a comment |
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.
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.
answered Mar 27 at 20:01
Afshin OroojlooyAfshin Oroojlooy
5479 silver badges21 bronze badges
5479 silver badges21 bronze badges
add a comment |
add a comment |
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