[GitHub] ChaiBapchya commented on issue #12594: [MXNET-867] Pooling1D with "same" padding

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ChaiBapchya commented on issue #12594: [MXNET-867] Pooling1D with "same" padding
URL: https://github.com/apache/incubator-mxnet/pull/12594#issuecomment-424465389
 
 
   As discussed with Sina Afrooze and Lin, this is just the first step towards complete solution/fix.
   
   Immediate demand was for Max Pool 1 Dimension to support "same" padding (the way Tensorflow does). Above pull request addresses that.
   
   However, for a complete solution, following things need to be understood
   
   ### Asymmetric Padding
   Feature request is about "Asymmetric padding"
   Currently, MXNet supports Symmetric padding
   e.g. Input data -> `1,2,3,4` and `pad`=`2` 
   Output data -> `0,0,1,2,3,4,0,0`
    
   #### Why isn't Asymmetric padding supported by CUDnn, MXNet?
   
   Due to misalignment of input-output if padding happens only on one side (left or right alone).
   It is called as *"Causal Convolution"* (only one side pad)
   
   ### Current work-around
   Level of abstraction introduced.
   By default, one can use `pad` parameter while performing `Pooling` or `Convolution` which performs symmetric padding. But, if there is a specific use-case for performing asymmetric padding, one has to perform following steps,
   Perform independent padding (using `pad` operator) and then perform `Pooling` or `Convolution` as desired.
   
   ### Future steps
   If one has to fully implement asymmetric padding, following changes need to be made
   1. Pooling
   - Add another parameter such as pad_type (because currently, `pooling_conv` which allows - 'full', 'valid', 'same' has been misused. Original meaning of `pooling_conv` was not supposed to take care of "type of padding"
   
   2. Convolution
   - Incorporate asymmetric padding for the same
   
   

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