Thanks for your contribution @ChaiBapchya
@mxnet-label-bot[pr-awaiting-review]

Thanks for making this contribution. LGTM overall. You may also want to update the operator description in operator/nn/pooling.cc since you added a new type "same"

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

@ChaiBapchya can you create and issue for the future steps, giving the full description as in the above comment? Issues will be tracked, the above comment could get lost.

Sure. Thanks for suggesting :)