[GitHub] ThomasDelteil commented on issue #7895: how can i keep the width and height same after Conv2d layer

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ThomasDelteil commented on issue #7895: how can i  keep the width and height same after Conv2d layer
URL: https://github.com/apache/incubator-mxnet/issues/7895#issuecomment-402570884
 
 
   @itisianlee typically `stride` and `dilation` are behavioral parameter that you want to use when you try to achieve specific behavior. `dilation` when you want to enlarge your receptive field. `stride` when you want to decrease your output resolution. `padding` is typically used in order to control the output size.
   Let's first check your formula after fixing `stride` `dilation` and `kernel`.
   
   Using your example:
   `stride = (1,1)`
   `dilation = (1,1)`
   `kernel = (4,4)`
   
   ```python
   out_height = floor((height+2*padding[0]-1*(4-1)-1)/1)+1
   out_width = floor((width+2*padding[1]-1*(4-1)-1)/1)+1
   
   out_height = floor((height+2*padding[0]-3-1))+1
   out_width = floor((width+2*padding[1]-3-1))+1
   
   out_height = height+2*padding[0]-3
   out_width = width+2*padding[1]-3
   
   padding[0] = 3/2
   padding[1] = 3/2
   ```
   You get indeed a non-integer value for your padding, and unfortunately Conv2D does not support asymetric padding.
   
   Two solutions come to mind to solve that:
   - use `nd.pad()` that supports asymmetric padding followed by a convolution with (0,0) padding.
   - over-pad, for example here using `padding=(2,2)` and use the `nd.slice()` operator to slice one column and one row from your output feature maps.
   
   @szha can you please close this issue? Thanks!
   
   @itisianlee if you would like to follow-up, please create an issue on https://discuss.mxnet.io, thanks!

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