[GitHub] eldercrow commented on issue #7717: Subpixel convolution(state of art) implementation rather than using Deconvolution.

[gi...@git.apache.org]

eldercrow commented on issue #7717: Subpixel convolution(state of art) implementation rather than using Deconvolution.
URL: https://github.com/apache/incubator-mxnet/issues/7717#issuecomment-327431788
 
 
   As far as I know, mxnet does not support subpixel upsampling layer yet. Still, you can emulate it using a series of transpose and reshape layers. I am currently using the followings, messy but works anyway.
   
   ``` python
   def subpixel_upsample(data, ch, c, r, name=None):
       '''
       Transform input data shape of (n, ch*r*c, h, w) to (n, ch, h*r, c*w).
   
       ch: number of channels after upsample
       r: row scale factor
       c: column scale factor
       '''
       if r == 1 and c == 1:
           return data
       X = mx.sym.reshape(data=data, shape=(-3, 0, 0))  # (n*ch*r*c, h, w)
       X = mx.sym.reshape(data=X, shape=(-4, -1, r * c, 0, 0))  # (n*ch, r*c, h, w)
       X = mx.sym.transpose(data=X, axes=(0, 3, 2, 1))  # (n*ch, w, h, r*c)
       X = mx.sym.reshape(data=X, shape=(0, 0, -1, c))  # (n*ch, w, h*r, c)
       X = mx.sym.transpose(data=X, axes=(0, 2, 1, 3))  # (n*ch, h*r, w, c)
       X = mx.sym.reshape(data=X, name=name, shape=(-4, -1, ch, 0, -3))  # (n, ch, h*r, w*c)
       return X
   ```
 
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