code: def interp(net, layers):
      
      for l in layers:
          m, k, h, w = net.params[l][0].data.shape
          if m != k and k != 1:
              print 'input + output channels need to be the same or |output| == 1'
              raise
          if h != w:
              print 'filters need to be square'
              raise
          filt = upsample_filt(h)
          net.params[l][0].data[range(m), range(k), :, :] = filt
docs: Set weights of each layer in layers to bilinear kernels for interpolation.