Caffe

Fills a Blob with coefficients for bilinear interpolation. More...
#include <filler.hpp>
Public Member Functions  
BilinearFiller (const FillerParameter ¶m)  
virtual void  Fill (Blob< Dtype > *blob) 
Public Member Functions inherited from caffe::Filler< Dtype >  
Filler (const FillerParameter ¶m)  
Additional Inherited Members  
Protected Attributes inherited from caffe::Filler< Dtype >  
FillerParameter  filler_param_ 
Fills a Blob with coefficients for bilinear interpolation.
A common use case is with the DeconvolutionLayer acting as upsampling. You can upsample a feature map with shape of (B, C, H, W) by any integer factor using the following proto.
Please use this by replacing {{}}
with your values. By specifying num_output: {{C}} group: {{C}}
, it behaves as channelwise convolution. The filter shape of this deconvolution layer will be (C, 1, K, K) where K is kernel_size
, and this filler will set a (K, K) interpolation kernel for every channel of the filter identically. The resulting shape of the top feature map will be (B, C, factor * H, factor * W). Note that the learning rate and the weight decay are set to 0 in order to keep coefficient values of bilinear interpolation unchanged during training. If you apply this to an image, this operation is equivalent to the following call in Python with Scikit.Image.