The input train/test images must be resized to certain size （e.g. 100*100）?

[TaihuLight]

@y0umu
Your CS net is interesting, but when I try to train and test it. I observe that it only fits to the fixed size （e.g. 100*100） images during the process of training and testing due to the limitations of the EncoderLinear layer used to CS sampling.
(1) Is it correct for my suggestion?
(2) How can it support for train/test images of the arbitrary size?
(3) The code is reconstructed for running on local computers, which is show as below.
ResCSNet-v0.01.tar.gz

[y0umu]

@TaihuLight

(1) Is it correct for my suggestion?

Yes you are right. The code in the notebook has to deal with images of size 100x100.

(2) How to can it support for train/test images of the arbitrary size?

This is really a good question I am refecting on. One might think of processing by blocks and put them together in the end (like what is done in ReconNet). But this would lead to blocky artifacts even after applying something like BM3D denoiser. I am investigating how is that done in CNN based image super-resolution ( and maybe in detection framework such as Faster-RCNN ). Possibly won't add such feature in this repo in the short term.

[TaihuLight]

Thank you for your timely reply.