Unplausible results from MSGNet.m

[wiedemannc]

Hello,

first of all thank you very much for uploading the code of the paper to github!

I have tried to run MSGNet.m and I get the following output:

Splitting 1 of 2
Cleared 0 solvers and 0 stand-alone nets
Run CNN ... 
Finished. 
Splitting 2 of 2
Cleared 0 solvers and 1 stand-alone nets
Run CNN ... 
Finished. 
------------------------------------------
Model: ./models/MSGNet_x2
Testing set A: art
MSG-Net 2x upsampling, RMSE = 3.977

According to your paper, the RMSE should be 0.663. The whole code is stock with the following changes:

-root = './examples/MSGNet-release/';
+root = '../';

-gpu_id = 7;
+gpu_id = 0;

-    caffe.set_mode_gpu();
-    caffe.set_device(gpu_id);
+    caffe.set_mode_cpu();
+    %caffe.set_device(gpu_id);

Note that I had to use the CPU version of caffe to avoid memory problems. Caffe is installed from debian stretch caffe-cpu package, caffe version is 1.0.0-rc4, matlab version is 2018a.

I also tried the 8x model but got similar bad results (an RMSE of ~8 in this case).

Thanks in advance for your help!
Kind Regards

[wiedemannc]

It seems to be related to using the cpu mode of caffe. I was able to run the MSG net on a cropped image with dimensions 100x100. The used image is Arts index range: (501:600, 501:600).

CPU results:

Model: ./models/MSGNet_x2
Testing set B: art
MSG-Net 2x upsampling, RMSE = 2.647

GPU results:

Model: ./models/MSGNet_x2
Testing set B: art
MSG-Net 2x upsampling, RMSE = 0.197

[wiedemannc]

It turned out to be an issue with the installed BLAS library. After installing openblas, the issue was gone. That seems to be an issue of the debian caffe package.