Closed hobin-jeong closed 4 years ago
@hobin-jeong Hi, we directly print the number of trainable parameters instead of manually calculate it - so our numbers should be correct. Moreover your formula should be correct for each convolution - please carefully checkout the ch_in
and ch_out
in our WDSR-B.
Hello, @JiahuiYu !
I'm trying to implement your network.
When I checked your light models for WDSR-B, I found the difference between your results and my results. I saying WDSR-B with n resblocks as WDSR-B_nblks, you assert that the numbers of parameters of WDSR-A_nblks and WDSR-B_nblks are equal. ( e.g. the number of parameters of WDSR-A_1blks and WDSR-B_1blks is 0.08M )
According to my computation, the numbers of parameters of WDSR-A_nblks are almost equal, but those of WDSR-B_nblks are quite different. ( more precisely, my results are less than yours )
How to compute the number of parameters? I use this formula: kernel_size^2 ch_in ch_out (weight) + ch_out (bias) for each convolution.
Thanks in advance!
Hobin