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Hi, I really appreciate your research and efforts. I want to extend this work. I am trying to implementing selection channel on a CNN architecture. I am confused in coding section of m.alloc() and dealloc(). I have replaced conv2d with SelectiveConv as you mentioned in the paper.
I would be thankful for your concern and help. Thanks
The text was updated successfully, but these errors were encountered:
After replacing Conv2d to SelectiveConv2d, you can call m.dealloc() and m.realloc() during training whenever needed (e.g. for every epoch). This part can be found in L220 of main.py:
for m in model.modules():
if type(m).__name__ in ['SelectiveConv2d']:
if epoch < 0.5 * args['num_epochs']:
m.dealloc()
m.realloc()
Please let me know if I misunderstood your question.
Thanks for your fast response. I was doing this thing but I was facing issue in iterating through network. It seems working now. Let's see how it performs after training. Moreover, I would like to ask what if I add SelectiveConv2d in Unet. Will it work? and UNet also contains convTranspose layers. How to add it in Unet?
Again I really appreciate your concern.
Thanks,
Zirsha
Hi, I really appreciate your research and efforts. I want to extend this work. I am trying to implementing selection channel on a CNN architecture. I am confused in coding section of m.alloc() and dealloc(). I have replaced conv2d with SelectiveConv as you mentioned in the paper.
I would be thankful for your concern and help. Thanks
The text was updated successfully, but these errors were encountered: