Comments (4)
Hi @dandelin,
Thank you for finding it out. I think you can directly initialize them with ones and zeros instead.
Did you compare the difference of execution time between these two initializations? I thought the initialization function is only called once, so maybe the computation overhead is tolerable.
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@HobbitLong
I'm trying a dataset larger than the ImageNet, so the initializing took some time (>15 minutes).
After the fix, the initialization was done immediately.
And yes, you are right since the actual training of CMC takes much longer than the initialization :) but it irritated me a lot while tuning the code so I left the issue.
+) Also I noticed that the memory bank took a few gigabytes on GPU RAM, so I moved them to a CPU RAM and it works fine for me with almost no overhead.
Although I added additional lines of code to move read features to GPU RAM and store inferred features back to CPU RAM.
++) Are you planning releasing your experiments on NYU-Depth-V2? It will be helpful to understand the implementation of >2 views case.
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- Nice! Thanks for your experiment of moving memory back to CPU. How much is the relative decrease of speed?
- Probably not now due to my limited capacity, but some hints are available here #9 and also our paper.
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@HobbitLong
I did not measure the latency, but there was no difference in the feeling.
Again, thanks for sharing the code :) closing the issue.
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Related Issues (20)
- Weight shape in Linear classifier HOT 1
- Train/Test split when training linear classifier (ImageNet) HOT 3
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- index drawn by AliasMethod is not on the same gpu as the model HOT 1
- About the training epochs for ImageNet-1K
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