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Memory efficient implementation of Caffe #23
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Hello @haikuoyao , caffe version does not because Caffe does not support enough computational graph style optimizations for shareMemory. However, my implementation avoids the "2-sidedness" of Caffe, which means within DenseBlock's data memory I didn't use Blob, instead they are pointers. Another issue is Caffe, in multi-gpu case, don't have good load-balance for memory across GPUs. |
Thank you, Tongcheng. It works well on CIFAR data. |
Hello @haikuoyao, what is your input size? CIFAR image is 32x32. If your image is much larger, you may want to do a downsampling through a conv with stride 2, before feeding the image into the first dense block, to reduce the memory consumption. |
Thanks @liuzhuang13 . |
Hi,
I saw this caffe implementation which is memory efficient.
https://github.com/Tongcheng/DN_CaffeScript
And I also notice this in wiki
Does that caffe use the above memory efficient way to implementation?
Thanks.
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