Comments (7)
你好,你的gpu里有其他程序把显存占满了。NLSN可以在8G的显卡上训练。
from non-local-sparse-attention.
你好,你的gpu里有其他程序把显存占满了。NLSN可以在8G的显卡上训练。
谢谢您的回复。我用的是v100,训练用的是800张DIV2K测试用的是10张DIV2K,训练完后,紧接着用10张DIV2K测试时(用于选择最佳模型)显示内存不够了,请问这个原因是?您对数据进行了额外处理?
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需要训练命令中加入—chop。这是一个sr中比较常用的方法(例如RCAN/SAN/CSNLN)将大图切成四块,用来缓解测试时的显存占用。可以参考readme里的训练命令
python main.py --dir_data ../../ --n_GPUs 4 --rgb_range 1 --chunk_size 144 --n_hashes 4 --save_models --lr 1e-4 --decay 200-400-600-800 --epochs 1000 --chop --save_results --n_resblocks 32 --n_feats 256 --res_scale 0.1 --batch_size 16 --model NLSN --scale 2 --patch_size 96 --save NLSN_x2 --data_train DIV2K
from non-local-sparse-attention.
需要训练命令中加入—chop。这是一个sr中比较常用的方法(例如RCAN/SAN/CSNLN)将大图切成四块,用来缓解测试时的显存占用。可以参考readme里的训练命令
python main.py --dir_data ../../ --n_GPUs 4 --rgb_range 1 --chunk_size 144 --n_hashes 4 --save_models --lr 1e-4 --decay 200-400-600-800 --epochs 1000 --chop --save_results --n_resblocks 32 --n_feats 256 --res_scale 0.1 --batch_size 16 --model NLSN --scale 2 --patch_size 96 --save NLSN_x2 --data_train DIV2K
特别感谢,麻烦您了,我试试看。
from non-local-sparse-attention.
你好,你的gpu里有其他程序把显存占满了。NLSN可以在8G的显卡上训练。
x3,x4的都可以在8G的卡上训练吗?为什么我x2的就不可以在11G的卡上训练?两张11G的可以,我看了一下内存占用大概是16G,并且没有其他程序占用显存。
from non-local-sparse-attention.
你好,你的gpu里有其他程序把显存占满了。NLSN可以在8G的显卡上训练。
x3,x4的都可以在8G的卡上训练吗?为什么我x2的就不可以在11G的卡上训练?两张11G的可以,我看了一下内存占用大概是16G,并且没有其他程序占用显存。
您好,我是用的小模型--n_resblocks 8--n_feats 64 所以加上-chop之后就可以运行了。不知道是否符合您的问题。
from non-local-sparse-attention.
谢谢您,我用的32个residual blocks,减小了应该可以。
from non-local-sparse-attention.
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