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slurm #44
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单机单卡、单机多卡、不使用slurm实现语义分割。 2 batch_size参数修改,在semantic_segmentation/configs/base/datasets/ade20k_sfpn.py中,修改为samples_per_gpu=8, workers_per_gpu=8即可。 3 lr和iters参数修改,在semantic_segmentation/configs/ade20k/sfpn.biformer_small.py中,根据经验公式gpusbatchsizeiters=定值,lr=定值(gpus*batchsize)修改lr和iters即可。 4 多卡训练参数修改,只需修改slurm.sh文件即可。以下是我没有使用slurm的多卡训练脚本。 CONFIG_DIR=configs/ade20k PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ export CUDA_VISIBLE_DEVICES=0,1,2,3 # 暴露GPU编号 |
你好请问单机单卡也是这么设置的吗? |
还要导入一下ckpt权重文件,在作者的readme里面有,新建文件夹pretrained,下载到里面即可。 |
请问按这个改完后报错
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i want to know how to run the .sh without slurm to segmentation, because the slurm is hard to use.
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