GPU memory about setr HOT 6 CLOSED

Hi 2 * P40 probably are not enough for the task semantic segmentation for example 2*P40 even can’t run the Deeplab v3+ or DANet with ResNet-101.

Following is the minimum resource to run SETR (bs=8) on Cityscapes you can see it is on par with most existing segmentation models.

SETR-Naive-DeiT, 8 * 11.5G
SETR-PUP-DeiT, 8 * 12.8G
SETR-MLA-DeiT, 8 * 12.1G

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I tried to train SETR-Naive-DeiT&SETR-MLA-DeiT on 4* TITAN RTX 24g GPU, I set samples_per_gpu=1 on config/SETR/.py，so my batch size is 4. But I can not start training cus OOM. You said SETR-Naive-DeiT, 811.5G SETR-PUP-DeiT, 812.8G SETR-MLA-DeiT, 812.1G . But It is different from my experimental result. How could I do to reduce the memory used?

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we don't have such a problem from our side

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RuntimeError: CUDA out of memory. Tried to allocate 44.00 MiB (GPU 6; 23.70 GiB total capacity; 21.91 GiB already allocated; 36.81 MiB free; 22.28 GiB reserved in total by PyTorch)
return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
File "/home/ls/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/nn/functional.py", line 1605, in log_softmax
ret = input.log_softmax(dim)
RuntimeError: CUDA out of memory. Tried to allocate 44.00 MiB (GPU 0; 23.70 GiB total capacity; 21.91 GiB already allocated; 36.81 MiB free; 22.28 GiB reserved in total by PyTorch)
Traceback (most recent call last):
File "/home/ls/anaconda3/envs/open-mmlab/lib/python3.8/runpy.py", line 194, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/ls/anaconda3/envs/open-mmlab/lib/python3.8/runpy.py", line 87, in _run_code
exec(code, run_globals)
File "/home/ls/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/distributed/launch.py", line 260, in
main()
File "/home/ls/anaconda3/envs/open-mmlab/lib/python3.8/site-packages/torch/distributed/launch.py", line 255, in main
raise subprocess.CalledProcessError(returncode=process.returncode,
subprocess.CalledProcessError: Command '['/home/ls/anaconda3/envs/open-mmlab/bin/python', '-u', './tools/train.py', '--local_rank=7', 'configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py', '--launcher', 'pytorch']' returned non-zero exit status 1.

When i tried to train your model "SETR_PUP" by the "./tools/dist_train.sh configs/SETR/SETR_PUP_768x768_40k_cityscapes_bs_8.py 8", i get the above issues even my machine has 8*3090 with 24G. Can you help me to solve it? Thank you.

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@lisenbuaa

try following three variants with DeiT

SETR-Naive-DeiT, 8 * 11.5G
SETR-PUP-DeiT, 8 * 12.8G
SETR-MLA-DeiT, 8 * 12.1G

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I have a similar issue when training on my own dataset. Always it is CUDA out of memory. I am using 6 GPUs with 12GB (4 GTX 1080TI, 2 RTX 2080TI). Is there any way to train without getting error?

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