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RuntimeError: CUDA out of memory. #63

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kae1111 opened this issue Aug 10, 2024 · 0 comments
Open

RuntimeError: CUDA out of memory. #63

kae1111 opened this issue Aug 10, 2024 · 0 comments

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@kae1111
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kae1111 commented Aug 10, 2024

when i run'sh eval.sh ' it show :
~/LangSplat/eval$ sh eval.sh
ModuleList(
(0): Linear(in_features=512, out_features=256, bias=True)
(1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU()
(3): Linear(in_features=256, out_features=128, bias=True)
(4): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(5): ReLU()
(6): Linear(in_features=128, out_features=64, bias=True)
(7): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(8): ReLU()
(9): Linear(in_features=64, out_features=32, bias=True)
(10): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(11): ReLU()
(12): Linear(in_features=32, out_features=3, bias=True)
) ModuleList(
(0): Linear(in_features=3, out_features=16, bias=True)
(1): ReLU()
(2): Linear(in_features=16, out_features=32, bias=True)
(3): ReLU()
(4): Linear(in_features=32, out_features=64, bias=True)
(5): ReLU()
(6): Linear(in_features=64, out_features=128, bias=True)
(7): ReLU()
(8): Linear(in_features=128, out_features=256, bias=True)
(9): ReLU()
(10): Linear(in_features=256, out_features=256, bias=True)
(11): ReLU()
(12): Linear(in_features=256, out_features=512, bias=True)
)
0%| | 0/6 [00:00<?, ?it/s]
Traceback (most recent call last):
File "evaluate_iou_loc.py", line 339, in
evaluate(feat_dir, output_path, ae_ckpt_path, json_folder, mask_thresh, args.encoder_dims, args.decoder_dims, logger)
File "evaluate_iou_loc.py", line 260, in evaluate
restored_feat = model.decode(sem_feat.flatten(0, 2))
File "../autoencoder/model.py", line 45, in decode
x = x / x.norm(dim=-1, keepdim=True)
RuntimeError: CUDA out of memory. Tried to allocate 4.13 GiB (GPU 0; 10.75 GiB total capacity; 4.50 GiB already allocated; 2.92 GiB free; 6.58 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

any help?thanks!
GPU:2080ti

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