Check failed: error == cudaSuccess (2 vs. 0) out of memory about bottom-up-attention HOT 7 OPEN

@maoyj1998
I deleted my previous comment, I thought it was fixed but I still got the same error after running on few examples.
I switched to T4 GPU wchich has 16gb of memory and that fixed it later. I was getting errors on rtx 2070 super which has only 8gb memory.

I use this code to extract feature from VG datasets, and I found it was caused by some images with big difference in aspect ratio, for example 281 * 500, faster rcnn will resize the image based on the shorter edge, so making the larger edge too large.

I modified this part and it works.

from bottom-up-attention.

Hi,

Have you solved this problem?
I am facing similar problem for some images. Rest of the images it do work. But some images it gets stuck and throws this error.
F0324 13:21:55.416903 11805 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory

from bottom-up-attention.

I run demo to extract bounding box features on GTX 2080Ti, but I received this error.

F0115 10:36:21.001302  3456 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0)  out of memory

Looking forward to anyone's help

hi have you solved this problem ?

from bottom-up-attention.

Decreasing BATCH_SIZE and RPN_BATCHSIZE size in yml configuration file fixed it for me (experiments/cfgs/faster_rcnn.. .yml)

hi, I check the yml file, but all the config you mentioned are training settings, test settings have nothing todo with BATCH_SIZE, I wonder whether it will work if I change these config

from bottom-up-attention.

Hi,

Have you solved this problem?
I am facing similar problem for some images. Rest of the images it do work. But some images it gets stuck and throws this error.
F0324 13:21:55.416903 11805 syncedmem.cpp:71] Check failed: error == cudaSuccess (2 vs. 0) out of memory

hi, did you solve this problem?

from bottom-up-attention.

@maoyj1998
I deleted my previous comment, I thought it was fixed but I still got the same error after running on few examples.
I switched to T4 GPU wchich has 16gb of memory and that fixed it later. I was getting errors on rtx 2070 super which has only 8gb memory.

from bottom-up-attention.

Have you solved this problem? I will also encounter this problem when running demo.ipynb

from bottom-up-attention.