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lijiannuist avatar lijiannuist commented on April 28, 2024 4

Hi, demo.py adopts ResNet152 backbone and multi-scale test, which consumes much GPU memory.
You can try to use less image scales for inference.
We will release a more fine model.

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yihongXU avatar yihongXU commented on April 28, 2024 3

Dear all, I also had the 'out of memory' problem. My problem is that the volatile flag for tensors is now deprecated (torch>=0.4). So the network keeps gradient while doing inference.
If you just want to do evaluation, add torch.set_grad_enabled(False) inside test_oneimage() function (at the beginning of the function).

It works for me, and the memory takes only around 2Gb instead of 9 Gb.

Be careful that it works only for test time since you discard all the gradient.

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nyck33 avatar nyck33 commented on April 28, 2024

@yihongXU

This did not help. I also put with torch.no_grad(): at the beginning of def infer() but neither worked.

I also commented out parts for det_b and det_s and just let them equal to det0 and det1 (because I am too confused to refactor more) but still getting memory errors.

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