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WARNING:root:NaN or Inf found in input tensor. #34

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liaorongfan opened this issue Jan 11, 2021 · 16 comments
Closed

WARNING:root:NaN or Inf found in input tensor. #34

liaorongfan opened this issue Jan 11, 2021 · 16 comments

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@liaorongfan
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Training form scratch with "V_16_voc07.yaml" with batch size of 1 on one GForce 1080Ti GPU, after 6000 iters, the logger gave this warming, I have no idea where got things wrong. Could you help me with some clue
@LoveIsAGame
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Hello, I've been watching this job recently. Do you use your own data set? What is your error message?

@liaorongfan
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@LoveIsAGame Hi, I used my own dataset with tow classes labeled in voc format(xml) and the training encountered the issue I posted.

@jason718
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can you post the log / err message?

@liaorongfan
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Author

@jason718 sorry for posting the long error log, it said:

2021-01-13 02:05:49,856 wetectron.utils.miscellaneous INFO: Saving labels mapping into output/labels.json
2021-01-13 02:05:50,802 wetectron.trainer INFO: Start training
/opt/conda/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
WARNING:root:NaN or Inf found in input tensor.
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2021-01-13 02:06:32,200 wetectron.trainer INFO: eta: 17:14:12 iter: 20 loss: nan (nan) loss_img: nan (nan) loss_ref_cls0: nan (nan) loss_ref_reg0: nan (nan) loss_ref_cls1: nan (nan) loss_ref_reg1: nan (nan) loss_ref_cls2: nan (nan) loss_ref_reg2: nan (nan) acc_img: 0.0000 (0.1375) acc_ref0: 0.0000 (0.0437) acc_ref1: 0.0000 (0.0250) acc_ref2: 0.0000 (0.1187) time: 1.9256 (2.0698) data: 0.0394 (0.0893) lr: 0.004000 max mem: 17949

@liaorongfan
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I prepared corresponding selective search bboxes as the USE_YOUR_OWN_DATA instructions.

@LoveIsAGame
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@liaorongfan Can I add your wechat or QQ? It's more convenient to communicate in this way.

@LoveIsAGame
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I don't know how to prepare my own data. My data is supervised data. How can I generate his pkl file?
@liaorongfan

@liaorongfan
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@LoveIsAGame I guess, we may fully discuss the issues we encountered here.

@liaorongfan
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@LoveIsAGame Hi , for selective search proposals this repo would be helpful.

@jason718
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@jason718 sorry for posting the long error log, it said:

2021-01-13 02:05:49,856 wetectron.utils.miscellaneous INFO: Saving labels mapping into output/labels.json
2021-01-13 02:05:50,802 wetectron.trainer INFO: Start training
/opt/conda/lib/python3.6/site-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of lr_scheduler.step() before optimizer.step(). In PyTorch 1.1.0 and later, you should call them in the opposite order: optimizer.step() before lr_scheduler.step(). Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate", UserWarning)
WARNING:root:NaN or Inf found in input tensor.
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2021-01-13 02:06:32,200 wetectron.trainer INFO: eta: 17:14:12 iter: 20 loss: nan (nan) loss_img: nan (nan) loss_ref_cls0: nan (nan) loss_ref_reg0: nan (nan) loss_ref_cls1: nan (nan) loss_ref_reg1: nan (nan) loss_ref_cls2: nan (nan) loss_ref_reg2: nan (nan) acc_img: 0.0000 (0.1375) acc_ref0: 0.0000 (0.0437) acc_ref1: 0.0000 (0.0250) acc_ref2: 0.0000 (0.1187) time: 1.9256 (2.0698) data: 0.0394 (0.0893) lr: 0.004000 max mem: 17949

Maybe some items of your dataset/proposal are broken? Have you checked?

You can also make the batchsize=1 and print out loss at every iteration to see when the loss become Nan.

@liaorongfan
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@jason718 Hi, thanks very much for the instructions. The reason caused to that problem may be too few proposals for each images, average in 500/image, and then I select more proposals and it seems OK now. Very excellent work, thanks for sharing.

@JanuaryThomas
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I had the same issue, but I fixed it by updating the learning rate.

@Zhangmaopeng88
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I had the same issue, but I fixed it by updating the learning rate.

thanks，me too

@moonsh
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moonsh commented May 26, 2022

@JanuaryThomas @Zhangmaopeng88 How did you solve it? Changed the learning rate small?

@xiaoWen9246
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I had the same issue, but I fixed it by updating the learning rate.

Do you increase or decrease the learning rate to solve this problem？

@ooguz166
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ooguz166 commented Nov 3, 2023

If anyone suffers from the issue and if you double checked your data, first:

  • At the beginning of your script or before the training loop, add the line torch.autograd.set_detect_anomaly(True) to enable anomaly detection.
  • Later problem may appear due to exploding gradients, thus lr change may seem to work but it is better to clip like :
    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)

It is a general solution that worked for my 3090 single GPU, hope it helps

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8 participants
@jason718 @moonsh @JanuaryThomas @liaorongfan @LoveIsAGame @ooguz166 @Zhangmaopeng88 @xiaoWen9246