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chuanfushen avatar chuanfushen commented on August 30, 2024

Awesome!

we also find this issue, but haven't pointed it out at docs.

Thanks for letting people know, and feel free to make a PR! Thanks.

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jackhanyuan avatar jackhanyuan commented on August 30, 2024

I found that some other format files were generated in the OUMVLP_rearranged folder, so the processing would randomly break.

Just filtering the png files will solve this problem.

jackhanyuan@046083a

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chuanfushen avatar chuanfushen commented on August 30, 2024

I found that some other format files were generated in the OUMVLP_rearranged folder, so the processing would randomly break.

Just filtering the png files will solve this problem.

https://github.com/jackhanyuan/OpenGait/blob/master/misc/pretreatment.py

Okay, I see you filter png, so does it work perfectly now? Currently, I am working on verifying @gosiqueira pull_request . After I finish verification, I will try to merge both your and gosiqueira code.

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jackhanyuan avatar jackhanyuan commented on August 30, 2024

@noahshen98 Okay, After filtering png, it worked well.

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chuanfushen avatar chuanfushen commented on August 30, 2024

System information (version)

  • Pytorch => 1.10

  • Operating System / Platform =>Ubuntu 20.04

  • Cuda => Cuda 11.5

Detailed description

pretreatment.py for OUMVLP dataset results in random deadlocks.

Currently you are making asynchronous calls using a thread pool and storing the result e ordinary Python list. Thus, when I execute the code to convert OUMVLP files to pickles it's happening random deadlocks during the code execution.

I forked the directory and I'm trying to solve this myself to make a PR, bit I believe it's relevant to keep you apart of this problem.

Steps to reproduce

Steps:

  1. Execute the code for OUMVLP dataset using personal machine processor. The error occurs in a non-deterministic fashion.

Issue submission checklist

  • I checked the problem with documentation, FAQ, issues, and have not found solution.

Hi, I have verified the output of your modified pretreatment is identical to previous version. Besides, your modified version is much clear and understandable. Thanks for your contribution.

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