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Unable to reproduce inference results #48

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howard5758 opened this issue Aug 17, 2021 · 12 comments
Closed

Unable to reproduce inference results #48

howard5758 opened this issue Aug 17, 2021 · 12 comments

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@howard5758
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Hi,

First I'd like to sincerely thank the author for sharing this amazing work.
When I tried the provided colab with default setting (indoor), the resulting plot was quite poor.
Screenshot 2021-08-16 185650

Similar problem occurred when I cloned the repo and tested on both indoor and outdoor pairs on
indoor_ds.ckpt and outdoor_ds.ckpt

Can anyone advise on this?
Thank you very much!

@JiamingSuen
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Hi @howard5758, a recent bug fix (3955e99 and #41) breaks the compatibility of the released model weights. We will fix this issue very soon and release the new models, thanks for the heads up.

@howard5758
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Understood. Thank you so much for the quick response and the information :)

@angshine
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Hi, this issue has been fixed in the most recent commit.

@howard5758
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Hi, thanks for the prompt actions.

I cloned the repo again and first encountered the following error:
loftr_error1
I removed the line "temp_bug_fix=config['coarse']['temp_bug_fix'])" and it still gave similar inference result as yesterday.
I then downloaded the new indoor_ds model you shared earlier, but saw a bunch of missing keys in state_dict and was unable to perform inference.
loftr_error2

Really appreciate your help :)

@angshine
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Hi @howard5758, can you share the script(mytest.py) and the config you are using? I think this relates to a misconfiguration of the project (e.g. mixing old & new files🤔).

@howard5758
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Hi @angshine , mytest.py is basically the exact same code from demo_single_pair.ipynb
The only part I modified is commenting out "temp_bug_fix=config['coarse']['temp_bug_fix'])" in src/loftr/loftr.py
Currently the provided colab is still giving me the keyerror:
loftr_error3
Can you kindly double check the colab maybe then I can directly follow the steps there.
Thank you very much!

@angshine
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Hi, sorry for the inconvenience, and thank you for your time! I have updated the code and tested the colab and demo_single_pair.ipynb results, and they all work appropriately now. Just make sure you use temp_buf_fix=True with the new ckpt. We will polish the logic in later releases.

@howard5758
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Hi, I can confirm the provided colab work appropriately. Thank you so much for the help!

@GabbySuwichaya
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Hi! Just wonder if the new trained weight is updated ?

@JiamingSuen
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The updated weight is in the google drive folder here
image

@GabbySuwichaya
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Thanks! Such a good news. Are they all updated ? All the cases?

@xmba15
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xmba15 commented Mar 18, 2023

@JiamingSuen Can you also update outdoor_ds.ckpt? I still see the problem mentioned above with outdoor_ds.ckpt.

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5 participants