Improve the AI-Polygon experience #1279

TDA-2030 · 2023-05-17T07:14:45Z

TDA-2030
May 17, 2023

labelme with SAM integration is great, but my experience with it right now is a little annoying. Because I have to wait several seconds to compute image embedding for each image when I change to the next/prev image. Despite the caching mechanism, I still have to wait for calculations from time to time, which makes my computer very slow overall. I tried to change the provider of onnxruntime to GPU successfully, but it shows the following warning and doesn't seem to have much of a speedup.

[INFO   ] __init__:get_config:70 - Loading config file from: C:\Users\zhouli\.labelmerc
[DEBUG  ] canvas:initializeAiModel:140 - Initializing AI model: 'Segment-Anything (balanced)'
Computing MD5: C:\Users\zhouli\.cache/gdown\https-COLON--SLASH--SLASH-github.com-SLASH-wkentaro-SLASH-labelme-SLASH-releases-SLASH-download-SLASH-sam-20230416-SLASH-sam_vit_l_0b3195.quantized.encoder.onnx
MD5 matches: C:\Users\zhouli\.cache/gdown\https-COLON--SLASH--SLASH-github.com-SLASH-wkentaro-SLASH-labelme-SLASH-releases-SLASH-download-SLASH-sam-20230416-SLASH-sam_vit_l_0b3195.quantized.encoder.onnx
Computing MD5: C:\Users\zhouli\.cache/gdown\https-COLON--SLASH--SLASH-github.com-SLASH-wkentaro-SLASH-labelme-SLASH-releases-SLASH-download-SLASH-sam-20230416-SLASH-sam_vit_l_0b3195.quantized.decoder.onnx
MD5 matches: C:\Users\zhouli\.cache/gdown\https-COLON--SLASH--SLASH-github.com-SLASH-wkentaro-SLASH-labelme-SLASH-releases-SLASH-download-SLASH-sam-20230416-SLASH-sam_vit_l_0b3195.quantized.decoder.onnx
2023-05-17 15:03:20.0692669 [W:onnxruntime:, session_state.cc:1136 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.
2023-05-17 15:03:20.0767142 [W:onnxruntime:, session_state.cc:1138 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Rerunning with verbose output on a non-minimal build will show node assignments.
2023-05-17 15:03:21.4425535 [W:onnxruntime:, session_state.cc:1136 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.
2023-05-17 15:03:21.4493971 [W:onnxruntime:, session_state.cc:1138 onnxruntime::VerifyEachNodeIsAssignedToAnEp] Rerunning with verbose output on a non-minimal build will show node assignments.
[DEBUG  ] segment_anything:_compute_and_cache_image_embedding:52 - Computing image embedding...
[DEBUG  ] segment_anything:_compute_and_cache_image_embedding:63 - Done computing image embedding.

As with the bad experience mentioned above, I think it would be a smoother experience if the user could import all the image embeddings computed on a powerful GPU and then do the AI labeling. The project https://github.com/anuragxel/salt did just that.

wkentaro · 2023-05-18T11:31:07Z

wkentaro
May 18, 2023
Maintainer

Despite the caching mechanism, I still have to wait for calculations from time to time

If this happens with the already computed images, we should fix that first.

I tried to change the provider of onnxruntime to GPU successfully, but it shows the following warning and doesn't seem to have much of a speedup.

This is also weird and should give better experience if we use GPU successfully.

0 replies

mohamedhasan-nazarbasha · 2024-10-01T11:08:15Z

mohamedhasan-nazarbasha
Oct 1, 2024

I think still there is an issue. Takes a lot of time to compute for new & computed images no matter what. Is there any possible way.

0 replies

left0ver · 2024-10-24T06:02:28Z

left0ver
Oct 24, 2024

metoo，I want to know if it can compute use gpu？

0 replies

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Improve the AI-Polygon experience #1279

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Improve the AI-Polygon experience #1279

TDA-2030 May 17, 2023

Replies: 3 comments

wkentaro May 18, 2023 Maintainer

mohamedhasan-nazarbasha Oct 1, 2024

left0ver Oct 24, 2024

TDA-2030
May 17, 2023

wkentaro
May 18, 2023
Maintainer

mohamedhasan-nazarbasha
Oct 1, 2024

left0ver
Oct 24, 2024