Comments (7)
@oke-aditya Thanks a lot for sharing these ideas that could really be a great improvement !
The great thing would be if you were willing to participate ! :-)
My thoughts
- That would be interesting but this generator was initiated for pytorch-ignite firstly.
- Yep, the main point is to have a detection usecase. We are working on mAP, hope we will finish soon !
- I agree !!
- That sounds great ! Good idea !
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I would be willing to participate 😄 If few more people are interested from discord, it would be easy to team up and tackle in sprints
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@sayantan1410 I think we still need to discuss the scope of this item. @oke-aditya can you please expand a bit more the idea ?
@sayantan1410 if you would like to tackle something more well defined, for example, there is #173 which is still open. Tackling this issue IMO you'll get a better understanding of the app and the code. What do you think ?
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Hi! and sorry for the delay. I was not well yesterday.
@sayantan1410 and @vfdev-5 Let me elaborate the scope.
Connecting torch.hub and code generator.
torch.hub
provides a very good support for Open source libraries. All the libraries need a hubconf.py
which would provide the models. Note that all classification models in torchvision are provided in hub. And there are many models which have hub support. E.g. YOLO-P, YOLOv5, Tacotron, etc
Torch hub internally clones the entire Github repo, which allows easy model building. Note that the model which is built is a dynamic graph (not a static one, like JIT scripted from Torchscript). This makes it feasible to re-train or fine-tune with pretrained weights. Torch hub does the hard part of model_building
. It's hard to write and verify models from scratch.
What ignite code-gen can do?
Ignite codegen can provide boiler plate and the training script, which allows to train on any new dataset (and also with some standard datasets available in torchvision like VOC / COCO).
This is basically a rewrite of train.py with ignite. (Although we would need to verify if good results can be obtained with the new training recipe, which is slightly hard). Since ignite takes care of distributed training, etc it would be easier to train.
This makes retraining of models very simple, and with config.yaml (which has good defaults) it would be easy to fine-tune on custom data. (Maybe by passing data_dir
as parameter?)
P.S.
We could all meet over discord to discuss as well.
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Hey, I would like to work on implementing this
4. A direct torch.hub connection. Maybe all the models listed in torch.hub can be easily trained?
Can you please guide me on how to start with this.
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@vfdev-5 Yeah, that is great, I will continue with #173.
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@oke-aditya It will be more helpful for me to understand if we can have a meet.
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Related Issues (20)
- Add support for TPU devices HOT 18
- fix nav bar in 404 page HOT 2
- Dependency Dashboard
- Update torch.distributed.launch to torchrun HOT 2
- Add GHA CI autocancel HOT 1
- Error using Visdom as exp. tracking system for image-segmentation HOT 4
- Boolean config item always give 'False' value. HOT 8
- Fix LR scheduler issue on CI HOT 1
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- Correcting for running Template-vision-segmentation in colab HOT 2
- "Open in Colab" opens the same zip even if we update the config in UI
- Add comments for each parameter in config.yaml HOT 2
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- To extend the mapping capabilities of metadata.json to have very specific features for each templates HOT 1
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