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With the introduction of #567 we now have pretrained models in the library. But we don't have any examples showing how they might be used for inference. I think creating a new directory examples/06_pretrained and adding some example scripts for various use cases of using pretrained models.
Some use-cases that come to mind
Predicting on a dataset with/without config files.
Benchmarking multiple models against one another on the same dataset.
Downstream training. (Freezing the gnn-part, but replacing the task/final MLP (this might not be possible in the current GraphNeT and might deserve it's own issue)).
The text was updated successfully, but these errors were encountered:
AMHermansen
changed the title
Add example directory and example scripts of using a pretrained model to predict.
Add example directory and example scripts of using a pretrained model.
Sep 6, 2023
AMHermansen
changed the title
Add example directory and example scripts of using a pretrained model.
Add example directory and example scripts of using a pretrained model
Sep 6, 2023
@AMHermansen We do indeed have examples on how to use them for inference; but that is in deployment on i3 files. Are you looking for examples on how to load a pre-trained model and apply it to a graphnet dataset? If so, we could just modify the current training script that uses config files; the only difference is loading the state dict.
I was looking for how to apply it to a GraphNeT dataset, I know it shouldn't be too difficult to do, I'm just rather unsure about what can be added in a training script, without breaking what is logged. (Will a model log that I'm using pre-trained weights for example).
With the introduction of #567 we now have pretrained models in the library. But we don't have any examples showing how they might be used for inference. I think creating a new directory
examples/06_pretrained
and adding some example scripts for various use cases of using pretrained models.Some use-cases that come to mind
The text was updated successfully, but these errors were encountered: