Comments (3)
Got it working on my laptop. Will format the code and push here soon. Also, if we wanted we could host the model using git LFS so that it wasn’t inside the repo. (It’s only 11 MB so this might not be necessary.) ONNX models can be loaded anywhere similar to PyTorch models with a onnx.load
command (https://github.com/onnx/models)
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ONNX is universal so it allows for porting to tensorflow/keras as well. From my initial tests, loading the ONNX runtime package is a lot faster than importing PyTorch.
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That sounds like a great idea! I don't know ONNX, but I do like Tensorflow and Keras a lot :)
That might also make packaging simpler (or not?) especially when it comes to conda-forge
.
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Related Issues (20)
- [DOC] License link in documentation 404's HOT 1
- [GUI] Additional PyQt5 dependency? HOT 2
- [GUI] Missing or non-expandable ICA properties HOT 1
- [DOC] Clarify the types of data to be used HOT 2
- [JOSS] Nitpicks on the paper HOT 1
- No module named 'numpy.typing' HOT 2
- mne_icalabel ImportError HOT 4
- Example render for 0.4 stable website HOT 4
- Possibly want to freeze GH actions at Ubuntu 20.04? HOT 1
- ValueError: need at least one array to concatenate HOT 6
- "arrays used as indices must be of integer (or boolean) type" HOT 14
- Set black to max_chars=88 per line
- ICLabel doesn't work with combined MEG/EEG data
- Evaluate and support MEGNet HOT 2
- Notes on JOSS paper HOT 3
- Comments on the docsite HOT 6
- Release v0.3 HOT 12
- Improve test coverage HOT 5
- .pt not included in v0.3 HOT 8
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