FIX NeuralNetBinaryClassifier with torch.compile#1058
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Fixes #1057 NeuralNetBinaryClassifier was not working with torch.compile because the non-linearity was not correctly inferred. This inference depends on the instance type of the criterion. However, when using torch.compile, the criterion is wrapped, resulting in the isinstance check to miss. Now, we unwrap the criterion before checking the instance type.
ottonemo
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May 30, 2024
githubnemo
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Please welcome skorch 1.1.0 - a smaller release with a few fixes, a new notebook showcasing learning rate schedulers and mainly support for scikit-learn 1.6.0. Full list of changes: ### Added - Added a [notebook](https://github.com/skorch-dev/skorch/blob/master/notebooks/Learning_Rate_Scheduler.ipynb) that shows how to use Learning Rate Scheduler in skorch.(#1074) ### Changed - All neural net classes now inherit from sklearn's [`BaseEstimator`](https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html). This is to support compatibility with sklearn 1.6.0 and above. Classification models additionally inherit from [`ClassifierMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.ClassifierMixin.html) and regressors from [`RegressorMixin`](https://scikit-learn.org/stable/modules/generated/sklearn.base.RegressorMixin.html). (#1078) - When using the `ReduceLROnPlateau` learning rate scheduler, we now record the learning rate in the net history (`net.history[:, 'event_lr']` by default). It is now also possible to to step per batch, not only by epoch (#1075) - The learning rate scheduler `.simulate()` method now supports adding step args which is useful when simulation policies such as `ReduceLROnPlateau` which expect metrics to base their schedule on. (#1077) - Removed deprecated `skorch.callbacks.scoring.cache_net_infer` (#1088) ### Fixed - Fix an issue with using `NeuralNetBinaryClassifier` with `torch.compile` (#1058)
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Fixes #1057
NeuralNetBinaryClassifierwas not working withtorch.compilebecause the non-linearity was not correctly inferred. This inference depends on the instance type of the criterion. However, when usingtorch.compile, the criterion is wrapped, resulting in theisinstancecheck to miss. Now, we unwrap the criterion before checking the instance type.