ENH: fix frame classification model to work with BioSoundSegBench #774
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…ostProcess.__call__
… when they are present to post-process multi-class frame labels'
…datasets in eval/frame_classification.py, to make sure this variable exists when we build the DataFrame with eval results"
…ide eval/frame_classification.py so this variable exists when we get the post-processing transform
…de eval/frame_classification.py, using `constants.DEFAULT_BACKGROUND_LABEL`
…handle the case when the loss function returns a dict
…o numpy when we pass them in to self.post_tfm
…all it 'val_multi_acc' to distinguish from boundary_acc and for consistency with val_multi_acc_tfm
…so we have it as a separate variable; will use for post_tfm kwargs when we add those later
… for models with multiple accuracies
…ix so that we monitor 'val_multi_acc' when a model has multiple targets, and just 'val_acc' otherwise
…onitor in train/frame_classification.py
…onal.postprocess -- don't if boundary_labels is None
…non-background segments are predicted for any sample in dataset
…rom_frame_boundary_labels
…ndary_inds_from_boundary_labels
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Sep 9, 2024
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This PR consists mainly of fixes needed for FrameClassificationModel to work with the BioSoundSegBench dataset.