Hi!
I’m improving my DeepSlice integration into ABBA and would like to show a progress bar while DSModel.predict() is running (e.g., progress updates as each slice is predicted).
Currently DSModel.predict() calls neural_network.predictions_util(), which runs model.predict(..., verbose=1). This prints progress to stdout, but there’s no structured way for an external caller to monitor progress (callback/events), and parsing stdout is brittle.
Request: Please consider adding an optional progress mechanism, e.g.:
DSModel.predict(..., callbacks=None) and pass through to Keras model.predict(callbacks=callbacks), or- a simple
progress_callback(done:int, total:int, filename:str|None) called per predicted slice/batch.
This would make it much easier to integrate DeepSlice into GUIs and pipelines without forking.
Thanks!
(Disclaimer: issue redacted by copilot)
Hi!
I’m improving my DeepSlice integration into ABBA and would like to show a progress bar while
DSModel.predict()is running (e.g., progress updates as each slice is predicted).Currently
DSModel.predict()callsneural_network.predictions_util(), whichruns model.predict(..., verbose=1). This prints progress to stdout, but there’s no structured way for an external caller to monitor progress (callback/events), and parsing stdout is brittle.Request: Please consider adding an optional progress mechanism, e.g.:
DSModel.predict(..., callbacks=None)and pass through to Kerasmodel.predict(callbacks=callbacks), orprogress_callback(done:int, total:int, filename:str|None)called per predicted slice/batch.This would make it much easier to integrate DeepSlice into GUIs and pipelines without forking.
Thanks!
(Disclaimer: issue redacted by copilot)