Add stop_training=False flag to callbacks (#6365)
* New flag 'stop_training' in util.callbacks.Callbacks class to prematurely stop training from callback handler * Removed most of the new checks, leaving only the one after calling 'on_train_batch_end' * Cleanup Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@@ -352,6 +352,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
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pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
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pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
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f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
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f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
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callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
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callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
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if callbacks.stop_training:
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return
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# end batch ------------------------------------------------------------------------------------------------
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# end batch ------------------------------------------------------------------------------------------------
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# Scheduler
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# Scheduler
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@@ -35,6 +35,7 @@ class Callbacks:
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'on_params_update': [],
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'on_params_update': [],
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'teardown': [],
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'teardown': [],
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}
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}
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self.stop_training = False # set True to interrupt training
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def register_action(self, hook, name='', callback=None):
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def register_action(self, hook, name='', callback=None):
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"""
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"""
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