add scheduled confidence threshold within the ScheduledPseudoLabeler class #382
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Marei33
commented
Oct 15, 2024
| @@ -283,6 +283,7 @@ def load_model(checkpoint, model=None, name="best", state_key="model_state", dev | |||
| ckpt = checkpoint | |||
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| state = torch.load(ckpt, map_location=device)[state_key] | |||
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Hi @Marei33,
I had a quick look, and the overall design looks very good!
However, there is an issue with a test that seems to be caused by your changes:
ERROR: test_mean_teacher_unsupervised (self_training.test_mean_teacher.TestMeanTeacher.test_mean_teacher_unsupervised)
Could you check out why this fails?
You can run the test locally by going to test/self_training and then running
python test_mean_teacher.py
| @@ -235,6 +235,7 @@ def _train_epoch_unsupervised(self, progress, forward_context, backprop): | |||
| ) | |||
| lr = [pm["lr"] for pm in self.optimizer.param_groups][0] | |||
| self.logger.log_lr(self._iteration, lr) | |||
| self.logger.log_ct(self._iteration, self.pseudo_labeler.confidence_threshold) | |||
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Maybe we need to check here that the confidence_threshold is not None before logging?
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Yes, I did exactly that. Now it only logs the threshold if it is not None.
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test_mean_teacher.py should now be resolved |
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