[BUG] Early stopping does not seem to work
The early stopping callback does not work as expected, and crashes at the end of max_epochs with: ``` ~/miniconda3/envs/py38/lib/python3.8/site-packages/pytorch_lightning/trainer/callback_hook.py in on_train_end(self) 155 """Called when the train ends.""" 156 for callback in self.callbacks: --> 157 callback.on_train_end(self, self.lightning_module) 158 159 def on_pretrain_routine_start(self) -> None: ~/work/repos/prototorch_models/prototorch/models/callbacks.py in on_train_end(self, trainer, pl_module) 18 def on_train_end(self, trainer, pl_module): 19 # instead, do it at the end of training loop ---> 20 self._run_early_stopping_check(trainer, pl_module) 21 22 TypeError: _run_early_stopping_check() takes 2 positional arguments but 3 were given ```
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@ -46,19 +46,27 @@ if __name__ == "__main__":
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vis = pt.models.VisGLVQ2D(train_ds)
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pruning = pt.models.PruneLoserPrototypes(
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threshold=0.01, # prune prototype if it wins less than 1%
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prune_after_epochs=30, # pruning too early may cause problems
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idle_epochs=30, # pruning too early may cause problems
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prune_quota_per_epoch=1, # prune at most 1 prototype per epoch
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frequency=5, # prune every fifth epoch
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verbose=True,
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)
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es = pt.models.EarlyStopWithoutVal(
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monitor="loss",
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min_delta=0.1,
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patience=3,
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mode="min",
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verbose=True,
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)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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args,
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max_epochs=100,
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max_epochs=250,
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callbacks=[
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vis,
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pruning,
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es,
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],
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terminate_on_nan=True,
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weights_summary=None,
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@ -2,7 +2,8 @@
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from importlib.metadata import PackageNotFoundError, version
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from .callbacks import PruneLoserPrototypes
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from .callbacks import (EarlyStopWithoutVal, PrototypeConvergence,
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PruneLoserPrototypes)
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from .cbc import CBC, ImageCBC
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from .glvq import (
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GLVQ,
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@ -4,15 +4,31 @@ import pytorch_lightning as pl
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import torch
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class EarlyStopWithoutVal(pl.callbacks.EarlyStopping):
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"""Run early stopping at the end of training loop.
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See:
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https://pytorch-lightning.readthedocs.io/en/latest/common/early_stopping.html
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"""
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def on_validation_end(self, trainer, pl_module):
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# override this to disable early stopping at the end of val loop
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pass
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def on_train_end(self, trainer, pl_module):
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# instead, do it at the end of training loop
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self._run_early_stopping_check(trainer, pl_module)
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class PruneLoserPrototypes(pl.Callback):
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def __init__(self,
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threshold=0.01,
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prune_after_epochs=10,
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idle_epochs=10,
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prune_quota_per_epoch=-1,
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frequency=1,
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verbose=False):
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self.threshold = threshold # minimum win ratio
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self.prune_after_epochs = prune_after_epochs # epochs to wait
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self.idle_epochs = idle_epochs # epochs to wait before pruning
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self.prune_quota_per_epoch = prune_quota_per_epoch
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self.frequency = frequency
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self.verbose = verbose
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@ -21,7 +37,7 @@ class PruneLoserPrototypes(pl.Callback):
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pl_module.initialize_prototype_win_ratios()
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def on_epoch_end(self, trainer, pl_module):
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if (trainer.current_epoch + 1) < self.prune_after_epochs:
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if (trainer.current_epoch + 1) < self.idle_epochs:
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return None
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if (trainer.current_epoch + 1) % self.frequency:
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return None
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@ -40,3 +56,18 @@ class PruneLoserPrototypes(pl.Callback):
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print(f"`num_prototypes` reduced from {cur_num_protos} "
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f"to {new_num_protos}.")
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return True
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class PrototypeConvergence(pl.Callback):
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def __init__(self, min_delta=0.01, idle_epochs=10, verbose=False):
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self.min_delta = min_delta
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self.idle_epochs = idle_epochs # epochs to wait
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self.verbose = verbose
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def on_epoch_end(self, trainer, pl_module):
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if (trainer.current_epoch + 1) < self.idle_epochs:
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return None
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if self.verbose:
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print("Stopping...")
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# TODO
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return True
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