58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
"""Lightning Callbacks."""
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import pytorch_lightning as pl
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import torch
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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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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.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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def on_epoch_start(self, trainer, pl_module):
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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.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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ratios = pl_module.prototype_win_ratios.mean(dim=0)
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to_prune = torch.arange(len(ratios))[ratios < self.threshold]
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if self.prune_quota_per_epoch > 0:
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to_prune = to_prune[:self.prune_quota_per_epoch]
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if len(to_prune) > 0:
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if self.verbose:
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print(f"\nPrototype win ratios: {ratios}")
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print(f"Pruning prototypes at: {to_prune.tolist()}")
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cur_num_protos = pl_module.num_prototypes
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pl_module.remove_prototypes(indices=to_prune)
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new_num_protos = pl_module.num_prototypes
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if self.verbose:
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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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