prototorch_models/prototorch/models/callbacks.py

55 lines
1.9 KiB
Python
Raw Normal View History

"""Lightning Callbacks."""
import pytorch_lightning as pl
import torch
class PruneLoserPrototypes(pl.Callback):
def __init__(self,
threshold=0.01,
idle_epochs=10,
prune_quota_per_epoch=-1,
frequency=1,
verbose=False):
self.threshold = threshold # minimum win ratio
self.idle_epochs = idle_epochs # epochs to wait before pruning
self.prune_quota_per_epoch = prune_quota_per_epoch
self.frequency = frequency
self.verbose = verbose
def on_epoch_end(self, trainer, pl_module):
if (trainer.current_epoch + 1) < self.idle_epochs:
return None
if (trainer.current_epoch + 1) % self.frequency:
return None
ratios = pl_module.prototype_win_ratios.mean(dim=0)
to_prune = torch.arange(len(ratios))[ratios < self.threshold]
if self.prune_quota_per_epoch > 0:
to_prune = to_prune[:self.prune_quota_per_epoch]
if len(to_prune) > 0:
if self.verbose:
print(f"\nPrototype win ratios: {ratios}")
print(f"Pruning prototypes at: {to_prune.tolist()}")
cur_num_protos = pl_module.num_prototypes
pl_module.remove_prototypes(indices=to_prune)
new_num_protos = pl_module.num_prototypes
if self.verbose:
print(f"`num_prototypes` reduced from {cur_num_protos} "
f"to {new_num_protos}.")
return True
class PrototypeConvergence(pl.Callback):
def __init__(self, min_delta=0.01, idle_epochs=10, verbose=False):
self.min_delta = min_delta
self.idle_epochs = idle_epochs # epochs to wait
self.verbose = verbose
def on_epoch_end(self, trainer, pl_module):
if (trainer.current_epoch + 1) < self.idle_epochs:
return None
if self.verbose:
print("Stopping...")
# TODO
return True