[FEATURE] Add example to showcase dynamic pruning of prototypes

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Jensun Ravichandran 2021-06-02 02:36:37 +02:00
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"""Dynamically prune prototypes in GLVQ-type models."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import Callback
class PrototypePruning(Callback):
def __init__(self, threshold=0.01, prune_after=10, verbose=False):
self.threshold = threshold
self.prune_after = prune_after
self.verbose = verbose
def on_epoch_start(self, trainer, pl_module):
pl_module.initialize_prototype_win_ratios()
def on_epoch_end(self, trainer, pl_module):
if (trainer.current_epoch + 1) > self.prune_after:
ratios = pl_module.prototype_win_ratios.mean(dim=0)
to_prune = torch.arange(len(ratios))[ratios < self.threshold]
if len(to_prune) > 0:
if self.verbose:
print(f"\nPrototype win ratios: {ratios}")
print(f"Pruning prototypes at indices: {to_prune}")
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}.")
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
num_classes = 4
num_features = 2
num_clusters = 1
train_ds = pt.datasets.Random(num_samples=500,
num_classes=num_classes,
num_features=num_features,
num_clusters=num_clusters,
separation=3.0,
seed=42)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=256)
# Hyperparameters
prototypes_per_class = num_clusters * 5
hparams = dict(
distribution=(num_classes, prototypes_per_class),
lr=0.3,
)
# Initialize the model
model = pt.models.CELVQ(
hparams,
prototype_initializer=pt.components.Ones(2, scale=3),
)
# Summary
print(model)
# Callbacks
vis = pt.models.VisGLVQ2D(train_ds)
pruning = PrototypePruning(
threshold=0.01, # prune prototype if it wins less than 1%
prune_after=50,
verbose=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
max_epochs=100,
callbacks=[
vis,
pruning,
],
terminate_on_nan=True,
weights_summary=None,
accelerator="ddp",
)
# Training loop
trainer.fit(model, train_loader)