prototorch_models/examples/dynamic_pruning.py

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2021-06-02 00:40:29 +00:00
"""Dynamically prune 'loser' prototypes in GLVQ-type models."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
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 = pt.models.PruneLoserPrototypes(
threshold=0.01, # prune prototype if it wins less than 1%
idle_epochs=30, # pruning too early may cause problems
prune_quota_per_epoch=1, # prune at most 1 prototype per epoch
frequency=5, # prune every fifth epoch
verbose=True,
)
es = pt.models.EarlyStopWithoutVal(
monitor="loss",
min_delta=0.1,
patience=3,
mode="min",
verbose=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
max_epochs=250,
callbacks=[
vis,
pruning,
es,
],
terminate_on_nan=True,
weights_summary=None,
accelerator="ddp",
)
# Training loop
trainer.fit(model, train_loader)