prototorch_models/examples/warm_starting.py
Alexander Engelsberger 634ef86a2c
fix: example test fixed
2023-06-20 17:42:36 +02:00

130 lines
3.3 KiB
Python

"""Warm-starting GLVQ with prototypes from Growing Neural Gas."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from lightning_fabric.utilities.seed import seed_everything
from prototorch.models import (
GLVQ,
KNN,
GrowingNeuralGas,
PruneLoserPrototypes,
VisGLVQ2D,
)
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--gpus", type=int, default=0)
parser.add_argument("--fast_dev_run", type=bool, default=False)
args = parser.parse_args()
# Prepare the data
train_ds = pt.datasets.Iris(dims=[0, 2])
train_loader = DataLoader(train_ds, batch_size=64, num_workers=0)
# Initialize the gng
gng = GrowingNeuralGas(
hparams=dict(num_prototypes=5, insert_freq=2, lr=0.1),
prototypes_initializer=pt.initializers.ZCI(2),
lr_scheduler=ExponentialLR,
lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
)
# Callbacks
es = EarlyStopping(
monitor="loss",
min_delta=0.001,
patience=20,
mode="min",
verbose=False,
check_on_train_epoch_end=True,
)
# Setup trainer for GNG
trainer = pl.Trainer(
accelerator="cpu",
max_epochs=50 if args.fast_dev_run else
1000, # 10 epochs fast dev run reproducible DIV error.
callbacks=[
es,
],
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(gng, train_loader)
# Hyperparameters
hparams = dict(
distribution=[],
lr=0.01,
)
# Warm-start prototypes
knn = KNN(dict(k=1), data=train_ds)
prototypes = gng.prototypes
plabels = knn.predict(prototypes)
# Initialize the model
model = GLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.LCI(prototypes),
labels_initializer=pt.initializers.LLI(plabels),
lr_scheduler=ExponentialLR,
lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Callbacks
vis = VisGLVQ2D(data=train_ds)
pruning = PruneLoserPrototypes(
threshold=0.02,
idle_epochs=2,
prune_quota_per_epoch=5,
frequency=1,
verbose=True,
)
es = EarlyStopping(
monitor="train_loss",
min_delta=0.001,
patience=10,
mode="min",
verbose=True,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer(
accelerator="cuda" if args.gpus else "cpu",
devices=args.gpus if args.gpus else "auto",
fast_dev_run=args.fast_dev_run,
callbacks=[
vis,
pruning,
es,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)