83 lines
2.1 KiB
Python
83 lines
2.1 KiB
Python
"""GLVQ example using the Iris dataset."""
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import argparse
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import logging
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import warnings
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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from lightning_fabric.utilities.seed import seed_everything
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from prototorch.models import GLVQ, VisGLVQ2D
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from pytorch_lightning.utilities.warnings import PossibleUserWarning
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from torch.optim.lr_scheduler import ExponentialLR
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from torch.utils.data import DataLoader
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warnings.filterwarnings("ignore", category=UserWarning)
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warnings.filterwarnings("ignore", category=PossibleUserWarning)
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if __name__ == "__main__":
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# Reproducibility
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seed_everything(seed=4)
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser.add_argument("--gpus", type=int, default=0)
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parser.add_argument("--fast_dev_run", type=bool, default=False)
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args = parser.parse_args()
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# Dataset
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train_ds = pt.datasets.Iris(dims=[0, 2])
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# Dataloaders
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train_loader = DataLoader(train_ds, batch_size=64, num_workers=4)
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# Hyperparameters
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hparams = dict(
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distribution={
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"num_classes": 3,
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"per_class": 4
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},
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lr=0.01,
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)
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# Initialize the model
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model = GLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototypes_initializer=pt.initializers.SMCI(train_ds),
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lr_scheduler=ExponentialLR,
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lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
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)
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# Compute intermediate input and output sizes
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model.example_input_array = torch.zeros(4, 2)
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# Callbacks
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vis = VisGLVQ2D(data=train_ds)
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# Setup trainer
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trainer = pl.Trainer(
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accelerator="cuda" if args.gpus else "cpu",
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devices=args.gpus if args.gpus else "auto",
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fast_dev_run=args.fast_dev_run,
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callbacks=[
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vis,
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],
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max_epochs=100,
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log_every_n_steps=1,
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detect_anomaly=True,
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)
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# Training loop
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trainer.fit(model, train_loader)
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# Manual save
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trainer.save_checkpoint("./glvq_iris.ckpt")
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# Load saved model
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new_model = GLVQ.load_from_checkpoint(
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checkpoint_path="./glvq_iris.ckpt",
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strict=False,
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)
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logging.info(new_model)
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