105 lines
2.7 KiB
Python
105 lines
2.7 KiB
Python
"""GTLVQ example using the MNIST dataset."""
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import argparse
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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 torchvision import transforms
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from torchvision.datasets import MNIST
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if __name__ == "__main__":
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# Dataset
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train_ds = MNIST(
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"~/datasets",
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train=True,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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]),
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)
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test_ds = MNIST(
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"~/datasets",
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train=False,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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]),
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)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds,
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num_workers=0,
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batch_size=256)
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test_loader = torch.utils.data.DataLoader(test_ds,
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num_workers=0,
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batch_size=256)
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# Hyperparameters
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num_classes = 10
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prototypes_per_class = 1
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hparams = dict(
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input_dim=28 * 28,
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latent_dim=28,
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distribution=(num_classes, prototypes_per_class),
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proto_lr=0.01,
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bb_lr=0.01,
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)
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# Initialize the model
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model = pt.models.ImageGTLVQ(
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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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#Use one batch of data for subspace initiator.
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omega_initializer=pt.initializers.PCALinearTransformInitializer(
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next(iter(train_loader))[0].reshape(256, 28 * 28)))
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# Callbacks
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vis = pt.models.VisImgComp(
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data=train_ds,
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num_columns=10,
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show=False,
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tensorboard=True,
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random_data=100,
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add_embedding=True,
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embedding_data=200,
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flatten_data=False,
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)
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pruning = pt.models.PruneLoserPrototypes(
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threshold=0.01,
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idle_epochs=1,
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prune_quota_per_epoch=10,
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frequency=1,
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verbose=True,
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)
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es = pl.callbacks.EarlyStopping(
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monitor="train_loss",
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min_delta=0.001,
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patience=15,
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mode="min",
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check_on_train_epoch_end=True,
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)
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# Setup trainer
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# using GPUs here is strongly recommended!
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trainer = pl.Trainer.from_argparse_args(
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args,
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callbacks=[
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vis,
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pruning,
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# es,
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],
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terminate_on_nan=True,
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weights_summary=None,
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accelerator="ddp",
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)
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# Training loop
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trainer.fit(model, train_loader)
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