124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
"""Prototorch Data Modules
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This allows to store the used dataset inside a Lightning Module.
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Mainly used for PytorchLightningCLI configurations.
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"""
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from typing import Any, Optional, Type
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import prototorch as pt
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import pytorch_lightning as pl
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from torch.utils.data import DataLoader, Dataset, random_split
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from torchvision import transforms
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from torchvision.datasets import MNIST
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# MNIST
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class MNISTDataModule(pl.LightningDataModule):
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def __init__(self, batch_size=32):
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super().__init__()
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self.batch_size = batch_size
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# Download mnist dataset as side-effect, only called on the first cpu
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def prepare_data(self):
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MNIST("~/datasets", train=True, download=True)
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MNIST("~/datasets", train=False, download=True)
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# called for every GPU/machine (assigning state is OK)
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def setup(self, stage=None):
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# Transforms
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transform = transforms.Compose([
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transforms.ToTensor(),
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])
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# Split dataset
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if stage in (None, "fit"):
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mnist_train = MNIST("~/datasets", train=True, transform=transform)
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self.mnist_train, self.mnist_val = random_split(
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mnist_train,
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[55000, 5000],
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)
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if stage == (None, "test"):
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self.mnist_test = MNIST(
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"~/datasets",
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train=False,
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transform=transform,
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)
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# Dataloaders
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def train_dataloader(self):
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mnist_train = DataLoader(self.mnist_train, batch_size=self.batch_size)
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return mnist_train
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def val_dataloader(self):
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mnist_val = DataLoader(self.mnist_val, batch_size=self.batch_size)
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return mnist_val
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def test_dataloader(self):
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mnist_test = DataLoader(self.mnist_test, batch_size=self.batch_size)
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return mnist_test
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# def train_on_mnist(batch_size=256) -> type:
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# class DataClass(pl.LightningModule):
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# datamodule = MNISTDataModule(batch_size=batch_size)
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# def __init__(self, *args, **kwargs):
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# prototype_initializer = kwargs.pop(
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# "prototype_initializer", pt.components.Zeros((28, 28, 1)))
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# super().__init__(*args,
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# prototype_initializer=prototype_initializer,
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# **kwargs)
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# dc: Type[DataClass] = DataClass
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# return dc
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# ABSTRACT
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class GeneralDataModule(pl.LightningDataModule):
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def __init__(self, dataset: Dataset, batch_size: int = 32) -> None:
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super().__init__()
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self.train_dataset = dataset
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self.batch_size = batch_size
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def train_dataloader(self) -> DataLoader:
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return DataLoader(self.train_dataset, batch_size=self.batch_size)
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# def train_on_dataset(dataset: Dataset, batch_size: int = 256):
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# class DataClass(pl.LightningModule):
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# datamodule = GeneralDataModule(dataset, batch_size)
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# datashape = dataset[0][0].shape
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# example_input_array = torch.zeros_like(dataset[0][0]).unsqueeze(0)
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# def __init__(self, *args: Any, **kwargs: Any) -> None:
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# prototype_initializer = kwargs.pop(
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# "prototype_initializer",
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# pt.components.Zeros(self.datashape),
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# )
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# super().__init__(*args,
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# prototype_initializer=prototype_initializer,
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# **kwargs)
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# return DataClass
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# if __name__ == "__main__":
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# from prototorch.models import GLVQ
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# demo_dataset = pt.datasets.Iris()
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# TrainingClass: Type = train_on_dataset(demo_dataset)
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# class DemoGLVQ(TrainingClass, GLVQ):
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# """Model Definition."""
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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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# "prototypes_per_class": 4
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# },
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# lr=0.01,
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# )
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# initialized = DemoGLVQ(hparams)
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# print(initialized)
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