prototorch_models/prototorch/models/data.py

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import prototorch as pt
import pytorch_lightning as pl
from torch.utils.data import DataLoader, random_split
from torchvision import transforms
from torchvision.datasets import MNIST
class MNISTDataModule(pl.LightningDataModule):
def __init__(self, batch_size=32):
super().__init__()
self.batch_size = batch_size
# Download mnist dataset as side-effect, only called on the first cpu
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def prepare_data(self):
MNIST("~/datasets", train=True, download=True)
MNIST("~/datasets", train=False, download=True)
# called for every GPU/machine (assigning state is OK)
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def setup(self, stage=None):
# Transforms
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transform = transforms.Compose([
transforms.ToTensor(),
])
# Split dataset
if stage in (None, "fit"):
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mnist_train = MNIST("~/datasets", train=True, transform=transform)
self.mnist_train, self.mnist_val = random_split(
mnist_train,
[55000, 5000],
)
if stage == (None, "test"):
self.mnist_test = MNIST(
"~/datasets",
train=False,
transform=transform,
)
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# Dataloaders
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def train_dataloader(self):
mnist_train = DataLoader(self.mnist_train, batch_size=self.batch_size)
return mnist_train
def val_dataloader(self):
mnist_val = DataLoader(self.mnist_val, batch_size=self.batch_size)
return mnist_val
def test_dataloader(self):
mnist_test = DataLoader(self.mnist_test, batch_size=self.batch_size)
return mnist_test
def train_on_mnist(batch_size=256) -> type:
class DataClass(pl.LightningModule):
datamodule = MNISTDataModule(batch_size=batch_size)
def prototype_initializer(self, **kwargs):
return pt.components.Zeros((28, 28, 1))
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return DataClass