Source code for torchgeo.datamodules.ucmerced
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""UC Merced datamodule."""
from typing import Any, Dict, Optional
import matplotlib.pyplot as plt
import pytorch_lightning as pl
import torchvision
from torch.utils.data import DataLoader
from torchvision.transforms import Compose
from ..datasets import UCMerced
# https://github.com/pytorch/pytorch/issues/60979
# https://github.com/pytorch/pytorch/pull/61045
DataLoader.__module__ = "torch.utils.data"
class UCMercedDataModule(pl.LightningDataModule):
"""LightningDataModule implementation for the UC Merced dataset.
Uses random train/val/test splits.
"""
[docs] def __init__(
self, root_dir: str, batch_size: int = 64, num_workers: int = 0, **kwargs: Any
) -> None:
"""Initialize a LightningDataModule for UCMerced based DataLoaders.
Args:
root_dir: The ``root`` arugment to pass to the UCMerced Dataset classes
batch_size: The batch size to use in all created DataLoaders
num_workers: The number of workers to use in all created DataLoaders
"""
super().__init__() # type: ignore[no-untyped-call]
self.root_dir = root_dir
self.batch_size = batch_size
self.num_workers = num_workers
[docs] def preprocess(self, sample: Dict[str, Any]) -> Dict[str, Any]:
"""Transform a single sample from the Dataset.
Args:
sample: dictionary containing image
Returns:
preprocessed sample
"""
sample["image"] = sample["image"].float()
sample["image"] /= 255.0
c, h, w = sample["image"].shape
if h != 256 or w != 256:
sample["image"] = torchvision.transforms.functional.resize(
sample["image"], size=(256, 256)
)
return sample
[docs] def prepare_data(self) -> None:
"""Make sure that the dataset is downloaded.
This method is only called once per run.
"""
UCMerced(self.root_dir, download=False, checksum=False)
[docs] def setup(self, stage: Optional[str] = None) -> None:
"""Initialize the main ``Dataset`` objects.
This method is called once per GPU per run.
Args:
stage: stage to set up
"""
transforms = Compose([self.preprocess])
self.train_dataset = UCMerced(self.root_dir, "train", transforms=transforms)
self.val_dataset = UCMerced(self.root_dir, "val", transforms=transforms)
self.test_dataset = UCMerced(self.root_dir, "test", transforms=transforms)
[docs] def train_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for training.
Returns:
training data loader
"""
return DataLoader(
self.train_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
shuffle=True,
)
[docs] def val_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for validation.
Returns:
validation data loader
"""
return DataLoader(
self.val_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
shuffle=False,
)
[docs] def test_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for testing.
Returns:
testing data loader
"""
return DataLoader(
self.test_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
shuffle=False,
)
[docs] def plot(self, *args: Any, **kwargs: Any) -> plt.Figure:
"""Run :meth:`torchgeo.datasets.UCMerced.plot`.
.. versionadded:: 0.2
"""
return self.val_dataset.plot(*args, **kwargs)