Source code for torchgeo.datamodules.cowc

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""COWC datamodule."""

from typing import Any, Dict, Optional

import matplotlib.pyplot as plt
import pytorch_lightning as pl
from torch import Generator  # type: ignore[attr-defined]
from torch.utils.data import DataLoader, random_split

from ..datasets import COWCCounting

# https://github.com/pytorch/pytorch/issues/60979
# https://github.com/pytorch/pytorch/pull/61045
DataLoader.__module__ = "torch.utils.data"


class COWCCountingDataModule(pl.LightningDataModule):
    """LightningDataModule implementation for the COWC Counting dataset."""

    def __init__(
        self,
        root_dir: str,
        seed: int,
        batch_size: int = 64,
        num_workers: int = 0,
        **kwargs: Any,
    ) -> None:
        """Initialize a LightningDataModule for COWC Counting based DataLoaders.

        Args:
            root_dir: The ``root`` arugment to pass to the COWCCounting Dataset class
            seed: The seed value to use when doing the dataset random_split
            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.seed = seed
        self.batch_size = batch_size
        self.num_workers = num_workers

    def custom_transform(self, sample: Dict[str, Any]) -> Dict[str, Any]:
        """Transform a single sample from the Dataset.

        Args:
            sample: dictionary containing image and target

        Returns:
            preprocessed sample
        """
        sample["image"] = sample["image"] / 255.0  # scale to [0, 1]
        sample["label"] = sample["label"].float()
        return sample

    def prepare_data(self) -> None:
        """Initialize the main ``Dataset`` objects for use in :func:`setup`.

        This includes optionally downloading the dataset. This is done once per node,
        while :func:`setup` is done once per GPU.
        """
        COWCCounting(self.root_dir, download=False)

    def setup(self, stage: Optional[str] = None) -> None:
        """Create the train/val/test splits based on the original Dataset objects.

        The splits should be done here vs. in :func:`__init__` per the docs:
        https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html#setup.

        Args:
            stage: stage to set up
        """
        train_val_dataset = COWCCounting(
            self.root_dir, split="train", transforms=self.custom_transform
        )
        self.test_dataset = COWCCounting(
            self.root_dir, split="test", transforms=self.custom_transform
        )
        self.train_dataset, self.val_dataset = random_split(
            train_val_dataset,
            [len(train_val_dataset) - len(self.test_dataset), len(self.test_dataset)],
            generator=Generator().manual_seed(self.seed),
        )

    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,
        )

    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,
        )

    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,
        )

    def plot(self, *args: Any, **kwargs: Any) -> plt.Figure:
        """Run :meth:`torchgeo.datasets.COWC.plot`."""
        return self.val_dataset.dataset.plot(*args, **kwargs)