Source code for torchgeo.datamodules.gid15
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""GID-15 datamodule."""
from typing import Any, Union
import kornia.augmentation as K
from ..datasets import GID15
from ..samplers.utils import _to_tuple
from ..transforms import AugmentationSequential
from ..transforms.transforms import _RandomNCrop
from .geo import NonGeoDataModule
from .utils import dataset_split
[docs]class GID15DataModule(NonGeoDataModule):
"""LightningDataModule implementation for the GID-15 dataset.
Uses the train/test splits from the dataset.
.. versionadded:: 0.4
"""
[docs] def __init__(
self,
batch_size: int = 64,
patch_size: Union[tuple[int, int], int] = 64,
val_split_pct: float = 0.2,
num_workers: int = 0,
**kwargs: Any,
) -> None:
"""Initialize a new GID15DataModule instance.
Args:
batch_size: Size of each mini-batch.
patch_size: Size of each patch, either ``size`` or ``(height, width)``.
Should be a multiple of 32 for most segmentation architectures.
val_split_pct: Percentage of the dataset to use as a validation set
num_workers: Number of workers for parallel data loading.
**kwargs: Additional keyword arguments passed to
:class:`~torchgeo.datasets.GID15`.
"""
super().__init__(GID15, 1, num_workers, **kwargs)
self.patch_size = _to_tuple(patch_size)
self.val_split_pct = val_split_pct
self.train_aug = self.val_aug = AugmentationSequential(
K.Normalize(mean=self.mean, std=self.std),
_RandomNCrop(self.patch_size, batch_size),
data_keys=["image", "mask"],
)
self.predict_aug = AugmentationSequential(
K.Normalize(mean=self.mean, std=self.std),
_RandomNCrop(self.patch_size, batch_size),
data_keys=["image"],
)
[docs] def setup(self, stage: str) -> None:
"""Set up datasets.
Args:
stage: Either 'fit', 'validate', 'test', or 'predict'.
"""
if stage in ["fit", "validate"]:
self.dataset = GID15(split="train", **self.kwargs)
self.train_dataset, self.val_dataset = dataset_split(
self.dataset, self.val_split_pct
)
if stage in ["test"]:
# Test set masks are not public, use for prediction instead
self.predict_dataset = GID15(split="test", **self.kwargs)