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Source code for torchgeo.datamodules.inria

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

"""InriaAerialImageLabeling datamodule."""

from typing import Any

import kornia.augmentation as K

from ..datasets import InriaAerialImageLabeling
from ..samplers.utils import _to_tuple
from ..transforms import AugmentationSequential
from ..transforms.transforms import _RandomNCrop
from .geo import NonGeoDataModule


[docs]class InriaAerialImageLabelingDataModule(NonGeoDataModule): """LightningDataModule implementation for the InriaAerialImageLabeling dataset. Uses the train/test splits from the dataset and further splits the train split into train/val splits. .. versionadded:: 0.3 """
[docs] def __init__( self, batch_size: int = 64, patch_size: tuple[int, int] | int = 64, num_workers: int = 0, **kwargs: Any, ) -> None: """Initialize a new InriaAerialImageLabelingDataModule 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. num_workers: Number of workers for parallel data loading. **kwargs: Additional keyword arguments passed to :class:`~torchgeo.datasets.InriaAerialImageLabeling`. """ super().__init__(InriaAerialImageLabeling, 1, num_workers, **kwargs) self.patch_size = _to_tuple(patch_size) self.train_aug = AugmentationSequential( K.Normalize(mean=self.mean, std=self.std), K.RandomHorizontalFlip(p=0.5), K.RandomVerticalFlip(p=0.5), _RandomNCrop(self.patch_size, batch_size), data_keys=['image', 'mask'], ) self.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']: self.train_dataset = InriaAerialImageLabeling(split='train', **self.kwargs) if stage in ['fit', 'validate']: self.val_dataset = InriaAerialImageLabeling(split='val', **self.kwargs) if stage in ['predict']: # Test set masks are not public, use for prediction instead self.predict_dataset = InriaAerialImageLabeling(split='test', **self.kwargs)

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