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Source code for torchgeo.datamodules.l8biome

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

"""L8 Biome datamodule."""

from typing import Any

import kornia.augmentation as K
import torch
from kornia.constants import DataKey, Resample

from ..datasets import L8Biome, random_bbox_assignment
from ..samplers import GridGeoSampler, RandomBatchGeoSampler
from ..samplers.utils import _to_tuple
from ..transforms import AugmentationSequential
from .geo import GeoDataModule


[docs]class L8BiomeDataModule(GeoDataModule): """LightningDataModule implementation for the L8 Biome dataset. .. versionadded:: 0.5 """
[docs] def __init__( self, batch_size: int = 1, patch_size: int | tuple[int, int] = 224, length: int | None = None, num_workers: int = 0, **kwargs: Any, ) -> None: """Initialize a new L8BiomeDataModule instance. Args: batch_size: Size of each mini-batch. patch_size: Size of each patch, either ``size`` or ``(height, width)``. length: Length of each training epoch. num_workers: Number of workers for parallel data loading. **kwargs: Additional keyword arguments passed to :class:`~torchgeo.datasets.L8Biome`. """ super().__init__( L8Biome, batch_size=batch_size, patch_size=patch_size, length=length, num_workers=num_workers, **kwargs, ) self.train_aug = AugmentationSequential( K.Normalize(mean=self.mean, std=self.std), K.RandomResizedCrop(_to_tuple(self.patch_size), scale=(0.6, 1.0)), K.RandomVerticalFlip(p=0.5), K.RandomHorizontalFlip(p=0.5), data_keys=['image', 'mask'], extra_args={ DataKey.MASK: {'resample': Resample.NEAREST, 'align_corners': None} }, )
[docs] def setup(self, stage: str) -> None: """Set up datasets. Args: stage: Either 'fit', 'validate', 'test', or 'predict'. """ dataset = L8Biome(**self.kwargs) generator = torch.Generator().manual_seed(0) (self.train_dataset, self.val_dataset, self.test_dataset) = ( random_bbox_assignment(dataset, [0.6, 0.2, 0.2], generator) ) if stage in ['fit']: self.train_batch_sampler = RandomBatchGeoSampler( self.train_dataset, self.patch_size, self.batch_size, self.length ) if stage in ['fit', 'validate']: self.val_sampler = GridGeoSampler( self.val_dataset, self.patch_size, self.patch_size ) if stage in ['test']: self.test_sampler = GridGeoSampler( self.test_dataset, self.patch_size, self.patch_size )

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