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Source code for torchgeo.datamodules.naip

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

"""National Agriculture Imagery Program (NAIP) datamodule."""

from typing import Any

import kornia.augmentation as K
from matplotlib.figure import Figure

from ..datasets import NAIP, BoundingBox, Chesapeake13
from ..samplers import GridGeoSampler, RandomBatchGeoSampler
from ..transforms import AugmentationSequential
from .geo import GeoDataModule


[docs]class NAIPChesapeakeDataModule(GeoDataModule): """LightningDataModule implementation for the NAIP and Chesapeake datasets. Uses the train/val/test splits from the dataset. """
[docs] def __init__( self, batch_size: int = 64, patch_size: int | tuple[int, int] = 256, length: int | None = None, num_workers: int = 0, **kwargs: Any, ) -> None: """Initialize a new NAIPChesapeakeDataModule 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.NAIP` (prefix keys with ``naip_``) and :class:`~torchgeo.datasets.Chesapeake13` (prefix keys with ``chesapeake_``). """ self.naip_kwargs = {} self.chesapeake_kwargs = {} for key, val in kwargs.items(): if key.startswith('naip_'): self.naip_kwargs[key[5:]] = val elif key.startswith('chesapeake_'): self.chesapeake_kwargs[key[11:]] = val super().__init__( Chesapeake13, batch_size, patch_size, length, num_workers, **self.chesapeake_kwargs, ) self.aug = AugmentationSequential( K.Normalize(mean=self.mean, std=self.std), data_keys=['image', 'mask'] )
[docs] def setup(self, stage: str) -> None: """Set up datasets and samplers. Args: stage: Either 'fit', 'validate', 'test', or 'predict'. """ self.chesapeake = Chesapeake13(**self.chesapeake_kwargs) self.naip = NAIP(**self.naip_kwargs) self.dataset = self.chesapeake & self.naip roi = self.dataset.bounds midx = roi.minx + (roi.maxx - roi.minx) / 2 midy = roi.miny + (roi.maxy - roi.miny) / 2 if stage in ['fit']: train_roi = BoundingBox( roi.minx, midx, roi.miny, roi.maxy, roi.mint, roi.maxt ) self.train_batch_sampler = RandomBatchGeoSampler( self.dataset, self.patch_size, self.batch_size, self.length, train_roi ) if stage in ['fit', 'validate']: val_roi = BoundingBox(midx, roi.maxx, roi.miny, midy, roi.mint, roi.maxt) self.val_sampler = GridGeoSampler( self.dataset, self.patch_size, self.patch_size, val_roi ) if stage in ['test']: test_roi = BoundingBox( roi.minx, roi.maxx, midy, roi.maxy, roi.mint, roi.maxt ) self.test_sampler = GridGeoSampler( self.dataset, self.patch_size, self.patch_size, test_roi )
[docs] def plot(self, *args: Any, **kwargs: Any) -> Figure: """Run NAIP plot method. Args: *args: Arguments passed to plot method. **kwargs: Keyword arguments passed to plot method. Returns: A matplotlib Figure with the image, ground truth, and predictions. .. versionadded:: 0.4 """ return self.naip.plot(*args, **kwargs)

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