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)