Source code for torchgeo.datamodules.l7irish
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""L7 Irish datamodule."""
from typing import Any
import kornia.augmentation as K
import torch
from kornia.constants import DataKey, Resample
from ..datasets import L7Irish, random_bbox_assignment
from ..samplers import GridGeoSampler, RandomBatchGeoSampler
from ..samplers.utils import _to_tuple
from ..transforms import AugmentationSequential
from .geo import GeoDataModule
[docs]class L7IrishDataModule(GeoDataModule):
"""LightningDataModule implementation for the L7 Irish 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 L7IrishDataModule 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.L7Irish`.
"""
super().__init__(
L7Irish,
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 = L7Irish(**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
)