Source code for torchgeo.datamodules.sentinel2_cdl
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Sentinel-2 and CDL datamodule."""
from typing import Any
import kornia.augmentation as K
import torch
from kornia.constants import DataKey, Resample
from matplotlib.figure import Figure
from ..datasets import CDL, Sentinel2, random_grid_cell_assignment
from ..samplers import GridGeoSampler, RandomBatchGeoSampler
from ..samplers.utils import _to_tuple
from ..transforms import AugmentationSequential
from .geo import GeoDataModule
[docs]class Sentinel2CDLDataModule(GeoDataModule):
"""LightningDataModule implementation for the Sentinel-2 and CDL datasets.
.. versionadded:: 0.6
"""
[docs] def __init__(
self,
batch_size: int = 64,
patch_size: int | tuple[int, int] = 64,
length: int | None = None,
num_workers: int = 0,
**kwargs: Any,
) -> None:
"""Initialize a new Sentinel2CDLDataModule 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.CDL` (prefix keys with ``cdl_``) and
:class:`~torchgeo.datasets.Sentinel2`
(prefix keys with ``sentinel2_``).
"""
# Define prefix for Cropland Data Layer (CDL) and Sentinel-2 arguments
cdl_signature = 'cdl_'
sentinel2_signature = 'sentinel2_'
self.cdl_kwargs = {}
self.sentinel2_kwargs = {}
for key, val in kwargs.items():
# Check if the current key starts with the CDL prefix
if key.startswith(cdl_signature):
# If so, extract the key-value pair to the CDL dictionary
self.cdl_kwargs[key[len(cdl_signature) :]] = val
# Check if the current key starts with the Sentinel-2 prefix
elif key.startswith(sentinel2_signature):
# If so, extract the key-value pair to the Sentinel-2 dictionary
self.sentinel2_kwargs[key[len(sentinel2_signature) :]] = val
super().__init__(
CDL, batch_size, patch_size, length, num_workers, **self.cdl_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}
},
)
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.sentinel2 = Sentinel2(**self.sentinel2_kwargs)
self.cdl = CDL(**self.cdl_kwargs)
self.dataset = self.sentinel2 & self.cdl
generator = torch.Generator().manual_seed(0)
(self.train_dataset, self.val_dataset, self.test_dataset) = (
random_grid_cell_assignment(
self.dataset, [0.8, 0.1, 0.1], grid_size=8, generator=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
)
[docs] def plot(self, *args: Any, **kwargs: Any) -> Figure:
"""Run CDL 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.
"""
return self.cdl.plot(*args, **kwargs)