Source code for torchgeo.datamodules.iobench
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""I/O benchmark datamodule."""
from typing import Any
from ..datasets import IOBench
from ..samplers import GridGeoSampler, RandomGeoSampler
from .geo import GeoDataModule
[docs]class IOBenchDataModule(GeoDataModule):
"""LightningDataModule implementation for the I/O benchmark dataset.
.. versionadded:: 0.6
"""
[docs] def __init__(
self,
batch_size: int = 32,
patch_size: int | tuple[int, int] = 256,
length: int | None = None,
num_workers: int = 0,
**kwargs: Any,
) -> None:
"""Initialize a new IOBenchDataModule 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.IOBench`.
"""
super().__init__(
IOBench,
batch_size=batch_size,
patch_size=patch_size,
length=length,
num_workers=num_workers,
**kwargs,
)
[docs] def setup(self, stage: str) -> None:
"""Set up datasets.
Args:
stage: Either 'fit', 'validate', 'test', or 'predict'.
"""
self.dataset = IOBench(**self.kwargs)
if stage in ['fit']:
self.train_sampler = RandomGeoSampler(
self.dataset, self.patch_size, self.length
)
if stage in ['fit', 'validate']:
self.val_sampler = GridGeoSampler(
self.dataset, self.patch_size, self.patch_size
)
if stage in ['test']:
self.test_sampler = GridGeoSampler(
self.dataset, self.patch_size, self.patch_size
)