.. module:: torchgeo.samplers
Samplers are used to index a dataset, retrieving a single query at a time. For :class:`~torchgeo.datasets.VisionDataset`, dataset objects can be indexed with integers, and PyTorch's builtin samplers are sufficient. For :class:`~torchgeo.datasets.GeoDataset`, dataset objects require a bounding box for indexing. For this reason, we define our own :class:`GeoSampler` implementations below. These can be used like so:
from torch.utils.data import DataLoader
from torchgeo.datasets import Landsat
from torchgeo.samplers import RandomGeoSampler
dataset = Landsat(...)
sampler = RandomGeoSampler(dataset, size=1000, length=100)
dataloader = DataLoader(dataset, sampler=sampler).. autoclass:: RandomGeoSampler
.. autoclass:: GridGeoSampler
When working with large tile-based datasets, randomly sampling patches from each tile can be extremely time consuming. It's much more efficient to choose a tile, load it, warp it to the appropriate :term:`coordinate reference system (CRS)` and resolution, and then sample random patches from that tile to construct a mini-batch of data. For this reason, we define our own :class:`BatchGeoSampler` implementations below. These can be used like so:
from torch.utils.data import DataLoader
from torchgeo.datasets import Landsat
from torchgeo.samplers import RandomBatchGeoSampler
dataset = Landsat(...)
sampler = RandomBatchGeoSampler(dataset, size=1000, batch_size=10, length=100)
dataloader = DataLoader(dataset, batch_sampler=sampler).. autoclass:: RandomBatchGeoSampler
If you want to write your own custom sampler, you can extend one of these abstract base classes.
.. autoclass:: GeoSampler
.. autoclass:: BatchGeoSampler