Source code for torchgeo.datasets.chesapeake
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Chesapeake Bay High-Resolution Land Cover Project datasets."""
import abc
import os
import sys
from typing import Any, Callable, Dict, List, Optional, Sequence
import fiona
import numpy as np
import pyproj
import rasterio
import rasterio.mask
import shapely.geometry
import shapely.ops
import torch
import torch.nn.functional as F
from pytorch_lightning.core.datamodule import LightningDataModule
from rasterio.crs import CRS
from torch import Tensor
from torch.utils.data import DataLoader
from torchvision.transforms import Compose
from ..samplers.batch import RandomBatchGeoSampler
from ..samplers.single import GridGeoSampler
from .geo import GeoDataset, RasterDataset
from .utils import BoundingBox, download_url, extract_archive
# https://github.com/pytorch/pytorch/issues/60979
# https://github.com/pytorch/pytorch/pull/61045
DataLoader.__module__ = "torch.utils.data"
class Chesapeake(RasterDataset, abc.ABC):
"""Abstract base class for all Chesapeake datasets.
`Chesapeake Bay High-Resolution Land Cover Project
<https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/>`_
dataset.
This dataset was collected by the Chesapeake Conservancy's Conservation Innovation
Center (CIC) in partnership with the University of Vermont and WorldView Solutions,
Inc. It consists of one-meter resolution land cover information for the Chesapeake
Bay watershed (~100,000 square miles of land).
For more information, see:
* `User Guide
<https://chesapeakeconservancy.org/wp-content/uploads/2017/01/LandCover101Guide.pdf>`_
* `Class Descriptions
<https://chesapeakeconservancy.org/wp-content/uploads/2020/03/LC_Class_Descriptions.pdf>`_
* `Accuracy Assessment
<https://chesapeakeconservancy.org/wp-content/uploads/2017/01/Chesapeake_Conservancy_Accuracy_Assessment_Methodology.pdf>`_
"""
# TODO: this shouldn't be needed, but .tif.ovr file is getting picked up
filename_glob = "*.tif"
is_image = False
@property
@abc.abstractmethod
def base_folder(self) -> str:
"""Parent directory of dataset in URL."""
@property
@abc.abstractmethod
def filename(self) -> str:
"""Filename to find/store dataset in."""
@property
@abc.abstractmethod
def zipfile(self) -> str:
"""Name of zipfile in download URL."""
@property
@abc.abstractmethod
def md5(self) -> str:
"""MD5 checksum to verify integrity of dataset."""
@property
def url(self) -> str:
"""URL to download dataset from."""
url = "https://cicwebresources.blob.core.windows.net/chesapeakebaylandcover"
url += f"/{self.base_folder}/{self.zipfile}"
return url
[docs] def __init__(
self,
root: str = "data",
crs: Optional[CRS] = None,
res: Optional[float] = None,
transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None,
cache: bool = True,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new Dataset instance.
Args:
root: root directory where dataset can be found
crs: :term:`coordinate reference system (CRS)` to warp to
(defaults to the CRS of the first file found)
res: resolution of the dataset in units of CRS
(defaults to the resolution of the first file found)
transforms: a function/transform that takes an input sample
and returns a transformed version
cache: if True, cache file handle to speed up repeated sampling
download: if True, download dataset and store it in the root directory
checksum: if True, check the MD5 of the downloaded files (may be slow)
Raises:
FileNotFoundError: if no files are found in ``root``
RuntimeError: if ``download=False`` but dataset is missing or checksum fails
"""
self.root = root
self.download = download
self.checksum = checksum
self._verify()
super().__init__(root, crs, res, transforms, cache)
def _verify(self) -> None:
"""Verify the integrity of the dataset.
Raises:
RuntimeError: if ``download=False`` but dataset is missing or checksum fails
"""
# Check if the extracted file already exists
if os.path.exists(os.path.join(self.root, self.filename)):
return
# Check if the zip file has already been downloaded
if os.path.exists(os.path.join(self.root, self.zipfile)):
self._extract()
return
# Check if the user requested to download the dataset
if not self.download:
raise RuntimeError(
f"Dataset not found in `root={self.root}` and `download=False`, "
"either specify a different `root` directory or use `download=True` "
"to automaticaly download the dataset."
)
# Download the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
download_url(self.url, self.root, filename=self.zipfile, md5=self.md5)
def _extract(self) -> None:
"""Extract the dataset."""
extract_archive(os.path.join(self.root, self.zipfile))
class Chesapeake7(Chesapeake):
"""Complete 7-class dataset.
This version of the dataset is composed of 7 classes:
0. No Data: Background values
1. Water: All areas of open water including ponds, rivers, and lakes
2. Tree Canopy and Shrubs: All woody vegetation including trees and shrubs
3. Low Vegetation: Plant material less than 2 meters in height including lawns
4. Barren: Areas devoid of vegetation consisting of natural earthen material
5. Impervious Surfaces: Human-constructed surfaces less than 2 meters in height
6. Impervious Roads: Impervious surfaces that are used for transportation
7. Aberdeen Proving Ground: U.S. Army facility with no labels
"""
base_folder = "BAYWIDE"
filename = "Baywide_7class_20132014.tif"
zipfile = "Baywide_7Class_20132014.zip"
md5 = "61a4e948fb2551840b6557ef195c2084"
cmap = {
0: (0, 0, 0, 0),
1: (0, 197, 255, 255),
2: (38, 115, 0, 255),
3: (163, 255, 115, 255),
4: (255, 170, 0, 255),
5: (156, 156, 156, 255),
6: (0, 0, 0, 255),
7: (197, 0, 255, 255),
8: (0, 0, 0, 0),
9: (0, 0, 0, 0),
10: (0, 0, 0, 0),
11: (0, 0, 0, 0),
12: (0, 0, 0, 0),
13: (0, 0, 0, 0),
14: (0, 0, 0, 0),
15: (0, 0, 0, 0),
}
class Chesapeake13(Chesapeake):
"""Complete 13-class dataset.
This version of the dataset is composed of 13 classes:
0. No Data: Background values
1. Water: All areas of open water including ponds, rivers, and lakes
2. Wetlands: Low vegetation areas located along marine or estuarine regions
3. Tree Canopy: Deciduous and evergreen woody vegetation over 3-5 meters in height
4. Shrubland: Heterogeneous woody vegetation including shrubs and young trees
5. Low Vegetation: Plant material less than 2 meters in height including lawns
6. Barren: Areas devoid of vegetation consisting of natural earthen material
7. Structures: Human-constructed objects made of impervious materials
8. Impervious Surfaces: Human-constructed surfaces less than 2 meters in height
9. Impervious Roads: Impervious surfaces that are used for transportation
10. Tree Canopy over Structures: Tree cover overlapping impervious structures
11. Tree Canopy over Impervious Surfaces: Tree cover overlapping impervious surfaces
12. Tree Canopy over Impervious Roads: Tree cover overlapping impervious roads
13. Aberdeen Proving Ground: U.S. Army facility with no labels
"""
base_folder = "BAYWIDE"
filename = "Baywide_13Class_20132014.tif"
zipfile = "Baywide_13Class_20132014.zip"
md5 = "7e51118923c91e80e6e268156d25a4b9"
class ChesapeakeDC(Chesapeake):
"""This subset of the dataset contains data only for Washington, D.C."""
base_folder = "DC"
filename = os.path.join("DC_11001", "DC_11001.img")
zipfile = "DC_11001.zip"
md5 = "ed06ba7570d2955e8857d7d846c53b06"
class ChesapeakeDE(Chesapeake):
"""This subset of the dataset contains data only for Delaware."""
base_folder = "DE"
filename = "DE_STATEWIDE.tif"
zipfile = "_DE_STATEWIDE.zip"
md5 = "5e12eff3b6950c01092c7e480b38e544"
class ChesapeakeMD(Chesapeake):
"""This subset of the dataset contains data only for Maryland."""
base_folder = "MD"
filename = "MD_STATEWIDE.tif"
zipfile = "_MD_STATEWIDE.zip"
md5 = "40c7cd697a887f2ffdb601b5c114e567"
class ChesapeakeNY(Chesapeake):
"""This subset of the dataset contains data only for New York."""
base_folder = "NY"
filename = "NY_STATEWIDE.tif"
zipfile = "_NY_STATEWIDE.zip"
md5 = "1100078c526616454ef2e508affda915"
class ChesapeakePA(Chesapeake):
"""This subset of the dataset contains data only for Pennsylvania."""
base_folder = "PA"
filename = "PA_STATEWIDE.tif"
zipfile = "_PA_STATEWIDE.zip"
md5 = "20a2a857c527a4dbadd6beed8b47e5ab"
class ChesapeakeVA(Chesapeake):
"""This subset of the dataset contains data only for Virginia."""
base_folder = "VA"
filename = "CIC2014_VA_STATEWIDE.tif"
zipfile = "_VA_STATEWIDE.zip"
md5 = "6f2c97deaf73bb3e1ea9b21bd7a3fc8e"
class ChesapeakeWV(Chesapeake):
"""This subset of the dataset contains data only for West Virginia."""
base_folder = "WV"
filename = "WV_STATEWIDE.tif"
zipfile = "_WV_STATEWIDE.zip"
md5 = "350621ea293651fbc557a1c3e3c64cc3"
class ChesapeakeCVPR(GeoDataset):
"""CVPR 2019 Chesapeake Land Cover dataset.
The `CVPR 2019 Chesapeake Land Cover
<https://lila.science/datasets/chesapeakelandcover>`_ dataset contains two layers of
NAIP aerial imagery, Landsat 8 leaf-on and leaf-off imagery, Chesapeake Bay land
cover labels, NLCD land cover labels, and Microsoft building footprint labels.
This dataset was organized to accompany the 2019 CVPR paper, "Large Scale
High-Resolution Land Cover Mapping with Multi-Resolution Data".
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1109/cvpr.2019.01301
"""
url = "https://lilablobssc.blob.core.windows.net/lcmcvpr2019/cvpr_chesapeake_landcover.zip" # noqa: E501
filename = "cvpr_chesapeake_landcover.zip"
md5 = "1225ccbb9590e9396875f221e5031514"
crs = CRS.from_epsg(3857)
res = 1
valid_layers = [
"naip-new",
"naip-old",
"landsat-leaf-on",
"landsat-leaf-off",
"nlcd",
"lc",
"buildings",
]
states = ["de", "md", "va", "wv", "pa", "ny"]
splits = (
[f"{state}-train" for state in states]
+ [f"{state}-val" for state in states]
+ [f"{state}-test" for state in states]
)
files = [
"de_1m_2013_extended-debuffered-test_tiles",
"de_1m_2013_extended-debuffered-train_tiles",
"de_1m_2013_extended-debuffered-val_tiles",
"md_1m_2013_extended-debuffered-test_tiles",
"md_1m_2013_extended-debuffered-train_tiles",
"md_1m_2013_extended-debuffered-val_tiles",
"ny_1m_2013_extended-debuffered-test_tiles",
"ny_1m_2013_extended-debuffered-train_tiles",
"ny_1m_2013_extended-debuffered-val_tiles",
"pa_1m_2013_extended-debuffered-test_tiles",
"pa_1m_2013_extended-debuffered-train_tiles",
"pa_1m_2013_extended-debuffered-val_tiles",
"va_1m_2014_extended-debuffered-test_tiles",
"va_1m_2014_extended-debuffered-train_tiles",
"va_1m_2014_extended-debuffered-val_tiles",
"wv_1m_2014_extended-debuffered-test_tiles",
"wv_1m_2014_extended-debuffered-train_tiles",
"wv_1m_2014_extended-debuffered-val_tiles",
"spatial_index.geojson",
]
p_src_crs = pyproj.CRS("epsg:3857")
p_transformers = {
"epsg:26917": pyproj.Transformer.from_crs(
p_src_crs, pyproj.CRS("epsg:26917"), always_xy=True
).transform,
"epsg:26918": pyproj.Transformer.from_crs(
p_src_crs, pyproj.CRS("epsg:26918"), always_xy=True
).transform,
}
[docs] def __init__(
self,
root: str = "data",
splits: Sequence[str] = ["de-train"],
layers: List[str] = ["naip-new", "lc"],
transforms: Optional[Callable[[Dict[str, Any]], Dict[str, Any]]] = None,
cache: bool = True,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new Dataset instance.
Args:
root: root directory where dataset can be found
splits: a list of strings in the format "{state}-{train,val,test}"
indicating the subset of data to use, for example "ny-train"
layers: a list containing a subset of "naip-new", "naip-old", "lc", "nlcd",
"landsat-leaf-on", "landsat-leaf-off", "buildings" indicating which
layers to load
transforms: a function/transform that takes an input sample
and returns a transformed version
cache: if True, cache file handle to speed up repeated sampling
download: if True, download dataset and store it in the root directory
checksum: if True, check the MD5 of the downloaded files (may be slow)
Raises:
FileNotFoundError: if no files are found in ``root``
RuntimeError: if ``download=False`` but dataset is missing or checksum fails
"""
for split in splits:
assert split in self.splits
assert all([layer in self.valid_layers for layer in layers])
self.root = root
self.layers = layers
self.cache = cache
self.download = download
self.checksum = checksum
self._verify()
super().__init__(transforms)
# Add all tiles into the index in epsg:3857 based on the included geojson
mint: float = 0
maxt: float = sys.maxsize
with fiona.open(os.path.join(root, "spatial_index.geojson"), "r") as f:
for i, row in enumerate(f):
if row["properties"]["split"] in splits:
box = shapely.geometry.shape(row["geometry"])
minx, miny, maxx, maxy = box.bounds
coords = (minx, maxx, miny, maxy, mint, maxt)
self.index.insert(
i,
coords,
{
"naip-new": row["properties"]["naip-new"],
"naip-old": row["properties"]["naip-old"],
"landsat-leaf-on": row["properties"]["landsat-leaf-on"],
"landsat-leaf-off": row["properties"]["landsat-leaf-off"],
"lc": row["properties"]["lc"],
"nlcd": row["properties"]["nlcd"],
"buildings": row["properties"]["buildings"],
},
)
[docs] def __getitem__(self, query: BoundingBox) -> Dict[str, Any]:
"""Retrieve image/mask and metadata indexed by query.
Args:
query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index
Returns:
sample of image/mask and metadata at that index
Raises:
IndexError: if query is not found in the index
"""
hits = self.index.intersection(query, objects=True)
filepaths = [hit.object for hit in hits]
sample = {"image": [], "mask": [], "crs": self.crs, "bbox": query}
if len(filepaths) == 0:
raise IndexError(
f"query: {query} not found in index with bounds: {self.bounds}"
)
elif len(filepaths) == 1:
filenames = filepaths[0]
query_geom_transformed = None # is set by the first layer
minx, maxx, miny, maxy, mint, maxt = query
query_box = shapely.geometry.box(minx, miny, maxx, maxy)
for layer in self.layers:
fn = filenames[layer]
with rasterio.open(os.path.join(self.root, fn)) as f:
dst_crs = f.crs.to_string().lower()
if query_geom_transformed is None:
query_box_transformed = shapely.ops.transform(
self.p_transformers[dst_crs], query_box
).envelope
query_geom_transformed = shapely.geometry.mapping(
query_box_transformed
)
data, _ = rasterio.mask.mask(
f, [query_geom_transformed], crop=True, all_touched=True
)
if layer in [
"naip-new",
"naip-old",
"landsat-leaf-on",
"landsat-leaf-off",
]:
sample["image"].append(data)
elif layer in ["lc", "nlcd", "buildings"]:
sample["mask"].append(data)
else:
raise IndexError(f"query: {query} spans multiple tiles which is not valid")
sample["image"] = np.concatenate( # type: ignore[no-untyped-call]
sample["image"], axis=0
)
sample["mask"] = np.concatenate( # type: ignore[no-untyped-call]
sample["mask"], axis=0
)
sample["image"] = torch.from_numpy( # type: ignore[attr-defined]
sample["image"]
)
sample["mask"] = torch.from_numpy(sample["mask"]) # type: ignore[attr-defined]
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _verify(self) -> None:
"""Verify the integrity of the dataset.
Raises:
RuntimeError: if ``download=False`` but dataset is missing or checksum fails
"""
# Check if the extracted files already exist
def exists(filename: str) -> bool:
return os.path.exists(os.path.join(self.root, filename))
if all(map(exists, self.files)):
return
# Check if the zip files have already been downloaded
if os.path.exists(os.path.join(self.root, self.filename)):
self._extract()
return
# Check if the user requested to download the dataset
if not self.download:
raise RuntimeError(
f"Dataset not found in `root={self.root}` and `download=False`, "
"either specify a different `root` directory or use `download=True` "
"to automaticaly download the dataset."
)
# Download the dataset
self._download()
self._extract()
def _download(self) -> None:
"""Download the dataset."""
download_url(self.url, self.root, filename=self.filename, md5=self.md5)
def _extract(self) -> None:
"""Extract the dataset."""
extract_archive(os.path.join(self.root, self.filename))
class ChesapeakeCVPRDataModule(LightningDataModule):
"""LightningDataModule implementation for the Chesapeake CVPR Land Cover dataset.
Uses the random splits defined per state to partition tiles into train, val,
and test sets.
"""
[docs] def __init__(
self,
root_dir: str,
train_splits: List[str],
val_splits: List[str],
test_splits: List[str],
patches_per_tile: int = 200,
patch_size: int = 256,
batch_size: int = 64,
num_workers: int = 0,
class_set: int = 7,
**kwargs: Any,
) -> None:
"""Initialize a LightningDataModule for Chesapeake CVPR based DataLoaders.
Args:
root_dir: The ``root`` arugment to pass to the ChesapeakeCVPR Dataset
classes
train_splits: The splits used to train the model, e.g. ["ny-train"]
val_splits: The splits used to validate the model, e.g. ["ny-val"]
test_splits: The splits used to test the model, e.g. ["ny-test"]
patches_per_tile: The number of patches per tile to sample
patch_size: The size of each patch in pixels (test patches will be 1.5 times
this size)
batch_size: The batch size to use in all created DataLoaders
num_workers: The number of workers to use in all created DataLoaders
class_set: The high-resolution land cover class set to use - 5 or 7
"""
super().__init__() # type: ignore[no-untyped-call]
for state in train_splits + val_splits + test_splits:
assert state in ChesapeakeCVPR.splits
assert class_set in [5, 7]
self.root_dir = root_dir
self.train_splits = train_splits
self.val_splits = val_splits
self.test_splits = test_splits
self.layers = ["naip-new", "lc"]
self.patches_per_tile = patches_per_tile
self.patch_size = patch_size
# This is a rough estimate of how large of a patch we will need to sample in
# EPSG:3857 in order to guarantee a large enough patch in the local CRS.
self.original_patch_size = int(patch_size * 2.0)
self.batch_size = batch_size
self.num_workers = num_workers
self.class_set = class_set
[docs] def pad_to(
self, size: int = 512, image_value: int = 0, mask_value: int = 0
) -> Callable[[Dict[str, Tensor]], Dict[str, Tensor]]:
"""Returns a function to perform a padding transform on a single sample.
Args:
size: output image size
image_value: value to pad image with
mask_value: value to pad mask with
Returns:
function to perform padding
"""
def pad_inner(sample: Dict[str, Tensor]) -> Dict[str, Tensor]:
_, height, width = sample["image"].shape
assert height <= size and width <= size
height_pad = size - height
width_pad = size - width
# See https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
# for a description of the format of the padding tuple
sample["image"] = F.pad(
sample["image"],
(0, width_pad, 0, height_pad),
mode="constant",
value=image_value,
)
sample["mask"] = F.pad(
sample["mask"],
(0, width_pad, 0, height_pad),
mode="constant",
value=mask_value,
)
return sample
return pad_inner
[docs] def center_crop(
self, size: int = 512
) -> Callable[[Dict[str, Tensor]], Dict[str, Tensor]]:
"""Returns a function to perform a center crop transform on a single sample.
Args:
size: output image size
Returns:
function to perform center crop
"""
def center_crop_inner(sample: Dict[str, Tensor]) -> Dict[str, Tensor]:
_, height, width = sample["image"].shape
y1 = (height - size) // 2
x1 = (width - size) // 2
sample["image"] = sample["image"][:, y1 : y1 + size, x1 : x1 + size]
sample["mask"] = sample["mask"][:, y1 : y1 + size, x1 : x1 + size]
return sample
return center_crop_inner
[docs] def preprocess(self, sample: Dict[str, Any]) -> Dict[str, Any]:
"""Preprocesses a single sample.
Args:
sample: sample dictionary containing image and mask
Returns:
preprocessed sample
"""
sample["image"] = sample["image"] / 255.0
sample["mask"] = sample["mask"]
sample["mask"] = sample["mask"].squeeze()
if self.class_set == 5:
sample["mask"][sample["mask"] == 5] = 4
sample["mask"][sample["mask"] == 6] = 4
sample["image"] = sample["image"].float()
sample["mask"] = sample["mask"].long()
return sample
[docs] def nodata_check(
self, size: int = 512
) -> Callable[[Dict[str, Tensor]], Dict[str, Tensor]]:
"""Returns a function to check for nodata or mis-sized input.
Args:
size: output image size
Returns:
function to check for nodata values
"""
def nodata_check_inner(sample: Dict[str, Tensor]) -> Dict[str, Tensor]:
num_channels, height, width = sample["image"].shape
if height < size or width < size:
sample["image"] = torch.zeros( # type: ignore[attr-defined]
(num_channels, size, size)
)
sample["mask"] = torch.zeros((size, size)) # type: ignore[attr-defined]
return sample
return nodata_check_inner
[docs] def prepare_data(self) -> None:
"""Confirms that the dataset is downloaded on the local node.
This method is called once per node, while :func:`setup` is called once per GPU.
"""
ChesapeakeCVPR(
self.root_dir,
splits=self.train_splits,
layers=self.layers,
transforms=None,
download=False,
checksum=False,
)
[docs] def setup(self, stage: Optional[str] = None) -> None:
"""Create the train/val/test splits based on the original Dataset objects.
The splits should be done here vs. in :func:`__init__` per the docs:
https://pytorch-lightning.readthedocs.io/en/latest/extensions/datamodules.html#setup.
Args:
stage: stage to set up
"""
train_transforms = Compose(
[
self.center_crop(self.patch_size),
self.nodata_check(self.patch_size),
self.preprocess,
]
)
val_transforms = Compose(
[
self.center_crop(self.patch_size),
self.nodata_check(self.patch_size),
self.preprocess,
]
)
test_transforms = Compose(
[
self.pad_to(self.original_patch_size, image_value=0, mask_value=0),
self.preprocess,
]
)
self.train_dataset = ChesapeakeCVPR(
self.root_dir,
splits=self.train_splits,
layers=self.layers,
transforms=train_transforms,
download=False,
checksum=False,
)
self.val_dataset = ChesapeakeCVPR(
self.root_dir,
splits=self.val_splits,
layers=self.layers,
transforms=val_transforms,
download=False,
checksum=False,
)
self.test_dataset = ChesapeakeCVPR(
self.root_dir,
splits=self.test_splits,
layers=self.layers,
transforms=test_transforms,
download=False,
checksum=False,
)
[docs] def train_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for training.
Returns:
training data loader
"""
sampler = RandomBatchGeoSampler(
self.train_dataset,
size=self.original_patch_size,
batch_size=self.batch_size,
length=self.patches_per_tile * len(self.train_dataset),
)
return DataLoader(
self.train_dataset, batch_sampler=sampler, num_workers=self.num_workers
)
[docs] def val_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for validation.
Returns:
validation data loader
"""
sampler = GridGeoSampler(
self.val_dataset,
size=self.original_patch_size,
stride=self.original_patch_size,
)
return DataLoader(
self.val_dataset,
batch_size=self.batch_size,
sampler=sampler,
num_workers=self.num_workers,
)
[docs] def test_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for testing.
Returns:
testing data loader
"""
sampler = GridGeoSampler(
self.test_dataset,
size=self.original_patch_size,
stride=self.original_patch_size,
)
return DataLoader(
self.test_dataset,
batch_size=self.batch_size,
sampler=sampler,
num_workers=self.num_workers,
)
