Add AgriFieldNet datamodule

docs/api/datamodules.rst

@@ -6,6 +6,11 @@ torchgeo.datamodules
 Geospatial DataModules
 ----------------------
 
+AgriFieldNet
+^^^^^^^^^^^^
+
+.. autoclass:: AgriFieldNetDataModule
+
 Chesapeake Land Cover
 ^^^^^^^^^^^^^^^^^^^^^
 

tests/conf/agrifieldnet.yaml

@@ -0,0 +1,17 @@
+model:
+  class_path: SemanticSegmentationTask
+  init_args:
+    loss: "ce"
+    model: "unet"
+    backbone: "resnet18"
+    in_channels: 12
+    num_classes: 14
+    num_filters: 1
+    ignore_index: 0
+data:
+  class_path: AgriFieldNetDataModule
+  init_args:
+    batch_size: 2
+    patch_size: 16
+  dict_kwargs:
+    paths: "tests/data/agrifieldnet"

tests/trainers/test_segmentation.py

@@ -55,6 +55,7 @@ class TestSemanticSegmentationTask:
     @pytest.mark.parametrize(
         "name",
         [
+            "agrifieldnet",
             "chabud",
             "chesapeake_cvpr_5",
             "chesapeake_cvpr_7",

torchgeo/datamodules/__init__.py

@@ -3,6 +3,7 @@
 
 """TorchGeo datamodules."""
 
+from .agrifieldnet import AgriFieldNetDataModule
 from .bigearthnet import BigEarthNetDataModule
 from .chabud import ChaBuDDataModule
 from .chesapeake import ChesapeakeCVPRDataModule
@@ -43,6 +44,7 @@
 
 __all__ = (
     # GeoDataset
+    "AgriFieldNetDataModule",
     "ChesapeakeCVPRDataModule",
     "L7IrishDataModule",
     "L8BiomeDataModule",

torchgeo/datamodules/agrifieldnet.py

@@ -0,0 +1,86 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""AgriFieldNet datamodule."""
+
+from typing import Any, Optional, Union
+
+import kornia.augmentation as K
+import torch
+from kornia.constants import DataKey, Resample
+
+from ..datasets import AgriFieldNet, random_bbox_assignment
+from ..samplers import GridGeoSampler, RandomBatchGeoSampler
+from ..samplers.utils import _to_tuple
+from ..transforms import AugmentationSequential
+from .geo import GeoDataModule
+
+
+class AgriFieldNetDataModule(GeoDataModule):
+    """LightningDataModule implementation for the AgriFieldNet dataset.
+
+    .. versionadded:: 0.6
+    """
+
+    def __init__(
+        self,
+        batch_size: int = 1,
+        patch_size: Union[int, tuple[int, int]] = 256,
+        length: Optional[int] = None,
+        num_workers: int = 0,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a new AgriFieldNetDataModule 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.AgriFieldNet`.
+        """
+        super().__init__(
+            AgriFieldNet,
+            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}
+            },
+        )
+
+    def setup(self, stage: str) -> None:
+        """Set up datasets.
+
+        Args:
+            stage: Either 'fit', 'validate', 'test', or 'predict'.
+        """
+        dataset = AgriFieldNet(**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
+            )