Add Inria datamodule

conf/inria.yaml

@@ -0,0 +1,30 @@
+program:
+  overwrite: True
+
+
+trainer:
+  gpus: 1
+  min_epochs: 5
+  max_epochs: 100
+  benchmark: True
+  log_every_n_steps: 2
+
+experiment:
+  task: "inria"
+  name: "inria_test"
+  module:
+    loss: "ce"
+    segmentation_model: "unet"
+    encoder_name: "resnet18"
+    encoder_weights: "imagenet"
+    learning_rate: 1e-3
+    learning_rate_schedule_patience: 6
+    in_channels: 3
+    num_classes: 2
+    ignore_zeros: True  # class 0 not used for scoring
+  datamodule:
+      root_dir: "data/inria"
+      batch_size: 2
+      num_workers: 32
+      patch_size: 512
+      num_patches_per_tile: 4

docs/api/datamodules.rst

@@ -44,6 +44,11 @@ FAIR1M (Fine-grAined object recognItion in high-Resolution imagery)
 
 .. autoclass:: FAIR1MDataModule
 
+Inria Aerial Image Labeling
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: InriaAerialImageLabelingDataModule
+
 LandCover.ai (Land Cover from Aerial Imagery)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

docs/conf.py

@@ -104,6 +104,7 @@
 
 # sphinx.ext.intersphinx
 intersphinx_mapping = {
+    "kornia": ("https://kornia.readthedocs.io/en/stable/", None),
     "matplotlib": ("https://matplotlib.org/stable/", None),
     "numpy": ("https://numpy.org/doc/stable/", None),
     "python": ("https://docs.python.org/3", None),

setup.cfg

@@ -31,8 +31,8 @@ install_requires =
     einops
     # fiona 1.5+ required for fiona.transform module
     fiona>=1.5
-    # kornia 0.5.11+ required for kornia.augmentation.PadTo
-    kornia>=0.5.11
+    # kornia 0.6.4+ required for kornia.contrib.compute_padding
+    kornia>=0.6.4
     matplotlib
     numpy
     # omegaconf 2.1+ required for to_object method

tests/conf/inria.yaml

@@ -0,0 +1,20 @@
+experiment:
+  task: "inria"
+  module:
+    loss: "ce"
+    segmentation_model: "unet"
+    encoder_name: "resnet18"
+    encoder_weights: "imagenet"
+    learning_rate: 1e-3
+    learning_rate_schedule_patience: 6
+    in_channels: 3
+    num_classes: 2
+    ignore_zeros: True  # class 0 not used for scoring
+  datamodule:
+      root_dir: "tests/data/inria"
+      batch_size: 1
+      num_workers: 0
+      val_split_pct: 0.2
+      test_split_pct: 0.2
+      patch_size: 2
+      num_patches_per_tile: 2

tests/data/inria/data.py

@@ -43,7 +43,7 @@ def generate_test_data(root: str, n_samples: int = 2) -> str:
         str: md5 hash of created archive
     """
     dtype = np.dtype("uint8")
-    size = (64, 64)
+    size = (8, 8)
 
     driver = "GTiff"
     transform = Affine(0.3, 0.0, 616500.0, 0.0, -0.3, 3345000.0)
@@ -83,9 +83,9 @@ def generate_test_data(root: str, n_samples: int = 2) -> str:
         archive_path, "zip", root_dir=root, base_dir="AerialImageDataset"
     )
     shutil.rmtree(folder_path)
-    return calculate_md5(archive_path + ".zip")
+    return calculate_md5(f"{archive_path}.zip")
 
 
 if __name__ == "__main__":
-    md5_hash = generate_test_data(os.getcwd(), 2)
+    md5_hash = generate_test_data(os.getcwd(), 5)
     print(md5_hash)

tests/datamodules/test_inria.py

@@ -0,0 +1,70 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+import os
+
+import pytest
+from _pytest.fixtures import SubRequest
+
+from torchgeo.datamodules import InriaAerialImageLabelingDataModule
+
+TEST_DATA_DIR = os.path.join("tests", "data", "inria")
+
+
+class TestInriaAerialImageLabelingDataModule:
+    @pytest.fixture(
+        params=zip([0.2, 0.2, 0.0], [0.2, 0.0, 0.0], ["test", "test", "test"])
+    )
+    def datamodule(self, request: SubRequest) -> InriaAerialImageLabelingDataModule:
+        val_split_pct, test_split_pct, predict_on = request.param
+        patch_size = 2  # (2,2)
+        num_patches_per_tile = 2
+        root = TEST_DATA_DIR
+        batch_size = 1
+        num_workers = 0
+        dm = InriaAerialImageLabelingDataModule(
+            root,
+            batch_size,
+            num_workers,
+            val_split_pct,
+            test_split_pct,
+            patch_size,
+            num_patches_per_tile,
+            predict_on=predict_on,
+        )
+        dm.prepare_data()
+        dm.setup()
+        return dm
+
+    def test_train_dataloader(
+        self, datamodule: InriaAerialImageLabelingDataModule
+    ) -> None:
+        sample = next(iter(datamodule.train_dataloader()))
+        assert sample["image"].shape[-2:] == sample["mask"].shape[-2:] == (2, 2)
+        assert sample["image"].shape[0] == sample["mask"].shape[0] == 2
+        assert sample["image"].shape[1] == 3
+        assert sample["mask"].shape[1] == 1
+
+    def test_val_dataloader(
+        self, datamodule: InriaAerialImageLabelingDataModule
+    ) -> None:
+        sample = next(iter(datamodule.val_dataloader()))
+        if datamodule.val_split_pct > 0.0:
+            assert sample["image"].shape[-2:] == sample["mask"].shape[-2:] == (2, 2)
+            assert sample["image"].shape[0] == sample["mask"].shape[0] == 2
+
+    def test_test_dataloader(
+        self, datamodule: InriaAerialImageLabelingDataModule
+    ) -> None:
+        sample = next(iter(datamodule.test_dataloader()))
+        if datamodule.test_split_pct > 0.0:
+            assert sample["image"].shape[-2:] == sample["mask"].shape[-2:] == (2, 2)
+            assert sample["image"].shape[0] == sample["mask"].shape[0] == 2
+
+    def test_predict_dataloader(
+        self, datamodule: InriaAerialImageLabelingDataModule
+    ) -> None:
+        sample = next(iter(datamodule.predict_dataloader()))
+        assert len(sample["image"].shape) == 5
+        assert sample["image"].shape[-2:] == (2, 2)
+        assert sample["image"].shape[2] == 3

tests/datasets/test_inria.py

@@ -21,7 +21,7 @@ def dataset(
     ) -> InriaAerialImageLabeling:
 
         root = os.path.join("tests", "data", "inria")
-        test_md5 = "f23caf363389ef59de55fad11197c161"
+        test_md5 = "478688944e4797c097d9387fd0b3f038"
         monkeypatch.setattr(InriaAerialImageLabeling, "md5", test_md5)
         transforms = nn.Identity()  # type: ignore[no-untyped-call]
         return InriaAerialImageLabeling(
@@ -39,7 +39,7 @@ def test_getitem(self, dataset: InriaAerialImageLabeling) -> None:
         assert x["image"].ndim == 3
 
     def test_len(self, dataset: InriaAerialImageLabeling) -> None:
-        assert len(dataset) == 2
+        assert len(dataset) == 5
 
     def test_already_downloaded(self, dataset: InriaAerialImageLabeling) -> None:
         InriaAerialImageLabeling(root=dataset.root)

tests/trainers/test_segmentation.py

@@ -14,6 +14,7 @@
 from torchgeo.datamodules import (
     ChesapeakeCVPRDataModule,
     ETCI2021DataModule,
+    InriaAerialImageLabelingDataModule,
     LandCoverAIDataModule,
     NAIPChesapeakeDataModule,
     OSCDDataModule,
@@ -34,6 +35,7 @@ class TestSemanticSegmentationTask:
         [
             ("chesapeake_cvpr_5", ChesapeakeCVPRDataModule),
             ("etci2021", ETCI2021DataModule),
+            ("inria", InriaAerialImageLabelingDataModule),
             ("landcoverai", LandCoverAIDataModule),
             ("naipchesapeake", NAIPChesapeakeDataModule),
             ("oscd_all", OSCDDataModule),

torchgeo/datamodules/__init__.py

@@ -10,6 +10,7 @@
 from .etci2021 import ETCI2021DataModule
 from .eurosat import EuroSATDataModule
 from .fair1m import FAIR1MDataModule
+from .inria import InriaAerialImageLabelingDataModule
 from .landcoverai import LandCoverAIDataModule
 from .loveda import LoveDADataModule
 from .naip import NAIPChesapeakeDataModule
@@ -33,6 +34,7 @@
     "ETCI2021DataModule",
     "EuroSATDataModule",
     "FAIR1MDataModule",
+    "InriaAerialImageLabelingDataModule",
     "LandCoverAIDataModule",
     "LoveDADataModule",
     "NASAMarineDebrisDataModule",