Merge branch 'main' into sweden

.pre-commit-config.yaml

@@ -24,9 +24,9 @@ repos:
             additional_dependencies: ["toml"]
 
     -   repo: https://github.com/pre-commit/mirrors-mypy
-        rev: v0.931
+        rev: v0.940
         hooks:
         -   id: mypy
             args: [--strict, --ignore-missing-imports, --show-error-codes]
-            additional_dependencies: [torch>=1.7, torchmetrics>=0.7, pytorch-lightning>=1.3, pytest>=6, omegaconf>=2.1, kornia>=0.6, numpy>=1.22.0]
+            additional_dependencies: [torch>=1.11, torchmetrics>=0.7, pytorch-lightning>=1.3, pytest>=6, omegaconf>=2.1, kornia>=0.6, numpy>=1.22.0]
             exclude: (build|data|dist|logo|logs|output)/

.readthedocs.yaml

@@ -4,6 +4,12 @@
 # Required
 version: 2
 
+# Set the version of Python
+build:
+  os: ubuntu-20.04
+  tools:
+    python: "3.9"
+
 # Configuration of the Python environment to be used
 python:
    install:

benchmark.py

@@ -208,17 +208,15 @@ def main(args: argparse.Namespace) -> None:
     # Benchmark model
     model = resnet34()
     # Change number of input channels to match Landsat
-    model.conv1 = nn.Conv2d(  # type: ignore[attr-defined]
+    model.conv1 = nn.Conv2d(
         len(bands), 64, kernel_size=7, stride=2, padding=3, bias=False
     )
 
-    criterion = nn.CrossEntropyLoss()  # type: ignore[attr-defined]
+    criterion = nn.CrossEntropyLoss()
     params = model.parameters()
     optimizer = optim.SGD(params, lr=0.0001)
 
-    device = torch.device(  # type: ignore[attr-defined]
-        "cuda" if torch.cuda.is_available() else "cpu", args.device
-    )
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu", args.device)
     model = model.to(device)
 
     tic = time.time()
@@ -227,7 +225,7 @@ def main(args: argparse.Namespace) -> None:
         num_total_patches += args.batch_size
         x = torch.rand(args.batch_size, len(bands), args.patch_size, args.patch_size)
         # y = torch.randint(0, 256, (args.batch_size, args.patch_size, args.patch_size))
-        y = torch.randint(0, 256, (args.batch_size,))  # type: ignore[attr-defined]
+        y = torch.randint(0, 256, (args.batch_size,))
         x = x.to(device)
         y = y.to(device)
 

evaluate.py

@@ -12,7 +12,7 @@
 
 import pytorch_lightning as pl
 import torch
-from torchmetrics import Accuracy, JaccardIndex, Metric, MetricCollection
+from torchmetrics import Accuracy, JaccardIndex, MetricCollection
 
 from torchgeo.trainers import ClassificationTask, SemanticSegmentationTask
 from train import TASK_TO_MODULES_MAPPING
@@ -85,19 +85,20 @@ def set_up_parser() -> argparse.ArgumentParser:
 def run_eval_loop(
     model: pl.LightningModule,
     dataloader: Any,
-    device: torch.device,  # type: ignore[name-defined]
-    metrics: Metric,
+    device: torch.device,
+    metrics: MetricCollection,
 ) -> Any:
     """Runs a standard test loop over a dataloader and records metrics.
 
     Args:
         model: the model used for inference
         dataloader: the dataloader to get samples from
         device: the device to put data on
-        metrics: a torchmetrics compatible Metric to score the output from the model
+        metrics: a torchmetrics compatible metric collection to score the output
+            from the model
 
     Returns:
-        the result of ``metric.compute()``
+        the result of ``metrics.compute()``
     """
     for batch in dataloader:
         x = batch["image"].to(device)
@@ -158,7 +159,7 @@ def main(args: argparse.Namespace) -> None:
             "loss": model.hparams["loss"],
         }
     elif issubclass(TASK, SemanticSegmentationTask):
-        val_row: Dict[str, Union[str, float]] = {  # type: ignore[no-redef]
+        val_row = {
             "split": "val",
             "segmentation_model": model.hparams["segmentation_model"],
             "encoder_name": model.hparams["encoder_name"],
@@ -167,7 +168,7 @@ def main(args: argparse.Namespace) -> None:
             "loss": model.hparams["loss"],
         }
 
-        test_row: Dict[str, Union[str, float]] = {  # type: ignore[no-redef]
+        test_row = {
             "split": "test",
             "segmentation_model": model.hparams["segmentation_model"],
             "encoder_name": model.hparams["encoder_name"],
@@ -179,7 +180,7 @@ def main(args: argparse.Namespace) -> None:
         raise ValueError(f"{TASK} is not supported")
 
     # Compute metrics
-    device = torch.device("cuda:%d" % (args.gpu))  # type: ignore[attr-defined]
+    device = torch.device("cuda:%d" % (args.gpu))
     model = model.to(device)
 
     if args.task == "etci2021":  # Custom metric setup for testing ETCI2021

tests/datamodules/test_chesapeake.py

@@ -25,17 +25,10 @@ def datamodule(self) -> ChesapeakeCVPRDataModule:
 
     def test_nodata_check(self, datamodule: ChesapeakeCVPRDataModule) -> None:
         nodata_check = datamodule.nodata_check(4)
-        sample = {
-            "image": torch.ones(1, 2, 2),  # type: ignore[attr-defined]
-            "mask": torch.ones(2, 2),  # type: ignore[attr-defined]
-        }
+        sample = {"image": torch.ones(1, 2, 2), "mask": torch.ones(2, 2)}
         out = nodata_check(sample)
-        assert torch.equal(  # type: ignore[attr-defined]
-            out["image"], torch.zeros(1, 4, 4)  # type: ignore[attr-defined]
-        )
-        assert torch.equal(  # type: ignore[attr-defined]
-            out["mask"], torch.zeros(4, 4)  # type: ignore[attr-defined]
-        )
+        assert torch.equal(out["image"], torch.zeros(1, 4, 4))
+        assert torch.equal(out["mask"], torch.zeros(4, 4))
 
     def test_invalid_param_config(self) -> None:
         with pytest.raises(ValueError, match="The pre-generated prior labels"):

tests/datamodules/test_utils.py

@@ -9,8 +9,8 @@
 
 def test_dataset_split() -> None:
     num_samples = 24
-    x = torch.ones(num_samples, 5)  # type: ignore[attr-defined]
-    y = torch.randint(low=0, high=2, size=(num_samples,))  # type: ignore[attr-defined]
+    x = torch.ones(num_samples, 5)
+    y = torch.randint(low=0, high=2, size=(num_samples,))
     ds = TensorDataset(x, y)
 
     # Test only train/val set split

tests/datasets/test_advance.py

@@ -5,7 +5,7 @@
 import os
 import shutil
 from pathlib import Path
-from typing import Any, Generator
+from typing import Any
 
 import matplotlib.pyplot as plt
 import pytest
@@ -23,38 +23,30 @@ def download_url(url: str, root: str, *args: str) -> None:
 
 class TestADVANCE:
     @pytest.fixture
-    def dataset(
-        self, monkeypatch: Generator[MonkeyPatch, None, None], tmp_path: Path
-    ) -> ADVANCE:
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            torchgeo.datasets.utils, "download_url", download_url
-        )
+    def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> ADVANCE:
+        monkeypatch.setattr(torchgeo.datasets.utils, "download_url", download_url)
         data_dir = os.path.join("tests", "data", "advance")
         urls = [
             os.path.join(data_dir, "ADVANCE_vision.zip"),
             os.path.join(data_dir, "ADVANCE_sound.zip"),
         ]
         md5s = ["43acacecebecd17a82bc2c1e719fd7e4", "039b7baa47879a8a4e32b9dd8287f6ad"]
-        monkeypatch.setattr(ADVANCE, "urls", urls)  # type: ignore[attr-defined]
-        monkeypatch.setattr(ADVANCE, "md5s", md5s)  # type: ignore[attr-defined]
+        monkeypatch.setattr(ADVANCE, "urls", urls)
+        monkeypatch.setattr(ADVANCE, "md5s", md5s)
         root = str(tmp_path)
-        transforms = nn.Identity()  # type: ignore[attr-defined]
+        transforms = nn.Identity()  # type: ignore[no-untyped-call]
         return ADVANCE(root, transforms, download=True, checksum=True)
 
     @pytest.fixture
-    def mock_missing_module(
-        self, monkeypatch: Generator[MonkeyPatch, None, None]
-    ) -> None:
+    def mock_missing_module(self, monkeypatch: MonkeyPatch) -> None:
         import_orig = builtins.__import__
 
         def mocked_import(name: str, *args: Any, **kwargs: Any) -> Any:
             if name == "scipy.io":
                 raise ImportError()
             return import_orig(name, *args, **kwargs)
 
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            builtins, "__import__", mocked_import
-        )
+        monkeypatch.setattr(builtins, "__import__", mocked_import)
 
     def test_getitem(self, dataset: ADVANCE) -> None:
         pytest.importorskip("scipy", minversion="0.9.0")

tests/datasets/test_agb_live_woody_density.py

@@ -4,7 +4,6 @@
 import os
 import shutil
 from pathlib import Path
-from typing import Generator
 
 import matplotlib.pyplot as plt
 import pytest
@@ -28,11 +27,11 @@ def download_url(url: str, root: str, *args: str, **kwargs: str) -> None:
 class TestAbovegroundLiveWoodyBiomassDensity:
     @pytest.fixture
     def dataset(
-        self, monkeypatch: Generator[MonkeyPatch, None, None], tmp_path: Path
+        self, monkeypatch: MonkeyPatch, tmp_path: Path
     ) -> AbovegroundLiveWoodyBiomassDensity:
 
-        transforms = nn.Identity()  # type: ignore[attr-defined]
-        monkeypatch.setattr(  # type: ignore[attr-defined]
+        transforms = nn.Identity()  # type: ignore[no-untyped-call]
+        monkeypatch.setattr(
             torchgeo.datasets.agb_live_woody_density, "download_url", download_url
         )
         url = os.path.join(
@@ -41,9 +40,7 @@ def dataset(
             "agb_live_woody_density",
             "Aboveground_Live_Woody_Biomass_Density.geojson",
         )
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            AbovegroundLiveWoodyBiomassDensity, "url", url
-        )
+        monkeypatch.setattr(AbovegroundLiveWoodyBiomassDensity, "url", url)
 
         root = str(tmp_path)
         return AbovegroundLiveWoodyBiomassDensity(

tests/datasets/test_astergdem.py

@@ -20,7 +20,7 @@ def dataset(self, tmp_path: Path) -> AsterGDEM:
         zipfile = os.path.join("tests", "data", "astergdem", "astergdem.zip")
         shutil.unpack_archive(zipfile, tmp_path, "zip")
         root = str(tmp_path)
-        transforms = nn.Identity()  # type: ignore[attr-defined]
+        transforms = nn.Identity()  # type: ignore[no-untyped-call]
         return AsterGDEM(root, transforms=transforms)
 
     def test_datasetmissing(self, tmp_path: Path) -> None:

tests/datasets/test_benin_cashews.py

@@ -5,7 +5,6 @@
 import os
 import shutil
 from pathlib import Path
-from typing import Generator
 
 import matplotlib.pyplot as plt
 import pytest
@@ -31,25 +30,17 @@ def fetch(dataset_id: str, **kwargs: str) -> Dataset:
 class TestBeninSmallHolderCashews:
     @pytest.fixture
     def dataset(
-        self, monkeypatch: Generator[MonkeyPatch, None, None], tmp_path: Path
+        self, monkeypatch: MonkeyPatch, tmp_path: Path
     ) -> BeninSmallHolderCashews:
         radiant_mlhub = pytest.importorskip("radiant_mlhub", minversion="0.2.1")
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            radiant_mlhub.Dataset, "fetch", fetch
-        )
+        monkeypatch.setattr(radiant_mlhub.Dataset, "fetch", fetch)
         source_md5 = "255efff0f03bc6322470949a09bc76db"
         labels_md5 = "ed2195d93ca6822d48eb02bc3e81c127"
-        monkeypatch.setitem(  # type: ignore[attr-defined]
-            BeninSmallHolderCashews.image_meta, "md5", source_md5
-        )
-        monkeypatch.setitem(  # type: ignore[attr-defined]
-            BeninSmallHolderCashews.target_meta, "md5", labels_md5
-        )
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            BeninSmallHolderCashews, "dates", ("2019_11_05",)
-        )
+        monkeypatch.setitem(BeninSmallHolderCashews.image_meta, "md5", source_md5)
+        monkeypatch.setitem(BeninSmallHolderCashews.target_meta, "md5", labels_md5)
+        monkeypatch.setattr(BeninSmallHolderCashews, "dates", ("2019_11_05",))
         root = str(tmp_path)
-        transforms = nn.Identity()  # type: ignore[attr-defined]
+        transforms = nn.Identity()  # type: ignore[no-untyped-call]
         bands = BeninSmallHolderCashews.ALL_BANDS
 
         return BeninSmallHolderCashews(

tests/datasets/test_bigearthnet.py

@@ -4,7 +4,6 @@
 import os
 import shutil
 from pathlib import Path
-from typing import Generator
 
 import matplotlib.pyplot as plt
 import pytest
@@ -26,14 +25,9 @@ class TestBigEarthNet:
         params=zip(["all", "s1", "s2"], [43, 19, 19], ["train", "val", "test"])
     )
     def dataset(
-        self,
-        monkeypatch: Generator[MonkeyPatch, None, None],
-        tmp_path: Path,
-        request: SubRequest,
+        self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
     ) -> BigEarthNet:
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            torchgeo.datasets.bigearthnet, "download_url", download_url
-        )
+        monkeypatch.setattr(torchgeo.datasets.bigearthnet, "download_url", download_url)
         data_dir = os.path.join("tests", "data", "bigearthnet")
         metadata = {
             "s1": {
@@ -66,15 +60,11 @@ def dataset(
                 "md5": "851a6bdda484d47f60e121352dcb1bf5",
             },
         }
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            BigEarthNet, "metadata", metadata
-        )
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            BigEarthNet, "splits_metadata", splits_metadata
-        )
+        monkeypatch.setattr(BigEarthNet, "metadata", metadata)
+        monkeypatch.setattr(BigEarthNet, "splits_metadata", splits_metadata)
         bands, num_classes, split = request.param
         root = str(tmp_path)
-        transforms = nn.Identity()  # type: ignore[attr-defined]
+        transforms = nn.Identity()  # type: ignore[no-untyped-call]
         return BigEarthNet(
             root, split, bands, num_classes, transforms, download=True, checksum=True
         )
@@ -85,8 +75,8 @@ def test_getitem(self, dataset: BigEarthNet) -> None:
         assert isinstance(x["image"], torch.Tensor)
         assert isinstance(x["label"], torch.Tensor)
         assert x["label"].shape == (dataset.num_classes,)
-        assert x["image"].dtype == torch.int32  # type: ignore[attr-defined]
-        assert x["label"].dtype == torch.int64  # type: ignore[attr-defined]
+        assert x["image"].dtype == torch.int32
+        assert x["label"].dtype == torch.int64
 
         if dataset.bands == "all":
             assert x["image"].shape == (14, 120, 120)

tests/datasets/test_cbf.py

@@ -4,7 +4,6 @@
 import os
 import shutil
 from pathlib import Path
-from typing import Generator
 
 import matplotlib.pyplot as plt
 import pytest
@@ -29,11 +28,9 @@ def download_url(url: str, root: str, *args: str) -> None:
 class TestCanadianBuildingFootprints:
     @pytest.fixture
     def dataset(
-        self, monkeypatch: Generator[MonkeyPatch, None, None], tmp_path: Path
+        self, monkeypatch: MonkeyPatch, tmp_path: Path
     ) -> CanadianBuildingFootprints:
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            torchgeo.datasets.utils, "download_url", download_url
-        )
+        monkeypatch.setattr(torchgeo.datasets.utils, "download_url", download_url)
         md5s = [
             "8a4a0a57367f67c69608d1452e30df13",
             "1829f4054a9a81bb23871ca797a3895c",
@@ -49,18 +46,12 @@ def dataset(
             "067664d066c4152fb96a5c129cbabadf",
             "474bc084bc41b124aa4919e7a37a9648",
         ]
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            CanadianBuildingFootprints, "md5s", md5s
-        )
+        monkeypatch.setattr(CanadianBuildingFootprints, "md5s", md5s)
         url = os.path.join("tests", "data", "cbf") + os.sep
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            CanadianBuildingFootprints, "url", url
-        )
-        monkeypatch.setattr(  # type: ignore[attr-defined]
-            plt, "show", lambda *args: None
-        )
+        monkeypatch.setattr(CanadianBuildingFootprints, "url", url)
+        monkeypatch.setattr(plt, "show", lambda *args: None)
         root = str(tmp_path)
-        transforms = nn.Identity()  # type: ignore[attr-defined]
+        transforms = nn.Identity()  # type: ignore[no-untyped-call]
         return CanadianBuildingFootprints(
             root, res=0.1, transforms=transforms, download=True, checksum=True
         )