Add unit tests

tests/datasets/test_benin_cashews.py

@@ -0,0 +1,93 @@
+import glob
+import os
+from pathlib import Path
+import shutil
+from typing import Generator
+
+import pytest
+from pytest import MonkeyPatch
+import torch
+
+from torchgeo.datasets import BeninSmallHolderCashews, ZipDataset
+from torchgeo.transforms import Identity
+
+
+class Dataset:
+    def download(self, output_dir: str, **kwargs: str) -> None:
+        glob_path = os.path.join("tests", "data", "ts_cashew_benin", "*.tar.gz")
+        for tarball in glob.iglob(glob_path):
+            shutil.copy(tarball, output_dir)
+
+
+def fetch(collection_id: str, **kwargs: str) -> Dataset:
+    return Dataset()
+
+
+class TestBeninSmallHolderCashews:
+    @pytest.fixture
+    def dataset(
+        self,
+        monkeypatch: Generator[MonkeyPatch, None, None],
+        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
+        )
+        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",)
+        )
+        (tmp_path / "ts_cashew_benin").mkdir()
+        root = str(tmp_path)
+        transforms = Identity()
+        return BeninSmallHolderCashews(
+            root,
+            transforms=transforms,
+            download=True,
+            api_key="",
+            checksum=True,
+            verbose=True,
+        )
+
+    def test_getitem(self, dataset: BeninSmallHolderCashews) -> None:
+        x = dataset[0]
+        assert isinstance(x, dict)
+        assert isinstance(x["image"], torch.Tensor)
+        assert isinstance(x["mask"], torch.Tensor)
+        assert isinstance(x["x"], torch.Tensor)
+        assert isinstance(x["y"], torch.Tensor)
+
+    def test_len(self, dataset: BeninSmallHolderCashews) -> None:
+        assert len(dataset) == 72
+
+    def test_add(self, dataset: BeninSmallHolderCashews) -> None:
+        ds = dataset + dataset
+        assert isinstance(ds, ZipDataset)
+        assert len(ds) == 72
+
+    def test_already_downloaded(self, dataset: BeninSmallHolderCashews) -> None:
+        BeninSmallHolderCashews(root=dataset.root, download=True, api_key="")
+
+    def test_missing_api_key(self) -> None:
+        match = "You must pass an MLHub API key if download=True."
+        with pytest.raises(RuntimeError, match=match):
+            BeninSmallHolderCashews(download=True)
+
+    def test_not_downloaded(self, tmp_path: Path) -> None:
+        with pytest.raises(RuntimeError, match="Dataset not found or corrupted."):
+            BeninSmallHolderCashews(str(tmp_path))
+
+    def test_invalid_bands(self) -> None:
+        with pytest.raises(AssertionError):
+            BeninSmallHolderCashews(bands=["B01", "B02"])  # type: ignore[arg-type]
+
+        with pytest.raises(ValueError, match="is an invalid band name."):
+            BeninSmallHolderCashews(bands=("foo", "bar"))

torchgeo/datasets/benin_cashews.py

@@ -432,9 +432,6 @@ class BeninSmallHolderCashews(GeoDataset):
             output_dir=os.path.join(self.root, self.dataset_id), api_key=api_key
         )
 
-        if not self._check_integrity():
-            raise RuntimeError("Dataset files not found or corrupted.")
-
         image_archive_path = os.path.join(
             self.root, self.dataset_id, self.image_meta["filename"]
         )