Support for radiant-mlhub 0.5+ (#1102)

* update datasets and tests to support radiant-mlhub>0.5

* add test coverage for nasa_marine_debris corrupted cases

* style fixes

* Correct return type in test_nasa_marine_debris.py

* Update setup.cfg to limit radiant-mlhub version

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

* radiant-mlhub version updates to <0.6

* Update environment.yml to not upper bound radiant-mlhub

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

---------

Co-authored-by: Adam J. Stewart <ajstewart426@gmail.com>

.github/dependabot.yml

@@ -9,9 +9,6 @@ updates:
     schedule:
       interval: "daily"
     ignore:
-      # radiant-mlhub 0.5+ changed download behavior:
-      # https://github.com/radiantearth/radiant-mlhub/pull/104
-      - dependency-name: "radiant-mlhub"
       # setuptools releases new versions almost daily
       - dependency-name: "setuptools"
         update-types: ["version-update:semver-patch"]

environment.yml

@@ -36,6 +36,7 @@ dependencies:
     - pytest-cov>=2.4
     - pytorch-lightning>=1.5.1
     - git+https://github.com/pytorch/pytorch_sphinx_theme
+    - pyupgrade>=2.4
     - radiant-mlhub>=0.2.1
     - rtree>=1
     - scikit-image>=0.18

setup.cfg

@@ -84,13 +84,11 @@ datasets =
     pycocotools>=2.0.1,<3
     # pyvista 0.25.2 required for wheels
     pyvista>=0.25.2,<0.39
-    # radiant-mlhub 0.2.1+ required for api_key bugfix:
-    # https://github.com/radiantearth/radiant-mlhub/pull/48
     # radiant-mlhub 0.5+ changed download behavior:
     # https://github.com/radiantearth/radiant-mlhub/pull/104
-    radiant-mlhub>=0.2.1,<0.5
-    # rarfile 3+ required for correct Rar file detection
-    rarfile>=3,<5
+    radiant-mlhub>=0.2.1,<0.6
+    # rarfile 4+ required for wheels
+    rarfile>=4,<5
     # scikit-image 0.18+ required for numpy 1.17+ compatibility
     # https://github.com/scikit-image/scikit-image/issues/3655
     scikit-image>=0.18,<0.20

tests/datasets/test_benin_cashews.py

@@ -16,15 +16,15 @@ from torch.utils.data import ConcatDataset
 from torchgeo.datasets import BeninSmallHolderCashews
 
 
-class Dataset:
+class Collection:
     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(dataset_id: str, **kwargs: str) -> Dataset:
-    return Dataset()
+def fetch(dataset_id: str, **kwargs: str) -> Collection:
+    return Collection()
 
 
 class TestBeninSmallHolderCashews:
@@ -33,7 +33,7 @@ class TestBeninSmallHolderCashews:
         self, monkeypatch: MonkeyPatch, tmp_path: Path
     ) -> BeninSmallHolderCashews:
         radiant_mlhub = pytest.importorskip("radiant_mlhub", minversion="0.2.1")
-        monkeypatch.setattr(radiant_mlhub.Dataset, "fetch", fetch)
+        monkeypatch.setattr(radiant_mlhub.Collection, "fetch", fetch)
         source_md5 = "255efff0f03bc6322470949a09bc76db"
         labels_md5 = "ed2195d93ca6822d48eb02bc3e81c127"
         monkeypatch.setitem(BeninSmallHolderCashews.image_meta, "md5", source_md5)

tests/datasets/test_cloud_cover.py

@@ -15,7 +15,7 @@ from _pytest.monkeypatch import MonkeyPatch
 from torchgeo.datasets import CloudCoverDetection
 
 
-class Dataset:
+class Collection:
     def download(self, output_dir: str, **kwargs: str) -> None:
         glob_path = os.path.join(
             "tests", "data", "ref_cloud_cover_detection_challenge_v1", "*.tar.gz"
@@ -24,15 +24,15 @@ class Dataset:
             shutil.copy(tarball, output_dir)
 
 
-def fetch(dataset_id: str, **kwargs: str) -> Dataset:
-    return Dataset()
+def fetch(dataset_id: str, **kwargs: str) -> Collection:
+    return Collection()
 
 
 class TestCloudCoverDetection:
     @pytest.fixture
     def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> CloudCoverDetection:
         radiant_mlhub = pytest.importorskip("radiant_mlhub", minversion="0.2.1")
-        monkeypatch.setattr(radiant_mlhub.Dataset, "fetch", fetch)
+        monkeypatch.setattr(radiant_mlhub.Collection, "fetch", fetch)
 
         test_image_meta = {
             "filename": "ref_cloud_cover_detection_challenge_v1_test_source.tar.gz",

tests/datasets/test_cv4a_kenya_crop_type.py

@@ -16,7 +16,7 @@ from torch.utils.data import ConcatDataset
 from torchgeo.datasets import CV4AKenyaCropType
 
 
-class Dataset:
+class Collection:
     def download(self, output_dir: str, **kwargs: str) -> None:
         glob_path = os.path.join(
             "tests", "data", "ref_african_crops_kenya_02", "*.tar.gz"
@@ -25,15 +25,15 @@ class Dataset:
             shutil.copy(tarball, output_dir)
 
 
-def fetch(dataset_id: str, **kwargs: str) -> Dataset:
-    return Dataset()
+def fetch(dataset_id: str, **kwargs: str) -> Collection:
+    return Collection()
 
 
 class TestCV4AKenyaCropType:
     @pytest.fixture
     def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> CV4AKenyaCropType:
         radiant_mlhub = pytest.importorskip("radiant_mlhub", minversion="0.2.1")
-        monkeypatch.setattr(radiant_mlhub.Dataset, "fetch", fetch)
+        monkeypatch.setattr(radiant_mlhub.Collection, "fetch", fetch)
         source_md5 = "7f4dcb3f33743dddd73f453176308bfb"
         labels_md5 = "95fc59f1d94a85ec00931d4d1280bec9"
         monkeypatch.setitem(CV4AKenyaCropType.image_meta, "md5", source_md5)

tests/datasets/test_cyclone.py

@@ -17,14 +17,14 @@ from torch.utils.data import ConcatDataset
 from torchgeo.datasets import TropicalCyclone
 
 
-class Dataset:
+class Collection:
     def download(self, output_dir: str, **kwargs: str) -> None:
         for tarball in glob.iglob(os.path.join("tests", "data", "cyclone", "*.tar.gz")):
             shutil.copy(tarball, output_dir)
 
 
-def fetch(collection_id: str, **kwargs: str) -> Dataset:
-    return Dataset()
+def fetch(collection_id: str, **kwargs: str) -> Collection:
+    return Collection()
 
 
 class TestTropicalCyclone:
@@ -33,7 +33,7 @@ class TestTropicalCyclone:
         self, monkeypatch: MonkeyPatch, tmp_path: Path, request: SubRequest
     ) -> TropicalCyclone:
         radiant_mlhub = pytest.importorskip("radiant_mlhub", minversion="0.2.1")
-        monkeypatch.setattr(radiant_mlhub.Dataset, "fetch", fetch)
+        monkeypatch.setattr(radiant_mlhub.Collection, "fetch", fetch)
         md5s = {
             "train": {
                 "source": "2b818e0a0873728dabf52c7054a0ce4c",

tests/datasets/test_nasa_marine_debris.py

@@ -15,23 +15,35 @@ from _pytest.monkeypatch import MonkeyPatch
 from torchgeo.datasets import NASAMarineDebris
 
 
-class Dataset:
+class Collection:
     def download(self, output_dir: str, **kwargs: str) -> None:
         glob_path = os.path.join("tests", "data", "nasa_marine_debris", "*.tar.gz")
         for tarball in glob.iglob(glob_path):
             shutil.copy(tarball, output_dir)
 
 
-def fetch(dataset_id: str, **kwargs: str) -> Dataset:
-    return Dataset()
+def fetch(collection_id: str, **kwargs: str) -> Collection:
+    return Collection()
+
+
+class Collection_corrupted:
+    def download(self, output_dir: str, **kwargs: str) -> None:
+        filenames = NASAMarineDebris.filenames
+        for filename in filenames:
+            with open(os.path.join(output_dir, filename), "w") as f:
+                f.write("bad")
+
+
+def fetch_corrupted(collection_id: str, **kwargs: str) -> Collection_corrupted:
+    return Collection_corrupted()
 
 
 class TestNASAMarineDebris:
     @pytest.fixture()
     def dataset(self, monkeypatch: MonkeyPatch, tmp_path: Path) -> NASAMarineDebris:
         radiant_mlhub = pytest.importorskip("radiant_mlhub", minversion="0.2.1")
-        monkeypatch.setattr(radiant_mlhub.Dataset, "fetch", fetch)
-        md5s = ["fe8698d1e68b3f24f0b86b04419a797d", "d8084f5a72778349e07ac90ec1e1d990"]
+        monkeypatch.setattr(radiant_mlhub.Collection, "fetch", fetch)
+        md5s = ["6f4f0d2313323950e45bf3fc0c09b5de", "540cf1cf4fd2c13b609d0355abe955d7"]
         monkeypatch.setattr(NASAMarineDebris, "md5s", md5s)
         root = str(tmp_path)
         transforms = nn.Identity()
@@ -58,9 +70,25 @@ class TestNASAMarineDebris:
     ) -> None:
         shutil.rmtree(dataset.root)
         os.makedirs(str(tmp_path), exist_ok=True)
-        Dataset().download(output_dir=str(tmp_path))
+        Collection().download(output_dir=str(tmp_path))
         NASAMarineDebris(root=str(tmp_path), download=False)
 
+    def test_corrupted_previously_downloaded(self, tmp_path: Path) -> None:
+        filenames = NASAMarineDebris.filenames
+        for filename in filenames:
+            with open(os.path.join(tmp_path, filename), "w") as f:
+                f.write("bad")
+        with pytest.raises(RuntimeError, match="Dataset checksum mismatch."):
+            NASAMarineDebris(root=str(tmp_path), download=False, checksum=True)
+
+    def test_corrupted_new_download(
+        self, tmp_path: Path, monkeypatch: MonkeyPatch
+    ) -> None:
+        with pytest.raises(RuntimeError, match="Dataset checksum mismatch."):
+            radiant_mlhub = pytest.importorskip("radiant_mlhub", minversion="0.2.1")
+            monkeypatch.setattr(radiant_mlhub.Collection, "fetch", fetch_corrupted)
+            NASAMarineDebris(root=str(tmp_path), download=True, checksum=True)
+
     def test_not_downloaded(self, tmp_path: Path) -> None:
         err = "Dataset not found in `root` directory and `download=False`, "
         "either specify a different `root` directory or use `download=True` "

torchgeo/datasets/benin_cashews.py

@@ -17,7 +17,7 @@ from rasterio.crs import CRS
 from torch import Tensor
 
 from .geo import NonGeoDataset
-from .utils import check_integrity, download_radiant_mlhub_dataset, extract_archive
+from .utils import check_integrity, download_radiant_mlhub_collection, extract_archive
 
 
 # TODO: read geospatial information from stac.json files
@@ -56,6 +56,7 @@ class BeninSmallHolderCashews(NonGeoDataset):
     """
 
     dataset_id = "ts_cashew_benin"
+    collection_ids = ["ts_cashew_benin_source", "ts_cashew_benin_labels"]
     image_meta = {
         "filename": "ts_cashew_benin_source.tar.gz",
         "md5": "957272c86e518a925a4e0d90dab4f92d",
@@ -416,7 +417,8 @@ class BeninSmallHolderCashews(NonGeoDataset):
             print("Files already downloaded and verified")
             return
 
-        download_radiant_mlhub_dataset(self.dataset_id, self.root, api_key)
+        for collection_id in self.collection_ids:
+            download_radiant_mlhub_collection(collection_id, self.root, api_key)
 
         image_archive_path = os.path.join(self.root, self.image_meta["filename"])
         target_archive_path = os.path.join(self.root, self.target_meta["filename"])

torchgeo/datasets/cloud_cover.py

@@ -14,7 +14,7 @@ import torch
 from torch import Tensor
 
 from .geo import NonGeoDataset
-from .utils import check_integrity, download_radiant_mlhub_dataset, extract_archive
+from .utils import check_integrity, download_radiant_mlhub_collection, extract_archive
 
 
 # TODO: read geospatial information from stac.json files
@@ -54,7 +54,12 @@ class CloudCoverDetection(NonGeoDataset):
     .. versionadded:: 0.4
     """
 
-    dataset_id = "ref_cloud_cover_detection_challenge_v1"
+    collection_ids = [
+        "ref_cloud_cover_detection_challenge_v1_train_source",
+        "ref_cloud_cover_detection_challenge_v1_train_labels",
+        "ref_cloud_cover_detection_challenge_v1_test_source",
+        "ref_cloud_cover_detection_challenge_v1_test_labels",
+    ]
 
     image_meta = {
         "train": {
@@ -332,7 +337,8 @@ class CloudCoverDetection(NonGeoDataset):
             print("Files already downloaded and verified")
             return
 
-        download_radiant_mlhub_dataset(self.dataset_id, self.root, api_key)
+        for collection_id in self.collection_ids:
+            download_radiant_mlhub_collection(collection_id, self.root, api_key)
 
         image_archive_path = os.path.join(
             self.root, self.image_meta[self.split]["filename"]

torchgeo/datasets/cv4a_kenya_crop_type.py

@@ -15,7 +15,7 @@ from PIL import Image
 from torch import Tensor
 
 from .geo import NonGeoDataset
-from .utils import check_integrity, download_radiant_mlhub_dataset, extract_archive
+from .utils import check_integrity, download_radiant_mlhub_collection, extract_archive
 
 
 # TODO: read geospatial information from stac.json files
@@ -56,7 +56,10 @@ class CV4AKenyaCropType(NonGeoDataset):
          imagery and labels from the Radiant Earth MLHub
     """
 
-    dataset_id = "ref_african_crops_kenya_02"
+    collection_ids = [
+        "ref_african_crops_kenya_02_labels",
+        "ref_african_crops_kenya_02_source",
+    ]
     image_meta = {
         "filename": "ref_african_crops_kenya_02_source.tar.gz",
         "md5": "9c2004782f6dc83abb1bf45ba4d0da46",
@@ -394,7 +397,8 @@ class CV4AKenyaCropType(NonGeoDataset):
             print("Files already downloaded and verified")
             return
 
-        download_radiant_mlhub_dataset(self.dataset_id, self.root, api_key)
+        for collection_id in self.collection_ids:
+            download_radiant_mlhub_collection(collection_id, self.root, api_key)
 
         image_archive_path = os.path.join(self.root, self.image_meta["filename"])
         target_archive_path = os.path.join(self.root, self.target_meta["filename"])

torchgeo/datasets/cyclone.py

@@ -15,7 +15,7 @@ from PIL import Image
 from torch import Tensor
 
 from .geo import NonGeoDataset
-from .utils import check_integrity, download_radiant_mlhub_dataset, extract_archive
+from .utils import check_integrity, download_radiant_mlhub_collection, extract_archive
 
 
 class TropicalCyclone(NonGeoDataset):
@@ -45,6 +45,12 @@ class TropicalCyclone(NonGeoDataset):
     """
 
     collection_id = "nasa_tropical_storm_competition"
+    collection_ids = [
+        "nasa_tropical_storm_competition_train_source",
+        "nasa_tropical_storm_competition_test_source",
+        "nasa_tropical_storm_competition_train_labels",
+        "nasa_tropical_storm_competition_test_labels",
+    ]
     md5s = {
         "train": {
             "source": "97e913667a398704ea8d28196d91dad6",
@@ -207,7 +213,8 @@ class TropicalCyclone(NonGeoDataset):
             print("Files already downloaded and verified")
             return
 
-        download_radiant_mlhub_dataset(self.collection_id, self.root, api_key)
+        for collection_id in self.collection_ids:
+            download_radiant_mlhub_collection(collection_id, self.root, api_key)
 
         for split, resources in self.md5s.items():
             for resource_type in resources:

torchgeo/datasets/nasa_marine_debris.py

@@ -14,7 +14,7 @@ from torch import Tensor
 from torchvision.utils import draw_bounding_boxes
 
 from .geo import NonGeoDataset
-from .utils import download_radiant_mlhub_dataset, extract_archive
+from .utils import check_integrity, download_radiant_mlhub_collection, extract_archive
 
 
 class NASAMarineDebris(NonGeoDataset):
@@ -51,7 +51,7 @@ class NASAMarineDebris(NonGeoDataset):
     .. versionadded:: 0.2
     """
 
-    dataset_id = "nasa_marine_debris"
+    collection_ids = ["nasa_marine_debris_source", "nasa_marine_debris_labels"]
     directories = ["nasa_marine_debris_source", "nasa_marine_debris_labels"]
     filenames = ["nasa_marine_debris_source.tar.gz", "nasa_marine_debris_labels.tar.gz"]
     md5s = ["fe8698d1e68b3f24f0b86b04419a797d", "d8084f5a72778349e07ac90ec1e1d990"]
@@ -189,9 +189,11 @@ class NASAMarineDebris(NonGeoDataset):
 
         # Check if zip file already exists (if so then extract)
         exists = []
-        for filename in self.filenames:
+        for filename, md5 in zip(self.filenames, self.md5s):
             filepath = os.path.join(self.root, filename)
             if os.path.exists(filepath):
+                if self.checksum and not check_integrity(filepath, md5):
+                    raise RuntimeError("Dataset checksum mismatch.")
                 exists.append(True)
                 extract_archive(filepath)
             else:
@@ -208,11 +210,13 @@ class NASAMarineDebris(NonGeoDataset):
                 "to automatically download the dataset."
             )
 
-        # TODO: need a checksum check in here post downloading
         # Download and extract the dataset
-        download_radiant_mlhub_dataset(self.dataset_id, self.root, self.api_key)
-        for filename in self.filenames:
+        for collection_id in self.collection_ids:
+            download_radiant_mlhub_collection(collection_id, self.root, self.api_key)
+        for filename, md5 in zip(self.filenames, self.md5s):
             filepath = os.path.join(self.root, filename)
+            if self.checksum and not check_integrity(filepath, md5):
+                raise RuntimeError("Dataset checksum mismatch.")
             extract_archive(filepath)
 
     def plot(