Check to make sure bands is a tuple, fixed some typing issues

torchgeo/datasets/cv4a_kenya_crop_type.py

@@ -97,7 +97,7 @@ class CV4AKenyaCropType(VisionDataset):
         root: str = "data",
         chip_size: int = 256,
         stride: int = 128,
-        bands: Optional[Tuple[str]] = None,
+        bands: Optional[Tuple[str, ...]] = None,
         transform: Optional[Callable[[Image.Image], Any]] = None,
         target_transform: Optional[Callable[[Image.Image], Any]] = None,
         transforms: Optional[Callable[[Image.Image, Image.Image], Any]] = None,
@@ -153,7 +153,7 @@ class CV4AKenyaCropType(VisionDataset):
                 ]:
                     self.chips_metadata.append((tile_index, y, x))
 
-    def __getitem__(self, index: int) -> Dict:
+    def __getitem__(self, index: int) -> Dict[str, Any]:
         """Return an index within the dataset.
 
         Parameters:
@@ -223,12 +223,13 @@ class CV4AKenyaCropType(VisionDataset):
 
         return (labels, field_ids)
 
-    def _validate_bands(self, bands: Optional[Tuple[str]]) -> Tuple[str]:
+    def _validate_bands(self, bands: Optional[Tuple[str, ...]]) -> Tuple[str, ...]:
         """Routine for validating a list of bands / filling in a default value"""
 
         if bands is None:
             return self.band_names
         else:
+            assert isinstance(bands, tuple), "The list of bands must be a tuple"
             for band in bands:
                 if band not in self.band_names:
                     raise ValueError(f"'{band}' is an invalid band name.")
@@ -236,7 +237,7 @@ class CV4AKenyaCropType(VisionDataset):
 
     @lru_cache
     def _load_all_image_tiles(
-        self, tile_name_: str, bands: Optional[Tuple[str]] = None
+        self, tile_name_: str, bands: Optional[Tuple[str, ...]] = None
     ) -> np.ndarray:
         """Load all the imagery (across time) for a single _tile_. Optionally allows
         for subsetting of the bands that are loaded.
@@ -263,7 +264,7 @@ class CV4AKenyaCropType(VisionDataset):
 
     @lru_cache
     def _load_single_image_tile(
-        self, tile_name_: str, date_: str, bands: Optional[Tuple[str]] = None
+        self, tile_name_: str, date_: str, bands: Optional[Tuple[str, ...]] = None
     ) -> np.ndarray:
         """Loads the imagery for a single tile for a single date. Optionally allows
         for subsetting of the bands that are loaded."""