fix bigearthnet and so2sat

terratorch/datasets/m_bigearthnet.py

@@ -14,7 +14,6 @@
 
 
 class MBigEarthNonGeo(NonGeoDataset):
-
     all_band_names = (
         "COASTAL_AEROSOL",
         "BLUE",
@@ -28,77 +27,79 @@ class MBigEarthNonGeo(NonGeoDataset):
         "WATER_VAPOR",
         "SWIR_1",
         "SWIR_2",
-        "CLOUD_PROBABILITY"
+        "CLOUD_PROBABILITY",
     )
 
     rgb_bands = ("RED", "GREEN", "BLUE")
 
     BAND_SETS = {"all": all_band_names, "rgb": rgb_bands}
 
-    def __init__(self, data_root: str, bands: Sequence[str] = BAND_SETS["all"], transform: A.Compose | None = None, split="train", **kwargs: any) -> None:
+    def __init__(
+        self,
+        data_root: str,
+        bands: Sequence[str] = BAND_SETS["all"],
+        transform: A.Compose | None = None,
+        split="train",
+        partition="default",
+    ) -> None:
         super().__init__()
         if split not in ["train", "test", "val"]:
             msg = "Split must be one of train, test, val."
             raise Exception(msg)
         if split == "val":
             split = "valid"
-            
+
         self.transform = transform if transform else lambda **batch: to_tensor(batch)
         self._validate_bands(bands)
         self.bands = bands
-        self.band_indices = np.array(
-            [self.all_band_names.index(b) for b in bands if b in self.all_band_names]
-        )
+        self.band_indices = np.array([self.all_band_names.index(b) for b in bands if b in self.all_band_names])
         self.split = split
         data_root = Path(data_root)
         self.data_directory = data_root / "m-bigearthnet"
-        
+
         label_map_file = self.data_directory / "label_stats.json"
-        with open(label_map_file, 'r') as file:
+        with open(label_map_file, "r") as file:
             self.label_map = json.load(file)
 
-        partition_file = self.data_directory / "default_partition.json"
-        with open(partition_file, 'r') as file:
+        partition_file = self.data_directory / f"{partition}_partition.json"
+        with open(partition_file, "r") as file:
             partitions = json.load(file)
 
         if split not in partitions:
             raise ValueError(f"Split '{split}' not found.")
 
         self.image_files = [self.data_directory / (filename + ".hdf5") for filename in partitions[split]]
 
-
     def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
         file_path = self.image_files[index]
-        image_id = file_path.stem 
+        image_id = file_path.stem
 
-        with h5py.File(file_path, 'r') as h5file:
+        with h5py.File(file_path, "r") as h5file:
             keys = sorted(h5file.keys())
-            keys = np.array([key for key in keys if key != 'label'])[self.band_indices]
+            keys = np.array([key for key in keys if key != "label"])[self.band_indices]
             bands = [np.array(h5file[key]) for key in keys]
-            
+
             image = np.stack(bands, axis=-1)
-        
+
         labels_vector = self.label_map[image_id]
         labels_tensor = torch.tensor(labels_vector, dtype=torch.float)
 
-        output =  {
-            "image": image
-        }
+        output = {"image": image}
 
         output = self.transform(**output)
 
         output["label"] = labels_tensor
         return output
-    
+
     def _validate_bands(self, bands: Sequence[str]) -> None:
         assert isinstance(bands, Sequence), "'bands' must be a sequence"
         for band in bands:
             if band not in self.all_band_names:
                 raise ValueError(f"'{band}' is an invalid band name.")
-            
+
     def __len__(self):
         return len(self.image_files)
-        
+
     def plot(self, arg, suptitle: str | None = None) -> None:
         if isinstance(arg, int):
             sample = self.__getitem__(arg)
@@ -120,25 +121,20 @@ def plot(self, arg, suptitle: str | None = None) -> None:
         rgb_image = image[rgb_indices, :, :]
         rgb_image = np.transpose(rgb_image, (1, 2, 0))
         rgb_image = (rgb_image - np.min(rgb_image)) / (np.max(rgb_image) - np.min(rgb_image))
-    
+
         active_labels = [i for i, label in enumerate(labels) if label == 1]
 
-        self._plot_sample(
-            image=rgb_image,
-            label_indices=active_labels,
-            suptitle=suptitle
-        )
-
+        self._plot_sample(image=rgb_image, label_indices=active_labels, suptitle=suptitle)
+
     @staticmethod
     def _plot_sample(image, label_indices, suptitle=None) -> None:
         fig, ax = plt.subplots(figsize=(6, 6))
         ax.imshow(image)
-        ax.axis('off')
+        ax.axis("off")
 
-        title = f'Active Labels: {label_indices}'
+        title = f"Active Labels: {label_indices}"
         if suptitle:
-            title = f'{suptitle} - {title}'
+            title = f"{suptitle} - {title}"
         ax.set_title(title)
-
-        return fig
 
+        return fig

terratorch/datasets/m_so2sat.py

@@ -86,10 +86,12 @@ def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
             attr_dict = pickle.loads(ast.literal_eval(h5file.attrs["pickle"]))
             class_index = attr_dict["label"]
 
-        output = {"image": image.astype(np.float32), "label": class_index}
+        output = {"image": image.astype(np.float32)}
 
         output = self.transform(**output)
 
+        output["label"] = class_index
+
         return output
 
     def _validate_bands(self, bands: Sequence[str]) -> None: