Add band selection to So2Sat Dataset and adopt plot method (#394)

* add band selection and adopt plot method

* np array typing and raise error doc

tests/datasets/test_so2sat.py

@@ -33,7 +33,7 @@ class TestSo2Sat:
         root = os.path.join("tests", "data", "so2sat")
         split = request.param
         transforms = nn.Identity()  # type: ignore[attr-defined]
-        return So2Sat(root, split, transforms, checksum=True)
+        return So2Sat(root=root, split=split, transforms=transforms, checksum=True)
 
     @pytest.fixture
     def mock_missing_module(
@@ -69,6 +69,10 @@ class TestSo2Sat:
         with pytest.raises(AssertionError):
             So2Sat(split="foo")
 
+    def test_invalid_bands(self) -> None:
+        with pytest.raises(ValueError):
+            So2Sat(bands=("OK", "BK"))
+
     def test_not_downloaded(self, tmp_path: Path) -> None:
         with pytest.raises(RuntimeError, match="Dataset not found or corrupted."):
             So2Sat(str(tmp_path))
@@ -83,6 +87,11 @@ class TestSo2Sat:
         dataset.plot(x)
         plt.close()
 
+    def test_plot_rgb(self, dataset: So2Sat) -> None:
+        dataset = So2Sat(root=dataset.root, bands=("B03",))
+        with pytest.raises(ValueError, match="doesn't contain some of the RGB bands"):
+            dataset.plot(dataset[0], suptitle="Single Band")
+
     def test_mock_missing_module(
         self, dataset: So2Sat, mock_missing_module: None
     ) -> None:

torchgeo/datasets/so2sat.py

@@ -4,7 +4,7 @@
 """So2Sat dataset."""
 
 import os
-from typing import Callable, Dict, Optional, cast
+from typing import Callable, Dict, Optional, Sequence, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -105,10 +105,34 @@ class So2Sat(VisionDataset):
         "Water",
     ]
 
+    all_s1_band_names = ("S1B1", "S1B2", "S1B3", "S1B4", "S1B5", "S1B6", "S1B7", "S1B8")
+    all_s2_band_names = (
+        "B02",
+        "B03",
+        "B04",
+        "B05",
+        "B06",
+        "B07",
+        "B08",
+        "B08A",
+        "B11 SWIR",
+        "B12 SWIR",
+    )
+    all_band_names = all_s1_band_names + all_s2_band_names
+
+    RGB_BANDS = ["B04", "B03", "B02"]
+
+    BAND_SETS = {
+        "all": all_band_names,
+        "s1": all_s1_band_names,
+        "s2": all_s2_band_names,
+    }
+
     def __init__(
         self,
         root: str = "data",
         split: str = "train",
+        bands: Sequence[str] = BAND_SETS["all"],
         transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None,
         checksum: bool = False,
     ) -> None:
@@ -117,6 +141,8 @@ class So2Sat(VisionDataset):
         Args:
             root: root directory where dataset can be found
             split: one of "train", "validation", or "test"
+            bands: a sequence of band names to use where the indices correspond to the
+                array index of combined Sentinel 1 and Sentinel 2
             transforms: a function/transform that takes input sample and its target as
                 entry and returns a transformed version
             checksum: if True, check the MD5 of the downloaded files (may be slow)
@@ -134,16 +160,39 @@ class So2Sat(VisionDataset):
 
         assert split in ["train", "validation", "test"]
 
+        self._validate_bands(bands)
+        self.s1_band_indices: "np.typing.NDArray[np.int_]" = np.array(
+            [
+                self.all_s1_band_names.index(b)
+                for b in bands
+                if b in self.all_s1_band_names
+            ]
+        ).astype(int)
+
+        self.s1_band_names = [self.all_s1_band_names[i] for i in self.s1_band_indices]
+
+        self.s2_band_indices: "np.typing.NDArray[np.int_]" = np.array(
+            [
+                self.all_s2_band_names.index(b)
+                for b in bands
+                if b in self.all_s2_band_names
+            ]
+        ).astype(int)
+
+        self.s2_band_names = [self.all_s2_band_names[i] for i in self.s2_band_indices]
+
+        self.bands = bands
+
         self.root = root
         self.split = split
         self.transforms = transforms
         self.checksum = checksum
 
+        self.fn = os.path.join(self.root, self.filenames[split])
+
         if not self._check_integrity():
             raise RuntimeError("Dataset not found or corrupted.")
 
-        self.fn = os.path.join(self.root, self.filenames[split])
-
         with h5py.File(self.fn, "r") as f:
             self.size = int(f["label"].shape[0])
 
@@ -160,7 +209,10 @@ class So2Sat(VisionDataset):
 
         with h5py.File(self.fn, "r") as f:
             s1 = f["sen1"][index].astype(np.float64)  # convert from <f8 to float64
+            s1 = np.take(s1, indices=self.s1_band_indices, axis=2)
             s2 = f["sen2"][index].astype(np.float64)  # convert from <f8 to float64
+            s2 = np.take(s2, indices=self.s2_band_indices, axis=2)
+
             # convert one-hot encoding to int64 then torch int
             label = torch.tensor(  # type: ignore[attr-defined]
                 f["label"][index].argmax()
@@ -196,13 +248,28 @@ class So2Sat(VisionDataset):
         Returns:
             True if dataset files are found and/or MD5s match, else False
         """
-        for split_name, filename in self.filenames.items():
-            filepath = os.path.join(self.root, filename)
-            md5 = self.md5s[split_name]
-            if not check_integrity(filepath, md5 if self.checksum else None):
-                return False
+        md5 = self.md5s[self.split]
+        if not check_integrity(self.fn, md5 if self.checksum else None):
+            return False
         return True
 
+    def _validate_bands(self, bands: Sequence[str]) -> None:
+        """Validate list of bands.
+
+        Args:
+            bands: user-provided sequence of bands to load
+
+        Raises:
+            AssertionError: if ``bands`` is not a sequence
+            ValueError: if an invalid band name is provided
+
+        .. versionadded:: 0.3
+        """
+        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 plot(
         self,
         sample: Dict[str, Tensor],
@@ -219,10 +286,23 @@ class So2Sat(VisionDataset):
         Returns:
             a matplotlib Figure with the rendered sample
 
+        Raises:
+            ValueError: if RGB bands are not found in dataset
+
         .. versionadded:: 0.2
         """
-        image = np.rollaxis(sample["image"][[10, 9, 8]].numpy(), 0, 3)
+        rgb_indices = []
+        for band in self.RGB_BANDS:
+            if band in self.s2_band_names:
+                idx = self.s2_band_names.index(band) + len(self.s1_band_names)
+                rgb_indices.append(idx)
+            else:
+                raise ValueError("Dataset doesn't contain some of the RGB bands")
+
+        image = np.take(sample["image"].numpy(), indices=rgb_indices, axis=0)
+        image = np.rollaxis(image, 0, 3)
         image = percentile_normalization(image, 0, 100)
+
         label = cast(int, sample["label"].item())
         label_class = self.classes[label]