Support custom palette (open-mmlab#157)

* Fix split

* Update tests/test_data/test_dataset.py

Co-authored-by: Jerry Jiarui XU <[email protected]>

Co-authored-by: Jerry Jiarui XU <[email protected]>

Author: daavoo

mmseg/datasets/custom.py

@@ -60,6 +60,10 @@ class CustomDataset(Dataset):
             Default: False
         classes (str | Sequence[str], optional): Specify classes to load.
             If is None, ``cls.CLASSES`` will be used. Default: None.
+        palette (Sequence[Sequence[int]]] | np.ndarray | None):
+            The palette of segmentation map. If None is given, and
+            self.PALETTE is None, random palette will be generated.
+            Default: None
     """
 
     CLASSES = None
@@ -77,7 +81,8 @@ def __init__(self,
                  test_mode=False,
                  ignore_index=255,
                  reduce_zero_label=False,
-                 classes=None):
+                 classes=None,
+                 palette=None):
         self.pipeline = Compose(pipeline)
         self.img_dir = img_dir
         self.img_suffix = img_suffix
@@ -89,7 +94,8 @@ def __init__(self,
         self.ignore_index = ignore_index
         self.reduce_zero_label = reduce_zero_label
         self.label_map = None
-        self.CLASSES, self.PALETTE = self.get_classes_and_palette(classes)
+        self.CLASSES, self.PALETTE = self.get_classes_and_palette(
+            classes, palette)
 
         # join paths if data_root is specified
         if self.data_root is not None:
@@ -241,7 +247,7 @@ def get_gt_seg_maps(self):
 
         return gt_seg_maps
 
-    def get_classes_and_palette(self, classes=None):
+    def get_classes_and_palette(self, classes=None, palette=None):
         """Get class names of current dataset.
 
         Args:
@@ -250,6 +256,9 @@ def get_classes_and_palette(self, classes=None):
                 string, take it as a file name. The file contains the name of
                 classes where each line contains one class name. If classes is
                 a tuple or list, override the CLASSES defined by the dataset.
+            palette (Sequence[Sequence[int]]] | np.ndarray | None):
+                The palette of segmentation map. If None is given, random
+                palette will be generated. Default: None
         """
         if classes is None:
             self.custom_classes = False
@@ -278,11 +287,11 @@ def get_classes_and_palette(self, classes=None):
                 else:
                     self.label_map[i] = classes.index(c)
 
-        palette = self.get_palette_for_custom_classes()
+        palette = self.get_palette_for_custom_classes(class_names, palette)
 
         return class_names, palette
 
-    def get_palette_for_custom_classes(self):
+    def get_palette_for_custom_classes(self, class_names, palette=None):
 
         if self.label_map is not None:
             # return subset of palette
@@ -293,8 +302,11 @@ def get_palette_for_custom_classes(self):
                     palette.append(self.PALETTE[old_id])
             palette = type(self.PALETTE)(palette)
 
-        else:
-            palette = self.PALETTE
+        elif palette is None:
+            if self.PALETTE is None:
+                palette = np.random.randint(0, 255, size=(len(class_names), 3))
+            else:
+                palette = self.PALETTE
 
         return palette
 

tests/test_data/test_dataset.py

@@ -231,3 +231,33 @@ def test_custom_classes_override_default(dataset, classes):
         test_mode=True)
 
     assert custom_dataset.CLASSES == original_classes
+
+
+@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
+@patch('mmseg.datasets.CustomDataset.__getitem__',
+       MagicMock(side_effect=lambda idx: idx))
+def test_custom_dataset_random_palette_is_generated():
+    dataset = CustomDataset(
+        pipeline=[],
+        img_dir=MagicMock(),
+        split=MagicMock(),
+        classes=('bus', 'car'),
+        test_mode=True)
+    assert len(dataset.PALETTE) == 2
+    for class_color in dataset.PALETTE:
+        assert len(class_color) == 3
+        assert all(x >= 0 and x <= 255 for x in class_color)
+
+
+@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)
+@patch('mmseg.datasets.CustomDataset.__getitem__',
+       MagicMock(side_effect=lambda idx: idx))
+def test_custom_dataset_custom_palette():
+    dataset = CustomDataset(
+        pipeline=[],
+        img_dir=MagicMock(),
+        split=MagicMock(),
+        classes=('bus', 'car'),
+        palette=[[100, 100, 100], [200, 200, 200]],
+        test_mode=True)
+    assert tuple(dataset.PALETTE) == tuple([[100, 100, 100], [200, 200, 200]])