fix issue at inference without label

dataset/aoi.py

@@ -330,7 +330,7 @@ def from_dict(cls,
         if not {'tif', 'gpkg', 'split'}.issubset(set(aoi_dict.keys())):
             raise ValueError(f"Input data should minimally contain the following keys: \n"
                              f"'tif', 'gpkg', 'split'.")
-        if not aoi_dict['gpkg']:
+        if aoi_dict['gpkg'] is None:
             logging.warning(f"No ground truth data found for {aoi_dict['tif']}.\n"
                             f"Only imagery will be processed from now on")
         if "aoi_id" not in aoi_dict.keys() or not aoi_dict['aoi_id']:

inference_segmentation.py

@@ -325,7 +325,7 @@ def main(params: Union[DictConfig, dict]) -> None:
     )
 
     # Dataset params
-    bands_requested = get_key_def('bands', params['dataset'], default=("red", "blue", "green"), expected_type=Sequence)
+    bands_requested = get_key_def('bands', params['dataset'], default=[1, 2, 3], expected_type=Sequence)
     classes_dict = get_key_def('classes_dict', params['dataset'], expected_type=DictConfig)
     num_classes = len(classes_dict)
     num_classes = num_classes + 1 if num_classes > 1 else num_classes  # multiclass account for background

tests/dataset/test_aoi.py

@@ -10,7 +10,7 @@
 from shapely.geometry import box
 from torchgeo.datasets.utils import extract_archive
 
-from dataset.aoi import AOI
+from dataset.aoi import AOI, aois_from_csv
 from utils.utils import read_csv
 
 
@@ -92,14 +92,21 @@ def test_stac_url_input(self):
             os.remove("data/SpaceNet_AOI_2_Las_Vegas-056155973080_01_P001-WV03-R.tif")
 
     def test_missing_label(self):
-        """Tests error when missing label file"""
+        """Tests error when provided label file is missing"""
         extract_archive(src="tests/data/spacenet.zip")
         data = read_csv("tests/tiling/tiling_segmentation_binary-multiband_ci.csv")
         for row in data:
             row['gpkg'] = "missing_file.gpkg"
             with pytest.raises(AttributeError):
                 aoi = AOI(raster=row['tif'], label=row['gpkg'], split=row['split'])
                 aoi.close_raster()
+
+    def test_no_label(self):
+        """Test when no label are provided. Should pass for inference. """
+        extract_archive(src="tests/data/new_brunswick_aerial.zip")
+        csv_path = "tests/inference/inference_segmentation_multiclass_no_label.csv"
+        aois = aois_from_csv(csv_path=csv_path, bands_requested=[1,2,3])
+        assert aois[0].label is None
 
     def test_parse_input_raster(self) -> None:
         """Tests parsing for three accepted patterns to reference input raster data with band selection"""

tests/inference/inference_segmentation_multiclass_no_label.csv

@@ -0,0 +1 @@
+tests/data/new_brunswick_aerial/23322E759967N_clipped_1m_inference.tif,,inference

utils/utils.py

@@ -320,13 +320,17 @@ def read_csv(csv_file_name: str) -> Dict:
             row_lengths_set.update([len(row)])
             if not len(row_lengths_set) == 1:
                 raise ValueError(f"Rows in csv should be of same length. Got rows with length: {row_lengths_set}")
+            row = [str(i) or None for i in row]  # replace empty strings to None.
             row.extend([None] * (4 - len(row)))  # fill row with None values to obtain row of length == 5
+
             row[0] = to_absolute_path(row[0]) if not is_url(row[0]) else row[0] # Convert relative paths to absolute with hydra's util to_absolute_path()
-            row[1] = to_absolute_path(row[1]) if not is_url(row[1]) else row[1]
-
+            try:
+                row[1] = str(to_absolute_path(row[1]) if not is_url(row[1]) else row[1])
+            except TypeError:
+                row[1] = None
             # save all values
             list_values.append(
-                {'tif': str(row[0]), 'gpkg': str(row[1]), 'split': row[2], 'aoi_id': row[3]})
+                {'tif': str(row[0]), 'gpkg': row[1], 'split': row[2], 'aoi_id': row[3]})
     try:
         # Try sorting according to dataset name (i.e. group "train", "val" and "test" rows together)
         list_values = sorted(list_values, key=lambda k: k['split'])