[SYSTEMDS-3913] Adapt visual representations to image modality

src/main/python/systemds/scuro/dataloader/image_loader.py

@@ -54,7 +54,7 @@ def extract(self, file: str, index: Optional[Union[str, List[str]]] = None):
         else:
             height, width, channels = image.shape
 
-        image = image.astype(np.float32) / 255.0
+        image = image.astype(np.uint8, copy=False)
 
         self.metadata[file] = self.modality_type.create_metadata(
             width, height, channels

src/main/python/systemds/scuro/dataloader/json_loader.py

@@ -55,6 +55,6 @@ def extract(self, file: str, index: Optional[Union[str, List[str]]] = None):
                 except:
                     text = json_file[self.field]
 
-                text = " ".join(text)
+                text = " ".join(text) if isinstance(text, list) else text
                 self.data.append(text)
                 self.metadata[idx] = self.modality_type.create_metadata(len(text), text)

src/main/python/systemds/scuro/drsearch/hyperparameter_tuner.py

@@ -177,11 +177,11 @@ def visit_node(node_id):
 
         if self.maximize_metric:
             best_params, best_score = max(
-                all_results, key=lambda x: x[1].scores[self.scoring_metric]
+                all_results, key=lambda x: x[1].average_scores[self.scoring_metric]
             )
         else:
             best_params, best_score = min(
-                all_results, key=lambda x: x[1].scores[self.scoring_metric]
+                all_results, key=lambda x: x[1].average_scores[self.scoring_metric]
             )
 
         tuning_time = time.time() - start_time

src/main/python/systemds/scuro/representations/color_histogram.py

@@ -22,17 +22,19 @@
 import numpy as np
 import cv2
 
+from systemds.scuro.drsearch.operator_registry import register_representation
 from systemds.scuro.modality.type import ModalityType
 from systemds.scuro.representations.unimodal import UnimodalRepresentation
 from systemds.scuro.modality.transformed import TransformedModality
 
 
+@register_representation(ModalityType.IMAGE)
 class ColorHistogram(UnimodalRepresentation):
     def __init__(
         self,
         color_space="RGB",
-        bins=32,
-        normalize=True,
+        bins=64,
+        normalize=False,
         aggregation="mean",
         output_file=None,
     ):
@@ -48,7 +50,7 @@ def __init__(
     def _get_parameters(self):
         return {
             "color_space": ["RGB", "HSV", "GRAY"],
-            "bins": [8, 16, 32, 64, 128, 256, (8, 8, 8), (16, 16, 16)],
+            "bins": [8, 16, 32, 64, 128, 256],
             "normalize": [True, False],
             "aggregation": ["mean", "max", "concat"],
         }

src/main/python/systemds/scuro/representations/fusion.py

@@ -86,7 +86,7 @@ def transform_with_training(self, modalities: List[Modality], task):
             (len(modalities[0].data), transformed_train.shape[1])
         )
         transformed_data[task.train_indices] = transformed_train
-        transformed_data[task.val_indices] = transformed_val
+        transformed_data[task.test_indices] = transformed_other
 
         return transformed_data
 

src/main/python/systemds/scuro/representations/resnet.py

@@ -114,7 +114,7 @@ def _get_parameters(self, high_level=True):
         return parameters
 
     def transform(self, modality):
-        self.data_type = numpy_dtype_to_torch_dtype(modality.data_type)
+        self.data_type = torch.float32
         if next(self.model.parameters()).dtype != self.data_type:
             self.model = self.model.to(self.data_type)
 

src/main/python/systemds/scuro/representations/vgg.py

@@ -65,7 +65,7 @@ def _get_parameters(self):
         return parameters
 
     def transform(self, modality):
-        self.data_type = numpy_dtype_to_torch_dtype(modality.data_type)
+        self.data_type = torch.float32
         if next(self.model.parameters()).dtype != self.data_type:
             self.model = self.model.to(self.data_type)
 

src/main/python/systemds/scuro/utils/torch_dataset.py

@@ -62,7 +62,6 @@ def __getitem__(self, index) -> Dict[str, object]:
 
         if isinstance(data, np.ndarray) and data.ndim == 3:
             # image
-            data = torch.tensor(data).permute(2, 0, 1)
             output = self.tf(data).to(self.device)
         else:
             for i, d in enumerate(data):