Improved data generation to cover evaluation cases as well

Signed-off-by: Jim Martens <github@2martens.de>

src/twomartens/masterthesis/data.py

@@ -236,7 +236,8 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
                        coco_path: str, predictor_sizes: np.ndarray,
                        batch_size: int,
                        resized_shape: Sequence[int],
-                       training: bool) -> Tuple[callable, int]:
+                       training: bool,
+                       evaluation: bool) -> Tuple[callable, int]:
     """
     Loads the SceneNet RGB-D data and returns a data set.
     
@@ -248,6 +249,7 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
         batch_size: size of every batch
         resized_shape: shape of input images to SSD
         training: True if training data is desired
+        evaluation: True if evaluation-ready data is desired
 
     Returns:
         scenenet data set generator
@@ -305,14 +307,15 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
         
     returns = {'processed_images', 'encoded_labels'}
     
-    if not training:
-        returns.update({'inverse_transform'})
-    
-    generator = data_generator.generate(
-        batch_size=batch_size,
-        shuffle=shuffle,
-        transformations=transformations,
-        label_encoder=ssd_input_encoder.SSDInputEncoder(
+    if not training and evaluation:
+        returns = {
+            'processed_images',
+            'filenames',
+            'inverse_transform',
+            'original_labels'}
+        label_encoder = None
+    else:
+        label_encoder = ssd_input_encoder.SSDInputEncoder(
             img_height=resized_shape[0],
             img_width=resized_shape[1],
             n_classes=len(cats_to_classes),  # 80
@@ -324,7 +327,13 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
                                      [1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
                                      [1.0, 2.0, 0.5],
                                      [1.0, 2.0, 0.5]]
-        ),
+        )
+    
+    generator = data_generator.generate(
+        batch_size=batch_size,
+        shuffle=shuffle,
+        transformations=transformations,
+        label_encoder=label_encoder,
         returns=returns,
         keep_images_without_gt=False
     )