paper preperations and notebooks, optuna callbacks

modules/model_parts.py

@@ -190,10 +190,6 @@ class BaseCNNEncoder(ShapeMixin, nn.Module):
         kernels = kernels if not isinstance(kernels, int) else [kernels] * len(filters)
         assert len(kernels) == len(filters), 'Length of "Filters" and "Kernels" has to be same.'
 
-        # Optional Padding for odd image-sizes
-        # Obsolet, cdan be done by autopadding module on incoming tensors
-        # in_shape = [tensor+1 if tensor % 2 != 0 and idx else tensor for idx, tensor in enumerate(in_shape)]
-
         # Parameters
         self.lat_dim = lat_dim
         self.in_shape = in_shape

modules/util.py

@@ -14,6 +14,8 @@ from sklearn.metrics import ConfusionMatrixDisplay
 
 # Utility - Modules
 ###################
+from ..metrics.binary_class_classifictaion import BinaryScores
+from ..metrics.multi_class_classification import MultiClassScores
 from ..utils.model_io import ModelParameters
 from ..utils.tools import add_argparse_args
 
@@ -133,9 +135,6 @@ try:
         def size(self):
             return self.shape
 
-        def additional_scores(self, outputs):
-            raise NotImplementedError
-
         def save_to_disk(self, model_path):
             Path(model_path, exist_ok=True).mkdir(parents=True, exist_ok=True)
             if not (model_path / 'model_class.obj').exists():
@@ -174,6 +173,12 @@ try:
             weight_initializer = WeightInit(in_place_init_function=self._weight_init)
             self.apply(weight_initializer)
 
+        def additional_scores(self, outputs):
+            if self.params.n_classes > 2:
+                return MultiClassScores(self)(outputs)
+            else:
+                return BinaryScores(self)(outputs)
+
     module_types = (LightningBaseModule, nn.Module,)
 
 except ImportError: