fixed conversion of classes

model/src/models/visual_communication.py

@@ -4,19 +4,7 @@ from __future__ import annotations
 
 from torch import nn
 
-from shared.dto import (
-    AngleData,
-    ContactData,
-    DistanceData,
-    FramingData,
-    InformationValueData,
-    ModalityColorData,
-    ModalityDepthData,
-    ModalityLightingData,
-    PointOfViewData,
-    SalienceData,
-    VisualSyntaxData,
-)
+from shared.dto import ModelData
 
 from .angle import AngleTail
 from .contact import ContactTail
@@ -51,76 +39,24 @@ class VisualCommunicationModel(nn.Module):
         self.framing_tail = FramingTail()
         self.salience_tail = SalienceTail()
 
-    def forward(self, x):
+    def forward(self, x) -> ModelData:
         """Calculate model output on data."""
         # generate visual representation
-        vis_rep = self.resnet_head(x)
-        # prepare result map
-        results = {}
-        # predict visual syntax
-        results['visual_syntax'] = VisualSyntaxData.from_list(
-            self.visual_syntax_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict contact
-        results['contact'] = ContactData.from_list(
-            self.contact_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict angle
-        results['angle'] = AngleData.from_list(
-            self.angle_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict point of view
-        results['point_of_view'] = PointOfViewData.from_list(
-            self.point_of_view_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict distance
-        results['distance'] = DistanceData.from_list(
-            self.distance_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict modality lighting
-        results['modality_lighting'] = ModalityLightingData.from_list(
-            self.modality_lighting_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict modality color
-        results['modality_color'] = ModalityColorData.from_list(
-            self.modality_color_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict modality depth
-        results['modality_depth'] = ModalityDepthData.from_list(
-            self.modality_depth_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict information value
-        results['information_value'] = InformationValueData.from_list(
-            self.information_value_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict framing
-        results['framing'] = FramingData.from_list(
-            self.framing_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        # predict salience
-        results['salience'] = SalienceData.from_list(
-            self.salience_tail(
-                vis_rep,
-            ).cpu(),
-        )
-        return results
+        features = self.resnet_head(x)
+        # make predictions
+        prediction_dict = {
+            'visual_syntax': self.visual_syntax_tail(features),
+            'contact': self.contact_tail(features),
+            'angle': self.angle_tail(features),
+            'point_of_view': self.point_of_view_tail(features),
+            'distance': self.distance_tail(features),
+            'modality_lighting': self.modality_lighting_tail(features),
+            'modality_color': self.modality_color_tail(features),
+            'modality_depth': self.modality_depth_tail(features),
+            'information_value': self.information_value_tail(features),
+            'framing': self.framing_tail(features),
+            'salience': self.salience_tail(features),
+        }
+        # convert to respective classes
+        data = ModelData.from_prediction_dict(prediction_dict)
+        return data