Fix for issue #27: Fix Accuracy Hack to work with sequence preds.

lab1/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)
 
 

lab2/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)
 
 

lab3/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)
 
 

lab4/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)
 
 

lab5/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)
 
 

lab7/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)
 
 

lab8/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)
 
 

lab9/text_recognizer/lit_models/base.py

@@ -21,7 +21,7 @@ def update(self, preds: torch.Tensor, target: torch.Tensor) -> None:
         Normalized preds are not necessary for accuracy computation as we just care about argmax().
         """
         if preds.min() < 0 or preds.max() > 1:
-            preds = torch.nn.functional.softmax(preds, dim=-1)
+            preds = torch.nn.functional.softmax(preds, dim=1)
         super().update(preds=preds, target=target)