Undoing a massive error

This reverts commit bc81082.

Author: Seilmast

main.py

@@ -107,7 +107,8 @@ def main():
             optimizer.step()
             optimizer.zero_grad(set_to_none=True)
 
-            metrics(y, logits)
+            preds = th.argmax(logits, dim=1)
+            metrics(y, preds)
 
             break
         print(metrics.accumulate())
@@ -133,7 +134,8 @@ def main():
             optimizer.zero_grad(set_to_none=True)
             trainingloss.append(loss.item())
 
-            metrics(y, logits)
+            preds = th.argmax(logits, dim=1)
+            metrics(y, preds)
 
         wandb.log(metrics.accumulate(str_prefix="Train "))
         metrics.reset()
@@ -148,7 +150,8 @@ def main():
                 loss = criterion(logits, y)
                 evalloss.append(loss.item())
 
-                metrics(y, logits)
+                preds = th.argmax(logits, dim=1)
+                metrics(y, preds)
 
         wandb.log(metrics.accumulate(str_prefix="Evaluation "))
         metrics.reset()

test.py

@@ -1,7 +1,4 @@
-import numpy as np 
-from scipy.stats import entropy
+from torch.utils.data import Dataset
+from torchvision.datasets import SVHN 
 
-a = np.array([0,0,0,1,1,5,4,3]).reshape(8,1)
-b = np.random.rand(8,10)
-
-print(entropy(a, qk=b))
+SVHN('data/', download=True)

utils/metrics/EntropyPred.py

@@ -1,32 +1,12 @@
-import numpy as np
 import torch.nn as nn
-from scipy.stats import entropy
 
 
 class EntropyPrediction(nn.Module):
-    def __init__(self, averages: str = 'average'):
-        """
-        Initializes the EntropyPrediction module.
-        Args:
-            averages (str): Specifies the method of aggregation for entropy values. 
-                            Must be either 'average' or 'sum'.
-        Raises:
-            AssertionError: If the averages parameter is not 'average' or 'sum'.
-        """
+    def __init__(self):
         super().__init__()
-        
-        assert averages == 'average' or averages == 'sum'
-        self.averages = averages
-        self.stored_entropy_values = []
-        
-    def __call__(self, y_true, y_pred_logits):
-        """
-        Computes the entropy between true labels and predicted logits, storing the results.
-        Args:
-            y_true: The true labels.
-            y_pred_logits: The predicted logits.
-        Side Effects:
-            Appends the computed entropy values to the stored_entropy_values list.
-        """
-        entropy_values = entropy(y_true, qk=y_pred_logits)
-        return np.mean(entropy_values)
+
+    def __call__(self, y_true, y_false_logits):
+        return
+
+    def __reset__(self):
+        pass