Moved loss function comparison back to logit vs onehot labels

detector/modeler.py

@@ -153,17 +153,17 @@ def train(
                 # Forward pass
                 outputs = self.model(b_input_ids, attention_mask=b_input_mask, labels=b_labels)
                 logits = outputs.logits
+
+                # sigmoid_output = torch.sigmoid(logits[:, 1])
 
-                sigmoid_output = torch.sigmoid(logits[:, 1])
-
-                # Thresholding to convert probabilities to binary values (0 or 1)
-                binary_output = (sigmoid_output > 0.5)
+                # # Thresholding to convert probabilities to binary values (0 or 1)
+                # binary_output = (sigmoid_output > 0.5)
 
                 # # Convert labels to one-hot encoding
-                # b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
+                b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
 
                 # Calculate the loss using the weighted loss function
-                loss = loss_function(binary_output, b_labels.float())
+                loss = loss_function(logits, b_labels_one_hot)
                 total_train_loss += loss.item()
 
                 # Backward pass
@@ -197,16 +197,16 @@ def train(
                     # loss = outputs[0]
                     logits = outputs.logits
 
-                sigmoid_output = torch.sigmoid(logits[:, 1])
+                # sigmoid_output = torch.sigmoid(logits[:, 1])
 
-                # Thresholding to convert probabilities to binary values (0 or 1)
-                binary_output = (sigmoid_output > 0.5)
+                # # Thresholding to convert probabilities to binary values (0 or 1)
+                # binary_output = (sigmoid_output > 0.5)
 
                 # # Convert labels to one-hot encoding
-                # b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
+                b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
 
                 # Calculate the loss using the weighted loss function
-                loss = loss_function(binary_output, b_labels.float())
+                loss = loss_function(logits, b_labels_one_hot)
                 total_eval_loss += loss.item()
                 logits = logits.detach().cpu().numpy()
                 label_ids = b_labels.detach().cpu().numpy()
@@ -460,17 +460,17 @@ def train(
                 # Forward pass
                 outputs = self.model(b_input_ids, attention_mask=b_input_mask, labels=b_labels)
                 logits = outputs.logits
+
+                # sigmoid_output = torch.sigmoid(logits[:, 1])
 
-                sigmoid_output = torch.sigmoid(logits[:, 1])
-
-                # Thresholding to convert probabilities to binary values (0 or 1)
-                binary_output = (sigmoid_output > 0.5)
+                # # Thresholding to convert probabilities to binary values (0 or 1)
+                # binary_output = (sigmoid_output > 0.5)
 
                 # # Convert labels to one-hot encoding
-                # b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
+                b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
 
                 # Calculate the loss using the weighted loss function
-                loss = loss_function(binary_output, b_labels.float())
+                loss = loss_function(logits, b_labels_one_hot)
 
                 total_train_loss += loss.item()
 
@@ -504,16 +504,16 @@ def train(
                     outputs = self.model(b_input_ids, attention_mask=b_input_mask, labels=b_labels)
                     logits = outputs.logits
 
-                sigmoid_output = torch.sigmoid(logits[:, 1])
+                # sigmoid_output = torch.sigmoid(logits[:, 1])
 
-                # Thresholding to convert probabilities to binary values (0 or 1)
-                binary_output = (sigmoid_output > 0.5)
+                # # Thresholding to convert probabilities to binary values (0 or 1)
+                # binary_output = (sigmoid_output > 0.5)
 
                 # # Convert labels to one-hot encoding
-                # b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
+                b_labels_one_hot = F.one_hot(b_labels, num_classes=2).float()
 
                 # Calculate the loss using the weighted loss function
-                loss = loss_function(binary_output, b_labels.float())
+                loss = loss_function(logits, b_labels_one_hot)
                 total_eval_loss += loss.item()
                 logits = logits.detach().cpu().numpy()
                 label_ids = b_labels.detach().cpu().numpy()