Fixed bug: replaced bce_loss_with_logits with bce_loss (#7096)

* Fixed bug: replaced bce_loss_with_logits with bec_loss

* Fixed bug: removed sigmoid activation from forward pass

* switched names for scores and logits

Co-authored-by: Alexey Misev <[email protected]>
Co-authored-by: Alexey Misev <[email protected]>
Co-authored-by: Adrian Wälchli <[email protected]>

Author: 4 people

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -225,7 +225,7 @@ def forward(self, x):
         # 2. Classifier (returns logits):
         x = self.fc(x)
 
-        return torch.sigmoid(x)
+        return x
 
     def loss(self, logits, labels):
         return self.loss_func(input=logits, target=labels)
@@ -234,27 +234,29 @@ def training_step(self, batch, batch_idx):
         # 1. Forward pass:
         x, y = batch
         y_logits = self.forward(x)
+        y_scores = torch.sigmoid(y_logits)
         y_true = y.view((-1, 1)).type_as(x)
 
         # 2. Compute loss
         train_loss = self.loss(y_logits, y_true)
 
         # 3. Compute accuracy:
-        self.log("train_acc", self.train_acc(y_logits, y_true.int()), prog_bar=True)
+        self.log("train_acc", self.train_acc(y_scores, y_true.int()), prog_bar=True)
 
         return train_loss
 
     def validation_step(self, batch, batch_idx):
         # 1. Forward pass:
         x, y = batch
         y_logits = self.forward(x)
+        y_scores = torch.sigmoid(y_logits)
         y_true = y.view((-1, 1)).type_as(x)
 
         # 2. Compute loss
         self.log("val_loss", self.loss(y_logits, y_true), prog_bar=True)
 
         # 3. Compute accuracy:
-        self.log("val_acc", self.valid_acc(y_logits, y_true.int()), prog_bar=True)
+        self.log("val_acc", self.valid_acc(y_scores, y_true.int()), prog_bar=True)
 
     def configure_optimizers(self):
         parameters = list(self.parameters())