Remove *_step_end from LitModular (#170)

Merge train/validation/test_step_end into train/validation/test_step.

Author: dxoigmn

mart/models/modular.py

@@ -134,19 +134,15 @@ def training_step(self, batch, batch_idx):
         for log_name, output_key in self.training_step_log.items():
             self.log(f"training/{log_name}", output[output_key])
 
-        assert "loss" in output
-        return output
-
-    def training_step_end(self, output):
         if self.training_metrics is not None:
             # Some models only return loss in the training mode.
             if self.output_preds_key not in output or self.output_target_key not in output:
                 raise ValueError(
                     f"You have specified training_metrics, but the model does not return {self.output_preds_key} or {self.output_target_key} during training. You can either nullify training_metrics or configure the model to return {self.output_preds_key} and {self.output_target_key} in the training output."
                 )
             self.training_metrics(output[self.output_preds_key], output[self.output_target_key])
-        loss = output.pop(self.output_loss_key)
-        return loss
+
+        return output[self.output_loss_key]
 
     def training_epoch_end(self, outputs):
         if self.training_metrics is not None:
@@ -168,13 +164,9 @@ def validation_step(self, batch, batch_idx):
         for log_name, output_key in self.validation_step_log.items():
             self.log(f"validation/{log_name}", output[output_key])
 
-        return output
-
-    def validation_step_end(self, output):
         self.validation_metrics(output[self.output_preds_key], output[self.output_target_key])
 
-        # I don't know why this is required to prevent CUDA memory leak in validaiton and test. (Not required in training.)
-        output.clear()
+        return None
 
     def validation_epoch_end(self, outputs):
         metrics = self.validation_metrics.compute()
@@ -194,13 +186,9 @@ def test_step(self, batch, batch_idx):
         for log_name, output_key in self.test_step_log.items():
             self.log(f"test/{log_name}", output[output_key])
 
-        return output
-
-    def test_step_end(self, output):
         self.test_metrics(output[self.output_preds_key], output[self.output_target_key])
 
-        # I don't know why this is required to prevent CUDA memory leak in validaiton and test. (Not required in training.)
-        output.clear()
+        return None
 
     def test_epoch_end(self, outputs):
         metrics = self.test_metrics.compute()