added testing for metrics

pytorch_lightning/examples/new_project_templates/lightning_module_template.py

@@ -152,9 +152,6 @@ class LightningTemplateModel(LightningModule):
         tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
         return tqdm_dic
 
-    def update_tng_log_metrics(self, logs):
-        return logs
-
     # ---------------------
     # MODEL SAVING
     # ---------------------

pytorch_lightning/models/trainer.py

@@ -678,7 +678,7 @@ class Trainer(TrainerIO):
                     # nb_params, nb_tensors = count_mem_items()
 
                     model = self.__get_model()
-                    metrics = model.update_tng_log_metrics(self.__tng_tqdm_dic)
+                    metrics = self.__tng_tqdm_dic
 
                     # add gpu memory
                     if self.on_gpu:

pytorch_lightning/root_module/root_module.py

@@ -71,15 +71,6 @@ class LightningModule(GradInformation, ModelIO, ModelHooks):
         """
         raise NotImplementedError
 
-    def update_tng_log_metrics(self, logs):
-        """
-        Chance to update metrics to be logged for training step.
-        For example, add music, images, etc... to log
-        :param logs:
-        :return:
-        """
-        return logs
-
     def loss(self, *args, **kwargs):
         """
         Expand model_out into your components

pytorch_lightning/testing_models/lm_test_module.py

@@ -166,8 +166,8 @@ class LightningTestModel(LightningModule):
         tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
         return tqdm_dic
 
-    def update_tng_log_metrics(self, logs):
-        return logs
+    def on_tng_metrics(self, logs):
+        logs['some_tensor_to_test'] = torch.rand(1)
 
     # ---------------------
     # MODEL SAVING