Make default tqdm dict overridable (#749)

* overridable tqdm_dict

* Slim down default tqdm_metrics

* gpu fix

pytorch_lightning/core/lightning.py

@@ -1210,6 +1210,25 @@ def on_save_checkpoint(self, checkpoint):
 
         """
 
+    def get_tqdm_dict(self):
+        r"""
+        Additional items to be displayed in the progress bar.
+
+        Return:
+            Dictionary with the items to be displayed in the progress bar.
+        """
+        tqdm_dict = {
+            'loss': '{:.3f}'.format(self.trainer.avg_loss)
+        }
+
+        if self.trainer.truncated_bptt_steps is not None:
+            tqdm_dict['split_idx'] = self.trainer.split_idx
+
+        if self.trainer.logger is not None and self.trainer.logger.version is not None:
+            tqdm_dict['v_num'] = self.trainer.logger.version
+
+        return tqdm_dict
+
 
 def load_hparams_from_tags_csv(tags_csv):
     if not os.path.isfile(tags_csv):

pytorch_lightning/trainer/evaluation_loop.py

@@ -295,7 +295,7 @@ def run_evaluation(self, test=False):
         desc = 'Testing' if test else 'Validating'
         pbar = tqdm(desc=desc, total=max_batches, leave=test, position=position,
                     disable=not self.show_progress_bar, dynamic_ncols=True,
-                    unit='batch', file=sys.stdout)
+                    file=sys.stdout)
         setattr(self, f'{"test" if test else "val"}_progress_bar', pbar)
 
         # run evaluation
@@ -319,9 +319,8 @@ def run_evaluation(self, test=False):
         model.on_post_performance_check()
 
         # add model specific metrics
-        tqdm_metrics = self.training_tqdm_dict
         if not test:
-            self.main_progress_bar.set_postfix(**tqdm_metrics)
+            self.main_progress_bar.set_postfix(**self.training_tqdm_dict)
 
         # close progress bar
         if test:

pytorch_lightning/trainer/trainer.py

@@ -681,23 +681,9 @@ def training_tqdm_dict(self):
         """Read-only for tqdm metrics.
         :return:
         """
-        tqdm_dict = {
-            'loss': '{0:.3f}'.format(self.avg_loss),
-            'batch_idx': '{}'.format(self.batch_idx),
-        }
+        ref_model = self.model if not self.data_parallel else self.model.module
 
-        if self.truncated_bptt_steps is not None:
-            tqdm_dict['split_idx'] = self.split_idx
-
-        if self.logger is not None and self.logger.version is not None:
-            tqdm_dict['v_num'] = self.logger.version
-
-        tqdm_dict.update(self.tqdm_metrics)
-
-        if self.on_gpu:
-            tqdm_dict['gpu'] = '{}'.format(torch.cuda.current_device())
-
-        return tqdm_dict
+        return dict(**ref_model.get_tqdm_dict(), **self.tqdm_metrics)
 
     @property
     def tng_tqdm_dic(self):
@@ -855,7 +841,7 @@ def run_pretrain_routine(self, model):
             pbar = tqdm(desc='Validation sanity check',
                              total=self.num_sanity_val_steps * len(self.get_val_dataloaders()),
                              leave=False, position=2 * self.process_position,
-                             disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch')
+                             disable=not self.show_progress_bar, dynamic_ncols=True)
             self.main_progress_bar = pbar
             # dummy validation progress bar
             self.val_progress_bar = tqdm(disable=True)
@@ -873,7 +859,7 @@ def run_pretrain_routine(self, model):
 
         # init progress bar
         pbar = tqdm(leave=True, position=2 * self.process_position,
-                    disable=not self.show_progress_bar, dynamic_ncols=True, unit='batch',
+                    disable=not self.show_progress_bar, dynamic_ncols=True,
                     file=sys.stdout)
         self.main_progress_bar = pbar