ref: fixes logging for eval steps (#3763)

* fixes logging for eval steps

pl_examples/basic_examples/image_classifier.py

@@ -54,13 +54,14 @@ class LitClassifier(pl.LightningModule):
         x, y = batch
         y_hat = self.backbone(x)
         loss = F.cross_entropy(y_hat, y)
+        self.log('train_loss', loss, on_epoch=True)
         return loss
 
     def validation_step(self, batch, batch_idx):
         x, y = batch
         y_hat = self.backbone(x)
         loss = F.cross_entropy(y_hat, y)
-        self.log('valid_loss', loss)
+        self.log('valid_loss', loss, on_step=True)
 
     def test_step(self, batch, batch_idx):
         x, y = batch

pytorch_lightning/callbacks/model_checkpoint.py

@@ -296,6 +296,9 @@ class ModelCheckpoint(Callback):
             raise ValueError(".save_function() not set")
 
     def check_monitor_top_k(self, current) -> bool:
+        if current is None:
+            return False
+
         if self.save_top_k == -1:
             return True
 
@@ -421,7 +424,7 @@ class ModelCheckpoint(Callback):
         if self.monitor is None and 'checkpoint_on' in metrics:
             self.monitor = 'checkpoint_on'
 
-        if self.save_top_k is None:
+        if self.save_top_k is None and self.monitor is not None:
             self.save_top_k = 1
 
     def _validate_monitor_key(self, trainer):
@@ -486,15 +489,7 @@ class ModelCheckpoint(Callback):
         if not isinstance(current, torch.Tensor) and current is not None:
             current = torch.tensor(current, device=pl_module.device)
 
-        if current is None:
-            m = f"Can save best model only with {self.monitor} available, skipping."
-            if self.monitor == 'checkpoint_on':
-                m = (
-                    'No checkpoint_on found. HINT: Did you set it in '
-                    'EvalResult(checkpoint_on=tensor) or TrainResult(checkpoint_on=tensor)?'
-                )
-            rank_zero_warn(m, RuntimeWarning)
-        elif self.check_monitor_top_k(current):
+        if self.check_monitor_top_k(current):
             self._update_best_and_save(filepath, current, epoch, trainer, pl_module)
         elif self.verbose:
             rank_zero_info(

pytorch_lightning/trainer/connectors/logger_connector.py

@@ -157,6 +157,9 @@ class LoggerConnector:
             # track the final results for the dataloader
             self.eval_loop_results.append(deepcopy(self.callback_metrics))
 
+            # actually log
+            self.log_metrics(logger_metrics, {}, step=self.trainer.global_step)
+
     def __rename_keys_by_dataloader_idx(self, metrics, dataloader_idx, num_loaders):
         if num_loaders == 1:
             return metrics