improve early stopping verbose logging

CHANGELOG.md

@@ -139,6 +139,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added warning when missing `Callback` and using `resume_from_checkpoint` ([#7254](https://github.com/PyTorchLightning/pytorch-lightning/pull/7254))
 
 
+- Improved verbose logging for `EarlyStopping` callback ([#6811](https://github.com/PyTorchLightning/pytorch-lightning/pull/6811))
+
+
 ### Changed
 
 

pytorch_lightning/callbacks/early_stopping.py

@@ -24,6 +24,7 @@
 import numpy as np
 import torch
 
+import pytorch_lightning as pl
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -196,8 +197,8 @@ def _run_early_stopping_check(self, trainer) -> None:
         trainer.should_stop = trainer.should_stop or should_stop
         if should_stop:
             self.stopped_epoch = trainer.current_epoch
-        if reason:
-            log.info(f"[{trainer.global_rank}] {reason}")
+        if reason and self.verbose:
+            self._log_info(trainer, reason)
 
     def _evalute_stopping_criteria(self, current: torch.Tensor) -> Tuple[bool, str]:
         should_stop = False
@@ -224,6 +225,7 @@ def _evalute_stopping_criteria(self, current: torch.Tensor) -> Tuple[bool, str]:
             )
         elif self.monitor_op(current - self.min_delta, self.best_score):
             should_stop = False
+            reason = self._improvement_message(current)
             self.best_score = current
             self.wait_count = 0
         else:
@@ -236,3 +238,21 @@ def _evalute_stopping_criteria(self, current: torch.Tensor) -> Tuple[bool, str]:
                 )
 
         return should_stop, reason
+
+    def _improvement_message(self, current: torch.Tensor) -> str:
+        """ Formats a log message that informs the user about an improvement in the monitored score. """
+        if torch.isfinite(self.best_score):
+            msg = (
+                f"Metric {self.monitor} improved by {abs(self.best_score - current):.3f} >="
+                f" min_delta = {abs(self.min_delta)}. New best score: {current:.3f}"
+            )
+        else:
+            msg = f"Metric {self.monitor} improved. New best score: {current:.3f}"
+        return msg
+
+    @staticmethod
+    def _log_info(trainer: Optional["pl.Trainer"], message: str) -> None:
+        if trainer is not None and trainer.world_size > 1:
+            log.info(f"[{trainer.global_rank}] {message}")
+        else:
+            log.info(message)