Prune deprecated EarlyStopping(mode='auto') (#6167)

Co-authored-by: Roger Shieh <sh.rog@protonmail.ch>
Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>

CHANGELOG.md

@@ -34,6 +34,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed deprecated `ModelCheckpoint` arguments `prefix`, `mode="auto"` ([#6162](https://github.com/PyTorchLightning/pytorch-lightning/pull/6162))
 
 
+- Removed `mode='auto'` from `EarlyStopping` ([#6167](https://github.com/PyTorchLightning/pytorch-lightning/pull/6167))
+
+
 ### Fixed
 
 - Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))

pytorch_lightning/callbacks/early_stopping.py

@@ -23,7 +23,7 @@ import numpy as np
 import torch
 
 from pytorch_lightning.callbacks.base import Callback
-from pytorch_lightning.utilities import rank_zero_info, rank_zero_warn
+from pytorch_lightning.utilities import rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
@@ -40,23 +40,18 @@ class EarlyStopping(Callback):
         patience: number of validation epochs with no improvement
             after which training will be stopped. Default: ``3``.
         verbose: verbosity mode. Default: ``False``.
-        mode: one of {auto, min, max}. In `min` mode,
+        mode: one of ``'min'``, ``'max'``. In ``'min'`` mode,
             training will stop when the quantity
-            monitored has stopped decreasing; in `max`
+            monitored has stopped decreasing and in ``'max'``
             mode it will stop when the quantity
-            monitored has stopped increasing; in `auto`
-            mode, the direction is automatically inferred
-            from the name of the monitored quantity.
-
-            .. warning::
-               Setting ``mode='auto'`` has been deprecated in v1.1 and will be removed in v1.3.
+            monitored has stopped increasing.
 
         strict: whether to crash the training if `monitor` is
             not found in the validation metrics. Default: ``True``.
 
     Raises:
         MisconfigurationException:
-            If ``mode`` is none of ``"min"``, ``"max"``, and ``"auto"``.
+            If ``mode`` is none of ``"min"`` or ``"max"``.
         RuntimeError:
             If the metric ``monitor`` is not available.
 
@@ -78,7 +73,7 @@ class EarlyStopping(Callback):
         min_delta: float = 0.0,
         patience: int = 3,
         verbose: bool = False,
-        mode: str = 'auto',
+        mode: str = 'min',
         strict: bool = True,
     ):
         super().__init__()
@@ -92,31 +87,13 @@ class EarlyStopping(Callback):
         self.mode = mode
         self.warned_result_obj = False
 
-        self.__init_monitor_mode()
+        if self.mode not in self.mode_dict:
+            raise MisconfigurationException(f"`mode` can be {', '.join(self.mode_dict.keys())}, got {self.mode}")
 
         self.min_delta *= 1 if self.monitor_op == torch.gt else -1
         torch_inf = torch.tensor(np.Inf)
         self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf
 
-    def __init_monitor_mode(self):
-        if self.mode not in self.mode_dict and self.mode != 'auto':
-            raise MisconfigurationException(f"`mode` can be auto, {', '.join(self.mode_dict.keys())}, got {self.mode}")
-
-        # TODO: Update with MisconfigurationException when auto mode is removed in v1.3
-        if self.mode == 'auto':
-            rank_zero_warn(
-                "mode='auto' is deprecated in v1.1 and will be removed in v1.3."
-                " Default value for mode with be 'min' in v1.3.", DeprecationWarning
-            )
-
-            if "acc" in self.monitor or self.monitor.startswith("fmeasure"):
-                self.mode = 'max'
-            else:
-                self.mode = 'min'
-
-            if self.verbose > 0:
-                rank_zero_info(f'EarlyStopping mode set to {self.mode} for monitoring {self.monitor}.')
-
     def _validate_condition_metric(self, logs):
         monitor_val = logs.get(self.monitor)
 

tests/callbacks/test_early_stopping.py

@@ -334,7 +334,7 @@ def test_min_steps_override_early_stopping_functionality(tmpdir, step_freeze, mi
     # Compute min_epochs latest step
     by_min_epochs = min_epochs * limit_train_batches
 
-    # Make sure the trainer stops for the max of all minimun requirements
+    # Make sure the trainer stops for the max of all minimum requirements
     assert trainer.global_step == max(min_steps, by_early_stopping, by_min_epochs), \
         (trainer.global_step, max(min_steps, by_early_stopping, by_min_epochs), step_freeze, min_steps, min_epochs)
 
@@ -342,5 +342,5 @@ def test_min_steps_override_early_stopping_functionality(tmpdir, step_freeze, mi
 
 
 def test_early_stopping_mode_options():
-    with pytest.raises(MisconfigurationException, match="`mode` can be auto, .* got unknown_option"):
+    with pytest.raises(MisconfigurationException, match="`mode` can be .* got unknown_option"):
         EarlyStopping(mode="unknown_option")

tests/deprecated_api/test_remove_1-3.py

@@ -19,7 +19,6 @@ from pytorch_lightning import LightningModule
 
 
 def test_v1_3_0_deprecated_arguments(tmpdir):
-
     with pytest.deprecated_call(match="The setter for self.hparams in LightningModule is deprecated"):
 
         class DeprecatedHparamsModel(LightningModule):