[BUG] Early stopping does not seem to work

The early stopping callback does not work as expected, and crashes at the end of
max_epochs with:

```
~/miniconda3/envs/py38/lib/python3.8/site-packages/pytorch_lightning/trainer/callback_hook.py in on_train_end(self)
    155         """Called when the train ends."""
    156         for callback in self.callbacks:
--> 157             callback.on_train_end(self, self.lightning_module)
    158
    159     def on_pretrain_routine_start(self) -> None:

~/work/repos/prototorch_models/prototorch/models/callbacks.py in on_train_end(self, trainer, pl_module)
     18     def on_train_end(self, trainer, pl_module):
     19         # instead, do it at the end of training loop
---> 20         self._run_early_stopping_check(trainer, pl_module)
     21
     22

TypeError: _run_early_stopping_check() takes 2 positional arguments but 3 were given
```

examples/dynamic_pruning.py

@@ -46,19 +46,27 @@ if __name__ == "__main__":
     vis = pt.models.VisGLVQ2D(train_ds)
     pruning = pt.models.PruneLoserPrototypes(
         threshold=0.01,  # prune prototype if it wins less than 1%
-        prune_after_epochs=30,  # pruning too early may cause problems
+        idle_epochs=30,  # pruning too early may cause problems
         prune_quota_per_epoch=1,  # prune at most 1 prototype per epoch
         frequency=5,  # prune every fifth epoch
         verbose=True,
     )
+    es = pt.models.EarlyStopWithoutVal(
+        monitor="loss",
+        min_delta=0.1,
+        patience=3,
+        mode="min",
+        verbose=True,
+    )
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(
         args,
-        max_epochs=100,
+        max_epochs=250,
         callbacks=[
             vis,
             pruning,
+            es,
         ],
         terminate_on_nan=True,
         weights_summary=None,

prototorch/models/__init__.py

@@ -2,7 +2,8 @@
 
 from importlib.metadata import PackageNotFoundError, version
 
-from .callbacks import PruneLoserPrototypes
+from .callbacks import (EarlyStopWithoutVal, PrototypeConvergence,
+                        PruneLoserPrototypes)
 from .cbc import CBC, ImageCBC
 from .glvq import (
     GLVQ,

prototorch/models/callbacks.py

@@ -4,15 +4,31 @@ import pytorch_lightning as pl
 import torch
 
 
+class EarlyStopWithoutVal(pl.callbacks.EarlyStopping):
+    """Run early stopping at the end of training loop.
+
+    See:
+    https://pytorch-lightning.readthedocs.io/en/latest/common/early_stopping.html
+
+    """
+    def on_validation_end(self, trainer, pl_module):
+        # override this to disable early stopping at the end of val loop
+        pass
+
+    def on_train_end(self, trainer, pl_module):
+        # instead, do it at the end of training loop
+        self._run_early_stopping_check(trainer, pl_module)
+
+
 class PruneLoserPrototypes(pl.Callback):
     def __init__(self,
                  threshold=0.01,
-                 prune_after_epochs=10,
+                 idle_epochs=10,
                  prune_quota_per_epoch=-1,
                  frequency=1,
                  verbose=False):
         self.threshold = threshold  # minimum win ratio
-        self.prune_after_epochs = prune_after_epochs  # epochs to wait
+        self.idle_epochs = idle_epochs  # epochs to wait before pruning
         self.prune_quota_per_epoch = prune_quota_per_epoch
         self.frequency = frequency
         self.verbose = verbose
@@ -21,7 +37,7 @@ class PruneLoserPrototypes(pl.Callback):
         pl_module.initialize_prototype_win_ratios()
 
     def on_epoch_end(self, trainer, pl_module):
-        if (trainer.current_epoch + 1) < self.prune_after_epochs:
+        if (trainer.current_epoch + 1) < self.idle_epochs:
             return None
         if (trainer.current_epoch + 1) % self.frequency:
             return None
@@ -40,3 +56,18 @@ class PruneLoserPrototypes(pl.Callback):
                 print(f"`num_prototypes` reduced from {cur_num_protos} "
                       f"to {new_num_protos}.")
         return True
+
+
+class PrototypeConvergence(pl.Callback):
+    def __init__(self, min_delta=0.01, idle_epochs=10, verbose=False):
+        self.min_delta = min_delta
+        self.idle_epochs = idle_epochs  # epochs to wait
+        self.verbose = verbose
+
+    def on_epoch_end(self, trainer, pl_module):
+        if (trainer.current_epoch + 1) < self.idle_epochs:
+            return None
+        if self.verbose:
+            print("Stopping...")
+        # TODO
+        return True