Squeeze the early stopping monitor

CHANGELOG.md

@@ -121,7 +121,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `CombinedLoader` and `max_size_cycle` didn't receive a `DistributedSampler` ([#10374](https://github.com/PyTorchLightning/pytorch-lightning/issues/10374))
 
 
--
+- Squeeze the early stopping monitor to remove empty tensor dimensions ([#10461](https://github.com/PyTorchLightning/pytorch-lightning/issues/10461))
 
 
 -

pytorch_lightning/callbacks/early_stopping.py

@@ -196,7 +196,7 @@ def _run_early_stopping_check(self, trainer: "pl.Trainer") -> None:
         ):  # short circuit if metric not present
             return
 
-        current = logs.get(self.monitor)
+        current = logs[self.monitor].squeeze()
         should_stop, reason = self._evaluate_stopping_criteria(current)
 
         # stop every ddp process if any world process decides to stop

tests/callbacks/test_early_stopping.py

@@ -469,3 +469,16 @@ def validation_step(self, batch, batch_idx):
         assert trainer.global_step == len(side_effect) * int(trainer.limit_train_batches * trainer.val_check_interval)
     else:
         assert trainer.current_epoch == len(side_effect) * trainer.check_val_every_n_epoch - 1
+
+
+def test_early_stopping_squeezes():
+    early_stopping = EarlyStopping(monitor="foo")
+    trainer = Trainer()
+    trainer.callback_metrics["foo"] = torch.tensor([[[0]]])
+
+    with mock.patch(
+        "pytorch_lightning.callbacks.EarlyStopping._evaluate_stopping_criteria", return_value=(False, "")
+    ) as es_mock:
+        early_stopping._run_early_stopping_check(trainer)
+
+    es_mock.assert_called_once_with(torch.tensor(0))