Deprecate early_stop_callback Trainer argument (part 2) (#3845)

* update tests with EarlyStopping default * imports * revert legacy tests * fix test * revert * revert
Lightning-AI · Oct 4, 2020 · cc9781a · cc9781a
1 parent 6723b92
commit cc9781a
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Showing 6 changed files with 12 additions and 10 deletions.
diff --git a/pytorch_lightning/callbacks/early_stopping.py b/pytorch_lightning/callbacks/early_stopping.py
@@ -181,7 +181,7 @@ def _run_early_stopping_check(self, trainer, pl_module):
         current = logs.get(self.monitor)
 
         # when in dev debugging
-        trainer.dev_debugger.track_early_stopping_history(current)
+        trainer.dev_debugger.track_early_stopping_history(self, current)
 
         if not isinstance(current, torch.Tensor):
             current = torch.tensor(current, device=pl_module.device)

diff --git a/pytorch_lightning/utilities/debugging.py b/pytorch_lightning/utilities/debugging.py
@@ -154,15 +154,14 @@ def track_pbar_metrics_history(self, metrics):
         self.pbar_added_metrics.append(metrics)
 
     @enabled_only
-    def track_early_stopping_history(self, current):
-        es = self.trainer.early_stop_callback
+    def track_early_stopping_history(self, callback, current):
         debug_dict = {
             'epoch': self.trainer.current_epoch,
             'global_step': self.trainer.global_step,
             'rank': self.trainer.global_rank,
             'current': current,
-            'best': es.best_score,
-            'patience': es.wait_count
+            'best': callback.best_score,
+            'patience': callback.wait_count
         }
         self.early_stopping_history.append(debug_dict)
 

diff --git a/tests/backends/test_ddp_spawn.py b/tests/backends/test_ddp_spawn.py
@@ -3,6 +3,7 @@
 
 import tests.base.develop_pipelines as tpipes
 import tests.base.develop_utils as tutils
+from pytorch_lightning.callbacks import EarlyStopping
 from tests.base import EvalModelTemplate
 from pytorch_lightning.core import memory
 from pytorch_lightning.trainer import Trainer
@@ -15,7 +16,7 @@ def test_multi_gpu_early_stop_ddp_spawn(tmpdir):
 
     trainer_options = dict(
         default_root_dir=tmpdir,
-        early_stop_callback=True,
+        callbacks=[EarlyStopping()],
         max_epochs=50,
         limit_train_batches=10,
         limit_val_batches=10,

diff --git a/tests/callbacks/test_early_stopping.py b/tests/callbacks/test_early_stopping.py
@@ -77,7 +77,7 @@ def test_early_stopping_no_extraneous_invocations(tmpdir):
     expected_count = 4
     trainer = Trainer(
         default_root_dir=tmpdir,
-        early_stop_callback=True,
+        callbacks=[EarlyStopping()],
         val_check_interval=1.0,
         max_epochs=expected_count,
     )

diff --git a/tests/models/test_gpu.py b/tests/models/test_gpu.py
@@ -8,6 +8,7 @@
 import tests.base.develop_pipelines as tpipes
 import tests.base.develop_utils as tutils
 from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import EarlyStopping
 from pytorch_lightning.core import memory
 from pytorch_lightning.utilities import device_parser
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -25,7 +26,7 @@ def test_multi_gpu_early_stop_dp(tmpdir):
 
     trainer_options = dict(
         default_root_dir=tmpdir,
-        early_stop_callback=True,
+        callbacks=[EarlyStopping()],
         max_epochs=50,
         limit_train_batches=10,
         limit_val_batches=10,

diff --git a/tests/models/test_tpu.py b/tests/models/test_tpu.py
@@ -6,6 +6,7 @@
 import tests.base.develop_pipelines as tpipes
 from pytorch_lightning import Trainer, seed_everything
 from pytorch_lightning.accelerators import TPUBackend
+from pytorch_lightning.callbacks import EarlyStopping
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from tests.base import EvalModelTemplate
 from tests.base.datasets import TrialMNIST
@@ -155,7 +156,7 @@ def test_model_tpu_early_stop(tmpdir):
     """Test if single TPU core training works"""
     model = EvalModelTemplate()
     trainer = Trainer(
-        early_stop_callback=True,
+        callbacks=[EarlyStopping()],
         default_root_dir=tmpdir,
         progress_bar_refresh_rate=0,
         max_epochs=50,
@@ -261,7 +262,7 @@ def test_result_obj_on_tpu(tmpdir):
     trainer_options = dict(
         default_root_dir=tmpdir,
         max_epochs=epochs,
-        early_stop_callback=True,
+        callbacks=[EarlyStopping()],
         row_log_interval=2,
         limit_train_batches=batches,
         weights_summary=None,