test pickling

pytorch_lightning/callbacks/early_stopping.py

@@ -57,6 +57,7 @@ def __init__(self, monitor: str = 'val_loss', min_delta: float = 0.0, patience:
         self.min_delta = min_delta
         self.wait = 0
         self.stopped_epoch = 0
+        self.mode = mode
 
         mode_dict = {
             'min': torch.lt,
@@ -67,9 +68,8 @@ def __init__(self, monitor: str = 'val_loss', min_delta: float = 0.0, patience:
         if mode not in mode_dict:
             if self.verbose > 0:
                 log.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')
-            mode = 'auto'
+            self.mode = 'auto'
 
-        self.monitor_op = mode_dict[mode]
         self.min_delta *= 1 if self.monitor_op == torch.gt else -1
 
     def _validate_condition_metric(self, logs):
@@ -94,6 +94,15 @@ def _validate_condition_metric(self, logs):
 
         return True
 
+    @property
+    def monitor_op(self):
+        mode_dict = {
+            'min': torch.lt,
+            'max': torch.gt,
+            'auto': torch.gt if 'acc' in self.monitor else torch.lt
+        }
+        return mode_dict[self.mode]
+
     def on_train_start(self, trainer, pl_module):
         # Allow instances to be re-used
         self.wait = 0

tests/callbacks/test_callbacks.py

@@ -240,6 +240,15 @@ def training_step(self, *args, **kwargs):
     assert trainer.current_epoch < trainer.max_epochs
 
 
+def test_pickling(tmpdir):
+    import pickle
+    early_stopping = EarlyStopping()
+    ckpt = ModelCheckpoint(tmpdir)
+
+    pickle.dumps(ckpt)
+    pickle.dumps(early_stopping)
+
+
 def test_model_checkpoint_with_non_string_input(tmpdir):
     """ Test that None in checkpoint callback is valid and that chkp_path is
         set correctly """

tests/loggers/test_all.py

@@ -88,6 +88,9 @@ def test_loggers_pickle(tmpdir, monkeypatch, logger_class):
     logger_args = _get_logger_args(logger_class, tmpdir)
     logger = logger_class(**logger_args)
 
+    # test pickling loggers
+    pickle.dumps(logger)
+
     trainer = Trainer(
         max_epochs=1,
         logger=logger

tests/trainer/test_trainer.py

@@ -26,6 +26,13 @@
 )
 
 
+def test_model_pickle(tmpdir):
+    import pickle
+
+    model = TestModelBase(tutils.get_default_hparams())
+    pickle.dumps(model)
+
+
 def test_hparams_save_load(tmpdir):
     model = DictHparamsModel({'in_features': 28 * 28, 'out_features': 10, 'failed_key': lambda x: x})
 

tests/trainer/test_trainer_cli.py

@@ -1,6 +1,7 @@
 import inspect
 from argparse import ArgumentParser, Namespace
 from unittest import mock
+import pickle
 
 import pytest
 
@@ -42,14 +43,14 @@ def test_add_argparse_args_redefined(cli_args):
 
     args = parser.parse_args(cli_args)
 
+    # make sure we can pickle args
+    pickle.dumps(args)
+
     # Check few deprecated args are not in namespace:
     for depr_name in ('gradient_clip', 'nb_gpu_nodes', 'max_nb_epochs'):
         assert depr_name not in args
 
     trainer = Trainer.from_argparse_args(args=args)
-
-    # make sure trainer can be pickled
-    import pickle
     pickle.dumps(trainer)
 
     assert isinstance(trainer, Trainer)