Added changeable extension variable for model checkpoints

pytorch_lightning/callbacks/model_checkpoint.py

@@ -90,7 +90,7 @@ class ModelCheckpoint(Callback):
             Example::
 
                 # custom path
-                # saves a file like: my/path/epoch=0.ckpt
+                # saves a file like: my/path/epoch=0-step=10.ckpt
                 >>> checkpoint_callback = ModelCheckpoint(dirpath='my/path/')
 
             By default, dirpath is ``None`` and will be set at runtime to the location
@@ -140,6 +140,7 @@ class ModelCheckpoint(Callback):
 
     CHECKPOINT_JOIN_CHAR = "-"
     CHECKPOINT_NAME_LAST = "last"
+    FILE_EXTENSION = ".ckpt"
 
     def __init__(
         self,
@@ -442,7 +443,7 @@ def format_checkpoint_name(
         )
         if ver is not None:
             filename = self.CHECKPOINT_JOIN_CHAR.join((filename, f"v{ver}"))
-        ckpt_name = f"{filename}.ckpt"
+        ckpt_name = f"{filename}{self.FILE_EXTENSION}"
         return os.path.join(self.dirpath, ckpt_name) if self.dirpath else ckpt_name
 
     def __resolve_ckpt_dir(self, trainer, pl_module):
@@ -545,7 +546,7 @@ def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics, filepath)
                 ckpt_name_metrics,
                 prefix=self.prefix
             )
-            last_filepath = os.path.join(self.dirpath, f"{last_filepath}.ckpt")
+            last_filepath = os.path.join(self.dirpath, f"{last_filepath}{self.FILE_EXTENSION}")
 
         self._save_model(last_filepath, trainer, pl_module)
         if (

tests/checkpointing/test_model_checkpoint.py

@@ -261,6 +261,29 @@ def test_model_checkpoint_format_checkpoint_name(tmpdir):
     assert ckpt_name == filepath / 'test-epoch=3-step=2.ckpt'
 
 
+class ModelCheckpointExtensionTest(ModelCheckpoint):
+    FILE_EXTENSION = '.tpkc'
+
+
+def test_model_checkpoint_file_extension(tmpdir):
+    """
+    Test ModelCheckpoint with different file extension.
+    """
+
+    model = LogInTwoMethods()
+    model_checkpoint = ModelCheckpointExtensionTest(monitor='early_stop_on', dirpath=tmpdir, save_top_k=1, save_last=True)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        callbacks=[model_checkpoint],
+        max_steps=1,
+        logger=False,
+    )
+    trainer.fit(model)
+
+    expected = ['epoch=0-step=0.tpkc', 'last.tpkc']
+    assert set(expected) == set(os.listdir(tmpdir))
+
+
 def test_model_checkpoint_save_last(tmpdir):
     """Tests that save_last produces only one last checkpoint."""
     seed_everything()