Start version suffixes at 1

CHANGELOG.md

@@ -91,6 +91,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed the default of `find_unused_parameters` to `False` in DDP ([#5185](https://github.com/PyTorchLightning/pytorch-lightning/pull/5185))
 
 
+- Changed `ModelCheckpoint` version suffixes to start at 1 ([5008](https://github.com/PyTorchLightning/pytorch-lightning/pull/5008))
+
+
 - Changed the default value for the `progress_bar_refresh_rate` Trainer argument in Google COLAB notebooks to 20 ([#5516](https://github.com/PyTorchLightning/pytorch-lightning/pull/5516))
 
 

pytorch_lightning/callbacks/model_checkpoint.py

@@ -80,10 +80,10 @@ class ModelCheckpoint(Callback):
             the quantity monitored will be saved.
             if ``save_top_k == 0``, no models are saved.
             if ``save_top_k == -1``, all models are saved.
-            Please note that the monitors are checked every `period` epochs.
+            Please note that the monitors are checked every ``period`` epochs.
             if ``save_top_k >= 2`` and the callback is called multiple
             times inside an epoch, the name of the saved file will be
-            appended with a version count starting with `v0`.
+            appended with a version count starting with ``v1``.
         mode: one of {auto, min, max}.
             If ``save_top_k != 0``, the decision
             to overwrite the current save file is made
@@ -105,6 +105,17 @@ class ModelCheckpoint(Callback):
             .. warning::
                This argument has been deprecated in v1.1 and will be removed in v1.3
 
+    Note:
+        For extra customization, ModelCheckpoint includes the following attributes:
+
+        - ``CHECKPOINT_JOIN_CHAR = "-"``
+        - ``CHECKPOINT_NAME_LAST = "last"``
+        - ``FILE_EXTENSION = ".ckpt"``
+        - ``STARTING_VERSION = 1``
+
+        For example, you can change the default last checkpoint name by doing
+        ``checkpoint_callback.CHECKPOINT_NAME_LAST = "{epoch}-last"``
+
     Example::
 
         >>> from pytorch_lightning import Trainer
@@ -128,11 +139,13 @@ class ModelCheckpoint(Callback):
         model = ...
         trainer.fit(model)
         checkpoint_callback.best_model_path
+
     """
 
     CHECKPOINT_JOIN_CHAR = "-"
     CHECKPOINT_NAME_LAST = "last"
     FILE_EXTENSION = ".ckpt"
+    STARTING_VERSION = 1
 
     def __init__(
         self,
@@ -485,28 +498,24 @@ def _validate_monitor_key(self, trainer):
 
     def _get_metric_interpolated_filepath_name(
         self,
-        ckpt_name_metrics: Dict[str, Any],
+        monitor_candidates: Dict[str, Any],
         epoch: int,
         step: int,
         del_filepath: Optional[str] = None
     ) -> str:
-        filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics)
-
-        version_cnt = 0
+        filepath = self.format_checkpoint_name(epoch, step, monitor_candidates)
+        version = self.STARTING_VERSION
         while self._fs.exists(filepath) and filepath != del_filepath:
-            filepath = self.format_checkpoint_name(epoch, step, ckpt_name_metrics, ver=version_cnt)
-            version_cnt += 1
-
+            filepath = self.format_checkpoint_name(epoch, step, monitor_candidates, ver=version)
+            version += 1
         return filepath
 
     def _monitor_candidates(self, trainer):
-        ckpt_name_metrics = deepcopy(trainer.logger_connector.logged_metrics)
-        ckpt_name_metrics.update(trainer.logger_connector.callback_metrics)
-        ckpt_name_metrics.update(trainer.logger_connector.progress_bar_metrics)
-        ckpt_name_metrics.update({"step": trainer.global_step, "epoch": trainer.current_epoch})
-        return ckpt_name_metrics
+        monitor_candidates = deepcopy(trainer.logger_connector.callback_metrics)
+        monitor_candidates.update(step=trainer.global_step, epoch=trainer.current_epoch)
+        return monitor_candidates
 
-    def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
+    def _save_last_checkpoint(self, trainer, pl_module, monitor_candidates):
         should_save_last = self.monitor is None or self.save_last
         if not should_save_last:
             return
@@ -517,13 +526,13 @@ def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
                 self.CHECKPOINT_NAME_LAST,
                 trainer.current_epoch,
                 trainer.global_step,
-                ckpt_name_metrics,
-                prefix=self.prefix
+                monitor_candidates,
+                prefix=self.prefix,
             )
             last_filepath = os.path.join(self.dirpath, f"{last_filepath}{self.FILE_EXTENSION}")
         else:
             last_filepath = self._get_metric_interpolated_filepath_name(
-                ckpt_name_metrics, trainer.current_epoch, trainer.global_step
+                monitor_candidates, trainer.current_epoch, trainer.global_step
             )
 
         accelerator_backend = trainer.accelerator_backend
@@ -534,10 +543,10 @@ def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
         else:
             self._save_model(last_filepath, trainer, pl_module)
         if (
-                self.last_model_path
-                and self.last_model_path != last_filepath
-                and (self.save_top_k != -1 or self.save_last)
-                and trainer.is_global_zero
+            self.last_model_path
+            and self.last_model_path != last_filepath
+            and (self.save_top_k != -1 or self.save_last)
+            and trainer.is_global_zero
         ):
             self._del_model(self.last_model_path)
         self.last_model_path = last_filepath

tests/checkpointing/test_model_checkpoint.py

@@ -738,18 +738,20 @@ def test_val_check_interval_checkpoint_files(tmpdir):
         save_top_k=-1,
         monitor="val_acc",
         mode="max",
-        verbose=True
     )
     trainer = Trainer(
         default_root_dir=tmpdir,
         val_check_interval=0.2,
         max_epochs=1,
         limit_train_batches=10,
-        callbacks=[model_checkpoint]
+        callbacks=[model_checkpoint],
+        logger=False,
+        weights_summary=None,
+        progress_bar_refresh_rate=0,
     )
     trainer.fit(model)
-    files = sorted([p.name for p in Path(tmpdir).glob("*.ckpt")])
-    assert files == [f"epoch=0-step={s}.ckpt" for s in [1, 3, 5, 7, 9]]
+    files = {p.basename for p in tmpdir.listdir()}
+    assert files == {f"epoch=0-step={s}.ckpt" for s in [1, 3, 5, 7, 9]}
 
 
 def test_current_score(tmpdir):
@@ -844,43 +846,66 @@ def __init__(self, hparams):
         assert type(ckpt[model.CHECKPOINT_HYPER_PARAMS_KEY]) == hparams_type
 
 
-@pytest.mark.parametrize('max_epochs', [3, 4])
-@pytest.mark.parametrize(
-    'save_top_k, expected',
-    [
-        (1, ['curr_epoch.ckpt']),
-        (2, ['curr_epoch.ckpt', 'curr_epoch-v0.ckpt']),
-    ]
-)
-def test_model_checkpoint_file_already_exists(tmpdir, max_epochs, save_top_k, expected):
+def test_ckpt_version_after_rerun_new_trainer(tmpdir):
     """
-    Test that version is added to filename if required and it already exists in dirpath.
+    Check that previous checkpoints are renamed to have the correct
+    version suffix when new trainer instances are used
     """
-    model_checkpoint = ModelCheckpoint(
-        dirpath=tmpdir,
-        filename='curr_epoch',
-        save_top_k=save_top_k,
-        monitor='epoch',
-        mode='max',
-    )
+    epochs = 2
+    for i in range(epochs):
+        mc = ModelCheckpoint(dirpath=tmpdir, save_top_k=-1, monitor="epoch", filename="{epoch}")
+        trainer = Trainer(
+            max_epochs=epochs,
+            limit_train_batches=1,
+            limit_val_batches=1,
+            default_root_dir=tmpdir,
+            callbacks=[mc],
+            logger=False,
+            weights_summary=None,
+            progress_bar_refresh_rate=0,
+        )
+        trainer.fit(BoringModel())
+
+        # check best_k_models state
+        expected = {"epoch=0-v1.ckpt", "epoch=1-v1.ckpt"} if i else {"epoch=0.ckpt", "epoch=1.ckpt"}
+        assert {Path(f).name for f in mc.best_k_models.keys()} == expected
+
+    # check created ckpts
+    assert set(f.basename for f in tmpdir.listdir()) == {
+        "epoch=0.ckpt",
+        "epoch=1.ckpt",
+        "epoch=0-v1.ckpt",
+        "epoch=1-v1.ckpt",
+    }
+
+
+def test_ckpt_version_after_rerun_same_trainer(tmpdir):
+    """
+    Check that previous checkpoints are renamed to have the correct
+    version suffix when the same trainer instance is used
+    """
+    mc = ModelCheckpoint(dirpath=tmpdir, save_top_k=-1, monitor="epoch", filename="test")
+    mc.STARTING_VERSION = 9
     trainer = Trainer(
+        max_epochs=2,
+        limit_train_batches=1,
+        limit_val_batches=1,
         default_root_dir=tmpdir,
-        callbacks=[model_checkpoint],
-        max_epochs=max_epochs,
-        limit_train_batches=2,
-        limit_val_batches=2,
-        logger=None,
+        callbacks=[mc],
+        logger=False,
         weights_summary=None,
         progress_bar_refresh_rate=0,
     )
+    trainer.fit(BoringModel())
+    trainer.max_epochs = 4
+    trainer.fit(BoringModel())
 
-    model = BoringModel()
-    trainer.fit(model)
-    ckpt_files = os.listdir(tmpdir)
-    assert set(ckpt_files) == set(expected)
-
-    epochs_in_ckpt_files = [pl_load(os.path.join(tmpdir, f))['epoch'] - 1 for f in ckpt_files]
-    assert sorted(epochs_in_ckpt_files) == list(range(max_epochs - save_top_k, max_epochs))
+    ckpt_range = range(mc.STARTING_VERSION, trainer.max_epochs + mc.STARTING_VERSION)
+    expected = {'test.ckpt', *[f"test-v{i}.ckpt" for i in ckpt_range]}
+    # check best_k_models state
+    assert {Path(f).name for f in mc.best_k_models.keys()} == expected
+    # check created ckpts
+    assert set(sorted(os.listdir(tmpdir))) == expected
 
 
 def test_model_checkpoint_mode_options():