Prune deprecated checkpoint arguments (#6162)

* prune prefix

* prune mode=auto

* chlog

Author: Borda

CHANGELOG.md

@@ -31,6 +31,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * from `pytorch_lightning.metrics.functional.reduction` removed `reduce`, `class_reduce`	
 
 
+- Removed deprecated `ModelCheckpoint` arguments `prefix`, `mode="auto"` ([#6162](https://github.com/PyTorchLightning/pytorch-lightning/pull/6162))
+
+
 ### Fixed
 
 - Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))

docs/source/common/hyperparameters.rst

@@ -167,9 +167,6 @@ improve readability and reproducibility.
             def train_dataloader(self):
                 return DataLoader(mnist_train, batch_size=self.hparams.batch_size)
 
-    .. warning:: Deprecated since v1.1.0. This method of assigning hyperparameters to the LightningModule
-        will no longer be supported from v1.3.0. Use the ``self.save_hyperparameters()`` method from above instead.
-
 
 4.  You can also save full objects such as `dict` or `Namespace` to the checkpoint.
 

pytorch_lightning/callbacks/model_checkpoint.py

@@ -86,26 +86,14 @@ class ModelCheckpoint(Callback):
             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 ``v1``.
-        mode: one of {auto, min, max}.
-            If ``save_top_k != 0``, the decision
-            to overwrite the current save file is made
-            based on either the maximization or the
-            minimization of the monitored quantity. For `val_acc`,
-            this should be `max`, for `val_loss` this should
-            be `min`, etc. In `auto` mode, the direction is
-            automatically inferred from the name of the monitored quantity.
-
-            .. warning::
-               Setting ``mode='auto'`` has been deprecated in v1.1 and will be removed in v1.3.
-
+        mode: one of {min, max}.
+            If ``save_top_k != 0``, the decision to overwrite the current save file is made
+            based on either the maximization or the minimization of the monitored quantity.
+            For ``'val_acc'``, this should be ``'max'``, for ``'val_loss'`` this should be ``'min'``, etc.
         save_weights_only: if ``True``, then only the model's weights will be
             saved (``model.save_weights(filepath)``), else the full model
             is saved (``model.save(filepath)``).
         period: Interval (number of epochs) between checkpoints.
-        prefix: A string to put at the beginning of checkpoint filename.
-
-            .. 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:
@@ -122,7 +110,7 @@ class ModelCheckpoint(Callback):
         MisconfigurationException:
             If ``save_top_k`` is neither ``None`` nor more than or equal to ``-1``,
             if ``monitor`` is ``None`` and ``save_top_k`` is none of ``None``, ``-1``, and ``0``, or
-            if ``mode`` is none of ``"min"``, ``"max"``, and ``"auto"``.
+            if ``mode`` is none of ``"min"`` or ``"max"``.
         ValueError:
             If ``trainer.save_checkpoint`` is ``None``.
 
@@ -166,9 +154,8 @@ def __init__(
         save_last: Optional[bool] = None,
         save_top_k: Optional[int] = None,
         save_weights_only: bool = False,
-        mode: str = "auto",
+        mode: str = "min",
         period: int = 1,
-        prefix: str = "",
     ):
         super().__init__()
         self.monitor = monitor
@@ -178,7 +165,6 @@ def __init__(
         self.save_weights_only = save_weights_only
         self.period = period
         self._last_global_step_saved = -1
-        self.prefix = prefix
         self.current_score = None
         self.best_k_models = {}
         self.kth_best_model_path = ""
@@ -188,12 +174,6 @@ def __init__(
         self.save_function = None
         self.warned_result_obj = False
 
-        if prefix:
-            rank_zero_warn(
-                'Argument `prefix` is deprecated in v1.1 and will be removed in v1.3.'
-                ' Please prepend your prefix in `filename` instead.', DeprecationWarning
-            )
-
         self.__init_monitor_mode(monitor, mode)
         self.__init_ckpt_dir(dirpath, filename, save_top_k)
         self.__validate_init_configuration()
@@ -300,18 +280,8 @@ def __init_monitor_mode(self, monitor, mode):
             "max": (-torch_inf, "max"),
         }
 
-        if mode not in mode_dict and mode != 'auto':
-            raise MisconfigurationException(f"`mode` can be auto, {', '.join(mode_dict.keys())}, got {mode}")
-
-        # TODO: Update with MisconfigurationException when auto mode is removed in v1.3
-        if mode == 'auto':
-            rank_zero_warn(
-                "mode='auto' is deprecated in v1.1 and will be removed in v1.3."
-                " Default value for mode with be 'min' in v1.3.", DeprecationWarning
-            )
-
-            _condition = monitor is not None and ("acc" in monitor or monitor.startswith("fmeasure"))
-            mode_dict['auto'] = ((-torch_inf, "max") if _condition else (torch_inf, "min"))
+        if mode not in mode_dict:
+            raise MisconfigurationException(f"`mode` can be {', '.join(mode_dict.keys())} but got {mode}")
 
         self.kth_value, self.mode = mode_dict[mode]
 
@@ -410,7 +380,7 @@ def format_checkpoint_name(self, epoch: int, step: int, metrics: Dict[str, Any],
             'step=0.ckpt'
 
         """
-        filename = self._format_checkpoint_name(self.filename, epoch, step, metrics, prefix=self.prefix)
+        filename = self._format_checkpoint_name(self.filename, epoch, step, metrics)
         if ver is not None:
             filename = self.CHECKPOINT_JOIN_CHAR.join((filename, f"v{ver}"))
 
@@ -523,7 +493,6 @@ def _save_last_checkpoint(self, trainer, pl_module, ckpt_name_metrics):
                 trainer.current_epoch,
                 trainer.global_step,
                 ckpt_name_metrics,
-                prefix=self.prefix
             )
             last_filepath = os.path.join(self.dirpath, f"{last_filepath}{self.FILE_EXTENSION}")
         else:

pytorch_lightning/trainer/connectors/callback_connector.py

@@ -79,7 +79,7 @@ def configure_checkpoint_callbacks(self, checkpoint_callback: Union[ModelCheckpo
             )
 
         if not self._trainer_has_checkpoint_callbacks() and checkpoint_callback is True:
-            self.trainer.callbacks.append(ModelCheckpoint(dirpath=None, filename=None, mode='min'))
+            self.trainer.callbacks.append(ModelCheckpoint())
 
     def _configure_swa_callbacks(self):
         if not self.trainer._stochastic_weight_avg:

tests/checkpointing/test_model_checkpoint.py

@@ -417,9 +417,9 @@ def test_model_checkpoint_format_checkpoint_name(tmpdir):
     assert ckpt_name == str(Path('.').resolve() / 'epoch=3-step=4.ckpt')
 
     # with version
-    ckpt = ModelCheckpoint(monitor='early_stop_on', dirpath=tmpdir, filename='name', prefix='test')
+    ckpt = ModelCheckpoint(monitor='early_stop_on', dirpath=tmpdir, filename='name')
     ckpt_name = ckpt.format_checkpoint_name(3, 2, {}, ver=3)
-    assert ckpt_name == tmpdir / 'test-name-v3.ckpt'
+    assert ckpt_name == tmpdir / 'name-v3.ckpt'
 
     # using slashes
     ckpt = ModelCheckpoint(monitor='early_stop_on', dirpath=None, filename='{epoch}_{val/loss:.5f}')
@@ -1098,5 +1098,5 @@ def test_ckpt_version_after_rerun_same_trainer(tmpdir):
 
 
 def test_model_checkpoint_mode_options():
-    with pytest.raises(MisconfigurationException, match="`mode` can be auto, .* got unknown_option"):
+    with pytest.raises(MisconfigurationException, match="`mode` can be .* but got unknown_option"):
         ModelCheckpoint(mode="unknown_option")

tests/deprecated_api/test_remove_1-2.py

@@ -15,25 +15,10 @@
 
 import pytest
 
-from pytorch_lightning import LightningModule, Trainer
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
+from pytorch_lightning import LightningModule
 
 
 def test_v1_3_0_deprecated_arguments(tmpdir):
-    with pytest.deprecated_call(match='will no longer be supported in v1.3'):
-        callback = ModelCheckpoint()
-        Trainer(checkpoint_callback=callback, callbacks=[], default_root_dir=tmpdir)
-
-    # Deprecate prefix
-    with pytest.deprecated_call(match='will be removed in v1.3'):
-        ModelCheckpoint(prefix='temp')
-
-    # Deprecate auto mode
-    with pytest.deprecated_call(match='will be removed in v1.3'):
-        ModelCheckpoint(mode='auto')
-
-    with pytest.deprecated_call(match='will be removed in v1.3'):
-        EarlyStopping(mode='auto')
 
     with pytest.deprecated_call(match="The setter for self.hparams in LightningModule is deprecated"):
 

tests/deprecated_api/test_remove_1-3.py

@@ -421,34 +421,17 @@ def test_dp_output_reduce():
 
 
 @pytest.mark.parametrize(
-    ["save_top_k", "save_last", "file_prefix", "expected_files"],
+    "save_top_k,save_last,expected_files",
     [
-        pytest.param(
-            -1,
-            False,
-            "",
-            {"epoch=4.ckpt", "epoch=3.ckpt", "epoch=2.ckpt", "epoch=1.ckpt", "epoch=0.ckpt"},
-            id="CASE K=-1  (all)",
-        ),
-        pytest.param(1, False, "test_prefix", {"test_prefix-epoch=4.ckpt"}, id="CASE K=1 (2.5, epoch 4)"),
-        pytest.param(2, False, "", {"epoch=4.ckpt", "epoch=2.ckpt"}, id="CASE K=2 (2.5 epoch 4, 2.8 epoch 2)"),
-        pytest.param(
-            4,
-            False,
-            "",
-            {"epoch=1.ckpt", "epoch=4.ckpt", "epoch=3.ckpt", "epoch=2.ckpt"},
-            id="CASE K=4 (save all 4 base)",
-        ),
-        pytest.param(
-            3,
-            False,
-            "", {"epoch=2.ckpt", "epoch=3.ckpt", "epoch=4.ckpt"},
-            id="CASE K=3 (save the 2nd, 3rd, 4th model)"
-        ),
-        pytest.param(1, True, "", {"epoch=4.ckpt", "last.ckpt"}, id="CASE K=1 (save the 4th model and the last model)"),
+        pytest.param(-1, False, [f"epoch={i}.ckpt" for i in range(5)], id="CASE K=-1  (all)"),
+        pytest.param(1, False, {"epoch=4.ckpt"}, id="CASE K=1 (2.5, epoch 4)"),
+        pytest.param(2, False, [f"epoch={i}.ckpt" for i in (2, 4)], id="CASE K=2 (2.5 epoch 4, 2.8 epoch 2)"),
+        pytest.param(4, False, [f"epoch={i}.ckpt" for i in range(1, 5)], id="CASE K=4 (save all 4 base)"),
+        pytest.param(3, False, [f"epoch={i}.ckpt" for i in range(2, 5)], id="CASE K=3 (save the 2nd, 3rd, 4th model)"),
+        pytest.param(1, True, {"epoch=4.ckpt", "last.ckpt"}, id="CASE K=1 (save the 4th model and the last model)"),
     ],
 )
-def test_model_checkpoint_options(tmpdir, save_top_k, save_last, file_prefix, expected_files):
+def test_model_checkpoint_options(tmpdir, save_top_k, save_last, expected_files):
     """Test ModelCheckpoint options."""
 
     def mock_save_function(filepath, *args):
@@ -463,7 +446,6 @@ def mock_save_function(filepath, *args):
         monitor='checkpoint_on',
         save_top_k=save_top_k,
         save_last=save_last,
-        prefix=file_prefix,
         verbose=1
     )
     checkpoint_callback.save_function = mock_save_function