Merge branch 'master' into prune-earlystopping-auto

Author: carmocca

CHANGELOG.md

@@ -9,6 +9,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
+- Added a way to print to terminal without breaking up the progress bar ([#5470](https://github.com/PyTorchLightning/pytorch-lightning/pull/5470))
+
 
 ### Changed
 
@@ -18,6 +20,19 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Removed support for passing a bool value to `profiler` argument of Trainer ([#6164](https://github.com/PyTorchLightning/pytorch-lightning/pull/6164))
+
+
+- Removed deprecated Trainer argument `enable_pl_optimizer` and `automatic_optimization` ([#6163](https://github.com/PyTorchLightning/pytorch-lightning/pull/6163))
+
+
+- Removed deprecated metrics ([#6161](https://github.com/PyTorchLightning/pytorch-lightning/pull/6161))
+    * from `pytorch_lightning.metrics.functional.classification` removed `to_onehot`, `to_categorical`, `get_num_classes`, `roc`, `multiclass_roc`, `average_precision`, `precision_recall_curve`, `multiclass_precision_recall_curve`
+    * 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))
+
 
 - Removed `mode='auto'` from `EarlyStopping` ([#6167](https://github.com/PyTorchLightning/pytorch-lightning/pull/6167))
 
@@ -36,6 +51,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Expose DeepSpeed loss parameters to allow users to fix loss instability ([#6115](https://github.com/PyTorchLightning/pytorch-lightning/pull/6115))
 
 
+- Fixed epoch level schedulers not being called when `val_check_interval < 1.0` ([#6075](https://github.com/PyTorchLightning/pytorch-lightning/pull/6075))
+
+
 ## [1.2.1] - 2021-02-23
 
 ### Fixed
@@ -91,7 +109,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added `Trainer` flag to activate Stochastic Weight Averaging (SWA) `Trainer(stochastic_weight_avg=True)` ([#6038](https://github.com/PyTorchLightning/pytorch-lightning/pull/6038))
 - Added DeepSpeed integration ([#5954](https://github.com/PyTorchLightning/pytorch-lightning/pull/5954),
     [#6042](https://github.com/PyTorchLightning/pytorch-lightning/pull/6042))
-- Added a way to print to terminal without breaking up the progress bar ([#5470](https://github.com/PyTorchLightning/pytorch-lightning/pull/5470))
 
 ### Changed
 

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.
 

docs/source/common/optimizers.rst

@@ -300,8 +300,6 @@ override the :meth:`optimizer_step` function.
 
 For example, here step optimizer A every 2 batches and optimizer B every 4 batches
 
-.. note:: When using Trainer(enable_pl_optimizer=True), there is no need to call `.zero_grad()`.
-
 .. testcode::
 
     def optimizer_zero_grad(self, current_epoch, batch_idx, optimizer, opt_idx):

docs/source/starter/new-project.rst

@@ -737,7 +737,7 @@ Lightning has many tools for debugging. Here is an example of just a few of them
 .. testcode::
 
     # Profile your code to find speed/memory bottlenecks
-    Trainer(profiler=True)
+    Trainer(profiler="simple")
 
 ---------------
 

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/core/lightning.py

@@ -1324,9 +1324,6 @@ def optimizer_step(
         By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example
         once per optimizer.
 
-        .. tip:: With ``Trainer(enable_pl_optimizer=True)``, you can use ``optimizer.step()`` directly
-         and it will handle zero_grad, accumulated gradients, AMP, TPU and more automatically for you.
-
         Warning:
             If you are overriding this method, make sure that you pass the ``optimizer_closure`` parameter
             to ``optimizer.step()`` function as shown in the examples. This ensures that

pytorch_lightning/metrics/functional/__init__.py

@@ -21,7 +21,6 @@
     multiclass_auroc,
     stat_scores_multiple_classes,
     to_categorical,
-    to_onehot,
 )
 from pytorch_lightning.metrics.functional.confusion_matrix import confusion_matrix  # noqa: F401
 from pytorch_lightning.metrics.functional.explained_variance import explained_variance  # noqa: F401