Add docstrings

docs/source/engine.rst

@@ -69,7 +69,7 @@ Resuming the training
 It is possible to resume the training from a checkpoint and approximately reproduce original run's behaviour.
 Using Ignite, this can be easily done using :class:`~ignite.handlers.checkpoint.Checkpoint` handler. Engine provides two methods
 to serialize and deserialize its internal state :meth:`~ignite.engine.engine.Engine.state_dict` and
-:meth:`~ignite.engine.engine.Engine.load_state_dict`. In addition to serializing model, optimizer, lr scheduler etc user can
+:meth:`~ignite.engine.engine.Engine.load_state_dict`. In addition to serializing model, optimizer, lr scheduler, metrics, etc., user can
 store the trainer and then resume the training. For example:
 
 .. code-block:: python
@@ -82,8 +82,9 @@ store the trainer and then resume the training. For example:
     optimizer = ...
     lr_scheduler = ...
     data_loader = ...
+    metric = ...
 
-    to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}
+    to_save = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler, 'metric': metric}
     handler = Checkpoint(to_save, DiskSaver('/tmp/training', create_dir=True))
     trainer.add_event_handler(Events.EPOCH_COMPLETED, handler)
     trainer.run(data_loader, max_epochs=100)
@@ -104,8 +105,9 @@ We can then restore the training from the last checkpoint.
     optimizer = ...
     lr_scheduler = ...
     data_loader = ...
+    metric = ...
 
-    to_load = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler}
+    to_load = {'trainer': trainer, 'model': model, 'optimizer': optimizer, 'lr_scheduler': lr_scheduler, 'metric': metric}
     checkpoint = torch.load(checkpoint_file)
     Checkpoint.load_objects(to_load=to_load, checkpoint=checkpoint)
 

ignite/handlers/checkpoint.py

@@ -710,7 +710,7 @@ def state_dict(self) -> "OrderedDict[str, List[Tuple[int, str]]]":
         return OrderedDict([("saved", [(p, f) for p, f in self._saved])])
 
     def load_state_dict(self, state_dict: Mapping) -> None:
-        """Method replace internal state of the class with provided state dict data.
+        """Method replaces internal state of the class with provided state dict data.
 
         Args:
             state_dict: a dict with "saved" key and list of ``(priority, filename)`` pairs as values.

ignite/metrics/metric.py

@@ -551,6 +551,12 @@ def is_attached(self, engine: Engine, usage: Union[str, MetricUsage] = EpochWise
         return engine.has_event_handler(self.completed, usage.COMPLETED)
 
     def state_dict(self) -> OrderedDict:
+        """Method returns state dict with attributes of the metric specified in its
+        `_state_dict_all_req_keys` attribute. Can be used to save internal state of the class.
+
+        If there's an active distributed configuration, some collective operations is done and
+        the list of values across ranks is saved under each attribute's name in the dict.
+        """
         state = OrderedDict()
         for attr_name in self._state_dict_all_req_keys:
             attr = getattr(self, attr_name)
@@ -565,6 +571,15 @@ def state_dict(self) -> OrderedDict:
         return state
 
     def load_state_dict(self, state_dict: Mapping) -> None:
+        """Method replaces internal state of the class with provided state dict data.
+
+        If there's an active distributed configuration, the process uses its rank to pick the proper value from
+        the list of values saved under each attribute's name in the dict.
+
+        Args:
+            state_dict: a dict containing attributes of the metric specified in its `_state_dict_all_req_keys`
+                attribute.
+        """
         super().load_state_dict(state_dict)
         rank = idist.get_local_rank()
         for attr in self._state_dict_all_req_keys: