add some mypy fixes

ignite/handlers/fbresearch_logger.py

@@ -1,6 +1,9 @@
 """FBResearch logger and its helper handlers."""
 
 import datetime
+from typing import Any, Optional
+
+# from typing import Any, Dict, Optional, Union
 
 import torch
 
@@ -33,14 +36,16 @@ class FBResearchLogger:
             logger.attach(trainer, name="Train", every=10, optimizer=my_optimizer)
     """
 
-    def __init__(self, logger, delimiter="  ", show_output=False):
+    def __init__(self, logger: Any, delimiter: str = "  ", show_output: bool = False):
         self.delimiter = delimiter
-        self.logger = logger
-        self.iter_timer = None
-        self.data_timer = None
-        self.show_output = show_output
-
-    def attach(self, engine: Engine, name: str, every: int = 1, optimizer=None):
+        self.logger: Any = logger
+        self.iter_timer: Timer = Timer(average=True)
+        self.data_timer: Timer = Timer(average=True)
+        self.show_output: bool = show_output
+
+    def attach(
+        self, engine: Engine, name: str, every: int = 1, optimizer: Optional[torch.optim.Optimizer] = None
+    ) -> None:
         """Attaches all the logging handlers to the given engine.
 
         Args:
@@ -54,15 +59,15 @@ def attach(self, engine: Engine, name: str, every: int = 1, optimizer=None):
         engine.add_event_handler(Events.EPOCH_COMPLETED, self.log_epoch_completed, engine, name)
         engine.add_event_handler(Events.COMPLETED, self.log_completed, engine, name)
 
-        self.iter_timer = Timer(average=True)
+        self.iter_timer.reset()
         self.iter_timer.attach(
             engine,
             start=Events.EPOCH_STARTED,
             resume=Events.ITERATION_STARTED,
             pause=Events.ITERATION_COMPLETED,
             step=Events.ITERATION_COMPLETED,
         )
-        self.data_timer = Timer(average=True)
+        self.data_timer.reset()
         self.data_timer.attach(
             engine,
             start=Events.EPOCH_STARTED,
@@ -71,14 +76,15 @@ def attach(self, engine: Engine, name: str, every: int = 1, optimizer=None):
             step=Events.GET_BATCH_COMPLETED,
         )
 
-    def log_every(self, engine, optimizer=None):
+    def log_every(self, engine: Engine, optimizer: Optional[torch.optim.Optimizer] = None) -> None:
         """
         Logs the training progress at regular intervals.
 
         Args:
             engine (Engine): The training engine.
             optimizer (torch.optim.Optimizer, optional): The optimizer used for training. Defaults to None.
         """
+        assert engine.state.epoch_length is not None
         cuda_max_mem = ""
         if torch.cuda.is_available():
             cuda_max_mem = f"GPU Max Mem: {torch.cuda.max_memory_allocated() / MB:.0f} MB"
@@ -89,12 +95,12 @@ def log_every(self, engine, optimizer=None):
         eta_seconds = iter_avg_time * (engine.state.epoch_length - current_iter)
 
         outputs = []
-        if self.show_output:
+        if self.show_output and engine.state.output is not None:
             output = engine.state.output
             if isinstance(output, dict):
                 outputs += [f"{k}: {v:.4f}" for k, v in output.items()]
             else:
-                outputs += [f"{v:.4f}" if isinstance(v, float) else f"{v}" for v in output]
+                outputs += [f"{v:.4f}" if isinstance(v, float) else f"{v}" for v in output]  # type: ignore
 
         lrs = ""
         if optimizer is not None:
@@ -120,7 +126,7 @@ def log_every(self, engine, optimizer=None):
         )
         self.logger.info(msg)
 
-    def log_epoch_started(self, engine, name):
+    def log_epoch_started(self, engine: Engine, name: str) -> None:
         """
         Logs the start of an epoch.
 
@@ -132,37 +138,44 @@ def log_epoch_started(self, engine, name):
         msg = f"{name}: start epoch [{engine.state.epoch}/{engine.state.max_epochs}]"
         self.logger.info(msg)
 
-    def log_epoch_completed(self, engine, name):
+    def log_epoch_completed(self, engine: Engine, name: str) -> None:
         """
         Logs the completion of an epoch.
 
         Args:
-            engine (Engine): The engine object.
-            name (str): The name of the epoch.
+            engine (Engine): The engine object that triggered the event.
+            name (str): The name of the event.
 
+        Returns:
+            None
         """
         epoch_time = engine.state.times[Events.EPOCH_COMPLETED.name]
-        epoch_info = f"Epoch [{engine.state.epoch}/{engine.state.max_epochs}]" if engine.state.max_epochs > 1 else ""
+        epoch_info = (
+            f"Epoch [{engine.state.epoch}/{engine.state.max_epochs}]"
+            if engine.state.max_epochs > 1
+            else ""  # type: ignore
+        )
         msg = self.delimiter.join(
             [
                 f"{name}: {epoch_info}",
-                f"Total time: {datetime.timedelta(seconds=int(epoch_time))}",
-                f"({epoch_time / engine.state.epoch_length:.4f} s / it)",
+                f"Total time: {datetime.timedelta(seconds=int(epoch_time))}",  # type: ignore
+                f"({epoch_time / engine.state.epoch_length:.4f} s / it)",  # type: ignore
             ]
         )
         self.logger.info(msg)
 
-    def log_completed(self, engine, name):
+    def log_completed(self, engine: Engine, name: str) -> None:
         """
         Logs the completion of a run.
 
         Args:
-            engine (Engine): The engine object.
+            engine (Engine): The engine object representing the training/validation loop.
             name (str): The name of the run.
 
         """
-        if engine.state.max_epochs > 1:
+        if engine.state.max_epochs and engine.state.max_epochs > 1:
             total_time = engine.state.times[Events.COMPLETED.name]
+            assert total_time is not None
             msg = self.delimiter.join(
                 [
                     f"{name}: run completed",