Added state attributes for tqdm logger (#2162)

* added state attributes for tqdm logger

* autopep8 fix

* removed extra imports

* fix flake8

Co-authored-by: Ishan-Kumar2 <Ishan-Kumar2@users.noreply.github.com>
Co-authored-by: vfdev <vfdev.5@gmail.com>

Author: Ishan-Kumar2

ignite/contrib/handlers/tqdm_logger.py

@@ -77,6 +77,19 @@ class ProgressBar(BaseLogger):
             # Progress bar will looks like
             # Epoch [2/50]: [64/128]  50%|█████      , loss=0.123 [06:17<12:34]
 
+
+        Example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta``
+        are also logged along with the NLL and Accuracy after each iteration:
+
+        .. code-block:: python
+
+            pbar.attach(
+                trainer,
+                metric_names=["nll", "accuracy"],
+                state_attributes=["alpha", "beta"],
+            )
+
+
     Note:
         When adding attaching the progress bar to an engine, it is recommend that you replace
         every print operation in the engine's handlers triggered every iteration with
@@ -88,6 +101,9 @@ class ProgressBar(BaseLogger):
         Due to `tqdm notebook bugs <https://github.com/tqdm/tqdm/issues/594>`_, bar format may be needed to be set
         to an empty string value.
 
+    ..  versionchanged:: 0.5.0
+        `attach` now accepts an optional list of `state_attributes`
+
     """
 
     _events_order = [
@@ -161,6 +177,7 @@ def attach(  # type: ignore[override]
         output_transform: Optional[Callable] = None,
         event_name: Union[Events, CallableEventWithFilter] = Events.ITERATION_COMPLETED,
         closing_event_name: Union[Events, CallableEventWithFilter] = Events.EPOCH_COMPLETED,
+        state_attributes: Optional[List[str]] = None,
     ) -> None:
         """
         Attaches the progress bar to an engine object.
@@ -176,6 +193,7 @@ def attach(  # type: ignore[override]
                 :class:`~ignite.engine.events.Events`.
             closing_event_name: event's name on which the progress bar is closed. Valid events are from
                 :class:`~ignite.engine.events.Events`.
+            state_attributes: list of attributes of the ``trainer.state`` to plot.
 
         Note:
             Accepted output value types are numbers, 0d and 1d torch tensors and strings.
@@ -193,7 +211,13 @@ def attach(  # type: ignore[override]
         if not self._compare_lt(event_name, closing_event_name):
             raise ValueError(f"Logging event {event_name} should be called before closing event {closing_event_name}")
 
-        log_handler = _OutputHandler(desc, metric_names, output_transform, closing_event_name=closing_event_name)
+        log_handler = _OutputHandler(
+            desc,
+            metric_names,
+            output_transform,
+            closing_event_name=closing_event_name,
+            state_attributes=state_attributes,
+        )
 
         super(ProgressBar, self).attach(engine, log_handler, event_name)
         engine.add_event_handler(closing_event_name, self._close)
@@ -215,6 +239,7 @@ def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> Callable:
 class _OutputHandler(BaseOutputHandler):
     """Helper handler to log engine's output and/or metrics
 
+        pbar = ProgressBar()
     Args:
         description: progress bar description.
         metric_names: list of metric names to plot or a string "all" to plot all available
@@ -226,6 +251,7 @@ class _OutputHandler(BaseOutputHandler):
         closing_event_name: event's name on which the progress bar is closed. Valid events are from
             :class:`~ignite.engine.events.Events` or any `event_name` added by
             :meth:`~ignite.engine.engine.Engine.register_events`.
+        state_attributes: list of attributes of the ``trainer.state`` to plot.
 
     """
 
@@ -235,11 +261,14 @@ def __init__(
         metric_names: Optional[Union[str, List[str]]] = None,
         output_transform: Optional[Callable] = None,
         closing_event_name: Union[Events, CallableEventWithFilter] = Events.EPOCH_COMPLETED,
+        state_attributes: Optional[List[str]] = None,
     ):
         if metric_names is None and output_transform is None:
             # This helps to avoid 'Either metric_names or output_transform should be defined' of BaseOutputHandler
             metric_names = []
-        super(_OutputHandler, self).__init__(description, metric_names, output_transform, global_step_transform=None)
+        super(_OutputHandler, self).__init__(
+            description, metric_names, output_transform, global_step_transform=None, state_attributes=state_attributes
+        )
         self.closing_event_name = closing_event_name
 
     @staticmethod

tests/ignite/contrib/handlers/test_tqdm_logger.py

@@ -209,6 +209,44 @@ def step(engine, batch):
     assert actual == expected
 
 
+def test_pbar_with_state_attrs(capsys):
+
+    n_iters = 2
+    data = list(range(n_iters))
+    loss_values = iter(range(n_iters))
+
+    def step(engine, batch):
+        loss_value = next(loss_values)
+        return loss_value
+
+    trainer = Engine(step)
+    trainer.state.alpha = 3.899
+    trainer.state.beta = torch.tensor(12.21)
+    trainer.state.gamma = torch.tensor([21.0, 6.0])
+
+    RunningAverage(alpha=0.5, output_transform=lambda x: x).attach(trainer, "batchloss")
+
+    pbar = ProgressBar()
+    pbar.attach(trainer, metric_names=["batchloss",], state_attributes=["alpha", "beta", "gamma"])
+
+    trainer.run(data=data, max_epochs=1)
+
+    captured = capsys.readouterr()
+    err = captured.err.split("\r")
+    err = list(map(lambda x: x.strip(), err))
+    err = list(filter(None, err))
+    actual = err[-1]
+    if get_tqdm_version() < LooseVersion("4.49.0"):
+        expected = (
+            "Iteration: [1/2]  50%|█████     , batchloss=0.5, alpha=3.9, beta=12.2, gamma_0=21, gamma_1=6 [00:00<00:00]"
+        )
+    else:
+        expected = (
+            "Iteration: [1/2]  50%|█████     , batchloss=0.5, alpha=3.9, beta=12.2, gamma_0=21, gamma_1=6 [00:00<?]"
+        )
+    assert actual == expected
+
+
 def test_pbar_no_metric_names(capsys):
 
     n_epochs = 2