From dbeebfec267d26b1af5e5f81dc6d4b6e13ec567c Mon Sep 17 00:00:00 2001
From: senselessdev1 <johnlambert@hayoungs-mbp.lan>
Date: Sat, 12 Aug 2023 16:25:28 -0400
Subject: [PATCH] fix formatting and docstrings

---
 gtsfm/bundle/bundle_adjustment.py | 39 ++++++++++++++-----------------
 1 file changed, 17 insertions(+), 22 deletions(-)

diff --git a/gtsfm/bundle/bundle_adjustment.py b/gtsfm/bundle/bundle_adjustment.py
index 57f0794be..ebbaafb92 100644
--- a/gtsfm/bundle/bundle_adjustment.py
+++ b/gtsfm/bundle/bundle_adjustment.py
@@ -289,7 +289,7 @@ def __cameras_to_model(
 
         return sorted(list(cameras))
 
-    def run_ba_step(
+    def run_ba_at_threshold(
         self,
         initial_data: GtsfmData,
         absolute_pose_priors: List[Optional[PosePrior]],
@@ -297,7 +297,7 @@ def run_ba_step(
         reproj_error_thresh: Optional[float],
         verbose: bool = True,
     ) -> Tuple[GtsfmData, GtsfmData, List[bool], float]:
-        """TODO"""
+        """Run bundle adjustment and filter the resulting tracks by reprojection error."""
         cameras_to_model = self.__cameras_to_model(initial_data, absolute_pose_priors, relative_pose_priors)
         graph = self.__construct_factor_graph(
             cameras_to_model=cameras_to_model,
@@ -311,31 +311,24 @@ def run_ba_step(
         # Print error.
         final_error = graph.error(result_values)
         if verbose:
-            logger.info(f"initial error: {graph.error(initial_values):.2f}")
-            logger.info(f"final error: {final_error:.2f}")
+            logger.info("initial error: %.2f", graph.error(initial_values))
+            logger.info("final error: %.2f", final_error)
 
-        # Construct the results.
+        # Convert the `Values` results to a `GtsfmData` instance.
         optimized_data = values_to_gtsfm_data(result_values, initial_data, self._shared_calib)
 
         # Filter landmarks by reprojection error.
         if reproj_error_thresh is not None:
             if verbose:
-                logger.info(
-                    "[Result] Number of tracks before filtering: %d",
-                    optimized_data.number_tracks(),
-                )
+                logger.info("[Result] Number of tracks before filtering: %d", optimized_data.number_tracks())
             filtered_result, valid_mask = optimized_data.filter_landmarks(reproj_error_thresh)
             if verbose:
-                logger.info(
-                    "[Result] Number of tracks after filtering: %d",
-                    filtered_result.number_tracks(),
-                )
+                logger.info("[Result] Number of tracks after filtering: %d", filtered_result.number_tracks())
 
         else:
             valid_mask = [True] * optimized_data.number_tracks()
             filtered_result = optimized_data
 
-        # Set intermediate result as initial condition for next step.
         return optimized_data, filtered_result, valid_mask, final_error
 
     def run_ba(
@@ -361,7 +354,8 @@ def run_ba(
         """
         num_ba_steps = len(self._reproj_error_thresholds)
         for step, reproj_error_thresh in enumerate(self._reproj_error_thresholds):
-            (optimized_data, filtered_result, valid_mask, final_error,) = self.run_ba_step(
+            # Use intermediate result as initial condition for next step.
+            (optimized_data, filtered_result, valid_mask, final_error) = self.run_ba_at_threshold(
                 initial_data,
                 absolute_pose_priors,
                 relative_pose_priors,
@@ -460,20 +454,21 @@ def _run_ba_instrumented(
         num_ba_steps = len(self._reproj_error_thresholds)
         for step, reproj_error_thresh in enumerate(self._reproj_error_thresholds):
             step_start_time = time.time()
-            (optimized_data, filtered_result, valid_mask, final_error,) = self.run_ba_step(
-                initial_data,
-                absolute_pose_priors,
-                relative_pose_priors,
-                reproj_error_thresh,
-                verbose,
+            (optimized_data, filtered_result, valid_mask, final_error) = self.run_ba_at_threshold(
+                initial_data=initial_data,
+                absolute_pose_priors=absolute_pose_priors,
+                relative_pose_priors=relative_pose_priors,
+                reproj_error_thresh=reproj_error_thresh,
+                verbose=verbose,
             )
             step_times.append(time.time() - step_start_time)
 
             # Print intermediate results.
             if num_ba_steps > 1:
                 logger.info(
-                    "[BA Step %d/%d] Error: %.2f, Number of tracks: %d"
+                    "[BA Stage @ thresh=%.2f px %d/%d] Error: %.2f, Number of tracks: %d"
                     % (
+                        reproj_error_thresh,
                         step + 1,
                         num_ba_steps,
                         final_error,