Change optimize_using to simpler function call

Author: gchenfc

python/gtsam/tests/test_logging_optimizer.py

@@ -66,9 +66,9 @@ def hook(_, error):
 
         # Wrapper function sets the hook and calls optimizer.optimize() for us.
         params = gtsam.GaussNewtonParams()
-        actual = optimize_using(gtsam.GaussNewtonOptimizer, hook)(self.graph, self.initial)
+        actual = optimize_using(gtsam.GaussNewtonOptimizer, hook, self.graph, self.initial)
         self.check(actual)
-        actual = optimize_using(gtsam.GaussNewtonOptimizer, hook)(self.graph, self.initial, params)
+        actual = optimize_using(gtsam.GaussNewtonOptimizer, hook, self.graph, self.initial, params)
         self.check(actual)
         actual = gtsam_optimize(gtsam.GaussNewtonOptimizer(self.graph, self.initial, params),
                                 params, hook)
@@ -80,10 +80,10 @@ def hook(_, error):
             print(error)
 
         params = gtsam.LevenbergMarquardtParams()
-        actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook)(self.graph, self.initial)
+        actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook, self.graph, self.initial)
         self.check(actual)
-        actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook)(self.graph, self.initial,
-                                                                         params)
+        actual = optimize_using(gtsam.LevenbergMarquardtOptimizer, hook, self.graph, self.initial,
+                                params)
         self.check(actual)
         actual = gtsam_optimize(gtsam.LevenbergMarquardtOptimizer(self.graph, self.initial, params),
                                 params, hook)

python/gtsam/utils/logging_optimizer.py

@@ -17,39 +17,42 @@
 }
 
 
-def optimize_using(OptimizerClass, hook) -> Callable[[Any], gtsam.Values]:
+def optimize_using(OptimizerClass, hook, *args) -> gtsam.Values:
     """ Wraps the constructor and "optimize()" call for an Optimizer together and adds an iteration
         hook.
         Example usage:
-            solution = optimize_using(gtsam.GaussNewtonOptimizer, hook)(graph, init, params)
+            ```python
+            def hook(optimizer, error):
+                print("iteration {:}, error = {:}".format(optimizer.iterations(), error))
+            solution = optimize_using(gtsam.GaussNewtonOptimizer, hook, graph, init, params)
+            ```
+        Iteration hook's args are (optimizer, error) and return type should be None
 
     Args:
         OptimizerClass (T): A NonlinearOptimizer class (e.g. GaussNewtonOptimizer,
-            LevenbergMarquadrtOptimizer)
+            LevenbergMarquardtOptimizer)
         hook ([T, double] -> None): Function to callback after each iteration.  Args are (optimizer,
             error) and return should be None.
+        *args: Arguments that would be passed into the OptimizerClass constructor, usually:
+            graph, init, [params]
     Returns:
-        (Callable[*, gtsam.Values]): Call the returned function with the usual NonlinearOptimizer
-            arguments (will be forwarded to constructor) and it will return a Values object
-            representing the solution.  See example usage above.
+        (gtsam.Values): A Values object representing the optimization solution.
     """
-
-    def wrapped_optimize(*args):
-        for arg in args:
-            if isinstance(arg, gtsam.NonlinearOptimizerParams):
-                arg.iterationHook = lambda iteration, error_before, error_after: hook(
-                    optimizer, error_after)
-                break
-        else:
-            params = OPTIMIZER_PARAMS_MAP[OptimizerClass]()
-            params.iterationHook = lambda iteration, error_before, error_after: hook(
+    # Add the iteration hook to the NonlinearOptimizerParams
+    for arg in args:
+        if isinstance(arg, gtsam.NonlinearOptimizerParams):
+            arg.iterationHook = lambda iteration, error_before, error_after: hook(
                 optimizer, error_after)
-            args = (*args, params)
-        optimizer = OptimizerClass(*args)
-        hook(optimizer, optimizer.error())
-        return optimizer.optimize()
-
-    return wrapped_optimize
+            break
+    else:
+        params = OPTIMIZER_PARAMS_MAP[OptimizerClass]()
+        params.iterationHook = lambda iteration, error_before, error_after: hook(
+            optimizer, error_after)
+        args = (*args, params)
+    # Construct Optimizer and optimize
+    optimizer = OptimizerClass(*args)
+    hook(optimizer, optimizer.error())  # Call hook once with init values to match behavior below
+    return optimizer.optimize()
 
 
 def optimize(optimizer, check_convergence, hook):