undo CustomFactor changes

gtsam/nonlinear/CustomFactor.cpp

@@ -44,20 +44,14 @@ Vector CustomFactor::unwhitenedError(
        *    return error
        * ```
        */
-      std::pair<Vector, JacobianVector> errorAndJacobian =
+      return this->error_function_(*this, x, H.get_ptr());
-          this->error_function_(*this, x, H.get_ptr());
 
-      Vector error = errorAndJacobian.first;
-      (*H) = errorAndJacobian.second;
-
-      return error;
     } else {
       /*
        * In this case, we pass the a `nullptr` to pybind, and it will translate to `None` in Python.
        * Users can check for `None` in their callback to determine if the Jacobian is requested.
        */
-      auto errorAndJacobian = this->error_function_(*this, x, nullptr);
+      return this->error_function_(*this, x, nullptr);
-      return errorAndJacobian.first;
     }
   } else {
     return Vector::Zero(this->dim());

gtsam/nonlinear/CustomFactor.h

@@ -35,8 +35,7 @@ class CustomFactor;
  * This is safe because this is passing a const pointer, and pybind11 will maintain the `std::vector` memory layout.
  * Thus the pointer will never be invalidated.
  */
-using CustomErrorFunction = std::function<std::pair<Vector, JacobianVector>(
+using CustomErrorFunction = std::function<Vector(const CustomFactor &, const Values &, const JacobianVector *)>;
-    const CustomFactor &, const Values &, JacobianVector *)>;
 
 /**
  * @brief Custom factor that takes a std::function as the error
@@ -78,7 +77,7 @@ public:
     * Calls the errorFunction closure, which is a std::function object
     * One can check if a derivative is needed in the errorFunction by checking the length of Jacobian array
     */
-  Vector unwhitenedError(const Values &x, boost::optional<std::vector<Matrix>&> H = boost::none) const override;
+  Vector unwhitenedError(const Values &x, boost::optional<std::vector<Matrix> &> H = boost::none) const override;
 
   /** print */
   void print(const std::string &s,

python/gtsam/tests/test_custom_factor.py

@@ -33,7 +33,7 @@ class TestCustomFactor(GtsamTestCase):
 
         def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
             """Minimal error function stub"""
-            return np.array([1, 0, 0]), H
+            return np.array([1, 0, 0])
 
         noise_model = gtsam.noiseModel.Unit.Create(3)
         cf = CustomFactor(noise_model, [0], error_func)
@@ -46,7 +46,7 @@ class TestCustomFactor(GtsamTestCase):
             """Minimal error function with no Jacobian"""
             key0 = this.keys()[0]
             error = -v.atPose2(key0).localCoordinates(expected_pose)
-            return error, H
+            return error
 
         noise_model = gtsam.noiseModel.Unit.Create(3)
         cf = CustomFactor(noise_model, [0], error_func)
@@ -80,7 +80,7 @@ class TestCustomFactor(GtsamTestCase):
                 result = gT1.between(gT2)
                 H[0] = -result.inverse().AdjointMap()
                 H[1] = np.eye(3)
-            return error, H
+            return error
 
         noise_model = gtsam.noiseModel.Unit.Create(3)
         cf = CustomFactor(noise_model, [0, 1], error_func)
@@ -103,9 +103,9 @@ class TestCustomFactor(GtsamTestCase):
         gT1 = Pose2(1, 2, np.pi / 2)
         gT2 = Pose2(-1, 4, np.pi)
 
-        def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
+        def error_func(this: CustomFactor, v: gtsam.Values, _: List[np.ndarray]):
             """Minimal error function stub"""
-            return np.array([1, 0, 0]), H
+            return np.array([1, 0, 0])
 
         noise_model = gtsam.noiseModel.Unit.Create(3)
         from gtsam.symbol_shorthand import X
@@ -143,7 +143,7 @@ class TestCustomFactor(GtsamTestCase):
                 result = gT1.between(gT2)
                 H[0] = -result.inverse().AdjointMap()
                 H[1] = np.eye(3)
-            return error, H
+            return error
 
         noise_model = gtsam.noiseModel.Unit.Create(3)
         cf = CustomFactor(noise_model, [0, 1], error_func)
@@ -181,7 +181,7 @@ class TestCustomFactor(GtsamTestCase):
                 result = gT1.between(gT2)
                 H[0] = -result.inverse().AdjointMap()
                 H[1] = np.eye(3)
-            return error, H
+            return error
 
         noise_model = gtsam.noiseModel.Unit.Create(3)
         cf = CustomFactor(noise_model, [0, 1], error_func)