Merge pull request #7 from powei-lin/py-factor

Py factor

.gitignore

@@ -3,4 +3,6 @@
 __pycache__
 *.so
 .DS_Store
-.vscode/*
+.vscode/*
+.venv
+.pytest_cache

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "tiny-solver"
-version = "0.3.4"
+version = "0.4.0"
 edition = "2021"
 authors = ["Powei Lin <poweilin1994@gmail.com>"]
 readme = "README.md"
@@ -18,23 +18,26 @@ exclude = ["/.github/*", "*.ipynb", "./scripts/*", "examples/*"]
 faer = "0.18.2"
 faer-ext = { version = "0.1.0", features = ["nalgebra"] }
 nalgebra = "0.32.4"
-num-dual = "0.8.1"
+num-dual = "0.8.1" 
 num-traits = "0.2.18"
 numpy = { version = "0.20.0", features = ["nalgebra"] }
 pyo3 = { version = "0.20.3", features = ["abi3", "abi3-py38"] }
 rayon = "1.9.0"
 
 [[example]]
 name = "m3500_benchmark"
-# crate-type = ["bin"]
 path = "examples/m3500_benchmark.rs"
 
+[features]
+python = ["num-dual/python"]
+
 [dev-dependencies]
+itertools = "0.12.1"
 plotters = "0.3.5"
 
 [profile.dev.package.faer]
 opt-level = 3
 
 [lib]
 name = "tiny_solver"
-# crate-type = ["cdylib"]
+# crate-type = ["staticlib"]

examples/python/small_problem.py

@@ -0,0 +1,24 @@
+import numpy as np
+from tiny_solver import Problem, GaussNewtonOptimizer
+from tiny_solver.factors import PriorFactor, PyFactor
+
+
+def cost(x, y, z):
+  r0 = x[0] + 2*y[0] + 4*z[0]
+  r1 = y[0] * z[0]
+  return np.array([r0, r1])
+
+def main():
+    problem = Problem()
+    pf = PyFactor(cost)
+    problem.add_residual_block(2, [('x', 1), ('y', 1), ('z', 1),], pf, None)
+    pp = PriorFactor(np.array([3.0]))
+    problem.add_residual_block(1, [('x', 1)], pp, None)
+    gn = GaussNewtonOptimizer()
+    init_values = {"x": np.array([0.7]), "y": np.array([-30.2]), "z": np.array([123.9])}
+    result_values = gn.optimize(problem, init_values)
+    print(result_values)
+
+
+if __name__ == '__main__':
+    main()

examples/python/try_import.py

@@ -1,9 +1,17 @@
 import tiny_solver
-from tiny_solver import GaussNewtonOptimizer, Problem, LinearSolver, OptimizerOptions
-from tiny_solver.factors import PriorFactor, BetweenFactorSE2
+from tiny_solver import GaussNewtonOptimizer, Problem, LinearSolver, OptimizerOptions, first_derivative_test
+from tiny_solver.factors import PriorFactor, BetweenFactorSE2, PyFactor
 from tiny_solver.loss_functions import HuberLoss
 import numpy as np
 
+def f(x: np.ndarray, y: np.ndarray):
+    # print("py ", x*x)
+    return np.array([2*x[0], x[1]*x[1]*x[1], y[1]*4.0])
+
+def fa():
+    print("fa")
+    return 123
+
 
 def main():
     print(f"{tiny_solver.__version__=}")
@@ -14,6 +22,11 @@ def main():
     print(opt_option)
     loss = HuberLoss(1.0)
     print(loss)
+    a = np.array([1.0, 2.0])
+    # j = first_derivative_test(f, a)
+    # print(j)
+    a = PyFactor(f)
+    a.call_func()
     exit()
     # print(tiny_solver.sum_as_string(1, 2))
 

pyproject.toml

@@ -20,4 +20,4 @@ dependencies = ["numpy"]
 
 
 [tool.maturin]
-features = ["pyo3/extension-module"]
+features = ["pyo3/extension-module", "python"]

src/gauss_newton_optimizer.rs

@@ -21,7 +21,6 @@ impl optimizer::Optimizer for GaussNewtonOptimizer {
         let opt_option = optimizer_option.unwrap_or_default();
 
         let mut last_err: f64 = 1.0;
-
         let mut symbolic_pattern: Option<solvers::SymbolicCholesky<usize>> = None;
 
         for i in 0..opt_option.max_iteration {

src/lib.rs

@@ -4,13 +4,14 @@ pub mod linear;
 pub mod loss_functions;
 pub mod optimizer;
 pub mod problem;
-pub mod python;
 pub mod residual_block;
 pub mod tiny_solver_old;
 
 pub use gauss_newton_optimizer::*;
 pub use linear::*;
 pub use optimizer::*;
 pub use problem::*;
-pub use python::*;
 pub use residual_block::*;
+
+#[cfg(feature = "python")]
+pub mod python;