Serde Support

Cargo.toml

@@ -1,39 +1,78 @@
 [package]
-name = "samrs"
+name = "factrs"
 version = "0.1.0"
 edition = "2021"
 
 [dependencies]
+# base
 ahash = "0.8.11"
 derive_more = "0.99.17"
 paste = "1.0.15"
 downcast-rs = "1.2.1"
+log = "0.4.21"
 
-faer = { version = "0.19.0", default-features = false, features = ["perf-warn", "std"] }
+# numerical 
+faer = { version = "0.19.0", default-features = false, features = [
+    "perf-warn",
+    "std",
+] }
 faer-ext = { version = "0.2.0", features = ["nalgebra"] }
 nalgebra = { version = "0.32.5" }
 num-dual = "0.9.1"
 matrixcompare-core = { version = "0.1", optional = true }
-serde = {version = "1.0.203", optional = true }
-log = "0.4.21"
-rerun = { version = "0.16.1", optional = true}
 
-[[example]]
-name = "g2o-rerun"
-required-features = ["rerun"]
+# serialization
+serde = { version = "1.0.203", optional = true }
+typetag = { version = "0.2.16", optional = true }
+serde_json = { version = "1.0.120", optional = true }
 
+# rerun support
+rerun = { version = "0.16.1", optional = true, default-features = false, features = [
+    "sdk",
+] }
 
 [features]
+# Run everything with f32 instead of the defaul f64
 f32 = []
+
+# Use left instead of right for lie group updates
 left = []
+
+# use SO(n) x R instead of SE(n) for exponential map
 fake_exp = []
+
+# Necessary dependencies for matrix comparing
 compare = ["dep:matrixcompare-core", "nalgebra/compare", "faer/matrixcompare"]
-serde = ["dep:serde", "nalgebra/serde", "faer/serde", "ahash/serde"]
-multithread = ["faer/rayon"]
+
+# Add multithreaded support (may run slower on smaller problems)
+rayon = ["faer/rayon"]
+
+# Add support for serialization
+serde = [
+    "dep:serde",
+    "dep:typetag",
+    "nalgebra/serde-serialize",
+    "faer/serde",
+    "ahash/serde",
+]
+# just used for examples
+serde_json = ["dep:serde_json"]
+
+# Support for conversion to rerun variable types
 rerun = ["dep:rerun"]
 
 [dev-dependencies]
 matrixcompare = "0.3.0"
-plotters = "0.3.6"
 pretty_env_logger = "0.4"
 nalgebra = { version = "0.32.5", features = ["compare"] }
+
+[[example]]
+name = "g2o"
+
+[[example]]
+name = "g2o-rerun"
+required-features = ["rerun"]
+
+[[example]]
+name = "serde"
+required-features = ["serde", "serde_json"]

examples/g2o-rerun.rs

@@ -4,13 +4,13 @@ use std::{
     time::Instant,
 };
 
-use rerun::{Arrows2D, Arrows3D, Points2D, Points3D};
-use samrs::{
-    optimizers::{GaussNewton, Optimizer},
-    rerun::RerunSender,
+use factrs::{
+    optimizers::{GaussNewton, GraphOptimizer, Optimizer},
+    rerun::RerunObserver,
     utils::load_g20,
     variables::*,
 };
+use rerun::{Arrows2D, Arrows3D, Points2D, Points3D};
 
 fn main() -> Result<(), Box<dyn std::error::Error>> {
     // ------------------------- Parse Arguments & Load data ------------------------- //
@@ -46,20 +46,20 @@ fn main() -> Result<(), Box<dyn std::error::Error>> {
     let topic = "base/solution";
     match (dim, obj) {
         ("se2", "points") => {
-            let callback = RerunSender::<SE2, Points2D>::new(rec, topic);
-            optimizer.params.add_callback(callback)
+            let callback = RerunObserver::<SE2, Points2D>::new(rec, topic);
+            optimizer.observers.add(callback)
         }
         ("se2", "arrows") => {
-            let callback = RerunSender::<SE2, Arrows2D>::new(rec, topic);
-            optimizer.params.add_callback(callback)
+            let callback = RerunObserver::<SE2, Arrows2D>::new(rec, topic);
+            optimizer.observers.add(callback)
         }
         ("se3", "points") => {
-            let callback = RerunSender::<SE3, Points3D>::new(rec, topic);
-            optimizer.params.add_callback(callback)
+            let callback = RerunObserver::<SE3, Points3D>::new(rec, topic);
+            optimizer.observers.add(callback)
         }
         ("se3", "arrows") => {
-            let callback = RerunSender::<SE3, Arrows3D>::new(rec, topic);
-            optimizer.params.add_callback(callback)
+            let callback = RerunObserver::<SE3, Arrows3D>::new(rec, topic);
+            optimizer.observers.add(callback)
         }
         _ => panic!("Invalid arguments"),
     };

examples/g2o.rs

@@ -1,7 +1,7 @@
 use std::{env, time::Instant};
 
-use samrs::{
-    optimizers::{GaussNewton, Optimizer},
+use factrs::{
+    optimizers::{GaussNewton, GraphOptimizer, Optimizer},
     utils::load_g20,
 };
 fn main() {

examples/serde.rs

@@ -0,0 +1,45 @@
+use factrs::{
+    containers::{Graph, Values, X},
+    factors::Factor,
+    noise::GaussianNoise,
+    residuals::{BetweenResidual, PriorResidual},
+    robust::{GemanMcClure, L2},
+    variables::{SE2, SO2},
+};
+
+fn main() {
+    // ------------------------- Try with values ------------------------- //
+    let x = SO2::from_theta(0.6);
+    let y = SE2::new(1.0, 2.0, 0.3);
+    let mut values = Values::new();
+    values.insert(X(0), x.clone());
+    values.insert(X(1), y.clone());
+
+    let serialized = serde_json::to_string_pretty(&values).unwrap();
+    println!("serialized = {}", serialized);
+
+    // Convert the JSON string back to a Point.
+    let deserialized: Values = serde_json::from_str(&serialized).unwrap();
+    println!("deserialized = {}", deserialized);
+
+    // ------------------------- Try with graph ------------------------- //
+    let prior = PriorResidual::new(x);
+    let bet = BetweenResidual::new(y);
+
+    let prior = Factor::new_full(
+        &[X(0)],
+        prior,
+        GaussianNoise::from_scalar_cov(0.1),
+        GemanMcClure::default(),
+    );
+    let bet = Factor::new_full(&[X(0), X(1)], bet, GaussianNoise::from_scalar_cov(10.0), L2);
+    let mut graph = Graph::new();
+    graph.add_factor(prior);
+    graph.add_factor(bet);
+
+    let serialized = serde_json::to_string_pretty(&graph).unwrap();
+    println!("serialized = {}", serialized);
+
+    let deserialized: Graph = serde_json::from_str(&serialized).unwrap();
+    println!("deserialized = {:?}", deserialized);
+}

src/containers/graph.rs

@@ -3,7 +3,8 @@ use faer::sparse::SymbolicSparseColMat;
 use super::{Idx, Values, ValuesOrder};
 use crate::{dtype, factors::Factor, linear::LinearGraph};
 
-#[derive(Default)]
+#[derive(Default, Debug)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
 pub struct Graph {
     factors: Vec<Factor>,
 }

src/containers/symbol.rs

@@ -10,9 +10,14 @@ const CHR_MASK: u64 = (char::MAX as u64) << IDX_BITS;
 const IDX_MASK: u64 = !CHR_MASK;
 
 #[derive(Clone, Eq, Hash, PartialEq)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
 pub struct Symbol(u64);
 
 impl Symbol {
+    pub fn new_raw(key: u64) -> Self {
+        Symbol(key)
+    }
+
     pub fn chr(&self) -> char {
         ((self.0 & CHR_MASK) >> IDX_BITS) as u8 as char
     }

src/containers/values.rs

@@ -9,6 +9,7 @@ use crate::{linear::LinearValues, variables::VariableSafe};
 // we can just use dtype rather than using generics with Numeric
 
 #[derive(Clone)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
 pub struct Values {
     values: AHashMap<Symbol, Box<dyn VariableSafe>>,
 }
@@ -35,8 +36,7 @@ impl Values {
         key: Symbol,
         value: impl VariableSafe,
     ) -> Option<Box<dyn VariableSafe>> {
-        // TODO: Avoid cloning here?
-        self.values.insert(key, value.clone_box())
+        self.values.insert(key, Box::new(value))
     }
 
     pub fn get(&self, key: &Symbol) -> Option<&Box<dyn VariableSafe>> {
@@ -62,6 +62,13 @@ impl Values {
         self.values.get_mut(key)
     }
 
+    // TODO: This should be some kind of error
+    pub fn get_mut_cast<T: VariableSafe>(&mut self, key: &Symbol) -> Option<&mut T> {
+        self.values
+            .get_mut(key)
+            .and_then(|value| value.downcast_mut::<T>())
+    }
+
     pub fn remove(&mut self, key: &Symbol) -> Option<Box<dyn VariableSafe>> {
         self.values.remove(key)
     }

src/factors/factor.rs

@@ -3,11 +3,13 @@ use crate::{
     dtype,
     linalg::{AllocatorBuffer, Const, DefaultAllocator, DiffResult, DualAllocator, MatrixBlock},
     linear::LinearFactor,
-    noise::{GaussianNoise, NoiseModel, NoiseModelSafe},
+    noise::{NoiseModel, NoiseModelSafe, UnitNoise},
     residuals::{Residual, ResidualSafe},
-    robust::{RobustCost, RobustCostSafe, L2},
+    robust::{RobustCostSafe, L2},
 };
 
+#[derive(Debug)]
+#[cfg_attr(feature = "serde", derive(serde::Serialize, serde::Deserialize))]
 pub struct Factor {
     pub keys: Vec<Symbol>,
     residual: Box<dyn ResidualSafe>,
@@ -21,14 +23,15 @@ impl Factor {
         residual: R,
     ) -> Self
     where
-        R: 'static + Residual<NumVars = Const<NUM_VARS>, DimOut = Const<DIM_OUT>>,
+        R: 'static + Residual<NumVars = Const<NUM_VARS>, DimOut = Const<DIM_OUT>> + ResidualSafe,
         AllocatorBuffer<R::DimIn>: Sync + Send,
         DefaultAllocator: DualAllocator<R::DimIn>,
+        UnitNoise<DIM_OUT>: NoiseModelSafe,
     {
         Self {
             keys: keys.to_vec(),
             residual: Box::new(residual),
-            noise: Box::new(GaussianNoise::<DIM_OUT>::identity()),
+            noise: Box::new(UnitNoise::<DIM_OUT>),
             robust: Box::new(L2),
         }
     }
@@ -39,8 +42,8 @@ impl Factor {
         noise: N,
     ) -> Self
     where
-        R: 'static + Residual<NumVars = Const<NUM_VARS>, DimOut = Const<DIM_OUT>>,
-        N: 'static + NoiseModel<Dim = Const<DIM_OUT>>,
+        R: 'static + Residual<NumVars = Const<NUM_VARS>, DimOut = Const<DIM_OUT>> + ResidualSafe,
+        N: 'static + NoiseModel<Dim = Const<DIM_OUT>> + NoiseModelSafe,
         AllocatorBuffer<R::DimIn>: Sync + Send,
         DefaultAllocator: DualAllocator<R::DimIn>,
     {
@@ -59,11 +62,11 @@ impl Factor {
         robust: C,
     ) -> Self
     where
-        R: 'static + Residual<NumVars = Const<NUM_VARS>, DimOut = Const<DIM_OUT>>,
+        R: 'static + Residual<NumVars = Const<NUM_VARS>, DimOut = Const<DIM_OUT>> + ResidualSafe,
         AllocatorBuffer<R::DimIn>: Sync + Send,
         DefaultAllocator: DualAllocator<R::DimIn>,
-        N: 'static + NoiseModel<Dim = Const<DIM_OUT>>,
-        C: 'static + RobustCost,
+        N: 'static + NoiseModel<Dim = Const<DIM_OUT>> + NoiseModelSafe,
+        C: 'static + RobustCostSafe,
     {
         Self {
             keys: keys.to_vec(),
@@ -75,7 +78,7 @@ impl Factor {
 
     pub fn error(&self, values: &Values) -> dtype {
         let r = self.residual.residual(values, &self.keys);
-        let r = self.noise.whiten_vec(r.as_view());
+        let r = self.noise.whiten_vec(r);
         let norm2 = r.norm_squared();
         self.robust.loss(norm2)
     }
@@ -89,8 +92,8 @@ impl Factor {
         let DiffResult { value: r, diff: a } = self.residual.residual_jacobian(values, &self.keys);
 
         // Whiten residual and jacobian
-        let r = self.noise.whiten_vec(r.as_view());
-        let a = self.noise.whiten_mat(a.as_view());
+        let r = self.noise.whiten_vec(r);
+        let a = self.noise.whiten_mat(a);
 
         // Weight according to robust cost
         let norm2 = r.norm_squared();

src/lib.rs

@@ -19,3 +19,41 @@ pub mod variables;
 
 #[cfg(feature = "rerun")]
 pub mod rerun;
+
+#[cfg(feature = "serde")]
+pub mod serde {
+    pub trait Tagged: serde::Serialize {
+        const TAG: &'static str;
+    }
+
+    #[macro_export]
+    macro_rules! register_typetag {
+        ($trait:path, $ty:ty) => {
+            // TODO: It'd be great if this was a blanket implementation, but
+            // I had problems getting it to run over const generics
+            impl $crate::serde::Tagged for $ty {
+                const TAG: &'static str = stringify!($ty);
+            }
+
+            typetag::__private::inventory::submit! {
+                <dyn $trait>::typetag_register(
+                    <$ty as $crate::serde::Tagged>::TAG, // Tag of the type
+                    (|deserializer| typetag::__private::Result::Ok(
+                        typetag::__private::Box::new(
+                            typetag::__private::erased_serde::deserialize::<$ty>(deserializer)?
+                        ),
+                    )) as typetag::__private::DeserializeFn<<dyn $trait as typetag::__private::Strictest>::Object>
+                )
+            }
+        };
+    }
+}
+
+// Dummy implementation so things don't break when the serde feature is disabled
+#[cfg(not(feature = "serde"))]
+pub mod serde {
+    #[macro_export]
+    macro_rules! register_typetag {
+        ($trait:path, $ty:ty) => {};
+    }
+}

src/linalg/forward_prop.rs

@@ -7,6 +7,7 @@ use super::{
     MatrixDim,
 };
 use crate::{
+    dtype,
     linalg::{Const, DefaultAllocator, DiffResult, DimName, Dyn, MatrixX, VectorDim, VectorX},
     variables::Variable,
 };
@@ -19,7 +20,7 @@ macro_rules! forward_maker {
     ($num:expr, $( ($name:ident: $var:ident) ),*) => {
         paste! {
             #[allow(unused_assignments)]
-            fn [<jacobian_ $num>]<$( $var: Variable<Alias<f64> = $var>, )* F: Fn($($var::Alias<Self::D>,)*) -> VectorX<Self::D>>
+            fn [<jacobian_ $num>]<$( $var: Variable<Alias<dtype> = $var>, )* F: Fn($($var::Alias<Self::D>,)*) -> VectorX<Self::D>>
                     (f: F, $($name: &$var,)*) -> DiffResult<VectorX, MatrixX>{
                 // Prepare variables
                 let mut curr_dim = 0;