Merge pull request #1436 from JuliaRobotics/master

v0.25.5-rc1
JuliaRobotics · Oct 22, 2021 · 59e6dd7 · 59e6dd7
2 parents 959d766 + adbe12a
commit 59e6dd7
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Showing 7 changed files with 27 additions and 15 deletions.
diff --git a/NEWS.md b/NEWS.md
@@ -25,6 +25,7 @@ The list below highlights major breaking changes, and please note that significa
 - Removing obsolete `approxConvCircular`, use `approxConvBelief` instead.
 - `getSample` should return a single sample and no longer takes the N(number of samples) parameter.
 - `solveTree!` / `solveGraph!` now returns just one value `tree<:AbstractBayesTree`.  Previous version returned three values, `tree, smt, hist` (#1379).
+- **Note for v0.25.5** Serialization of newly introduced type `PackedHeatmapGridDensity` changed from v0.25.4, unlikely have yet been used publically, therefore emphasizing fastest possible standardization in this case (even though this particular event does not strictly follow semver).  General usage and operation is effectively unchanged,see #1435.
 
 # Major changes in v0.24
 

diff --git a/Project.toml b/Project.toml
@@ -2,7 +2,7 @@ name = "IncrementalInference"
 uuid = "904591bb-b899-562f-9e6f-b8df64c7d480"
 keywords = ["MM-iSAMv2", "Bayes tree", "junction tree", "Bayes network", "variable elimination", "graphical models", "SLAM", "inference", "sum-product", "belief-propagation"]
 desc = "Implements the Multimodal-iSAMv2 algorithm."
-version = "0.25.4"
+version = "0.25.5"
 
 [deps]
 ApproxManifoldProducts = "9bbbb610-88a1-53cd-9763-118ce10c1f89"

diff --git a/src/DispatchPackedConversions.jl b/src/DispatchPackedConversions.jl
@@ -29,7 +29,7 @@ end
 
 ## unpack converters------------------------------------------------------------
 
-
+# FIXME see #1424
 function convert(
             ::Type{GenericFunctionNodeData{CommonConvWrapper{F}}},
             packed::GenericFunctionNodeData{P} ) where {F <: AbstractFactor, P <: PackedInferenceType}

diff --git a/src/SerializingDistributions.jl b/src/SerializingDistributions.jl
@@ -158,7 +158,8 @@ mutable struct PackedHeatmapGridDensity <: PackedSamplableBelief
   domain::Tuple{Vector{Float64}, Vector{Float64}}
   hint_callback::String
   bw_factor::Float64
-  densityFnc::String
+  N::Int
+  # densityFnc::String # TODO rather rebuild at unpack
 end
 
 
@@ -171,28 +172,29 @@ function convert( ::Union{Type{<:SamplableBelief},Type{<:HeatmapGridDensity}},
   data__ = map(x->collect(x), data_)
   @cast data[i,j] := data__[j][i]
   _data__ = collect(data)
-  densFnc = convert(SamplableBelief, obj.densityFnc)
+  # densFnc = convert(SamplableBelief, obj.densityFnc)
   # build the final object, misses the hint...
   HeatmapGridDensity( _data__,
                       obj.domain,
                       obj.hint_callback == "" ? nothing : nothing,
-                      obj.bw_factor,
-                      densFnc )
+                      obj.bw_factor;
+                      N=obj.N )
 end
 
 function convert( ::Union{Type{<:PackedSamplableBelief},Type{<:PackedHeatmapGridDensity}}, 
                   obj::HeatmapGridDensity )
   #
   data_ = obj.data
   @cast data[j][i] := data_[i,j]
-  str = convert(SamplableBelief, obj.densityFnc)
+  # str = convert(SamplableBelief, obj.densityFnc)
+  N = Npts(obj.densityFnc)
   # TODO misses the hint...
   PackedHeatmapGridDensity( "IncrementalInference.PackedHeatmapGridDensity",
                             data,
                             obj.domain,
                             "", 
                             obj.bw_factor,
-                            str )
+                            N )
 end
 
 

diff --git a/src/entities/OptionalDensities.jl b/src/entities/OptionalDensities.jl
@@ -33,11 +33,14 @@ Notes
 - Density approximation is constructed on Guassian measurement assumption of level set and sigma variation.
 - Assume data is on a regular grid on TranslationGroup(2)
   - Assume on early implementation `x_grid, y_grid = domain`
+- Serialization currently does not store the hint callback.
+- To save space, serialization does not store the internal density, but rather reconstructs at unpacking.
 
 DevNotes:
 - Generalize to scalar fields on any Manifold.
 - Generalize to vector fields if interpolation is sensible.
 - TODO standardize with AliasingScalarSampler see IIF #1341
+- TODO store the hint function (at least any easy cases)
 """
 struct HeatmapGridDensity{T <: Real, H <: Union{<:Function, Nothing}, B <: Union{ManifoldKernelDensity, BallTreeDensity}}
   """intensity data, on regular grid"""

diff --git a/src/services/CalcFactor.jl b/src/services/CalcFactor.jl
@@ -121,7 +121,8 @@ function calcFactorResidualTemporary( fct::AbstractRelative,
                                       measurement,
                                       pts::Tuple;
                                       tfg::AbstractDFG = initfg(),
-                                      _blockRecursion::Bool=false )
+                                      _blockRecursion::Bool=false,
+                                      doTime::Bool=false  )
   #
 
   # build a new temporary graph
@@ -135,9 +136,14 @@ function calcFactorResidualTemporary( fct::AbstractRelative,
     cfo = CalcFactor(_getCCW(_dfgfct))
     sampleFactor(cfo, 1)[1]
   end
-
+  
   # assume a single sample point is being run
-  return calcFactorResidual(_dfgfct, _measurement, pts...) 
+  return if doTime
+    @time res = calcFactorResidual(_dfgfct, _measurement, pts...)
+    res
+  else
+    calcFactorResidual(_dfgfct, _measurement, pts...)
+  end
 end
 
 

diff --git a/src/services/HeatmapSampler.jl b/src/services/HeatmapSampler.jl
@@ -61,7 +61,7 @@ end
 # Helper function to construct HGD
 function HeatmapGridDensity(data::AbstractMatrix{<:Real}, 
                             domain::Tuple{<:AbstractVector{<:Real},<:AbstractVector{<:Real}},
-                            hist_callback::Union{<:Function, Nothing}=nothing,
+                            hint_callback::Union{<:Function, Nothing}=nothing,
                             bw_factor::Real=0.7;  # kde spread between domain points 
                             N::Int=10000  )
   #
@@ -101,7 +101,7 @@ function HeatmapGridDensity(data::AbstractMatrix{<:Real},
   density = ManifoldKernelDensity(TranslationGroup(Ndim(bel)), bel)
 
   # return `<:SamplableBelief` object
-  HeatmapGridDensity(data, domain, hist_callback, bw_factor, density)
+  HeatmapGridDensity(data, domain, hint_callback, bw_factor, density)
 end
 
 
@@ -111,7 +111,7 @@ function LevelSetGridNormal(data::AbstractMatrix{<:Real},
                             level::Real,
                             sigma::Real;
                             sigma_scale::Real=3,
-                            hist_callback::Union{<:Function, Nothing}=nothing,
+                            hint_callback::Union{<:Function, Nothing}=nothing,
                             bw_factor::Real=0.7,  # kde spread between domain points 
                             N::Int=10000  )
   #
@@ -128,7 +128,7 @@ function LevelSetGridNormal(data::AbstractMatrix{<:Real},
   #   l = 1/2 (h-z/σ)^2
   #   masked_roi = 0 .< κ^2 - l
 
-  hgd = HeatmapGridDensity(data, domain, hist_callback, bw_factor; N=N)
+  hgd = HeatmapGridDensity(data, domain, hint_callback, bw_factor; N=N)
 
   LevelSetGridNormal(level, sigma, float(sigma_scale), hgd)
 end