add readme and fix minor stuff

README.md

@@ -6,3 +6,18 @@ Read and use at your own risk.
 [1] Stephan Hilb and Andreas Langer. 'A General Decomposition Method
 for a Convex Problem Related to Total Variation Minimization'. In preparation.
 2022
+
+## Getting started
+
+1. `julia --project=scripts/`
+2. `]instantiate`
+3. execute statements in `include("scripts/run.jl")`. There is a memory leak
+   currently, so the Julia session should better be restarted after every
+   experiment.
+
+### Running parallel scaling tests
+
+1. start Julia with e.g. `julia -p 9 --project=scripts/`
+2. `@everywhere using Pkg`
+3. `@everywhere Pkg.activate("scripts/")`
+4. as above

scripts/run_experiments.jl

@@ -8,6 +8,7 @@ using CSV
 using Colors: HSV
 using DataFrames
 using Distributed
+@everywhere using DualTVDD
 @everywhere using DualTVDD:
     DualTVL1ROFOpProblem,
     DualTVDDAlgorithm, ChambolleAlgorithm, NStepAlgorithm, ProjGradAlgorithm,# TVNewtonAlgorithm,
@@ -60,7 +61,7 @@ function halve(img)
     return res
 end
 
-# Problems
+# Problem Definitions
 
 function DenoiseProblem(img::AbstractArray{T,d}; λ, β = 0.) where {T,d}
     B = (1 + β) * I
@@ -73,9 +74,6 @@ function InpaintProblem(img::AbstractArray{T,d}, imgmask::AbstractArray{Bool,d};
     pwop = imgmask .+ β .* Ref(I)
     B = Diagonal(vec(inv.(pwop)))
 
-    ## we grey-out the inpainting area.
-    ## since β > 0 this actually matters and should be a sane default
-    #g = imgmask .* img .+ .!imgmask .* 0.5
     g = imgmask .* img
     return DualTVL1ROFOpProblem(g, B, λ)
 end
@@ -280,21 +278,17 @@ function experiment_scaling_opticalflow(ctx)
     ntimings = 3
 
     prob = OptFlowProblem(f0, f1; λ, β)
-    #return prob
 
     galg = ChambolleAlgorithm(prob)
-    dalg(workers) =
-        DualTVDDAlgorithm(prob;
-            workers, M, overlap, ninner, parallel=false, σ=1.,
-            subalg = x -> ChambolleAlgorithm(x))
+    alg_ddseq(workers) = DualTVDDAlgorithm(prob; workers, M, overlap, ninner, parallel = false, σ = 1., subalg = x -> ChambolleAlgorithm(x))
+    alg_ddpar(workers) = DualTVDDAlgorithm(prob; workers, M, overlap, ninner, parallel = true, σ = 0.25, subalg = x -> ChambolleAlgorithm(x))
 
     function timeit(alg)
-        # precompile
+        # warmup for precompilation
         run_while((_, _) -> false, alg)
 
         times = map(1:ntimings) do _
             return @elapsed run_while(alg) do st, k
-                # min ~107
                 return energy(fetch(st), prob) > stopenergy
             end
         end
@@ -311,13 +305,14 @@ function experiment_scaling_opticalflow(ctx)
         nparallel % nw == 0 || continue
         push!(df, (
             nworkers = nw,
-            time = timeit(dalg(ws[1:nw]))))
+            time_ddseq = timeit(alg_ddseq(ws[1:nw])),
+            time_ddpar = timeit(alg_ddpar(ws[1:nw]))
+        ))
     end
 
     display(df)
 
     CSV.write(joinpath(ctx.outdir, "timings.csv"), df)
-    #saveimg(joinpath(ctx.outdir, "output_glob.png"), fetch_u(states.glob))
     savedata(joinpath(ctx.outdir, "data.tex");
         lambda=λ, beta=β, Mdir, M=prod(M),
         ntimings, stopenergy, ninner,
@@ -372,7 +367,7 @@ function _experiment_surrogate_outerinner(ctx, ref)
             #residual = residual(fetch(state), prob),
            )
     end
-    display(df)
+    #display(df)
 
     CSV.write(joinpath(ctx.outdir, "energies.csv"), df)
     #saveimg(joinpath(ctx.outdir, "output_glob.png"), fetch_u(states.glob))