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Update TotalVariation.jl to run, defining benchmark/Project.toml. #39

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Update TotalVariation.jl to run, defining benchmark/Project.toml. #39

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12 changes: 12 additions & 0 deletions benchmarks/Project.toml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,12 @@
[deps]
AbstractOperators = "d9c5613a-d543-52d8-9afd-8f241a8c3f1c"
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
Debugger = "31a5f54b-26ea-5ae9-a837-f05ce5417438"
ECOS = "e2685f51-7e38-5353-a97d-a921fd2c8199"
ImageView = "86fae568-95e7-573e-a6b2-d8a6b900c9ef"
Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
MathProgBase = "fdba3010-5040-5b88-9595-932c9decdf73"
SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
StructuredOptimization = "46cd3e9d-64ff-517d-a929-236bc1a1fc9d"
TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
34 changes: 18 additions & 16 deletions benchmarks/TotalVariation.jl
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Expand Up @@ -6,13 +6,14 @@ using AbstractOperators
using Images
using ImageView
using TestImages
using Random

function set_up()
srand(123)
Random.seed!(1234)
img = testimage("cameraman")

X = convert(Array{Float64},img) # convert image to array
Xt = X.+sqrt(0.006*vecnorm(X,Inf))*randn(size(X)) # add noise
Xt = X.+sqrt(0.006*norm(X,Inf))*randn(size(X)) # add noise
Xt[Xt .< 0] .= 0. #make sure pixels are in range
Xt[Xt .> 1] .= 1.

Expand All @@ -32,7 +33,7 @@ function run_demo()
end

function solve_problem!(slv,V, Y, Xt, X, lambda)
it, = @minimize ls(-V'*Y+Xt)+conj(lambda*norm(Y,2,1,2)) with slv
it = @minimize ls(-V'*Y+Xt)+conj(lambda*norm(Y,2,1,2)) with slv
return it
end

Expand All @@ -42,31 +43,32 @@ function benchmark(;verb = 0, samples = 5, seconds = 100, tol = 1e-3, maxit = 50

solvers = ["ZeroFPR",
"PANOC",
"FPG",
"PG"]
"ForwardBackward"]
slv_opt = ["(verbose = $verb, tol = $tol, gamma = 1/8, maxit = $maxit)",
"(verbose = $verb, tol = $tol, gamma = 1/8, maxit = $maxit)",
"(verbose = $verb, tol = $tol, gamma = 1/8, maxit = $maxit)",
"(verbose = $verb, tol = $tol, gamma = 1/8, maxit = $maxit)"]

its = Dict([(sol,0.) for sol in solvers])
for i in eachindex(solvers)

setup = set_up()
solver = eval(parse(solvers[i]*slv_opt[i]))
solver = eval(Meta.parse(solvers[i]*slv_opt[i]))

suite[solvers[i]] =
@benchmarkable(it = solve_problem!(solver, setup...),
setup = (
it = 0;
setup = deepcopy($setup);
solver = deepcopy($solver) ),
teardown = (
$its[$solvers[$i]] = it;
),
evals = 1, samples = samples, seconds = seconds)
@benchmarkable(
it = solve_problem!(solver, setup...),
setup = (
it = 0;
setup = deepcopy($setup);
solver = deepcopy($solver)
),
teardown = (
$its[$solvers[$i]] = it[2];
),
evals = 1, samples = samples, seconds = seconds)
end

println("Starting run")
results = run(suite, verbose = (verb != 0))
println("TotalVariation its")
println(its)
Expand Down