JSOTutorials.jl

A Julia repository of bundle adjustment problems from the Bundle Adjustment in the Large repository.

using BundleAdjustmentModels, DataFrames

Get the list of problems

The function problems_df() returns a Dataframe of all the bundle adjustment problems.

There are 74 different models organized in the Dataframe with the following elements:

the name of the model;
the group of the model;
the size of the jacobian matrix (nequ, nvar);
the number of non-zeros elements in the jacobian matrix (nnzj).

df = problems_df()

74×5 DataFrame
 Row │ name                     group      nequ      nvar     nnzj
     │ String                   String     Int64     Int64    Int64
─────┼──────────────────────────────────────────────────────────────────
│ problem-16-22106-pre     dubrovnik    167436    66462    2009232
│ problem-88-64298-pre     dubrovnik    767874   193686    9214488
│ problem-135-90642-pre    dubrovnik   1106672   273141   13280064
│ problem-142-93602-pre    dubrovnik   1131216   282084   13574592
│ problem-150-95821-pre    dubrovnik   1136238   288813   13634856
│ problem-161-103832-pre   dubrovnik   1184038   312945   14208456
│ problem-173-111908-pre   dubrovnik   1269140   337281   15229680
│ problem-182-116770-pre   dubrovnik   1337410   351948   16048920
  ⋮  │            ⋮                 ⋮         ⋮         ⋮         ⋮
│ problem-1158-802917-pre  venice      8261006  2419173   99132072
│ problem-1184-816583-pre  venice      8358094  2460405  100297128
│ problem-1238-843534-pre  venice      8581006  2541744  102972072
│ problem-1288-866452-pre  venice      8766012  2610948  105192144
│ problem-1350-894716-pre  venice      9034252  2696298  108411024
│ problem-1408-912229-pre  venice      9269060  2749359  111228720
│ problem-1778-993923-pre  venice     10003892  2997771  120046704
                                                         59 rows omitted

For instance, it is possible to select the problems where the Jacobian matrix of the residual has at least 50000 lines and less than 34000 columns.

filter_df = df[ ( df.nequ .≥ 50000 ) .& ( df.nvar .≤ 34000 ), :]

2×5 DataFrame
 Row │ name                  group    nequ   nvar   nnzj
     │ String                String   Int64  Int64  Int64
─────┼──────────────────────────────────────────────────────
   1 │ problem-49-7776-pre   ladybug  63686  23769   764232
   2 │ problem-73-11032-pre  ladybug  92244  33753  1106928

The Dataframe is listing the matrices that you can have access to, but they still need to be downloaded.

Following the example above, we filtered two problems. What we want to do now is to select the first one in the listing.

name = filter_df[1, :name] # select the name of the first problem

"problem-49-7776-pre"

Now that the name is selected, we need to access the problem itself, and there are 2 solutions:

You can download the problem’s archive file;
You can automatically create a nonlinear least squares problem using NLPModels from JuliaSmoothOptimizers.

Get the problem archive file

This package uses Julia Artifacts to handle the problems archives so that

The models are downloaded only once;
They are identified with a unique hash;
They can be deleted with a single command line.

The method fetch_ba_name will automatically download the problem (if needed) and return its path.

path = fetch_ba_name(name)

"/home/runner/.julia/artifacts/dd2da5f94014b5f9086a2b38a87f8c1bc171b9c2"

It is also possible to directly download and get access to an entire group of problems using fetch_ba_group.

paths = fetch_ba_group("ladybug")

30-element Vector{String}:
 "/home/runner/.julia/artifacts/dd2da5f94014b5f9086a2b38a87f8c1bc171b9c2"
 "/home/runner/.julia/artifacts/3d0853a3ca8e585814697fea9cd4d6956692e103"
 "/home/runner/.julia/artifacts/5c5c938c998d6c083f549bc584cfeb07bd296d89"
 "/home/runner/.julia/artifacts/e8ebdcec7cd5dc3ec4825dcb6642064e541fa625"
 "/home/runner/.julia/artifacts/111ebd1dcdf5670eac8a12f4e2ce5a3b79b4f7f6"
 "/home/runner/.julia/artifacts/3e8acd1ef4cb13d93a7140be1c3702b5eb165978"
 "/home/runner/.julia/artifacts/69595726ce3990604ef56b80c273fe7f95506f2e"
 "/home/runner/.julia/artifacts/1eab751f4850b1e22302a7f97894d4b769c7350e"
 "/home/runner/.julia/artifacts/90ce1588d82c76a81c2b07d9e6abcc984e0e4586"
 "/home/runner/.julia/artifacts/05e4248f24d897a97e9be1f386e18d1de7e511e5"
 ⋮
 "/home/runner/.julia/artifacts/72cdf832cd3eedb4cc4c589127e7ab5b91a60f00"
 "/home/runner/.julia/artifacts/1e01d6927d4d4e0acd3086d9c75d2ee783ab1220"
 "/home/runner/.julia/artifacts/b92a8dfd520e7440769c4a72685e817632f9ea43"
 "/home/runner/.julia/artifacts/366a8368c091027a23a190b1ed498158eef947a5"
 "/home/runner/.julia/artifacts/ee08f4f6898cc26471d3a63502fe908cfa9a5dcc"
 "/home/runner/.julia/artifacts/1a0ebf23b35d784ad3d39f4987bc7d785f5976b6"
 "/home/runner/.julia/artifacts/00be55410c27068ec73261e122a39258100a1a11"
 "/home/runner/.julia/artifacts/0303e7ae8256c494c9da052d977277f21265899b"
 "/home/runner/.julia/artifacts/389ecea5c2f2e2b637a2b4439af0bd4ca98e6d84"

Generate a nonlinear least squares model

Now, it is possible to load the model using BundleAdjustmentModel

df = problems_df()
filter_df = df[ ( df.nequ .≥ 50000 ) .& ( df.nvar .≤ 34000 ), :]
name = filter_df[1, :name]
model = BundleAdjustmentModel(name);

model = BundleAdjustmentModel("problem-49-7776-pre");

The function BundleAdjustmentModel will instantiate the model and automatically download it if needed. The resulting structure is an instance of AbstractNLPModel. So, it is possible to access its API as any other NLPModel.

using NLPModels

Using residual, it is possible to compute the residual of the model

model = BundleAdjustmentModel("problem-49-7776-pre.txt.bz2")
x = get_x0(model) # or `model.meta.x0`
Fx = residual(model, x)

63686-element Vector{Float64}:
 -9.020226301243213
 11.263958304987227
 -1.8332297149469525
  5.304698960898122
 -4.332321480806684
  7.117305031392988
 -0.5632751791501462
 -1.062178017695885
 -3.9692059546843836
 -2.2850712830954194
  ⋮
 -0.4377574792022081
 -0.024461522651677114
 -0.1961281328666189
  0.008375315306523134
  0.23044496991071384
  0.04927878647407624
  0.47289578243411867
 -0.01443314653496941
 -0.4486499211288866

or use the in-place method residual!

model = BundleAdjustmentModel("problem-49-7776-pre.txt.bz2")
x = get_x0(model) # or `model.meta.x0`
nequ = get_nequ(model) # or `model.nls_meta.nequ`
Fx = zeros(nequ)
residual!(model, x, Fx);

You can also have access to the LinearOperator of the Jacobian matrix of the residual of the model which is calculated by hand (in contradiction to automatic differentiation).

You need to call jac_structure_residual! at least once before calling jac_op_residual!.

model = BundleAdjustmentModel("problem-49-7776")
meta_nls = nls_meta(model)
nnzj = meta_nls.nnzj # number of nonzeros in the jacobian
rows = Vector{Int}(undef, nnzj)
cols = Vector{Int}(undef, nnzj)
jac_structure_residual!(model, rows, cols);

You need to call jac_coord_residual! to update it to the current point.

model = BundleAdjustmentModel("problem-49-7776")
x = get_x0(model)
vals = similar(x, nnzj)
jac_coord_residual!(model, x, vals)

764232-element Vector{Float64}:
   545.1179297695714
    -5.058282392703829
  -478.0666614182794
  -283.5120110272222
 -1296.3388697208218
  -320.60334752077165
   551.1773498438256
     0.00020469082949125088
  -471.09490058346296
  -409.36200783910084
     ⋮
  1246.0491342213195
   -96.64898276178089
   622.4628407807937
     2.852854717671426e-7
   305.00859581263086
    19.561762292226746
     6.5984133899730155
     1.680958440896614
     0.06413511779846102

Finally you can use jac_op_residual!:

model = BundleAdjustmentModel("problem-49-7776")
meta_nls = nls_meta(model)
nnzj = meta_nls.nnzj

rows = Vector{Int}(undef, nnzj)
cols = Vector{Int}(undef, nnzj)
jac_structure_residual!(model, rows, cols)

vals = similar(model.meta.x0, nnzj)
jac_coord_residual!(model, model.meta.x0, vals)

Jv = similar(model.meta.x0, meta_nls.nequ)
Jtv = similar(model.meta.x0, meta_nls.nvar)
Jx = jac_op_residual!(model, rows, cols, vals, Jv, Jtv)

Linear operator
  nrow: 63686
  ncol: 23769
  eltype: Float64
  symmetric: false
  hermitian: false
  nprod:   0
  ntprod:  0
  nctprod: 0

There is no second-order information available for the problems in this package.

Delete the problems

Once you have finished working with a specific model you can delete it the same way you downloaded it.

delete_ba_artifact!("problem-49-7776")

If you want to clean your workspace, you can also delete all the problems at once.

delete_all_ba_artifacts!()