Mercurial > repos > public > sbplib_julia
changeset 712:de2df1214394 feature/selectable_tests
Split testfile for LazyTensors
| author | Jonatan Werpers <jonatan@werpers.com> |
|---|---|
| date | Sat, 20 Feb 2021 20:59:32 +0100 |
| parents | df88aee35bb9 |
| children | be648c6d6686 |
| files | test/LazyTensors/LazyTensors_test.jl test/LazyTensors/lazy_array_test.jl test/LazyTensors/lazy_tensor_operations_test.jl test/LazyTensors/tensor_mapping_test.jl |
| diffstat | 4 files changed, 583 insertions(+), 580 deletions(-) [+] |
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--- a/test/LazyTensors/LazyTensors_test.jl Sat Feb 20 20:45:40 2021 +0100 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,580 +0,0 @@ -using Test -using Sbplib.LazyTensors -using Sbplib.RegionIndices - -using Tullio - -@testset "LazyTensors" begin - -@testset "Generic Mapping methods" begin - struct DummyMapping{T,R,D} <: TensorMapping{T,R,D} end - LazyTensors.apply(m::DummyMapping{T,R,D}, v, I::Vararg{Any,R}) where {T,R,D} = :apply - @test range_dim(DummyMapping{Int,2,3}()) == 2 - @test domain_dim(DummyMapping{Int,2,3}()) == 3 - @test apply(DummyMapping{Int,2,3}(), zeros(Int, (0,0,0)),0,0) == :apply - @test eltype(DummyMapping{Int,2,3}()) == Int - @test eltype(DummyMapping{Float64,2,3}()) == Float64 -end - -@testset "Mapping transpose" begin - struct DummyMapping{T,R,D} <: TensorMapping{T,R,D} end - - LazyTensors.apply(m::DummyMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = :apply - LazyTensors.apply_transpose(m::DummyMapping{T,R,D}, v, I::Vararg{Any,D}) where {T,R,D} = :apply_transpose - - LazyTensors.range_size(m::DummyMapping) = :range_size - LazyTensors.domain_size(m::DummyMapping) = :domain_size - - m = DummyMapping{Float64,2,3}() - @test m' isa TensorMapping{Float64, 3,2} - @test m'' == m - @test apply(m',zeros(Float64,(0,0)), 0, 0, 0) == :apply_transpose - @test apply(m'',zeros(Float64,(0,0,0)), 0, 0) == :apply - @test apply_transpose(m', zeros(Float64,(0,0,0)), 0, 0) == :apply - - @test range_size(m') == :domain_size - @test domain_size(m') == :range_size -end - -@testset "TensorApplication" begin - struct SizeDoublingMapping{T,R,D} <: TensorMapping{T,R,D} - domain_size::NTuple{D,Int} - end - - LazyTensors.apply(m::SizeDoublingMapping{T,R}, v, i::Vararg{Any,R}) where {T,R} = (:apply,v,i) - LazyTensors.range_size(m::SizeDoublingMapping) = 2 .* m.domain_size - LazyTensors.domain_size(m::SizeDoublingMapping) = m.domain_size - - - m = SizeDoublingMapping{Int, 1, 1}((3,)) - v = [0,1,2] - @test m*v isa AbstractVector{Int} - @test size(m*v) == 2 .*size(v) - @test (m*v)[0] == (:apply,v,(0,)) - @test m*m*v isa AbstractVector{Int} - @test (m*m*v)[1] == (:apply,m*v,(1,)) - @test (m*m*v)[3] == (:apply,m*v,(3,)) - @test (m*m*v)[6] == (:apply,m*v,(6,)) - @test_broken BoundsError == (m*m*v)[0] - @test_broken BoundsError == (m*m*v)[7] - @test_throws MethodError m*m - - m = SizeDoublingMapping{Int, 2, 1}((3,)) - @test_throws MethodError m*ones(Int,2,2) - @test_throws MethodError m*m*v - - m = SizeDoublingMapping{Float64, 2, 2}((3,3)) - v = ones(3,3) - @test size(m*v) == 2 .*size(v) - @test (m*v)[1,2] == (:apply,v,(1,2)) - - struct ScalingOperator{T,D} <: TensorMapping{T,D,D} - λ::T - size::NTuple{D,Int} - end - - LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...] - LazyTensors.range_size(m::ScalingOperator) = m.size - LazyTensors.domain_size(m::ScalingOperator) = m.size - - m = ScalingOperator{Int,1}(2,(3,)) - v = [1,2,3] - @test m*v isa AbstractVector - @test m*v == [2,4,6] - - m = ScalingOperator{Int,2}(2,(2,2)) - v = [[1 2];[3 4]] - @test m*v == [[2 4];[6 8]] - @test (m*v)[2,1] == 6 -end - -@testset "TensorMapping binary operations" begin - struct ScalarMapping{T,R,D} <: TensorMapping{T,R,D} - λ::T - range_size::NTuple{R,Int} - domain_size::NTuple{D,Int} - end - - LazyTensors.apply(m::ScalarMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = m.λ*v[I...] - LazyTensors.range_size(m::ScalarMapping) = m.domain_size - LazyTensors.domain_size(m::ScalarMapping) = m.range_size - - A = ScalarMapping{Float64,1,1}(2.0, (3,), (3,)) - B = ScalarMapping{Float64,1,1}(3.0, (3,), (3,)) - - v = [1.1,1.2,1.3] - for i ∈ eachindex(v) - @test ((A+B)*v)[i] == 2*v[i] + 3*v[i] - end - - for i ∈ eachindex(v) - @test ((A-B)*v)[i] == 2*v[i] - 3*v[i] - end - - @test range_size(A+B) == range_size(A) == range_size(B) - @test domain_size(A+B) == domain_size(A) == domain_size(B) -end - -@testset "LazyArray" begin - @testset "LazyConstantArray" begin - @test LazyTensors.LazyConstantArray(3,(3,2)) isa LazyArray{Int,2} - - lca = LazyTensors.LazyConstantArray(3.0,(3,2)) - @test eltype(lca) == Float64 - @test ndims(lca) == 2 - @test size(lca) == (3,2) - @test lca[2] == 3.0 - end - struct DummyArray{T,D, T1<:AbstractArray{T,D}} <: LazyArray{T,D} - data::T1 - end - Base.size(v::DummyArray) = size(v.data) - Base.getindex(v::DummyArray{T,D}, I::Vararg{Int,D}) where {T,D} = v.data[I...] - - # Test lazy operations - v1 = [1, 2.3, 4] - v2 = [1., 2, 3] - s = 3.4 - r_add_v = v1 .+ v2 - r_sub_v = v1 .- v2 - r_times_v = v1 .* v2 - r_div_v = v1 ./ v2 - r_add_s = v1 .+ s - r_sub_s = v1 .- s - r_times_s = v1 .* s - r_div_s = v1 ./ s - @test isa(v1 +̃ v2, LazyArray) - @test isa(v1 -̃ v2, LazyArray) - @test isa(v1 *̃ v2, LazyArray) - @test isa(v1 /̃ v2, LazyArray) - @test isa(v1 +̃ s, LazyArray) - @test isa(v1 -̃ s, LazyArray) - @test isa(v1 *̃ s, LazyArray) - @test isa(v1 /̃ s, LazyArray) - @test isa(s +̃ v1, LazyArray) - @test isa(s -̃ v1, LazyArray) - @test isa(s *̃ v1, LazyArray) - @test isa(s /̃ v1, LazyArray) - for i ∈ eachindex(v1) - @test (v1 +̃ v2)[i] == r_add_v[i] - @test (v1 -̃ v2)[i] == r_sub_v[i] - @test (v1 *̃ v2)[i] == r_times_v[i] - @test (v1 /̃ v2)[i] == r_div_v[i] - @test (v1 +̃ s)[i] == r_add_s[i] - @test (v1 -̃ s)[i] == r_sub_s[i] - @test (v1 *̃ s)[i] == r_times_s[i] - @test (v1 /̃ s)[i] == r_div_s[i] - @test (s +̃ v1)[i] == r_add_s[i] - @test (s -̃ v1)[i] == -r_sub_s[i] - @test (s *̃ v1)[i] == r_times_s[i] - @test (s /̃ v1)[i] == 1/r_div_s[i] - end - @test_throws BoundsError (v1 +̃ v2)[4] - v2 = [1., 2, 3, 4] - # Test that size of arrays is asserted when not specified inbounds - # TODO: Replace these errors with SizeMismatch - @test_throws DimensionMismatch v1 +̃ v2 - - # Test operations on LazyArray - v1 = DummyArray([1, 2.3, 4]) - v2 = [1., 2, 3] - @test isa(v1 + v2, LazyArray) - @test isa(v2 + v1, LazyArray) - @test isa(v1 - v2, LazyArray) - @test isa(v2 - v1, LazyArray) - for i ∈ eachindex(v2) - @test (v1 + v2)[i] == (v2 + v1)[i] == r_add_v[i] - @test (v1 - v2)[i] == -(v2 - v1)[i] == r_sub_v[i] - end - @test_throws BoundsError (v1 + v2)[4] - v2 = [1., 2, 3, 4] - # Test that size of arrays is asserted when not specified inbounds - # TODO: Replace these errors with SizeMismatch - @test_throws DimensionMismatch v1 + v2 -end - - -@testset "LazyFunctionArray" begin - @test LazyFunctionArray(i->i^2, (3,)) == [1,4,9] - @test LazyFunctionArray((i,j)->i*j, (3,2)) == [ - 1 2; - 2 4; - 3 6; - ] - - @test size(LazyFunctionArray(i->i^2, (3,))) == (3,) - @test size(LazyFunctionArray((i,j)->i*j, (3,2))) == (3,2) - - @inferred LazyFunctionArray(i->i^2, (3,))[2] - - @test_throws BoundsError LazyFunctionArray(i->i^2, (3,))[4] - @test_throws BoundsError LazyFunctionArray((i,j)->i*j, (3,2))[4,2] - @test_throws BoundsError LazyFunctionArray((i,j)->i*j, (3,2))[2,3] - -end - -@testset "TensorMappingComposition" begin - A = rand(2,3) - B = rand(3,4) - - à = LazyLinearMap(A, (1,), (2,)) - B̃ = LazyLinearMap(B, (1,), (2,)) - - @test Ã∘B̃ isa TensorMappingComposition - @test range_size(Ã∘B̃) == (2,) - @test domain_size(Ã∘B̃) == (4,) - @test_throws SizeMismatch B̃∘à - - # @test @inbounds B̃∘à # Should not error even though dimensions don't match. (Since ]test runs with forced boundschecking this is currently not testable 2020-10-16) - - v = rand(4) - @test Ã∘B̃*v ≈ A*B*v rtol=1e-14 - - v = rand(2) - @test (Ã∘B̃)'*v ≈ B'*A'*v rtol=1e-14 -end - -@testset "LazyLinearMap" begin - # Test a standard matrix-vector product - # mapping vectors of size 4 to vectors of size 3. - A = rand(3,4) - à = LazyLinearMap(A, (1,), (2,)) - v = rand(4) - w = rand(3) - - @test à isa LazyLinearMap{T,1,1} where T - @test à isa TensorMapping{T,1,1} where T - @test range_size(Ã) == (3,) - @test domain_size(Ã) == (4,) - - @test Ã*ones(4) ≈ A*ones(4) atol=5e-13 - @test Ã*v ≈ A*v atol=5e-13 - @test Ã'*w ≈ A'*w - - A = rand(2,3,4) - @test_throws DomainError LazyLinearMap(A, (3,1), (2,)) - - # Test more exotic mappings - B = rand(3,4,2) - # Map vectors of size 2 to matrices of size (3,4) - B̃ = LazyLinearMap(B, (1,2), (3,)) - v = rand(2) - - @test range_size(B̃) == (3,4) - @test domain_size(B̃) == (2,) - @test B̃ isa TensorMapping{T,2,1} where T - @test B̃*ones(2) ≈ B[:,:,1] + B[:,:,2] atol=5e-13 - @test B̃*v ≈ B[:,:,1]*v[1] + B[:,:,2]*v[2] atol=5e-13 - - # Map matrices of size (3,2) to vectors of size 4 - B̃ = LazyLinearMap(B, (2,), (1,3)) - v = rand(3,2) - - @test range_size(B̃) == (4,) - @test domain_size(B̃) == (3,2) - @test B̃ isa TensorMapping{T,1,2} where T - @test B̃*ones(3,2) ≈ B[1,:,1] + B[2,:,1] + B[3,:,1] + - B[1,:,2] + B[2,:,2] + B[3,:,2] atol=5e-13 - @test B̃*v ≈ B[1,:,1]*v[1,1] + B[2,:,1]*v[2,1] + B[3,:,1]*v[3,1] + - B[1,:,2]v[1,2] + B[2,:,2]*v[2,2] + B[3,:,2]*v[3,2] atol=5e-13 - - - # TODO: - # @inferred (B̃*v)[2] -end - - -@testset "IdentityMapping" begin - @test IdentityMapping{Float64}((4,5)) isa IdentityMapping{T,2} where T - @test IdentityMapping{Float64}((4,5)) isa TensorMapping{T,2,2} where T - @test IdentityMapping{Float64}((4,5)) == IdentityMapping{Float64}(4,5) - - @test IdentityMapping(3,2) isa IdentityMapping{Float64,2} - - for sz ∈ [(4,5),(3,),(5,6,4)] - I = IdentityMapping{Float64}(sz) - v = rand(sz...) - @test I*v == v - @test I'*v == v - - @test range_size(I) == sz - @test domain_size(I) == sz - end - - I = IdentityMapping{Float64}((4,5)) - v = rand(4,5) - @inferred (I*v)[3,2] - @inferred (I'*v)[3,2] - @inferred range_size(I) - - @inferred range_dim(I) - @inferred domain_dim(I) - - à = rand(4,2) - A = LazyLinearMap(Ã,(1,),(2,)) - I1 = IdentityMapping{Float64}(2) - I2 = IdentityMapping{Float64}(4) - @test A∘I1 == A - @test I2∘A == A - @test I1∘I1 == I1 - @test_throws SizeMismatch I1∘A - @test_throws SizeMismatch A∘I2 - @test_throws SizeMismatch I1∘I2 -end - -@testset "InflatedTensorMapping" begin - I(sz...) = IdentityMapping(sz...) - - à = rand(4,2) - B̃ = rand(4,2,3) - C̃ = rand(4,2,3) - - A = LazyLinearMap(Ã,(1,),(2,)) - B = LazyLinearMap(B̃,(1,2),(3,)) - C = LazyLinearMap(C̃,(1,),(2,3)) - - @testset "Constructors" begin - @test InflatedTensorMapping(I(3,2), A, I(4)) isa TensorMapping{Float64, 4, 4} - @test InflatedTensorMapping(I(3,2), B, I(4)) isa TensorMapping{Float64, 5, 4} - @test InflatedTensorMapping(I(3), C, I(2,3)) isa TensorMapping{Float64, 4, 5} - @test InflatedTensorMapping(C, I(2,3)) isa TensorMapping{Float64, 3, 4} - @test InflatedTensorMapping(I(3), C) isa TensorMapping{Float64, 2, 3} - @test InflatedTensorMapping(I(3), I(2,3)) isa TensorMapping{Float64, 3, 3} - end - - @testset "Range and domain size" begin - @test range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4) - @test domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4) - - @test range_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,4,2,4) - @test domain_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,3,4) - - @test range_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,4,2,3) - @test domain_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,2,3,2,3) - - @inferred range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4) - @inferred domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4) - end - - @testset "Application" begin - # Testing regular application and transposed application with inflation "before", "after" and "before and after". - # The inflated tensor mappings are chosen to preserve, reduce and increase the dimension of the result compared to the input. - tests = [ - ( - InflatedTensorMapping(I(3,2), A, I(4)), - (v-> @tullio res[a,b,c,d] := Ã[c,i]*v[a,b,i,d]), # Expected result of apply - (v-> @tullio res[a,b,c,d] := Ã[i,c]*v[a,b,i,d]), # Expected result of apply_transpose - ), - ( - InflatedTensorMapping(I(3,2), B, I(4)), - (v-> @tullio res[a,b,c,d,e] := B̃[c,d,i]*v[a,b,i,e]), - (v-> @tullio res[a,b,c,d] := B̃[i,j,c]*v[a,b,i,j,d]), - ), - ( - InflatedTensorMapping(I(3,2), C, I(4)), - (v-> @tullio res[a,b,c,d] := C̃[c,i,j]*v[a,b,i,j,d]), - (v-> @tullio res[a,b,c,d,e] := C̃[i,c,d]*v[a,b,i,e]), - ), - ( - InflatedTensorMapping(I(3,2), A), - (v-> @tullio res[a,b,c] := Ã[c,i]*v[a,b,i]), - (v-> @tullio res[a,b,c] := Ã[i,c]*v[a,b,i]), - ), - ( - InflatedTensorMapping(I(3,2), B), - (v-> @tullio res[a,b,c,d] := B̃[c,d,i]*v[a,b,i]), - (v-> @tullio res[a,b,c] := B̃[i,j,c]*v[a,b,i,j]), - ), - ( - InflatedTensorMapping(I(3,2), C), - (v-> @tullio res[a,b,c] := C̃[c,i,j]*v[a,b,i,j]), - (v-> @tullio res[a,b,c,d] := C̃[i,c,d]*v[a,b,i]), - ), - ( - InflatedTensorMapping(A,I(4)), - (v-> @tullio res[a,b] := Ã[a,i]*v[i,b]), - (v-> @tullio res[a,b] := Ã[i,a]*v[i,b]), - ), - ( - InflatedTensorMapping(B,I(4)), - (v-> @tullio res[a,b,c] := B̃[a,b,i]*v[i,c]), - (v-> @tullio res[a,b] := B̃[i,j,a]*v[i,j,b]), - ), - ( - InflatedTensorMapping(C,I(4)), - (v-> @tullio res[a,b] := C̃[a,i,j]*v[i,j,b]), - (v-> @tullio res[a,b,c] := C̃[i,a,b]*v[i,c]), - ), - ] - - @testset "apply" begin - for i ∈ 1:length(tests) - tm = tests[i][1] - v = rand(domain_size(tm)...) - true_value = tests[i][2](v) - @test tm*v ≈ true_value rtol=1e-14 - end - end - - @testset "apply_transpose" begin - for i ∈ 1:length(tests) - tm = tests[i][1] - v = rand(range_size(tm)...) - true_value = tests[i][3](v) - @test tm'*v ≈ true_value rtol=1e-14 - end - end - - @testset "Inference of application" begin - struct ScalingOperator{T,D} <: TensorMapping{T,D,D} - λ::T - size::NTuple{D,Int} - end - - LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...] - LazyTensors.range_size(m::ScalingOperator) = m.size - LazyTensors.domain_size(m::ScalingOperator) = m.size - - tm = InflatedTensorMapping(I(2,3),ScalingOperator(2.0, (3,2)),I(3,4)) - v = rand(domain_size(tm)...) - - @inferred apply(tm,v,1,2,3,2,2,4) - @inferred (tm*v)[1,2,3,2,2,4] - end - end - - @testset "InflatedTensorMapping of InflatedTensorMapping" begin - A = ScalingOperator(2.0,(2,3)) - itm = InflatedTensorMapping(I(3,2), A, I(4)) - @test InflatedTensorMapping(I(4), itm, I(2)) == InflatedTensorMapping(I(4,3,2), A, I(4,2)) - @test InflatedTensorMapping(itm, I(2)) == InflatedTensorMapping(I(3,2), A, I(4,2)) - @test InflatedTensorMapping(I(4), itm) == InflatedTensorMapping(I(4,3,2), A, I(4)) - - @test InflatedTensorMapping(I(2), I(2), I(2)) isa InflatedTensorMapping # The constructor should always return its type. - end -end - -@testset "split_index" begin - @test LazyTensors.split_index(Val(2),Val(1),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,5,6),(3,4)) - @test LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,:,:,5,6),(3,4)) - @test LazyTensors.split_index(Val(3),Val(1),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,5,6),(4,)) - @test LazyTensors.split_index(Val(3),Val(2),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,:,5,6),(4,)) - @test LazyTensors.split_index(Val(1),Val(1),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,4,5,6),(2,3)) - @test LazyTensors.split_index(Val(1),Val(2),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,:,4,5,6),(2,3)) - - @test LazyTensors.split_index(Val(0),Val(1),Val(3),Val(3),1,2,3,4,5,6) == ((:,4,5,6),(1,2,3)) - @test LazyTensors.split_index(Val(3),Val(1),Val(3),Val(0),1,2,3,4,5,6) == ((1,2,3,:),(4,5,6)) - - @inferred LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,2,2,4) -end - -@testset "slice_tuple" begin - @test LazyTensors.slice_tuple((1,2,3),Val(1), Val(3)) == (1,2,3) - @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(2), Val(5)) == (2,3,4,5) - @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(1), Val(3)) == (1,2,3) - @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(4), Val(6)) == (4,5,6) -end - -@testset "split_tuple" begin - @testset "2 parts" begin - @test LazyTensors.split_tuple((),Val(0)) == ((),()) - @test LazyTensors.split_tuple((1,),Val(0)) == ((),(1,)) - @test LazyTensors.split_tuple((1,),Val(1)) == ((1,),()) - - @test LazyTensors.split_tuple((1,2,3,4),Val(0)) == ((),(1,2,3,4)) - @test LazyTensors.split_tuple((1,2,3,4),Val(1)) == ((1,),(2,3,4)) - @test LazyTensors.split_tuple((1,2,3,4),Val(2)) == ((1,2),(3,4)) - @test LazyTensors.split_tuple((1,2,3,4),Val(3)) == ((1,2,3),(4,)) - @test LazyTensors.split_tuple((1,2,3,4),Val(4)) == ((1,2,3,4),()) - - @test LazyTensors.split_tuple((1,2,true,4),Val(3)) == ((1,2,true),(4,)) - - @inferred LazyTensors.split_tuple((1,2,3,4),Val(3)) - @inferred LazyTensors.split_tuple((1,2,true,4),Val(3)) - end - - @testset "3 parts" begin - @test LazyTensors.split_tuple((),Val(0),Val(0)) == ((),(),()) - @test LazyTensors.split_tuple((1,2,3),Val(1), Val(1)) == ((1,),(2,),(3,)) - @test LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) == ((1,),(true,),(3,)) - - @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(1),Val(2)) == ((1,),(2,3),(4,5,6)) - @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) == ((1,2,3),(4,5),(6,)) - - @inferred LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) - @inferred LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) - end -end - -@testset "flatten_tuple" begin - @test LazyTensors.flatten_tuple((1,)) == (1,) - @test LazyTensors.flatten_tuple((1,2,3,4,5,6)) == (1,2,3,4,5,6) - @test LazyTensors.flatten_tuple((1,2,(3,4),5,6)) == (1,2,3,4,5,6) - @test LazyTensors.flatten_tuple((1,2,(3,(4,5)),6)) == (1,2,3,4,5,6) - @test LazyTensors.flatten_tuple(((1,2),(3,4),(5,),6)) == (1,2,3,4,5,6) -end - - -@testset "LazyOuterProduct" begin - struct ScalingOperator{T,D} <: TensorMapping{T,D,D} - λ::T - size::NTuple{D,Int} - end - - LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...] - LazyTensors.range_size(m::ScalingOperator) = m.size - LazyTensors.domain_size(m::ScalingOperator) = m.size - - A = ScalingOperator(2.0, (5,)) - B = ScalingOperator(3.0, (3,)) - C = ScalingOperator(5.0, (3,2)) - - AB = LazyOuterProduct(A,B) - @test AB isa TensorMapping{T,2,2} where T - @test range_size(AB) == (5,3) - @test domain_size(AB) == (5,3) - - v = rand(range_size(AB)...) - @test AB*v == 6*v - - ABC = LazyOuterProduct(A,B,C) - - @test ABC isa TensorMapping{T,4,4} where T - @test range_size(ABC) == (5,3,3,2) - @test domain_size(ABC) == (5,3,3,2) - - @test A⊗B == AB - @test A⊗B⊗C == ABC - - A = rand(3,2) - B = rand(2,4,3) - - v₁ = rand(2,4,3) - v₂ = rand(4,3,2) - - à = LazyLinearMap(A,(1,),(2,)) - B̃ = LazyLinearMap(B,(1,),(2,3)) - - ÃB̃ = LazyOuterProduct(Ã,B̃) - @tullio ABv[i,k] := A[i,j]*B[k,l,m]*v₁[j,l,m] - @test ÃB̃*v₁ ≈ ABv - - B̃à = LazyOuterProduct(B̃,Ã) - @tullio BAv[k,i] := A[i,j]*B[k,l,m]*v₂[l,m,j] - @test B̃Ã*v₂ ≈ BAv - - @testset "Indentity mapping arguments" begin - @test LazyOuterProduct(IdentityMapping(3,2), IdentityMapping(1,2)) == IdentityMapping(3,2,1,2) - - à = LazyLinearMap(A,(1,),(2,)) - @test LazyOuterProduct(IdentityMapping(3,2), Ã) == InflatedTensorMapping(IdentityMapping(3,2),Ã) - @test LazyOuterProduct(Ã, IdentityMapping(3,2)) == InflatedTensorMapping(Ã,IdentityMapping(3,2)) - - I1 = IdentityMapping(3,2) - I2 = IdentityMapping(4) - @test I1⊗Ã⊗I2 == InflatedTensorMapping(I1, Ã, I2) - end - -end - -end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test/LazyTensors/lazy_array_test.jl Sat Feb 20 20:59:32 2021 +0100 @@ -0,0 +1,102 @@ +using Test +using Sbplib.LazyTensors +using Sbplib.RegionIndices + + +@testset "LazyArray" begin + @testset "LazyConstantArray" begin + @test LazyTensors.LazyConstantArray(3,(3,2)) isa LazyArray{Int,2} + + lca = LazyTensors.LazyConstantArray(3.0,(3,2)) + @test eltype(lca) == Float64 + @test ndims(lca) == 2 + @test size(lca) == (3,2) + @test lca[2] == 3.0 + end + struct DummyArray{T,D, T1<:AbstractArray{T,D}} <: LazyArray{T,D} + data::T1 + end + Base.size(v::DummyArray) = size(v.data) + Base.getindex(v::DummyArray{T,D}, I::Vararg{Int,D}) where {T,D} = v.data[I...] + + # Test lazy operations + v1 = [1, 2.3, 4] + v2 = [1., 2, 3] + s = 3.4 + r_add_v = v1 .+ v2 + r_sub_v = v1 .- v2 + r_times_v = v1 .* v2 + r_div_v = v1 ./ v2 + r_add_s = v1 .+ s + r_sub_s = v1 .- s + r_times_s = v1 .* s + r_div_s = v1 ./ s + @test isa(v1 +̃ v2, LazyArray) + @test isa(v1 -̃ v2, LazyArray) + @test isa(v1 *̃ v2, LazyArray) + @test isa(v1 /̃ v2, LazyArray) + @test isa(v1 +̃ s, LazyArray) + @test isa(v1 -̃ s, LazyArray) + @test isa(v1 *̃ s, LazyArray) + @test isa(v1 /̃ s, LazyArray) + @test isa(s +̃ v1, LazyArray) + @test isa(s -̃ v1, LazyArray) + @test isa(s *̃ v1, LazyArray) + @test isa(s /̃ v1, LazyArray) + for i ∈ eachindex(v1) + @test (v1 +̃ v2)[i] == r_add_v[i] + @test (v1 -̃ v2)[i] == r_sub_v[i] + @test (v1 *̃ v2)[i] == r_times_v[i] + @test (v1 /̃ v2)[i] == r_div_v[i] + @test (v1 +̃ s)[i] == r_add_s[i] + @test (v1 -̃ s)[i] == r_sub_s[i] + @test (v1 *̃ s)[i] == r_times_s[i] + @test (v1 /̃ s)[i] == r_div_s[i] + @test (s +̃ v1)[i] == r_add_s[i] + @test (s -̃ v1)[i] == -r_sub_s[i] + @test (s *̃ v1)[i] == r_times_s[i] + @test (s /̃ v1)[i] == 1/r_div_s[i] + end + @test_throws BoundsError (v1 +̃ v2)[4] + v2 = [1., 2, 3, 4] + # Test that size of arrays is asserted when not specified inbounds + # TODO: Replace these errors with SizeMismatch + @test_throws DimensionMismatch v1 +̃ v2 + + # Test operations on LazyArray + v1 = DummyArray([1, 2.3, 4]) + v2 = [1., 2, 3] + @test isa(v1 + v2, LazyArray) + @test isa(v2 + v1, LazyArray) + @test isa(v1 - v2, LazyArray) + @test isa(v2 - v1, LazyArray) + for i ∈ eachindex(v2) + @test (v1 + v2)[i] == (v2 + v1)[i] == r_add_v[i] + @test (v1 - v2)[i] == -(v2 - v1)[i] == r_sub_v[i] + end + @test_throws BoundsError (v1 + v2)[4] + v2 = [1., 2, 3, 4] + # Test that size of arrays is asserted when not specified inbounds + # TODO: Replace these errors with SizeMismatch + @test_throws DimensionMismatch v1 + v2 +end + + +@testset "LazyFunctionArray" begin + @test LazyFunctionArray(i->i^2, (3,)) == [1,4,9] + @test LazyFunctionArray((i,j)->i*j, (3,2)) == [ + 1 2; + 2 4; + 3 6; + ] + + @test size(LazyFunctionArray(i->i^2, (3,))) == (3,) + @test size(LazyFunctionArray((i,j)->i*j, (3,2))) == (3,2) + + @inferred LazyFunctionArray(i->i^2, (3,))[2] + + @test_throws BoundsError LazyFunctionArray(i->i^2, (3,))[4] + @test_throws BoundsError LazyFunctionArray((i,j)->i*j, (3,2))[4,2] + @test_throws BoundsError LazyFunctionArray((i,j)->i*j, (3,2))[2,3] + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test/LazyTensors/lazy_tensor_operations_test.jl Sat Feb 20 20:59:32 2021 +0100 @@ -0,0 +1,469 @@ +using Test +using Sbplib.LazyTensors +using Sbplib.RegionIndices + +using Tullio + +@testset "Mapping transpose" begin + struct DummyMapping{T,R,D} <: TensorMapping{T,R,D} end + + LazyTensors.apply(m::DummyMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = :apply + LazyTensors.apply_transpose(m::DummyMapping{T,R,D}, v, I::Vararg{Any,D}) where {T,R,D} = :apply_transpose + + LazyTensors.range_size(m::DummyMapping) = :range_size + LazyTensors.domain_size(m::DummyMapping) = :domain_size + + m = DummyMapping{Float64,2,3}() + @test m' isa TensorMapping{Float64, 3,2} + @test m'' == m + @test apply(m',zeros(Float64,(0,0)), 0, 0, 0) == :apply_transpose + @test apply(m'',zeros(Float64,(0,0,0)), 0, 0) == :apply + @test apply_transpose(m', zeros(Float64,(0,0,0)), 0, 0) == :apply + + @test range_size(m') == :domain_size + @test domain_size(m') == :range_size +end + +@testset "TensorApplication" begin + struct SizeDoublingMapping{T,R,D} <: TensorMapping{T,R,D} + domain_size::NTuple{D,Int} + end + + LazyTensors.apply(m::SizeDoublingMapping{T,R}, v, i::Vararg{Any,R}) where {T,R} = (:apply,v,i) + LazyTensors.range_size(m::SizeDoublingMapping) = 2 .* m.domain_size + LazyTensors.domain_size(m::SizeDoublingMapping) = m.domain_size + + + m = SizeDoublingMapping{Int, 1, 1}((3,)) + v = [0,1,2] + @test m*v isa AbstractVector{Int} + @test size(m*v) == 2 .*size(v) + @test (m*v)[0] == (:apply,v,(0,)) + @test m*m*v isa AbstractVector{Int} + @test (m*m*v)[1] == (:apply,m*v,(1,)) + @test (m*m*v)[3] == (:apply,m*v,(3,)) + @test (m*m*v)[6] == (:apply,m*v,(6,)) + @test_broken BoundsError == (m*m*v)[0] + @test_broken BoundsError == (m*m*v)[7] + @test_throws MethodError m*m + + m = SizeDoublingMapping{Int, 2, 1}((3,)) + @test_throws MethodError m*ones(Int,2,2) + @test_throws MethodError m*m*v + + m = SizeDoublingMapping{Float64, 2, 2}((3,3)) + v = ones(3,3) + @test size(m*v) == 2 .*size(v) + @test (m*v)[1,2] == (:apply,v,(1,2)) + + struct ScalingOperator{T,D} <: TensorMapping{T,D,D} + λ::T + size::NTuple{D,Int} + end + + LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...] + LazyTensors.range_size(m::ScalingOperator) = m.size + LazyTensors.domain_size(m::ScalingOperator) = m.size + + m = ScalingOperator{Int,1}(2,(3,)) + v = [1,2,3] + @test m*v isa AbstractVector + @test m*v == [2,4,6] + + m = ScalingOperator{Int,2}(2,(2,2)) + v = [[1 2];[3 4]] + @test m*v == [[2 4];[6 8]] + @test (m*v)[2,1] == 6 +end + +@testset "TensorMapping binary operations" begin + struct ScalarMapping{T,R,D} <: TensorMapping{T,R,D} + λ::T + range_size::NTuple{R,Int} + domain_size::NTuple{D,Int} + end + + LazyTensors.apply(m::ScalarMapping{T,R}, v, I::Vararg{Any,R}) where {T,R} = m.λ*v[I...] + LazyTensors.range_size(m::ScalarMapping) = m.domain_size + LazyTensors.domain_size(m::ScalarMapping) = m.range_size + + A = ScalarMapping{Float64,1,1}(2.0, (3,), (3,)) + B = ScalarMapping{Float64,1,1}(3.0, (3,), (3,)) + + v = [1.1,1.2,1.3] + for i ∈ eachindex(v) + @test ((A+B)*v)[i] == 2*v[i] + 3*v[i] + end + + for i ∈ eachindex(v) + @test ((A-B)*v)[i] == 2*v[i] - 3*v[i] + end + + @test range_size(A+B) == range_size(A) == range_size(B) + @test domain_size(A+B) == domain_size(A) == domain_size(B) +end + + +@testset "TensorMappingComposition" begin + A = rand(2,3) + B = rand(3,4) + + à = LazyLinearMap(A, (1,), (2,)) + B̃ = LazyLinearMap(B, (1,), (2,)) + + @test Ã∘B̃ isa TensorMappingComposition + @test range_size(Ã∘B̃) == (2,) + @test domain_size(Ã∘B̃) == (4,) + @test_throws SizeMismatch B̃∘à + + # @test @inbounds B̃∘à # Should not error even though dimensions don't match. (Since ]test runs with forced boundschecking this is currently not testable 2020-10-16) + + v = rand(4) + @test Ã∘B̃*v ≈ A*B*v rtol=1e-14 + + v = rand(2) + @test (Ã∘B̃)'*v ≈ B'*A'*v rtol=1e-14 +end + +@testset "LazyLinearMap" begin + # Test a standard matrix-vector product + # mapping vectors of size 4 to vectors of size 3. + A = rand(3,4) + à = LazyLinearMap(A, (1,), (2,)) + v = rand(4) + w = rand(3) + + @test à isa LazyLinearMap{T,1,1} where T + @test à isa TensorMapping{T,1,1} where T + @test range_size(Ã) == (3,) + @test domain_size(Ã) == (4,) + + @test Ã*ones(4) ≈ A*ones(4) atol=5e-13 + @test Ã*v ≈ A*v atol=5e-13 + @test Ã'*w ≈ A'*w + + A = rand(2,3,4) + @test_throws DomainError LazyLinearMap(A, (3,1), (2,)) + + # Test more exotic mappings + B = rand(3,4,2) + # Map vectors of size 2 to matrices of size (3,4) + B̃ = LazyLinearMap(B, (1,2), (3,)) + v = rand(2) + + @test range_size(B̃) == (3,4) + @test domain_size(B̃) == (2,) + @test B̃ isa TensorMapping{T,2,1} where T + @test B̃*ones(2) ≈ B[:,:,1] + B[:,:,2] atol=5e-13 + @test B̃*v ≈ B[:,:,1]*v[1] + B[:,:,2]*v[2] atol=5e-13 + + # Map matrices of size (3,2) to vectors of size 4 + B̃ = LazyLinearMap(B, (2,), (1,3)) + v = rand(3,2) + + @test range_size(B̃) == (4,) + @test domain_size(B̃) == (3,2) + @test B̃ isa TensorMapping{T,1,2} where T + @test B̃*ones(3,2) ≈ B[1,:,1] + B[2,:,1] + B[3,:,1] + + B[1,:,2] + B[2,:,2] + B[3,:,2] atol=5e-13 + @test B̃*v ≈ B[1,:,1]*v[1,1] + B[2,:,1]*v[2,1] + B[3,:,1]*v[3,1] + + B[1,:,2]v[1,2] + B[2,:,2]*v[2,2] + B[3,:,2]*v[3,2] atol=5e-13 + + + # TODO: + # @inferred (B̃*v)[2] +end + + +@testset "IdentityMapping" begin + @test IdentityMapping{Float64}((4,5)) isa IdentityMapping{T,2} where T + @test IdentityMapping{Float64}((4,5)) isa TensorMapping{T,2,2} where T + @test IdentityMapping{Float64}((4,5)) == IdentityMapping{Float64}(4,5) + + @test IdentityMapping(3,2) isa IdentityMapping{Float64,2} + + for sz ∈ [(4,5),(3,),(5,6,4)] + I = IdentityMapping{Float64}(sz) + v = rand(sz...) + @test I*v == v + @test I'*v == v + + @test range_size(I) == sz + @test domain_size(I) == sz + end + + I = IdentityMapping{Float64}((4,5)) + v = rand(4,5) + @inferred (I*v)[3,2] + @inferred (I'*v)[3,2] + @inferred range_size(I) + + @inferred range_dim(I) + @inferred domain_dim(I) + + à = rand(4,2) + A = LazyLinearMap(Ã,(1,),(2,)) + I1 = IdentityMapping{Float64}(2) + I2 = IdentityMapping{Float64}(4) + @test A∘I1 == A + @test I2∘A == A + @test I1∘I1 == I1 + @test_throws SizeMismatch I1∘A + @test_throws SizeMismatch A∘I2 + @test_throws SizeMismatch I1∘I2 +end + +@testset "InflatedTensorMapping" begin + I(sz...) = IdentityMapping(sz...) + + à = rand(4,2) + B̃ = rand(4,2,3) + C̃ = rand(4,2,3) + + A = LazyLinearMap(Ã,(1,),(2,)) + B = LazyLinearMap(B̃,(1,2),(3,)) + C = LazyLinearMap(C̃,(1,),(2,3)) + + @testset "Constructors" begin + @test InflatedTensorMapping(I(3,2), A, I(4)) isa TensorMapping{Float64, 4, 4} + @test InflatedTensorMapping(I(3,2), B, I(4)) isa TensorMapping{Float64, 5, 4} + @test InflatedTensorMapping(I(3), C, I(2,3)) isa TensorMapping{Float64, 4, 5} + @test InflatedTensorMapping(C, I(2,3)) isa TensorMapping{Float64, 3, 4} + @test InflatedTensorMapping(I(3), C) isa TensorMapping{Float64, 2, 3} + @test InflatedTensorMapping(I(3), I(2,3)) isa TensorMapping{Float64, 3, 3} + end + + @testset "Range and domain size" begin + @test range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4) + @test domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4) + + @test range_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,4,2,4) + @test domain_size(InflatedTensorMapping(I(3,2), B, I(4))) == (3,2,3,4) + + @test range_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,4,2,3) + @test domain_size(InflatedTensorMapping(I(3), C, I(2,3))) == (3,2,3,2,3) + + @inferred range_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,4,4) + @inferred domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4) + end + + @testset "Application" begin + # Testing regular application and transposed application with inflation "before", "after" and "before and after". + # The inflated tensor mappings are chosen to preserve, reduce and increase the dimension of the result compared to the input. + tests = [ + ( + InflatedTensorMapping(I(3,2), A, I(4)), + (v-> @tullio res[a,b,c,d] := Ã[c,i]*v[a,b,i,d]), # Expected result of apply + (v-> @tullio res[a,b,c,d] := Ã[i,c]*v[a,b,i,d]), # Expected result of apply_transpose + ), + ( + InflatedTensorMapping(I(3,2), B, I(4)), + (v-> @tullio res[a,b,c,d,e] := B̃[c,d,i]*v[a,b,i,e]), + (v-> @tullio res[a,b,c,d] := B̃[i,j,c]*v[a,b,i,j,d]), + ), + ( + InflatedTensorMapping(I(3,2), C, I(4)), + (v-> @tullio res[a,b,c,d] := C̃[c,i,j]*v[a,b,i,j,d]), + (v-> @tullio res[a,b,c,d,e] := C̃[i,c,d]*v[a,b,i,e]), + ), + ( + InflatedTensorMapping(I(3,2), A), + (v-> @tullio res[a,b,c] := Ã[c,i]*v[a,b,i]), + (v-> @tullio res[a,b,c] := Ã[i,c]*v[a,b,i]), + ), + ( + InflatedTensorMapping(I(3,2), B), + (v-> @tullio res[a,b,c,d] := B̃[c,d,i]*v[a,b,i]), + (v-> @tullio res[a,b,c] := B̃[i,j,c]*v[a,b,i,j]), + ), + ( + InflatedTensorMapping(I(3,2), C), + (v-> @tullio res[a,b,c] := C̃[c,i,j]*v[a,b,i,j]), + (v-> @tullio res[a,b,c,d] := C̃[i,c,d]*v[a,b,i]), + ), + ( + InflatedTensorMapping(A,I(4)), + (v-> @tullio res[a,b] := Ã[a,i]*v[i,b]), + (v-> @tullio res[a,b] := Ã[i,a]*v[i,b]), + ), + ( + InflatedTensorMapping(B,I(4)), + (v-> @tullio res[a,b,c] := B̃[a,b,i]*v[i,c]), + (v-> @tullio res[a,b] := B̃[i,j,a]*v[i,j,b]), + ), + ( + InflatedTensorMapping(C,I(4)), + (v-> @tullio res[a,b] := C̃[a,i,j]*v[i,j,b]), + (v-> @tullio res[a,b,c] := C̃[i,a,b]*v[i,c]), + ), + ] + + @testset "apply" begin + for i ∈ 1:length(tests) + tm = tests[i][1] + v = rand(domain_size(tm)...) + true_value = tests[i][2](v) + @test tm*v ≈ true_value rtol=1e-14 + end + end + + @testset "apply_transpose" begin + for i ∈ 1:length(tests) + tm = tests[i][1] + v = rand(range_size(tm)...) + true_value = tests[i][3](v) + @test tm'*v ≈ true_value rtol=1e-14 + end + end + + @testset "Inference of application" begin + struct ScalingOperator{T,D} <: TensorMapping{T,D,D} + λ::T + size::NTuple{D,Int} + end + + LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...] + LazyTensors.range_size(m::ScalingOperator) = m.size + LazyTensors.domain_size(m::ScalingOperator) = m.size + + tm = InflatedTensorMapping(I(2,3),ScalingOperator(2.0, (3,2)),I(3,4)) + v = rand(domain_size(tm)...) + + @inferred apply(tm,v,1,2,3,2,2,4) + @inferred (tm*v)[1,2,3,2,2,4] + end + end + + @testset "InflatedTensorMapping of InflatedTensorMapping" begin + A = ScalingOperator(2.0,(2,3)) + itm = InflatedTensorMapping(I(3,2), A, I(4)) + @test InflatedTensorMapping(I(4), itm, I(2)) == InflatedTensorMapping(I(4,3,2), A, I(4,2)) + @test InflatedTensorMapping(itm, I(2)) == InflatedTensorMapping(I(3,2), A, I(4,2)) + @test InflatedTensorMapping(I(4), itm) == InflatedTensorMapping(I(4,3,2), A, I(4)) + + @test InflatedTensorMapping(I(2), I(2), I(2)) isa InflatedTensorMapping # The constructor should always return its type. + end +end + +@testset "split_index" begin + @test LazyTensors.split_index(Val(2),Val(1),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,5,6),(3,4)) + @test LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,4,5,6) == ((1,2,:,:,:,5,6),(3,4)) + @test LazyTensors.split_index(Val(3),Val(1),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,5,6),(4,)) + @test LazyTensors.split_index(Val(3),Val(2),Val(1),Val(2),1,2,3,4,5,6) == ((1,2,3,:,:,5,6),(4,)) + @test LazyTensors.split_index(Val(1),Val(1),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,4,5,6),(2,3)) + @test LazyTensors.split_index(Val(1),Val(2),Val(2),Val(3),1,2,3,4,5,6) == ((1,:,:,4,5,6),(2,3)) + + @test LazyTensors.split_index(Val(0),Val(1),Val(3),Val(3),1,2,3,4,5,6) == ((:,4,5,6),(1,2,3)) + @test LazyTensors.split_index(Val(3),Val(1),Val(3),Val(0),1,2,3,4,5,6) == ((1,2,3,:),(4,5,6)) + + @inferred LazyTensors.split_index(Val(2),Val(3),Val(2),Val(2),1,2,3,2,2,4) +end + +@testset "slice_tuple" begin + @test LazyTensors.slice_tuple((1,2,3),Val(1), Val(3)) == (1,2,3) + @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(2), Val(5)) == (2,3,4,5) + @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(1), Val(3)) == (1,2,3) + @test LazyTensors.slice_tuple((1,2,3,4,5,6),Val(4), Val(6)) == (4,5,6) +end + +@testset "split_tuple" begin + @testset "2 parts" begin + @test LazyTensors.split_tuple((),Val(0)) == ((),()) + @test LazyTensors.split_tuple((1,),Val(0)) == ((),(1,)) + @test LazyTensors.split_tuple((1,),Val(1)) == ((1,),()) + + @test LazyTensors.split_tuple((1,2,3,4),Val(0)) == ((),(1,2,3,4)) + @test LazyTensors.split_tuple((1,2,3,4),Val(1)) == ((1,),(2,3,4)) + @test LazyTensors.split_tuple((1,2,3,4),Val(2)) == ((1,2),(3,4)) + @test LazyTensors.split_tuple((1,2,3,4),Val(3)) == ((1,2,3),(4,)) + @test LazyTensors.split_tuple((1,2,3,4),Val(4)) == ((1,2,3,4),()) + + @test LazyTensors.split_tuple((1,2,true,4),Val(3)) == ((1,2,true),(4,)) + + @inferred LazyTensors.split_tuple((1,2,3,4),Val(3)) + @inferred LazyTensors.split_tuple((1,2,true,4),Val(3)) + end + + @testset "3 parts" begin + @test LazyTensors.split_tuple((),Val(0),Val(0)) == ((),(),()) + @test LazyTensors.split_tuple((1,2,3),Val(1), Val(1)) == ((1,),(2,),(3,)) + @test LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) == ((1,),(true,),(3,)) + + @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(1),Val(2)) == ((1,),(2,3),(4,5,6)) + @test LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) == ((1,2,3),(4,5),(6,)) + + @inferred LazyTensors.split_tuple((1,2,3,4,5,6),Val(3),Val(2)) + @inferred LazyTensors.split_tuple((1,true,3),Val(1), Val(1)) + end +end + +@testset "flatten_tuple" begin + @test LazyTensors.flatten_tuple((1,)) == (1,) + @test LazyTensors.flatten_tuple((1,2,3,4,5,6)) == (1,2,3,4,5,6) + @test LazyTensors.flatten_tuple((1,2,(3,4),5,6)) == (1,2,3,4,5,6) + @test LazyTensors.flatten_tuple((1,2,(3,(4,5)),6)) == (1,2,3,4,5,6) + @test LazyTensors.flatten_tuple(((1,2),(3,4),(5,),6)) == (1,2,3,4,5,6) +end + + +@testset "LazyOuterProduct" begin + struct ScalingOperator{T,D} <: TensorMapping{T,D,D} + λ::T + size::NTuple{D,Int} + end + + LazyTensors.apply(m::ScalingOperator{T,D}, v, I::Vararg{Any,D}) where {T,D} = m.λ*v[I...] + LazyTensors.range_size(m::ScalingOperator) = m.size + LazyTensors.domain_size(m::ScalingOperator) = m.size + + A = ScalingOperator(2.0, (5,)) + B = ScalingOperator(3.0, (3,)) + C = ScalingOperator(5.0, (3,2)) + + AB = LazyOuterProduct(A,B) + @test AB isa TensorMapping{T,2,2} where T + @test range_size(AB) == (5,3) + @test domain_size(AB) == (5,3) + + v = rand(range_size(AB)...) + @test AB*v == 6*v + + ABC = LazyOuterProduct(A,B,C) + + @test ABC isa TensorMapping{T,4,4} where T + @test range_size(ABC) == (5,3,3,2) + @test domain_size(ABC) == (5,3,3,2) + + @test A⊗B == AB + @test A⊗B⊗C == ABC + + A = rand(3,2) + B = rand(2,4,3) + + v₁ = rand(2,4,3) + v₂ = rand(4,3,2) + + à = LazyLinearMap(A,(1,),(2,)) + B̃ = LazyLinearMap(B,(1,),(2,3)) + + ÃB̃ = LazyOuterProduct(Ã,B̃) + @tullio ABv[i,k] := A[i,j]*B[k,l,m]*v₁[j,l,m] + @test ÃB̃*v₁ ≈ ABv + + B̃à = LazyOuterProduct(B̃,Ã) + @tullio BAv[k,i] := A[i,j]*B[k,l,m]*v₂[l,m,j] + @test B̃Ã*v₂ ≈ BAv + + @testset "Indentity mapping arguments" begin + @test LazyOuterProduct(IdentityMapping(3,2), IdentityMapping(1,2)) == IdentityMapping(3,2,1,2) + + à = LazyLinearMap(A,(1,),(2,)) + @test LazyOuterProduct(IdentityMapping(3,2), Ã) == InflatedTensorMapping(IdentityMapping(3,2),Ã) + @test LazyOuterProduct(Ã, IdentityMapping(3,2)) == InflatedTensorMapping(Ã,IdentityMapping(3,2)) + + I1 = IdentityMapping(3,2) + I2 = IdentityMapping(4) + @test I1⊗Ã⊗I2 == InflatedTensorMapping(I1, Ã, I2) + end + +end
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test/LazyTensors/tensor_mapping_test.jl Sat Feb 20 20:59:32 2021 +0100 @@ -0,0 +1,12 @@ +using Test +using Sbplib.LazyTensors + +@testset "Generic Mapping methods" begin + struct DummyMapping{T,R,D} <: TensorMapping{T,R,D} end + LazyTensors.apply(m::DummyMapping{T,R,D}, v, I::Vararg{Any,R}) where {T,R,D} = :apply + @test range_dim(DummyMapping{Int,2,3}()) == 2 + @test domain_dim(DummyMapping{Int,2,3}()) == 3 + @test apply(DummyMapping{Int,2,3}(), zeros(Int, (0,0,0)),0,0) == :apply + @test eltype(DummyMapping{Int,2,3}()) == Int + @test eltype(DummyMapping{Float64,2,3}()) == Float64 +end