sbplib_julia: test/testLazyTensors.jl comparison

comparison test/testLazyTensors.jl @ 540:013ca4892540

Merge feature/inflated_tensormapping_transpose

author	Jonatan Werpers <jonatan@werpers.com>
date	Thu, 26 Nov 2020 17:53:40 +0100
parents	fc0f942b8792
children	62d96e2cd165

comparison

equal deleted inserted replaced

-:b6768d769f46
+:013ca4892540
 # 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]),
+(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]),
 ),
 ]
-for i ∈ 1:length(tests)
+@testset "apply" begin
-tm = tests[i][1]
+for i ∈ 1:length(tests)
-v = rand(domain_size(tm)...)
+tm = tests[i][1]
-true_value = tests[i][2](v)
+v = rand(domain_size(tm)...)
-@test tm*v ≈ true_value rtol=1e-14
+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
 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 LazyTensors.split_index(tm,1,2,3,2,2,4)
 @inferred apply(tm,v,Index{Unknown}.((1,2,3,2,2,4))...)
 @inferred (tm*v)[1,2,3,2,2,4]
 end
 end
 @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),())
+@inferred LazyTensors.split_tuple((1,2,3,4),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((1,2,3,4,5,6),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))
+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)

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comparison test/testLazyTensors.jl @ 540:013ca4892540