sbplib_julia: test/LazyTensors/lazy_tensor_operations

comparison test/LazyTensors/lazy_tensor_operations_test.jl @ 995:1ba8a398af9c refactor/lazy_tensors

Rename types

author	Jonatan Werpers <jonatan@werpers.com>
date	Fri, 18 Mar 2022 21:14:47 +0100
parents	2f9beee56a4c
children	20c376dffe84

comparison

equal deleted inserted replaced

-:55ab7801c45f
+:1ba8a398af9c
 using Sbplib.RegionIndices
 using Tullio
 @testset "Mapping transpose" begin
-struct DummyMapping{T,R,D} <: TensorMapping{T,R,D} end
+struct DummyMapping{T,R,D} <: LazyTensor{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' isa LazyTensor{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}
+struct SizeDoublingMapping{T,R,D} <: LazyTensor{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
 @inferred m*v
 @inferred (m*v)[1]
 end
 end
-@testset "TensorMapping binary operations" begin
+@testset "LazyTensor binary operations" begin
 A = ScalingTensor(2.0, (3,))
 B = ScalingTensor(3.0, (3,))
 v = [1.1,1.2,1.3]
 for i ∈ eachindex(v)
 @test ((A+B)*ComplexF64[1.1,1.2,1.3])[3] isa ComplexF64
 end
-@testset "TensorMappingComposition" begin
+@testset "LazyTensorComposition" begin
 A = rand(2,3)
 B = rand(3,4)
 Ã = LazyLinearMap(A, (1,), (2,))
 B̃ = LazyLinearMap(B, (1,), (2,))
-@test Ã∘B̃ isa TensorMappingComposition
+@test Ã∘B̃ isa LazyTensorComposition
 @test range_size(Ã∘B̃) == (2,)
 @test domain_size(Ã∘B̃) == (4,)
 @test_throws SizeMismatch B̃∘Ã
 # @test @inbounds B̃∘Ã # Should not error even though dimensions don't match. (Since ]test runs with forced boundschecking this is currently not testable 2020-10-16)
 Ã = LazyLinearMap(A, (1,), (2,))
 v = rand(4)
 w = rand(3)
 @test Ã isa LazyLinearMap{T,1,1} where T
-@test Ã isa TensorMapping{T,1,1} where T
+@test Ã isa LazyTensor{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
 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 isa LazyTensor{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 isa LazyTensor{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
+@testset "IdentityTensor" begin
-@test IdentityMapping{Float64}((4,5)) isa IdentityMapping{T,2} where T
+@test IdentityTensor{Float64}((4,5)) isa IdentityTensor{T,2} where T
-@test IdentityMapping{Float64}((4,5)) isa TensorMapping{T,2,2} where T
+@test IdentityTensor{Float64}((4,5)) isa LazyTensor{T,2,2} where T
-@test IdentityMapping{Float64}((4,5)) == IdentityMapping{Float64}(4,5)
+@test IdentityTensor{Float64}((4,5)) == IdentityTensor{Float64}(4,5)
-@test IdentityMapping(3,2) isa IdentityMapping{Float64,2}
+@test IdentityTensor(3,2) isa IdentityTensor{Float64,2}
 for sz ∈ [(4,5),(3,),(5,6,4)]
-I = IdentityMapping{Float64}(sz)
+I = IdentityTensor{Float64}(sz)
 v = rand(sz...)
 @test I*v == v
 @test I'*v == v
 v = rand(ComplexF64,sz...)
 @test range_size(I) == sz
 @test domain_size(I) == sz
 end
-I = IdentityMapping{Float64}((4,5))
+I = IdentityTensor{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)
 Ã = rand(4,2)
 A = LazyLinearMap(Ã,(1,),(2,))
-I1 = IdentityMapping{Float64}(2)
+I1 = IdentityTensor{Float64}(2)
-I2 = IdentityMapping{Float64}(4)
+I2 = IdentityTensor{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 "ScalingTensor" begin
 st = ScalingTensor(2.,(3,4))
-@test st isa TensorMapping{Float64, 2, 2}
+@test st isa LazyTensor{Float64, 2, 2}
 @test range_size(st) == (3,4)
 @test domain_size(st) == (3,4)
 v = rand(3,4)
 @test st*v == 2.0 .* v
 @inferred (st*v)[2,2]
 @inferred (st'*v)[2,2]
 end
-@testset "InflatedTensorMapping" begin
+@testset "InflatedLazyTensor" begin
-I(sz...) = IdentityMapping(sz...)
+I(sz...) = IdentityTensor(sz...)
 Ã = 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 InflatedLazyTensor(I(3,2), A, I(4)) isa LazyTensor{Float64, 4, 4}
-@test InflatedTensorMapping(I(3,2), B, I(4)) isa TensorMapping{Float64, 5, 4}
+@test InflatedLazyTensor(I(3,2), B, I(4)) isa LazyTensor{Float64, 5, 4}
-@test InflatedTensorMapping(I(3), C, I(2,3)) isa TensorMapping{Float64, 4, 5}
+@test InflatedLazyTensor(I(3), C, I(2,3)) isa LazyTensor{Float64, 4, 5}
-@test InflatedTensorMapping(C, I(2,3)) isa TensorMapping{Float64, 3, 4}
+@test InflatedLazyTensor(C, I(2,3)) isa LazyTensor{Float64, 3, 4}
-@test InflatedTensorMapping(I(3), C) isa TensorMapping{Float64, 2, 3}
+@test InflatedLazyTensor(I(3), C) isa LazyTensor{Float64, 2, 3}
-@test InflatedTensorMapping(I(3), I(2,3)) isa TensorMapping{Float64, 3, 3}
+@test InflatedLazyTensor(I(3), I(2,3)) isa LazyTensor{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 range_size(InflatedLazyTensor(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 domain_size(InflatedLazyTensor(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 range_size(InflatedLazyTensor(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 domain_size(InflatedLazyTensor(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 range_size(InflatedLazyTensor(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)
+@test domain_size(InflatedLazyTensor(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 range_size(InflatedLazyTensor(I(3,2), A, I(4))) == (3,2,4,4)
-@inferred domain_size(InflatedTensorMapping(I(3,2), A, I(4))) == (3,2,2,4)
+@inferred domain_size(InflatedLazyTensor(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)),
+InflatedLazyTensor(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)),
+InflatedLazyTensor(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)),
+InflatedLazyTensor(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),
+InflatedLazyTensor(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),
+InflatedLazyTensor(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),
+InflatedLazyTensor(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)),
+InflatedLazyTensor(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)),
+InflatedLazyTensor(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)),
+InflatedLazyTensor(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]),
 ),
 ]
 @test tm'*v ≈ true_value rtol=1e-14
 end
 end
 @testset "application to other type" begin
-tm = InflatedTensorMapping(I(3,2), A, I(4))
+tm = InflatedLazyTensor(I(3,2), A, I(4))
 v = rand(ComplexF64, domain_size(tm)...)
 @test (tm*v)[1,2,3,1] isa ComplexF64
 v = rand(ComplexF64, domain_size(tm')...)
 @test (tm'*v)[1,2,2,1] isa ComplexF64
 end
 @testset "Inference of application" begin
-tm = InflatedTensorMapping(I(2,3),ScalingTensor(2.0, (3,2)),I(3,4))
+tm = InflatedLazyTensor(I(2,3),ScalingTensor(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
+@testset "InflatedLazyTensor of InflatedLazyTensor" begin
 A = ScalingTensor(2.0,(2,3))
-itm = InflatedTensorMapping(I(3,2), A, I(4))
+itm = InflatedLazyTensor(I(3,2), A, I(4))
-@test  InflatedTensorMapping(I(4), itm, I(2)) == InflatedTensorMapping(I(4,3,2), A, I(4,2))
+@test  InflatedLazyTensor(I(4), itm, I(2)) == InflatedLazyTensor(I(4,3,2), A, I(4,2))
-@test  InflatedTensorMapping(itm, I(2)) == InflatedTensorMapping(I(3,2), A, I(4,2))
+@test  InflatedLazyTensor(itm, I(2)) == InflatedLazyTensor(I(3,2), A, I(4,2))
-@test  InflatedTensorMapping(I(4), itm) == InflatedTensorMapping(I(4,3,2), A, I(4))
+@test  InflatedLazyTensor(I(4), itm) == InflatedLazyTensor(I(4,3,2), A, I(4))
-@test InflatedTensorMapping(I(2), I(2), I(2)) isa InflatedTensorMapping # The constructor should always return its type.
+@test InflatedLazyTensor(I(2), I(2), I(2)) isa InflatedLazyTensor # 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))
 A = ScalingTensor(2.0, (5,))
 B = ScalingTensor(3.0, (3,))
 C = ScalingTensor(5.0, (3,2))
 AB = LazyOuterProduct(A,B)
-@test AB isa TensorMapping{T,2,2} where T
+@test AB isa LazyTensor{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 ABC isa LazyTensor{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
 B̃Ã = 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)
+@test LazyOuterProduct(IdentityTensor(3,2), IdentityTensor(1,2)) == IdentityTensor(3,2,1,2)
 Ã = LazyLinearMap(A,(1,),(2,))
-@test LazyOuterProduct(IdentityMapping(3,2), Ã) == InflatedTensorMapping(IdentityMapping(3,2),Ã)
+@test LazyOuterProduct(IdentityTensor(3,2), Ã) == InflatedLazyTensor(IdentityTensor(3,2),Ã)
-@test LazyOuterProduct(Ã, IdentityMapping(3,2)) == InflatedTensorMapping(Ã,IdentityMapping(3,2))
+@test LazyOuterProduct(Ã, IdentityTensor(3,2)) == InflatedLazyTensor(Ã,IdentityTensor(3,2))
-I1 = IdentityMapping(3,2)
+I1 = IdentityTensor(3,2)
-I2 = IdentityMapping(4)
+I2 = IdentityTensor(4)
-@test I1⊗Ã⊗I2 == InflatedTensorMapping(I1, Ã, I2)
+@test I1⊗Ã⊗I2 == InflatedLazyTensor(I1, Ã, I2)
 end
 end

Mercurial > repos > public > sbplib_julia

comparison test/LazyTensors/lazy_tensor_operations_test.jl @ 995:1ba8a398af9c refactor/lazy_tensors