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Fourier Layer Tests #35

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19 changes: 18 additions & 1 deletion test/deeponet.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -14,4 +14,21 @@ using Test, Random, Flux
# Accept only Int as architecture parameters
@test_throws MethodError DeepONet((32.5,64,72), (24,48,72), σ, tanh)
@test_throws MethodError DeepONet((32,64,72), (24.1,48,72))
end
end

#Just the first 16 datapoints from the Burgers' equation dataset
a = [0.83541104, 0.83479851, 0.83404712, 0.83315711, 0.83212979, 0.83096755, 0.82967374, 0.82825263, 0.82670928, 0.82504949, 0.82327962, 0.82140651, 0.81943734, 0.81737952, 0.8152405, 0.81302771]
sensors = collect(range(0, 1, length=16))'

model = DeepONet((16, 22, 30), (1, 16, 24, 30), σ, tanh; init_branch=Flux.glorot_normal, bias_trunk=false)

model(a,sensors)

#forward pass
@test size(model(a, sensors)) == (1, 16)

mgrad = Flux.Zygote.gradient((x,p)->sum(model(x,p)),a,sensors)

#gradients
@test !iszero(Flux.Zygote.gradient((x,p)->sum(model(x,p)),a,sensors)[1])
@test !iszero(Flux.Zygote.gradient((x,p)->sum(model(x,p)),a,sensors)[2])
25 changes: 24 additions & 1 deletion test/fourierlayer.jl
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Expand Up @@ -28,4 +28,27 @@ using Test, Random, Flux
# Test max amount of modes
@test_throws AssertionError FourierLayer(100, 100, 100, 60, σ)
@test_throws AssertionError FourierLayer(100, 100, 100, 60)
end
end

#Just the first 16 data points from Burgers' equation dataset
xtrain = Float32[0.83541104, 0.83479851, 0.83404712, 0.83315711, 0.83212979, 0.83096755, 0.82967374, 0.82825263, 0.82670928, 0.82504949, 0.82327962, 0.82140651, 0.81943734, 0.81737952, 0.8152405, 0.81302771]
grid = Float32.(collect(range(0, 1, length=16))')

x = cat(reshape(xtrain,(1,16,1)),
reshape(repeat(grid,1),(1,16,1));
dims=3)

x = permutedims(x,(3,2,1))
layer = FourierLayer(64, 64, 16, 8, gelu, bias_fourier=false)
model = Chain(Dense(2,64;bias=false), layer, layer, layer, layer,
Dense(64,2;bias=false))

model(x)

#forward pass
@test size(model(x)) == (2, 16, 1)

Flux.Zygote.gradient((x)->sum(model(x)), x)

#gradient test
@test !iszero(Flux.Zygote.gradient((x)->sum(model(x)), x)[1])