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🧪 (Schedulers) add some tests to check step
and add_noise
correctness
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parent
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@ -13,7 +13,8 @@ julia = "1"
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[extras]
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[extras]
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Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
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Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
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Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
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Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
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[targets]
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[targets]
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test = ["Test", "Aqua"]
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test = ["Test", "Aqua", "Statistics"]
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58
test/Schedulers.jl
Normal file
58
test/Schedulers.jl
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@ -0,0 +1,58 @@
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import Diffusers: step, add_noise, DDPM, cosine_beta_schedule, rescale_zero_terminal_snr
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using Statistics
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using Test
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@testset "Schedulers tests" begin
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@testset "check `step` correctness" begin
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T = 10
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batch_size = 8
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size = 128
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# create a DDPM with a cosine beta schedule
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ddpm = Diffusers.DDPM(
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Vector{Float32},
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Diffusers.cosine_beta_schedule(T),
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)
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# create some dummy data
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x₀ = ones(Float32, size, size, batch_size)
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ϵ = randn(Float32, size, size, batch_size)
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for t in 1:T
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t = ones(UInt32, batch_size) .* t
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# corrupt x₀ with noise
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xₜ = Diffusers.add_noise(ddpm, x₀, ϵ, t)
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# suppose a model predicted ϵ perfectly
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_, x̂₀ = Diffusers.step(ddpm, xₜ, ϵ, t)
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# test that we recover x₀
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@test x̂₀ ≈ x₀
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end
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end
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@testset "check `add_noise` terminal SNR" begin
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T = 10
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batch_size = 1
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size = 1000
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# create a DDPM with a terminal SNR cosine beta schedule
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ddpm = Diffusers.DDPM(
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Vector{Float32},
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Diffusers.rescale_zero_terminal_snr(
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Diffusers.cosine_beta_schedule(T),
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),
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)
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# create some dummy data
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x₀ = ones(Float32, size, size, batch_size)
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ϵ = randn(Float32, size, size, batch_size)
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t = ones(UInt32, batch_size) .* T
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# corrupt x₀ with noise
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xₜ = Diffusers.add_noise(ddpm, x₀, ϵ, t)
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@test std(xₜ) ≈ 1.0 atol=1f-3
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@test mean(xₜ) ≈ 0.0 atol=1f-3
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end
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end
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end
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end
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@ -3,4 +3,5 @@ using Aqua
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Aqua.test_all(Diffusers)
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Aqua.test_all(Diffusers)
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include("Schedulers.jl")
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include("BetaSchedules.jl")
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include("BetaSchedules.jl")
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