49 lines
1.2 KiB
Julia
49 lines
1.2 KiB
Julia
using Flux, CuArrays, Test
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trainmode(f, x...) = forward(f, x...)[1]
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#
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# @testset "CUDNN BatchNorm" begin
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# @testset "4D Input" begin
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# x = Float64.(collect(reshape(1:12, 2, 2, 3, 1)))
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# m = BatchNorm(3)
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# cx = gpu(x)
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# cm = gpu(m)
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#
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# y = trainmode(m, x)
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# cy = trainmode(cm, cx)
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#
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# # @test cy isa TrackedArray{Float32,4,CuArray{Float32,4}}
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#
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# @test cpu(data(cy)) ≈ data(y)
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#
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# g = rand(size(y)...)
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# Flux.back!(y, g)
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# Flux.back!(cy, gpu(g))
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#
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# @test m.γ.grad ≈ cpu(cm.γ.grad)
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# @test m.β.grad ≈ cpu(cm.β.grad)
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# @test x.grad ≈ cpu(x.grad)
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# end
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#
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# @testset "2D Input" begin
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# x = TrackedArray(Float64.(collect(reshape(1:12, 3, 4))))
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# m = BatchNorm(3)
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# cx = gpu(x)
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# cm = gpu(m)
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#
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# y = m(x)
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# cy = cm(cx)
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#
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# @test cy isa TrackedArray{Float32,2,CuArray{Float32,2}}
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#
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# @test cpu(data(cy)) ≈ data(y)
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#
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# g = rand(size(y)...)
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# Flux.back!(y, g)
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# Flux.back!(cy, gpu(g))
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#
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# @test m.γ.grad ≈ cpu(cm.γ.grad)
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# @test m.β.grad ≈ cpu(cm.β.grad)
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# @test x.grad ≈ cpu(x.grad)
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# end
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# end
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