Merge pull request #586 from KristofferC/kc/batchnorm

work around extreme slowdown in BatchNorm due to julia performance bug in broadcast fusion

src/layers/normalise.jl

@@ -138,7 +138,9 @@ function (BN::BatchNorm)(x)
   end
 
   let λ = BN.λ
-    λ.(reshape(γ, affine_shape...) .* ((x .- μ) ./ sqrt.(σ² .+ BN.ϵ)) .+ reshape(β, affine_shape...))
+    temp = reshape(γ, affine_shape...) .* ((x .- μ) ./ sqrt.(σ² .+ BN.ϵ)) 
+    # This is intentionally not fused because of an extreme slowdown doing so
+    λ.(temp .+ reshape(β, affine_shape...))
   end
 end
 

test/layers/normalisation.jl

@@ -98,4 +98,9 @@ end
     y = permutedims(reshape(m(y), 2, 2, 2, 3, 1), [2, 3, 4, 1, 5])
     @test m(x) == y
   end
+
+  let m = BatchNorm(32), x = randn(Float32, 416, 416, 32, 1);
+    m(x)
+    @test (@allocated m(x)) <  100_000_000
+  end
 end