Merge pull request #397 from vincentmolin/master

Move exports to main source file

src/NNlib.jl

@@ -5,13 +5,19 @@ using Requires
 using ChainRulesCore
 import ChainRulesCore: rrule
 using Base.Broadcast: broadcasted
+using Base.Threads
+using Statistics
 using Statistics: mean
+using LinearAlgebra
+using LinearAlgebra: BlasFloat, Transpose, Adjoint, AdjOrTransAbsMat
+using LinearAlgebra.BLAS: libblas, BlasInt, @blasfunc
 
 const IntOrIntTuple = Union{Integer, NTuple{N,<:Integer} where N}
 const Numeric = Union{AbstractArray{<:T}, T} where {T<:Number}
 
 # Include APIs
 include("dim_helpers.jl")
+export ConvDims, DenseConvDims, PoolDims, DepthwiseConvDims
 
 is_nnpack_available() = false
 
@@ -27,14 +33,46 @@ is_nnpack_available() = false
 end
 
 include("activations.jl")
+for f in ACTIVATIONS
+    @eval export $(f)
+end
+export sigmoid, hardsigmoid, logsigmoid, thresholdrelu # Aliases
+
 include("softmax.jl")
+export softmax, softmax!, ∇softmax, ∇softmax!, logsoftmax, 
+    logsoftmax!, ∇logsoftmax, ∇logsoftmax!, logsumexp
+
+include("batched/batchedadjtrans.jl")
 include("batched/batchedmul.jl")
+export batched_mul, batched_mul!, ⊠,  batched_vec,
+    batched_transpose, batched_adjoint
+
 include("gemm.jl")
+export grid_sample, ∇grid_sample
+
 include("conv.jl")
+export conv, conv!, ∇conv_data, ∇conv_data!, ∇conv_filter, 
+    ∇conv_filter!, depthwiseconv, depthwiseconv!, 
+    ∇depthwiseconv_data, ∇depthwiseconv_data!, 
+    ∇depthwiseconv_filter, ∇depthwiseconv_filter!
+
 include("conv_bias_act.jl")
+export conv_bias_act, conv_bias_act!
+
 include("pooling.jl")
+export maxpool, maxpool!, meanpool, meanpool!, 
+    ∇maxpool, ∇maxpool!, ∇meanpool, ∇meanpool!
+
 include("padding.jl")
+export pad_constant, pad_repeat, pad_reflect, pad_zeros
+
 include("upsample.jl")
+export upsample_nearest, ∇upsample_nearest,
+    upsample_linear, ∇upsample_linear,
+    upsample_bilinear, ∇upsample_bilinear,
+    upsample_trilinear, ∇upsample_trilinear,
+    pixel_shuffle
+
 include("gather.jl")
 include("scatter.jl")
 include("utils.jl")

src/activations.jl

@@ -11,13 +11,6 @@ ACTIVATIONS = [
     :tanh_fast, :sigmoid_fast,
 ]
 
-for f in ACTIVATIONS
-    @eval export $(f)
-end
-
-# Aliases
-export sigmoid, hardsigmoid, logsigmoid, thresholdrelu
-
 # of type float (to allow for integer inputs)
 oftf(x, y) = oftype(float(x), y)
 

src/batched/batchedadjtrans.jl

@@ -1,5 +1,3 @@
-using LinearAlgebra
-
 import Base: -
 import Adapt: adapt_structure, adapt
 

src/batched/batchedmul.jl

@@ -1,11 +1,3 @@
-
-export batched_mul, batched_mul!, ⊠,  batched_vec
-export batched_transpose, batched_adjoint
-
-include("./batchedadjtrans.jl")
-
-using LinearAlgebra: BlasFloat, Transpose, Adjoint, AdjOrTransAbsMat
-
 _unbatch(A) = A
 _unbatch(A::BatchedAdjOrTrans) = parent(A)
 

src/conv.jl

@@ -1,7 +1,3 @@
-export conv, conv!, ∇conv_data, ∇conv_data!, ∇conv_filter, ∇conv_filter!, depthwiseconv,
-        depthwiseconv!, ∇depthwiseconv_data, ∇depthwiseconv_data!, ∇depthwiseconv_filter,
-        ∇depthwiseconv_filter!
-
 ## Convolution API
 #
 #  We provide the following generic methods, for 3d, 4d, and 5d tensors, calculating 1d,

src/conv_bias_act.jl

@@ -1,5 +1,3 @@
-export conv_bias_act, conv_bias_act!
-
 function conv_bias_act(x::AbstractArray{xT,N}, w::AbstractArray{wT,N},
                 cdims::ConvDims, b::AbstractArray{bT,N}, σ=identity; kwargs...) where {xT, wT, bT, N}
     y = similar(x, promote_type(xT, wT, bT), output_size(cdims)..., channels_out(cdims), size(x,N))

src/dim_helpers/ConvDims.jl

@@ -1,5 +1,3 @@
-export ConvDims
-
 """
     ConvDims
 

src/dim_helpers/DenseConvDims.jl

@@ -1,5 +1,3 @@
-export DenseConvDims
-
 """
     DenseConvDims
 

src/dim_helpers/DepthwiseConvDims.jl

@@ -1,5 +1,3 @@
-export DepthwiseConvDims
-
 """
     DepthwiseConvDims
 

src/dim_helpers/PoolDims.jl

@@ -1,5 +1,3 @@
-export PoolDims
-
 """
     PoolDims(x_size::NTuple{M}, k::Union{NTuple{L, Int}, Int};
             stride=k, padding=0, dilation=1)  where {M, L}

src/functions.jl

@@ -1,4 +1,3 @@
-using NNlib: sigmoid
 """
     glu(x, dim = 1)
 

src/gemm.jl

@@ -1,9 +1,6 @@
 ## Low level gemm! call with pointers
 ## Borrowed from Knet.jl, adapted for compile-time constants
 
-using LinearAlgebra
-using LinearAlgebra.BLAS: libblas, BlasInt, @blasfunc
-
 using Compat: get_num_threads, set_num_threads # needs Compat 3.13, for any Julia < 1.6
 
 """

src/impl/conv_direct.jl

@@ -1,5 +1,4 @@
 ## This file contains direct Julia implementations of 2d and 3d convolutions
-using Base.Threads
 
 # Helper functions for restricting x/w overreach
 function clamp_lo(x, w)

src/impl/pooling_direct.jl

@@ -1,5 +1,3 @@
-using Statistics
-
 # Pooling is so similar, we abstract over meanpooling and maxpooling, simply replacing
 # the inner loop operation and a few initialization parameters.
 for name in (:max, :mean)

src/padding.jl

@@ -1,5 +1,3 @@
-export pad_constant, pad_repeat, pad_reflect, pad_zeros
-
 """
     pad_zeros(x, pad::Tuple; [dims])
     pad_zeros(x, pad::Int; [dims])

src/pooling.jl

@@ -1,5 +1,3 @@
-export maxpool, maxpool!, meanpool, meanpool!, ∇maxpool, ∇maxpool!, ∇meanpool, ∇meanpool!
-
 ## Pooling API
 #
 #  We provide the following generic methods, for 3d, 4d, and 5d tensors, calculating 1d,

src/sampling.jl

@@ -1,5 +1,3 @@
-export grid_sample, ∇grid_sample
-
 @inline in_bounds(h, w, H, W) = 1 ≤ h ≤ H && 1 ≤ w ≤ W
 # Borders are considered out-of-bounds for gradient.
 @inline clip_coordinate(coordinate, dim_size) = min(dim_size, max(1, coordinate))

src/softmax.jl

@@ -1,13 +1,3 @@
-export softmax,
-    softmax!,
-    ∇softmax,
-    ∇softmax!,
-    logsoftmax,
-    logsoftmax!,
-    ∇logsoftmax,
-    ∇logsoftmax!,
-    logsumexp
-
 """
     softmax(x; dims = 1)
 

src/upsample.jl

@@ -1,9 +1,3 @@
-export upsample_nearest, ∇upsample_nearest,
-    upsample_linear, ∇upsample_linear,
-    upsample_bilinear, ∇upsample_bilinear,
-    upsample_trilinear, ∇upsample_trilinear,
-    pixel_shuffle
-
 """
     upsample_nearest(x, scale::NTuple{S,Int})
     upsample_nearest(x; size::NTuple{S,Int})