TastyPancakes/Flux.jl
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cleaner API

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Dhairya Gandhi 2019-11-27 19:40:58 +05:30
parent eb41715d26
commit 245563077b
1 changed files with 29 additions and 41 deletions
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70
src/layers/conv.jl
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@ -22,8 +22,7 @@ In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array. and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields. Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
@ -44,17 +43,15 @@ Constructs the convolutional layer with user defined weight and bias arrays.
All other behaviours of the Conv layer apply with regard to data order and All other behaviours of the Conv layer apply with regard to data order and
forward pass. forward pass.
Setting `bias` to `nothing` or `Flux.Zeros()` would switch `bias` off for the Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
function Conv(w::AbstractArray{T,N}, b::Union{Nothing, Zeros, AbstractVector{T}}, σ = identity; function Conv(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N} stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride) stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad) pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation) dilation = expand(Val(N-2), dilation)
b = b isa Nothing ? Zeros((size(w, ndims(w)), )) : b
return Conv(σ, w, b, stride, pad, dilation) return Conv(σ, w, b, stride, pad, dilation)
end end
@ -70,14 +67,14 @@ distribution.
See also: [`depthwiseconvfilter`](@ref) See also: [`depthwiseconvfilter`](@ref)
""" """
convfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}; convfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
init = glorot_uniform) where N = init(filter..., ch...) init = glorot_uniform) where N = init(filter..., ch...)
function Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; function Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = glorot_uniform, stride = 1, pad = 0, dilation = 1, init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N
Conv(weight, bias, σ, Conv(weight, bias, σ,
stride = stride, pad = pad, dilation = dilation) stride = stride, pad = pad, dilation = dilation)
end end
@functor Conv @functor Conv
@ -114,8 +111,7 @@ Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array. be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields. Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
@ -136,23 +132,21 @@ Constructs the convolutional transpose layer with user defined weight and bias a
All other behaviours of the ConvTranspose layer apply with regard to data order and All other behaviours of the ConvTranspose layer apply with regard to data order and
forward pass. forward pass.
Setting `bias` to `nothing` or `Flux.Zeros()` would switch `bias` off for the Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
function ConvTranspose(w::AbstractArray{T,N}, b::Union{Nothing, Zeros, AbstractVector{T}}, σ = identity; function ConvTranspose(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N} stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride) stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad) pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation) dilation = expand(Val(N-2), dilation)
b = b isa Nothing ? Zeros((size(w, ndims(w)), )) : b
return ConvTranspose(σ, w, b, stride, pad, dilation) return ConvTranspose(σ, w, b, stride, pad, dilation)
end end
function ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; function ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = glorot_uniform, stride = 1, pad = 0, dilation = 1, init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
weight = convfilter(k, reverse(ch), init = init), bias = zeros(ch[2])) where N weight = convfilter(k, reverse(ch), init = init), bias = zeros(ch[2])) where N
ConvTranspose(weight, bias, σ, ConvTranspose(weight, bias, σ,
stride = stride, pad = pad, dilation = dilation) stride = stride, pad = pad, dilation = dilation)
@ -168,9 +162,9 @@ function conv_transpose_dims(c::ConvTranspose, x::AbstractArray)
batch_size = size(x)[end] batch_size = size(x)[end]
# Create DenseConvDims() that looks like the corresponding conv() # Create DenseConvDims() that looks like the corresponding conv()
return DenseConvDims((I..., C_in, batch_size), size(c.weight); return DenseConvDims((I..., C_in, batch_size), size(c.weight);
stride=c.stride, stride=c.stride,
padding=c.pad, padding=c.pad,
dilation=c.dilation, dilation=c.dilation,
) )
end end
@ -206,8 +200,7 @@ Data should be stored in WHCN order. In other words, a 100×100 RGB image would
be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array. be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields. Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
@ -228,17 +221,15 @@ Constructs the `DepthwiseConv` layer with user defined weight and bias arrays.
All other behaviours of the `DepthwiseConv` layer apply with regard to data order and All other behaviours of the `DepthwiseConv` layer apply with regard to data order and
forward pass. forward pass.
Setting `bias` to `nothing` or `Flux.Zeros()` would switch `bias` off for the Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Nothing, Zeros, AbstractVector{T}}, σ = identity; function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N} stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride) stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad) pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation) dilation = expand(Val(N-2), dilation)
b = b isa Nothing ? Zeros((size(w, ndims(w)), )) : b
return DepthwiseConv(σ, w, b, stride, pad, dilation) return DepthwiseConv(σ, w, b, stride, pad, dilation)
end end
@ -254,11 +245,11 @@ distribution.
See also: [`convfilter`](@ref) See also: [`convfilter`](@ref)
""" """
depthwiseconvfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}; depthwiseconvfilter(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
init = glorot_uniform) where N = init(filter..., div(ch[2], ch[1]), ch[1]) init = glorot_uniform) where N = init(filter..., div(ch[2], ch[1]), ch[1])
function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = glorot_uniform, stride = 1, pad = 0, dilation = 1, init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
weight = depthwiseconvfilter(k, ch, init = init), bias = zeros(ch[2])) where N weight = depthwiseconvfilter(k, ch, init = init), bias = zeros(ch[2])) where N
@assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels" @assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels"
return DepthwiseConv( return DepthwiseConv(
@ -312,8 +303,7 @@ In other words, a 100×100 RGB image would be a `100×100×3×1` array,
and a batch of 50 would be a `100×100×3×50` array. and a batch of 50 would be a `100×100×3×50` array.
Accepts keyword arguments `weight` and `bias` to set the corresponding fields. Accepts keyword arguments `weight` and `bias` to set the corresponding fields.
Setting `bias` to `Flux.Zeros()` will switch bias off for the Setting `bias` to `Flux.Zeros()` will switch bias off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
@ -334,23 +324,21 @@ Constructs the standard cross convolutional layer with user defined weight and b
arrays. All other behaviours of the CrossCor layer apply with regard to data order and arrays. All other behaviours of the CrossCor layer apply with regard to data order and
forward pass. forward pass.
Setting `bias` to `nothing` or `Flux.Zeros()` would switch `bias` off for the Setting `bias` to `Flux.Zeros()` would switch `bias` off for the layer.
layer.
Takes the keyword arguments `pad`, `stride` and `dilation`. Takes the keyword arguments `pad`, `stride` and `dilation`.
""" """
function CrossCor(w::AbstractArray{T,N}, b::Union{Nothing, Zeros, AbstractVector{T}}, σ = identity; function CrossCor(w::AbstractArray{T,N}, b::Union{Zeros, AbstractVector{T}}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N} stride = 1, pad = 0, dilation = 1) where {T,N}
stride = expand(Val(N-2), stride) stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad) pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation) dilation = expand(Val(N-2), dilation)
b = b isa Nothing ? Zeros((size(w, ndims(w)), )) : b
return CrossCor(σ, w, b, stride, pad, dilation) return CrossCor(σ, w, b, stride, pad, dilation)
end end
function CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; function CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = glorot_uniform, stride = 1, pad = 0, dilation = 1, init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N weight = convfilter(k, ch, init = init), bias = zeros(ch[2])) where N
CrossCor(weight, bias, σ, CrossCor(weight, bias, σ,
stride = stride, pad = pad, dilation = dilation) stride = stride, pad = pad, dilation = dilation)