add bias and weight kwarg
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Dhairya Gandhi
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f3904b4e04
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040697fb2b
@ -3,16 +3,16 @@ using NNlib: conv, ∇conv_data, depthwiseconv
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expand(N, i::Tuple) = i
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expand(N, i::Integer) = ntuple(_ -> i, N)
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"""
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Conv(size, in=>out)
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Conv(size, in=>out, relu)
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Conv(filter::Tuple, in=>out)
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Conv(filter::Tuple, in=>out, activation)
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Standard convolutional layer. `size` should be a tuple like `(2, 2)`.
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Standard convolutional layer. `filter` should be a tuple like `(2, 2)`.
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`in` and `out` specify the number of input and output channels respectively.
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Example: Applying Conv layer to a 1-channel input using a 2x2 window size,
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giving us a 16-channel output. Output is activated with ReLU.
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size = (2,2)
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filter = (2,2)
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in = 1
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out = 16
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Conv((2, 2), 1=>16, relu)
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@ -34,7 +34,7 @@ end
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"""
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Conv(weight::AbstractArray, bias::AbstractArray)
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Conv(weight::AbstractArray, bias::AbstractArray, relu)
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Conv(weight::AbstractArray, bias::AbstractArray, activation)
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Constructs the convolutional layer with user defined weight and bias arrays.
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All other behaviours of the Conv layer apply with regard to data order and
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@ -42,21 +42,32 @@ forward pass.
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Takes the keyword arguments `pad`, `stride` and `dilation`.
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"""
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function Conv(w::AbstractArray{T,N}, b::Union{Number, AbstractVector{T}}, σ = identity;
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function Conv(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;
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stride = 1, pad = 0, dilation = 1) where {T,N}
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stride = expand(Val(N-2), stride)
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pad = expand(Val(2*(N-2)), pad)
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dilation = expand(Val(N-2), dilation)
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b = b isa Nothing ? ZeroType((size(w, ndims(w)), )) : b
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return Conv(σ, w, b, stride, pad, dilation)
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end
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convweight(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
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init = glorot_uniform) where N = init(k..., ch...)
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const convbias = zeros
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"""
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convweight(filter::Tuple, in=>out)
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Constructs a standard convolutional weight matrix with given `filter` and
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channels from `in` to `out`.
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Accepts the keyword `init` (default: `glorot_uniform`) to control the sampling
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distribution.
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See also: [`depthwiseconvweight`](@ref)
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"""
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convweight(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
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init = glorot_uniform) where N = init(filter..., ch...)
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function Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
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init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
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weight = convweight(k, ch, init = init), bias = convbias(ch[2])) where N
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weight = convweight(k, ch, init = init), bias = zeros(ch[2])) where N
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Conv(weight, bias, σ,
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stride = stride, pad = pad, dilation = dilation)
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@ -86,10 +97,10 @@ end
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a(T.(x))
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"""
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ConvTranspose(size, in=>out)
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ConvTranspose(size, in=>out, relu)
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ConvTranspose(filter::Tuple, in=>out)
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ConvTranspose(filter::Tuple, in=>out, relu)
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Standard convolutional transpose layer. `size` should be a tuple like `(2, 2)`.
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Standard convolutional transpose layer. `filter` should be a tuple like `(2, 2)`.
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`in` and `out` specify the number of input and output channels respectively.
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Data should be stored in WHCN order. In other words, a 100×100 RGB image would
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@ -106,17 +117,28 @@ struct ConvTranspose{N,M,F,A,V}
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dilation::NTuple{N,Int}
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end
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function ConvTranspose(w::AbstractArray{T,N}, b::Union{Number, AbstractVector{T}}, σ = identity;
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"""
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ConvTranspose(weight::AbstractArray, bias::AbstractArray)
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ConvTranspose(weight::AbstractArray, bias::AbstractArray, activation)
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Constructs the convolutional transpose layer with user defined weight and bias arrays.
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All other behaviours of the ConvTranspose layer apply with regard to data order and
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forward pass.
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Takes the keyword arguments `pad`, `stride` and `dilation`.
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"""
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function ConvTranspose(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;
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stride = 1, pad = 0, dilation = 1) where {T,N}
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stride = expand(Val(N-2), stride)
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pad = expand(Val(2*(N-2)), pad)
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dilation = expand(Val(N-2), dilation)
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b = b isa Nothing ? ZeroType((size(w, ndims(w)), )) : b
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return ConvTranspose(σ, w, b, stride, pad, dilation)
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end
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function ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
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init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
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weight = convweight(k, reverse(ch), init = init), bias = convbias(ch[2])) where N
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weight = convweight(k, reverse(ch), init = init), bias = zeros(ch[2])) where N
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ConvTranspose(weight, bias, σ,
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stride = stride, pad = pad, dilation = dilation)
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@ -157,11 +179,12 @@ end
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(a::ConvTranspose{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
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a(T.(x))
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"""
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DepthwiseConv(size, in=>out)
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DepthwiseConv(size, in=>out, relu)
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Depthwise convolutional layer. `size` should be a tuple like `(2, 2)`.
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"""
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DepthwiseConv(filter::Tuple, in=>out)
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DepthwiseConv(filter::Tuple, in=>out, relu)
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Depthwise convolutional layer. `filter` should be a tuple like `(2, 2)`.
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`in` and `out` specify the number of input and output channels respectively.
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Note that `out` must be an integer multiple of `in`.
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@ -179,21 +202,44 @@ struct DepthwiseConv{N,M,F,A,V}
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dilation::NTuple{N,Int}
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end
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function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Number, AbstractVector{T}}, σ = identity;
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"""
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DepthwiseConv(weight::AbstractArray, bias::AbstractArray)
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DepthwiseConv(weight::AbstractArray, bias::AbstractArray, activation)
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Constructs the `DepthwiseConv` layer with user defined weight and bias arrays.
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All other behaviours of the `DepthwiseConv` layer apply with regard to data order and
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forward pass.
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Takes the keyword arguments `pad`, `stride` and `dilation`.
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"""
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function DepthwiseConv(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;
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stride = 1, pad = 0, dilation = 1) where {T,N}
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stride = expand(Val(N-2), stride)
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pad = expand(Val(2*(N-2)), pad)
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dilation = expand(Val(N-2), dilation)
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b = b isa Nothing ? ZeroType((size(w, ndims(w)), )) : b
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return DepthwiseConv(σ, w, b, stride, pad, dilation)
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end
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depthwiseconvweight(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
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init = glorot_uniform) where N = init(k..., div(ch[2], ch[1]), ch[1])
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"""
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depthwiseconvweight(filter::Tuple, in=>out)
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Constructs a depthwise convolutional weight array defined by `filter` and channels
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from `in` to `out`.
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Accepts the keyword `init` (default: `glorot_uniform`) to control the sampling
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distribution.
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See also: [`convweight`](@ref)
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"""
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depthwiseconvweight(filter::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer};
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init = glorot_uniform) where N = init(filter..., div(ch[2], ch[1]), ch[1])
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function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
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init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
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weight = depthwiseconvweight(k, ch, init = init), bias = convbias(ch[2])) where N
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weight = depthwiseconvweight(k, ch, init = init), bias = zeros(ch[2])) where N
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@assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels"
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return DepthwiseConv(
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weight,
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bias,
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@ -255,17 +301,29 @@ struct CrossCor{N,M,F,A,V}
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dilation::NTuple{N,Int}
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end
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function CrossCor(w::AbstractArray{T,N}, b::Union{Number, AbstractVector{T}}, σ = identity;
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"""
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CrossCor(weight::AbstractArray, bias::AbstractArray)
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CrossCor(weight::AbstractArray, bias::AbstractArray, activation)
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Constructs the standard cross convolutional layer with user defined weight and bias
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arrays. All other behaviours of the CrossCor layer apply with regard to data order and
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forward pass.
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Takes the keyword arguments `pad`, `stride` and `dilation`.
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"""
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function CrossCor(w::AbstractArray{T,N}, b::Union{Nothing, ZeroType, AbstractVector{T}}, σ = identity;
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stride = 1, pad = 0, dilation = 1) where {T,N}
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stride = expand(Val(N-2), stride)
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pad = expand(Val(2*(N-2)), pad)
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dilation = expand(Val(N-2), dilation)
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b = b isa Nothing ? ZeroType((size(w, ndims(w)), )) : b
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return CrossCor(σ, w, b, stride, pad, dilation)
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end
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function CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
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init = glorot_uniform, stride = 1, pad = 0, dilation = 1,
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weight = convweight(k, ch, init = init), bias = convbias(ch[2])) where N
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weight = convweight(k, ch, init = init), bias = zeros(ch[2])) where N
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CrossCor(weight, bias, σ,
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stride = stride, pad = pad, dilation = dilation)
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end
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