DepthwiseConv corrected again.
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@ -138,14 +138,11 @@ end
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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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`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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Data should be stored in WHCN order. In other words, a 100×100 RGB image would
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be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
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Takes the keyword arguments `pad`, `stride` and `dilation`.
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"""
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struct DepthwiseConv{N,M,F,A,V}
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@ -165,17 +162,18 @@ function DepthwiseConv(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identit
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return DepthwiseConv(σ, w, b, stride, pad, dilation)
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end
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DepthwiseConv(k::NTuple{N,Integer}, ch::Integer, σ = identity; init = glorot_uniform,
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stride = 1, pad = 0, dilation = 1) where N =
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DepthwiseConv(init(k..., 1, ch), zeros(ch), σ,
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stride = stride, pad = pad, dilation=dilation)
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DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = glorot_uniform,
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stride::NTuple{N,Integer} = map(_->1,k),
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pad::NTuple{N,Integer} = map(_->0,2 .* k),
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dilation::NTuple{N,Integer} = map(_->1,k)) where N =
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DepthwiseConv(init(k..., ch[2], ch[1]), zeros(ch[2]*ch[1]), σ,
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stride = stride, pad = pad)
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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) 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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init(k..., div(ch[2], ch[1]), ch[1]),
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zeros(ch[2]),
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σ;
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stride = stride,
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pad = pad,
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dilation = dilation
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)
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end
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@treelike DepthwiseConv
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@ -196,7 +194,7 @@ end
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invoke(a, Tuple{AbstractArray}, x)
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(a::DepthwiseConv{<: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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a(T.(x))
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"""
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CrossCor(size, in=>out)
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CrossCor(size, in=>out, relu)
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