Switched to using NNlib for conv.jl outdims.
This commit is contained in:
Kyle Daruwalla
parent
6265b1fa39
commit
a64378b112
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@ -169,16 +169,6 @@ function Base.show(io::IO, l::Diagonal)
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print(io, "Diagonal(", length(l.α), ")")
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end
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"""
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outdims(l::Diagonal, isize)
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Calculate the output dimensions given the input dimensions, `isize`.
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```julia
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m = Diagonal(10)
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outdims(m, (10,)) == (10,)
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```
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"""
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outdims(l::Diagonal, isize) = (length(l.α),)
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"""
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@ -228,16 +218,6 @@ function (mo::Maxout)(input::AbstractArray)
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mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over)
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end
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"""
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outdims(c::Maxout, isize)
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Calculate the output dimensions given the input dimensions, `isize`.
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```julia
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m = Maxout(() -> Conv((3, 3), 3 => 16), 2)
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outdims(m, (10, 10)) == (8, 8)
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```
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"""
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outdims(l::Maxout, isize) = outdims(first(l.over), isize)
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"""
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@ -1,6 +1,8 @@
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using NNlib: conv, ∇conv_data, depthwiseconv
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using NNlib: conv, ∇conv_data, depthwiseconv, output_size
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# pad dims of x with dims of y until ndims(x) == ndims(y)
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_paddims(x::Tuple, y::Tuple) = (x..., y[(end - (length(y) - length(x) - 1)):end]...)
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_convoutdims(isize, ksize, ssize, pad) = Int.(floor.((isize .- ksize .+ 2 .* pad) ./ ssize .+ 1))
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_convtransoutdims(isize, ksize, ssize, pad) = Int.(ssize .* (isize .- 1) .+ ksize .- 2 .* pad)
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expand(N, i::Tuple) = i
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@ -75,13 +77,16 @@ end
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outdims(l::Conv, isize::Tuple)
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Calculate the output dimensions given the input dimensions, `isize`.
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Batch size and channel size are ignored as per `NNlib.jl`.
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```julia
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m = Conv((3, 3), 3 => 16)
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outdims(m, (10, 10)) == (8, 8)
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outdims(m, (10, 10, 1, 3)) == (8, 8)
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```
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"""
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outdims(l::Conv{N}, isize) where N = _convoutdims(isize, size(l.weight)[1:N], l.stride, l.pad[1:N])
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outdims(l::Conv, isize) =
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output_size(DenseConvDims(_paddims(isize, size(l.weight)), size(l.weight); stride = l.stride, padding = l.pad, dilation = l.dilation))
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"""
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ConvTranspose(size, in=>out)
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@ -156,17 +161,7 @@ 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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outdims(l::ConvTranspose, isize::Tuple)
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Calculate the output dimensions given the input dimensions, `isize`.
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```julia
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m = ConvTranspose((3, 3), 3 => 16)
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outdims(m, (8, 8)) == (10, 10)
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```
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"""
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outdims(l::ConvTranspose{N}, isize) where N = _convtransoutdims(isize, size(l.weight)[1:N], l.stride, l.pad[1:N])
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outdims(l::ConvTranspose{N}, isize) where N = _convtransoutdims(isize[1:2], size(l.weight)[1:N], l.stride, l.pad[1:N])
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"""
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DepthwiseConv(size, in=>out)
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@ -232,17 +227,8 @@ end
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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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"""
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outdims(l::DepthwiseConv, isize::Tuple)
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Calculate the output dimensions given the input dimensions, `isize`.
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```julia
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m = DepthwiseConv((3, 3), 3 => 6)
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outdims(m, (10, 10)) == (8, 8)
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```
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"""
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outdims(l::DepthwiseConv{N}, isize) where N = _convoutdims(isize, size(l.weight)[1:N], l.stride, l.pad[1:N])
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outdims(l::DepthwiseConv, isize) =
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output_size(DepthwiseConvDims(_paddims(isize, (1, 1, size(l.weight)[end], 1)), size(l.weight); stride = l.stride, padding = l.pad, dilation = l.dilation))
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"""
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CrossCor(size, in=>out)
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@ -315,17 +301,8 @@ end
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(a::CrossCor{<: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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outdims(l::CrossCor, isize::Tuple)
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Calculate the output dimensions given the input dimensions, `isize`.
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```julia
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m = CrossCor((3, 3), 3 => 16)
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outdims(m, (10, 10)) == (8, 8)
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```
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"""
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outdims(l::CrossCor{N}, isize) where N = _convoutdims(isize, size(l.weight)[1:N], l.stride, l.pad[1:N])
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outdims(l::CrossCor, isize) =
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output_size(DenseConvDims(_paddims(isize, size(l.weight)), size(l.weight); stride = l.stride, padding = l.pad, dilation = l.dilation))
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"""
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MaxPool(k)
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@ -356,17 +333,7 @@ function Base.show(io::IO, m::MaxPool)
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print(io, "MaxPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
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end
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"""
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outdims(l::MaxPool, isize::Tuple)
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Calculate the output dimensions given the input dimensions, `isize`.
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```julia
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m = MaxPool((2, 2))
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outdims(m, (10, 10)) == (5, 5)
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```
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"""
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outdims(l::MaxPool{N}, isize) where N = _convoutdims(isize, l.k, l.stride, l.pad[1:N])
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outdims(l::MaxPool{N}, isize) where N = output_size(PoolDims(_paddims(isize, (l.k..., 1, 1)), l.k; stride = l.stride, padding = l.pad))
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"""
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MeanPool(k)
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@ -396,14 +363,4 @@ function Base.show(io::IO, m::MeanPool)
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print(io, "MeanPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
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end
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"""
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outdims(l::MeanPool, isize::Tuple)
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Calculate the output dimensions given the input dimensions, `isize`.
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```julia
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m = MeanPool((2, 2))
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outdims(m, (10, 10)) == (5, 5)
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```
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"""
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outdims(l::MeanPool{N}, isize) where N = _convoutdims(isize, l.k, l.stride, l.pad[1:N])
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outdims(l::MeanPool{N}, isize) where N = output_size(PoolDims(_paddims(isize, (l.k..., 1, 1)), l.k; stride = l.stride, padding = l.pad))
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