TastyPancakes/Flux.jl
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Updated with all basic and conv layers outdims

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This commit is contained in:
Kyle Daruwalla 2019-12-05 21:57:10 -06:00
parent b4ed16ad9c
commit 31dda0ce6c
2 changed files with 39 additions and 2 deletions
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16
src/layers/basic.jl
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@ -40,7 +40,7 @@ function Base.show(io::IO, c::Chain)
end end
""" """
outdims(c::Chain, isize::Tuple) outdims(c::Chain, isize)
Calculate the output dimensions given the input dimensions, `isize`. Calculate the output dimensions given the input dimensions, `isize`.
@ -49,7 +49,7 @@ m = Chain(Conv((3, 3), 3 => 16), Conv((3, 3), 16 => 32))
outdims(m, (10, 10)) == (6, 6) outdims(m, (10, 10)) == (6, 6)
``` ```
""" """
outdims(c::Chain, isize::Tuple) = foldl(∘, map(l -> (x -> outdims(l, x)), c.layers)) outdims(c::Chain, isize) = foldl(∘, map(l -> (x -> outdims(l, x)), c.layers))
# This is a temporary and naive implementation # This is a temporary and naive implementation
# it might be replaced in the future for better performance # it might be replaced in the future for better performance
@ -228,6 +228,18 @@ function (mo::Maxout)(input::AbstractArray)
mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over) mapreduce(f -> f(input), (acc, out) -> max.(acc, out), mo.over)
end end
"""
outdims(c::Maxout, isize)
Calculate the output dimensions given the input dimensions, `isize`.
```julia
m = Maxout(Conv((3, 3), 3 => 16), Conv((3, 3), 16 => 32))
outdims(m, (10, 10)) == (8, 8)
```
"""
outdims(l::Maxout, isize) = outdims(first(l.over))
""" """
SkipConnection(layers, connection) SkipConnection(layers, connection)

25
src/layers/conv.jl
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@ -1,6 +1,7 @@
using NNlib: conv, ∇conv_data, depthwiseconv using NNlib: conv, ∇conv_data, depthwiseconv
_convoutdims(isize, ksize, ssize, pad) = Int.(floor.((isize .- ksize .+ 2 .* pad) ./ ssize .+ 1)) _convoutdims(isize, ksize, ssize, pad) = Int.(floor.((isize .- ksize .+ 2 .* pad) ./ ssize .+ 1))
_convtransoutdims(isize, ksize, ssize, pad) = Int.(ssize .* (isize .- 1) .+ ksize .- 2 .* pad))
expand(N, i::Tuple) = i expand(N, i::Tuple) = i
expand(N, i::Integer) = ntuple(_ -> i, N) expand(N, i::Integer) = ntuple(_ -> i, N)
@ -155,6 +156,18 @@ end
(a::ConvTranspose{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = (a::ConvTranspose{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
a(T.(x)) a(T.(x))
"""
outdims(l::ConvTranspose, isize::Tuple)
Calculate the output dimensions given the input dimensions, `isize`.
```julia
m = ConvTranspose((3, 3), 3 => 16)
outdims(m, (8, 8)) == (10, 10)
```
"""
outdims(l::ConvTranspose{N}, isize) where N = _convtransoutdims(isize, size(l.weight)[1:N], l.stride, l.pad[1:N])
""" """
DepthwiseConv(size, in=>out) DepthwiseConv(size, in=>out)
DepthwiseConv(size, in=>out, relu) DepthwiseConv(size, in=>out, relu)
@ -302,6 +315,18 @@ end
(a::CrossCor{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = (a::CrossCor{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
a(T.(x)) a(T.(x))
"""
outdims(l::CrossCor, isize::Tuple)
Calculate the output dimensions given the input dimensions, `isize`.
```julia
m = CrossCor((3, 3), 3 => 16)
outdims(m, (10, 10)) == (8, 8)
```
"""
outdims(l::CrossCor{N}, isize) where N = _convoutdims(isize, size(l.weight)[1:N], l.stride, l.pad[1:N])
""" """
MaxPool(k) MaxPool(k)