TastyPancakes/Flux.jl
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

ditto remaining conv layers

Browse Source
This commit is contained in:
Dhairya Gandhi 2019-09-27 12:04:27 +05:30
parent 5ea6a33f44
commit 9f2ac8fdef
1 changed files with 34 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

52
src/layers/conv.jl
View File

@ -92,20 +92,21 @@ struct ConvTranspose{N,M,F,A,V}
stride::NTuple{N,Int}
pad::NTuple{M,Int}
dilation::NTuple{N,Int}
use_bias::Bool
end
function ConvTranspose(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N}
stride = 1, pad = 0, dilation = 1, use_bias = true) where {T,N}
stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation)
return ConvTranspose(σ, w, b, stride, pad, dilation)
return ConvTranspose(σ, w, b, stride, pad, dilation, use_bias)
end
ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N =
init = glorot_uniform, stride = 1, pad = 0, dilation = 1, use_bias = true) where N =
ConvTranspose(init(k..., reverse(ch)...), zeros(ch[2]), σ,
stride = stride, pad = pad, dilation = dilation)
stride = stride, pad = pad, dilation = dilation, use_bias = use_bias)
@functor ConvTranspose
@ -125,9 +126,13 @@ end
function (c::ConvTranspose)(x::AbstractArray)
# ndims(x) == ndims(c.weight)-1 && return squeezebatch(c(reshape(x, size(x)..., 1)))
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
cdims = conv_transpose_dims(c, x)
return σ.(∇conv_data(x, c.weight, cdims) .+ b)
if c.use_bias
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
σ.(∇conv_data(x, c.weight, cdims) .+ b)
else
c.σ.(∇conv_data(x, c.weight, cdims))
end
end
function Base.show(io::IO, l::ConvTranspose)
@ -162,18 +167,19 @@ struct DepthwiseConv{N,M,F,A,V}
stride::NTuple{N,Int}
pad::NTuple{M,Int}
dilation::NTuple{N,Int}
use_bias::Bool
end
function DepthwiseConv(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N}
stride = 1, pad = 0, dilation = 1, use_bias = true) where {T,N}
stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation)
return DepthwiseConv(σ, w, b, stride, pad, dilation)
return DepthwiseConv(σ, w, b, stride, pad, dilation, use_bias)
end
function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N
init = glorot_uniform, stride = 1, pad = 0, dilation = 1, use_bias = true) where N
@assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels"
return DepthwiseConv(
init(k..., div(ch[2], ch[1]), ch[1]),
@ -181,16 +187,21 @@ function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ =
σ;
stride = stride,
pad = pad,
dilation = dilation
dilation = dilation,
use_bias = use_bias
)
end
@functor DepthwiseConv
function (c::DepthwiseConv)(x)
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
cdims = DepthwiseConvDims(x, c.weight; stride=c.stride, padding=c.pad, dilation=c.dilation)
σ.(depthwiseconv(x, c.weight, cdims) .+ b)
if c.use_bias
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
σ.(depthwiseconv(x, c.weight, cdims) .+ b)
else
c.σ.(depthwiseconv(x, c.weight, cdims))
end
end
function Base.show(io::IO, l::DepthwiseConv)
@ -234,20 +245,21 @@ struct CrossCor{N,M,F,A,V}
stride::NTuple{N,Int}
pad::NTuple{M,Int}
dilation::NTuple{N,Int}
use_bias::Bool
end
function CrossCor(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
stride = 1, pad = 0, dilation = 1) where {T,N}
stride = 1, pad = 0, dilation = 1, use_bias = true) where {T,N}
stride = expand(Val(N-2), stride)
pad = expand(Val(2*(N-2)), pad)
dilation = expand(Val(N-2), dilation)
return CrossCor(σ, w, b, stride, pad, dilation)
return CrossCor(σ, w, b, stride, pad, dilation, use_bias)
end
CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N =
init = glorot_uniform, stride = 1, pad = 0, dilation = 1, use_bias = true) where N =
CrossCor(init(k..., ch...), zeros(ch[2]), σ,
stride = stride, pad = pad, dilation = dilation)
stride = stride, pad = pad, dilation = dilation, use_bias = use_bias)
@functor CrossCor
@ -259,9 +271,13 @@ end
function (c::CrossCor)(x::AbstractArray)
# TODO: breaks gpu broadcast :(
# ndims(x) == ndims(c.weight)-1 && return squeezebatch(c(reshape(x, size(x)..., 1)))
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
cdims = DenseConvDims(x, c.weight; stride=c.stride, padding=c.pad, dilation=c.dilation)
σ.(crosscor(x, c.weight, cdims) .+ b)
if c.use_bias
σ, b = c.σ, reshape(c.bias, map(_->1, c.stride)..., :, 1)
σ.(crosscor(x, c.weight, cdims) .+ b)
else
c.σ.(crosscor(x, c.weight, cdims))
end
end
function Base.show(io::IO, l::CrossCor)