clean code

src/layers/conv.jl

@@ -150,46 +150,6 @@ function Base.show(io::IO, l::DepthwiseConv)
   print(io, ")")
 end
 
-"""
-    ConvTranspose(size, in=>out)
-    ConvTranspose(size, in=>out, relu)
-
-Standard convolutional transpose layer. `size` should be a tuple like `(2, 2)`.
-`in` and `out` specify the number of input and output channels respectively.
-Data should be stored in WHCN order. In other words, a 100×100 RGB image would
-be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array.
-Takes the keyword arguments `pad`, `stride` and `dilation`.
-"""
-struct ConvTranspose{N,F,A,V}
-  σ::F
-  weight::A
-  bias::V
-  stride::NTuple{N,Int}
-  pad::NTuple{N,Int}
-  dilation::NTuple{N,Int}
-end
-
-ConvTranspose(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
-              stride = 1, pad = 0, dilation = 1) where {T,N} =
-  ConvTranspose(σ, w, b, expand.(sub2(Val(N)), (stride, pad, dilation))...)
-
-ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = initn,
-              stride = 1, pad = 0, dilation = 1) where N =
-ConvTranspose(param(init(k..., reverse(ch)...)), param(zeros(ch[2])), σ,
-              stride = stride, pad = pad, dilation = dilation)
-
-@treelike ConvTranspose
-
-function (c::ConvTranspose)(x)
-  # 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)
-  σ.(∇conv_data(x, c.weight, stride = c.stride, pad = c.pad, dilation = c.dilation) .+ b)
-end
-
-function Base.show(io::IO, l::ConvTranspose)
-  print(io, "ConvTranspose(", size(l.weight)[1:ndims(l.weight)-2])
-end
-
 """
     MaxPool(k)
 

src/tracker/lib/array.jl

@@ -356,12 +356,7 @@ x::TrackedVector  * y::TrackedVector  = track(*, x, y)
 # NNlib
 
 using NNlib
-<<<<<<< HEAD:src/tracker/lib/array.jl
 import NNlib: softmax, ∇softmax, logsoftmax, ∇logsoftmax, conv, ∇conv_data, depthwiseconv, maxpool, meanpool
-=======
-import NNlib: softmax, ∇softmax, logsoftmax, ∇logsoftmax,
-              conv, ∇conv_data, depthwiseconv, maxpool, meanpool
->>>>>>> a657c287d0590fdd9e49bb68c35bf96febe45e6d:src/tracker/array.jl
 
 softmax(xs::TrackedArray) = track(softmax, xs)