conflicts

src/layers/conv.jl

@@ -1,8 +1,4 @@
-<<<<<<< HEAD
 using NNlib: conv, ∇conv_data, depthwiseconv, crossconv
-=======
-using NNlib: conv, depthwiseconv, crosscor
->>>>>>> some final changes
 
 @generated sub2(::Val{N}) where N = :(Val($(N-2)))
 
@@ -73,8 +69,6 @@ end
 """
     ConvTranspose(size, in=>out)
     ConvTranspose(size, in=>out, relu)
-    CrossCor(size, in=>out)
-    CrossCor(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.
@@ -83,7 +77,6 @@ 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}
-struct CrossCor{N,F,A,V}
   σ::F
   weight::A
   bias::V
@@ -173,8 +166,8 @@ function Base.show(io::IO, l::DepthwiseConv)
 end
 
 """
-  CrossConv(size, in=>out)
-  CrossConv(size, in=>out, relu)
+  CrossCor(size, in=>out)
+  CrossCor(size, in=>out, relu)
 
 Standard cross convolutional layer. `size` should be a tuple like `(2, 2)`.
 `in` and `out` specify the number of input and output channels respectively.
@@ -260,48 +253,4 @@ MeanPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N =
 
 function Base.show(io::IO, m::MeanPool)
   print(io, "MeanPool(", m.k, ", pad = ", m.pad, ", stride = ", m.stride, ")")
-end
-<<<<<<< HEAD
-=======
-
-"""
-    CrossCor(size, in=>out)
-    CrossCor(size, in=>out, relu)
-Standard cross convolutional 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 CrossCor{N,F,A,V}
-  σ::F
-  weight::A
-  bias::V
-  stride::NTuple{N,Int}
-  pad::NTuple{N,Int}
-  dilation::NTuple{N,Int}
-end
-CrossCor(w::AbstractArray{T,N}, b::AbstractVector{T}, σ = identity;
-     stride = 1, pad = 0, dilation = 1) where {T,N} =
-  CrossCor(σ, w, b, expand.(sub2(Val(N)), (stride, pad, dilation))...)
-
-CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity;
-  init = glorot_uniform,  stride = 1, pad = 0, dilation = 1) where N =
-  CrossCor(param(init(k..., ch...)), param(zeros(ch[2])), σ,
-    stride = stride, pad = pad, dilation = dilation)
-
-@treelike CrossCor
-
-function (c::CrossCor)(x)
-  # 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)
-  σ.(crosscor(x, c.weight, stride = c.stride, pad = c.pad, dilation = c.dilation) .+ b)
-end
-function Base.show(io::IO, l::CrossCor)
-  print(io, "CrossCor(", size(l.weight)[1:ndims(l.weight)-2])
-  print(io, ", ", size(l.weight, ndims(l.weight)-1), "=>", size(l.weight, ndims(l.weight)))
-  l.σ == identity || print(io, ", ", l.σ)
-  print(io, ")")
-end
->>>>>>> some final changes
+end