merge conflict resolved

src/layers/conv.jl

@@ -57,6 +57,47 @@ end
 (a::Conv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} =
   a(T.(x))
 
+"""
+    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
+
+
 """
     DepthwiseConv(size, in)
     DepthwiseConv(size, in=>mul)

src/tracker/lib/array.jl

@@ -356,7 +356,12 @@ 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)
 

test/tracker.jl

@@ -1,6 +1,10 @@
 using Flux
 using Flux.Tracker, Test, NNlib
+<<<<<<< HEAD
 using Flux.Tracker: TrackedReal, gradcheck, grad, checkpoint
+=======
+using Flux.Tracker: TrackedReal, gradcheck, grad, derivative, checkpoint
+>>>>>>> a657c287d0590fdd9e49bb68c35bf96febe45e6d
 using NNlib: conv, ∇conv_data, depthwiseconv
 using Printf: @sprintf
 using LinearAlgebra: diagm, dot, LowerTriangular, norm
@@ -186,6 +190,10 @@ end
 @test gradtest(conv, rand(10, 10, 3, 2), randn(Float64, 2, 2, 3, 2))
 @test gradtest(conv, rand(10, 10, 10, 3, 2), randn(Float64, 2, 2, 2, 3, 2))
 
+@test gradtest(∇conv_data, rand(10, 3, 2), randn(Float64, 2, 2, 3))
+@test gradtest(∇conv_data, rand(10, 10, 3, 2), randn(Float64,2, 2, 2, 3))
+@test gradtest(∇conv_data, rand(10, 10, 10, 3, 2), randn(Float64,2, 2, 2, 2, 3))
+
 @test gradtest(depthwiseconv, rand(10,10,3,2), randn(2, 2, 2, 3))
 
 @test gradtest(∇conv_data, rand(10, 3, 2), randn(Float64, 2, 2, 3))