Split out conv_transpose_dims() so that Zygote can ignore it

src/layers/conv.jl

@@ -102,20 +102,24 @@ ConvTranspose(param(init(k..., reverse(ch)...)), param(zeros(ch[2])), σ,
 
 @treelike ConvTranspose
 
+function conv_transpose_dims(c::ConvTranspose, x::AbstractArray)
+    # Calculate size of "input", from ∇conv_data()'s perspective...
+    combined_pad = (c.pad[1:2:end] .+ c.pad[2:2:end])
+    I = (size(x)[1:end-2] .- 1).*c.stride .+ 1 .+ (size(c.weight)[1:end-2] .- 1).*c.dilation .- combined_pad
+    C_in = size(c.weight)[end-1]
+    batch_size = size(x)[end]
+    # Create DenseConvDims() that looks like the corresponding conv()
+    return DenseConvDims((I..., C_in, batch_size), size(c.weight);
+        stride=c.stride,
+        padding=c.pad,
+        dilation=c.dilation,
+    )
+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)
-  # Calculate size of "input", from ∇conv_data()'s perspective...
+  cdims = conv_transpose_dims(c, x)
-  combined_pad = (c.pad[1:2:end] .+ c.pad[2:2:end])
-  I = (size(x)[1:end-2] .- 1).*c.stride .+ 1 .+ (size(c.weight)[1:end-2] .- 1).*c.dilation .- combined_pad
-  C_in = size(c.weight)[end-1]
-  batch_size = size(x)[end]
-  # Create DenseConvDims() that looks like the corresponding conv()
-  cdims = DenseConvDims((I..., C_in, batch_size), size(c.weight);
-      stride=c.stride,
-      padding=c.pad,
-      dilation=c.dilation,
-  )
   return σ.(∇conv_data(x, c.weight, cdims) .+ b)
 end