Add SamePad for pooling layers

src/layers/conv.jl

@@ -308,6 +308,8 @@ end
 Max pooling layer. `k` stands for the size of the window for each dimension of the input.
 
 Takes the keyword arguments `pad` and `stride`.
+
+Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride
 """
 struct MaxPool{N,M}
   k::NTuple{N,Int}
@@ -317,8 +319,7 @@ end
 
 function MaxPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N
   stride = expand(Val(N), stride)
-  pad = expand(Val(2*N), pad)
-
+  pad = calc_padding(pad, k, 1, stride)
   return MaxPool(k, pad, stride)
 end
 
@@ -337,6 +338,8 @@ end
 Mean pooling layer. `k` stands for the size of the window for each dimension of the input.
 
 Takes the keyword arguments `pad` and `stride`.
+
+Use `pad=SamePad()` to apply padding so that outputsize == inputsize / stride
 """
 struct MeanPool{N,M}
     k::NTuple{N,Int}
@@ -346,7 +349,7 @@ end
 
 function MeanPool(k::NTuple{N,Integer}; pad = 0, stride = k) where N
   stride = expand(Val(N), stride)
-  pad = expand(Val(2*N), pad)
+  pad = calc_padding(pad, k, 1, stride)
   return MeanPool(k, pad, stride)
 end
 

test/layers/conv.jl

@@ -114,8 +114,15 @@ end
   stride = 3
   l = ltype(k, 1=>1, pad=SamePad(), stride = stride)
   if ltype == ConvTranspose
-    @test size(l(data))[1:end-2] == stride .* size(data)[1:end-2] .- stride .- 1
+    @test size(l(data))[1:end-2] == stride .* size(data)[1:end-2] .- stride .+ 1
   else
     @test size(l(data))[1:end-2] == ceil.(Int, size(data)[1:end-2] ./ stride)
   end
 end
+
+@testset "$ltype SamePad windowsize $k" for ltype in (MeanPool, MaxPool), k in ( (1,), (2,), (3,), (4,5), (6,7,8))
+    data = ones(Float32, (k .+ 3)..., 1,1)
+
+    l = ltype(k, pad=SamePad())
+    @test size(l(data))[1:end-2] == ceil.(Int, size(data)[1:end-2] ./ k)
+end