Merge pull request #99 from thebhatman/patch-1

Changed output size calculation to support kernel size such as (x,y)

Author: DhairyaLGandhi

src/impl/conv.jl

@@ -18,7 +18,7 @@ img_size(c::ConvDims{I,K,C,S,P,D,F}) where {I, K, C, S, P, D, F} = I
 # Calculate the output dimensions of this convolution
 function output_size(c::ConvDims{I,K,C,S,P,D,F}) where {I, K, C, S, P, D, F}
     O_w = div(I[1] + P[1] + P[2] - (K[1] - 1) * D[1] - 1, S[1]) + 1
-    O_h = div(I[2] + P[3] + P[4] - (K[1] - 1) * D[1] - 1, S[1]) + 1
+    O_h = div(I[2] + P[3] + P[4] - (K[2] - 1) * D[2] - 1, S[2]) + 1
     return (O_w, O_h)
 end
 kernel_size(c::ConvDims{I,K,C,S,P,D,F}) where {I, K, C, S, P, D, F} = K

test/conv.jl

@@ -3,6 +3,19 @@ using NNlib: conv, crosscor, ∇conv_filter, ∇conv_data, ∇maxpool, maxpool,
 @testset "conv2d" begin
     x = reshape(Float64[1:20;], 5, 4, 1, 1)
     w = reshape(Float64[1:4;], 2, 2, 1, 1)
+    w1 = reshape(Float64[1:6;], 2, 3, 1, 1)
+    w2 = reshape(Float64[1:6;], 3, 2, 1, 1)
+
+    @test dropdims(conv(x, w1), dims = (3,4)) == [
+        95.0 200.0;
+        116.0 221.0;
+        137.0 242.0;
+        158.0 263.0]
+
+    @test dropdims(conv(x, w2), dims = (3,4)) == [
+        68.0 173.0 278.0;
+        89.0 194.0 299.0;
+        110.0 215.0 320.0]
 
     @test dropdims(conv(x, w), dims = (3,4)) == [
         29 79 129;