Cant seem to understand the output of this convolution.
Hi, I’m coding the beginnings of a conv c++ class using cudnn, I already did
so using pure cpu, and I’m trying to get my head around some of the calls.
I have, as input, two 3x3 features, so, in NCHW format I=1233.
I have a single 2x2 filter, again, in NCHW format, F=1122.
I expect as output, from my cpu code, O=1222.
Now, to correctly configure the filters for the convolution, the N and C terms become:
(from the API reference)
Now… C is the c from the previous layer right? Inputs have two channels, so C=2.
But what is K?
Now, how does one compute K? To me, what I expect is for K to be 2.
So, with that in mind, we redo the dimensions of the filter to be (KCHW) F=2222
When I run my code, instead of geting, on the output, two channels of 2x2, I get back 8
values, the first 4 are the sum of what I expect the sum of the output channles would be,
and the last 4 values are zeroed.
So, even though I ran cudnnGetConvolutionNdForwardOutputDim, and it returned O=1222, and
even though I did get 8 values out, the last 4 are zeroes, and the first 4 seems to be the
sum of the expected two output channels…
Any idea where I’m messing this up?
Convolution is set up as CUDNN_CROSS_CORRELATION.
Forward algo selected is: CUDNN_CONVOLUTION_FWD_ALGO_GEMM
input values (dimensions are 1233), from 1 to 18:
1 2 3 10 11 12
4 5 6 13 14 15
7 8 9 16 17 18
166 214 598 646
310 358 742 790
764 860 0 0
1052 1148 0 0
Thank you to anyone helping out.