@el_samou_samou Yes your are correct.
Here is what i could deduce, the architecture has to be understood before programming or training on pytorch.
Input image dimension HxWxC = n x n x c
filter f x f x cf
Output dimension = ((n+2p-f)/s)+1
p = padding
s = stride
here p=0 and s=1(default)
Here in this case
32x32x3  is the input image convolution with 5x5x6 filter which gives the output of 28x28x6
Maxpooling same equation for output dimension
28x28x6 is the input image , convolution with 2x2x6 filter which gives the output of 14x14x6 IMPORTANT: STRIDE IN MAXPOOLING EQUALS THE FILTER/KERNEL SIZE
Same for other convolution and Maxpooling
Convolution O/P = 10x10x16
Maxpooling O/P = 5x5x16
Hope it helps someone