This is probably very silly question. However, I could not find an answer for it.
given that I have Matrix A (with the size of NxN), and Kernel K (with the size of MxM) how I can get the output B, where: B = A*K? where * is the 2d-convolution sign
P.S. I did looked at torch.nn.functional.conv2d, but im not sure how we can define the kernel in that. I am not even sure if it is doing what I need…
I’m not sure if
conv2d is what you’re looking for, but here’s how to adapt it to take your inputs.
import torch.nn.functional as F
from torch.autograd import Variable
N = 4
M = 3
A = Variable(torch.randn(1, 1, N, N))
M = Variable(torch.ones(1, 1, M, M))
output = F.conv2d(A, M)
You can set
A to be any
N x N matrix with
A[0,0,:] = A2, where
N x N. Same with
M[0, 0, :] = M2 where
M by M.
Yeah, Can you please tell me how would you do 2d convolution in pytorch if you dont want to use conv2d? or if you wanna use another way other than conv2…
I mean is there any other way to do it here in pytorch?
with the functional.conv2d, the kernel is the
sorry for retrieving this question.
lets say i wanna do convolution with a specific filter on tensors.
e.g. my kernel is:
H = torch.Tensor([[1 ,0, -1],[2, 0 ,-2], [1, 0 ,-1]]), so it is
3x3 originally, but i can expand it in a size that is needed.
If my tensor T, has size of
BxCxDxD, What should be the size and shape of my kernel so i can do
conv2d(T,H) and get the output of
what if i have RGB image (3xNxN) and 2D kernel (MxM)?