And you are right. For one kernel, all results will be added into one. So we can just get one result. So we can just use this kind of way that is one computing with only one in_channel and one out_channel, and input only one vector. To do this operation for three times, we can get three results. But it won’t like what your pic showing, computing all at the same time. But you can set the three in_channels and three out_channels, and use group convolution to make the output of three channels won’t be added into one
Code goes like
import torch
import torch.nn as nn

ind=torch.arange(12,dtype=torch.float32).reshape([1,3,4])
# print(ind)
#
# conv=nn.Conv1d(in_channels=1,out_channels=1,kernel_size=4)
# for data in ind:
#     print(conv(data.unsqueeze(0).unsqueeze(0)))
conv=nn.Conv1d(in_channels=3,out_channels=3,kernel_size=4,groups=3)
print(conv(ind))