Hi,
I read the doc about group of the Conv2d().
e.g.  If I use group=10,  does it mean that 10 convolution layers side by side and the 10 layers share the same parameters?
If so, is there an elegant way to  use 10 layers of different parameters ?
i.e:
I have a tensor  whose size is [batch_size, channel=100, H, W]
and I want to have 5 Conv layers, each looks at only 20 of the 100 channel, how can I do?
Must I use slice operation?  Is there a better way?
Thank you in advance!