involution | CVPR 2021 ] Involution : Inverting the Inherence | Machine Learning library

channel_filter = torch.ones(filter_size)
output = ddf(input, channel_filter, spatial_filter,
             kernel_size, dilation, stride, head, 'mul')

channel_filter = torch.zeros(filter_size)
output = ddf(input, channel_filter, spatial_filter,
       

import torch
from involution import Involution2d

involution = Involution2d(in_channels=32, out_channels=64)
output = involution(torch.rand(1, 32, 128, 128))

import torch
from involution import Involution3d

involution = Involution3d(in_channels=8,   

def InvolutionChanges(n):
    """Generate change sequence for involutions on n items.
    Uses a variation of the Steinhaus-Johnson-Trotter idea,
    in which we first recurse for n-1, generating involutions
    in which the last item is fixed, and t  

def Involutions(n):
    """Generate involutions on n items.
    The first involution is always the one in which all items
    are mapped to themselves, and the last involution is the one
    in which only the final two items are swapped.
    Each two