This project is the PyTorch implementation of our CVPR 2022 paper:

Dear Professor Huang: Hello! Is the final XBNBlock-P2 module the following code?

def GroupNorm(num_features, num_groups=32, eps=1e-5, affine=True, *args, **kwargs):
    if num_groups>num_features:
        print('------arrive maxum groub numbers of:', num_features)
        num_groups=num_features
    return nn.GroupNorm(num_groups, num_features, eps=eps, affine=affine)


class Bottleneck_XBNBlock_P2(nn.Module):
        expansion = 4
    
        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(Bottleneck_XBNBlock_P2, self).__init__()
            self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
            self.bn1 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
            self.bn2 = GroupNorm(planes)
            self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
            self.bn3 = nn.BatchNorm2d(planes * 4)
            self.relu = nn.ReLU(inplace=True)
            self.downsample = downsample
            self.stride = stride

        def forward(self, x):
            residual = x
    
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
    
            out = self.conv2(out)
            out = self.bn2(out)
            out = self.relu(out)
    
            out = self.conv3(out)
            out = self.bn3(out)
    
            if self.downsample is not None:
                residual = self.downsample(x)
    
            out += residual
            out = self.relu(out)
    
            return out

I read your code and only this class matches the module above. Also, I use the standard Bottleneck in my work: conv 11->bn->relu->conv33->bn->relu. The structure of this Bottleneck is different from the one in your article. Can I replace the bn after 3*3 convolution with BFN(GN)? Thank you very much.