"Res FFT-Conv Block" in MPRNet

[misakshoyan]

In MPRNet, the Channel Attention Block (CAB) has the following structure:

`class CAB(nn.Module):
def init(self, n_feat, kernel_size, reduction, bias, act):
super(CAB, self).init()
modules_body = []

    # ResBlock
    modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
    modules_body.append(act)
    modules_body.append(conv(n_feat, n_feat, kernel_size, bias=bias))
    ###

    self.CA = CALayer(n_feat, reduction, bias=bias)
    self.body = nn.Sequential(*modules_body)

def forward(self, x):
    res = self.body(x)
    res = self.CA(res)
    res += x
    return res`

In the paper, for Table 1, it is stated that ResBlock is replaced by "Res FFT-Conv Block".
I wonder how do you integrate the "Res FFT-Conv Block" into CAB in your experiments?
Do you just replace the ResBlock structure by "Res FFT-Conv Block" (please see my comments in the code) or you replace the whole CAB by "Res FFT-Conv Block"?

Thanks in advance.

[INVOKERer]

Table 1 replace the whole CAB by "Res FFT-Conv Block".