layers.py 文件源码

def normalized_cross_correlation(self):
        w = self.weight.view(self.weight.size(0), -1)
        t_norm = torch.norm(w, p=2, dim=1)
        if self.in_channels == 1 & sum(self.kernel_size) == 1:
            ncc = w.squeeze() / torch.norm(self.t0_norm, p=2)
            ncc = ncc - self.start_ncc
            return ncc
        #mean = torch.mean(w, dim=1).unsqueeze(1).expand_as(w)
        mean = torch.mean(w, dim=1).unsqueeze(1) # 0.2 broadcasting
        t_factor = w - mean
        h_product = self.t0_factor * t_factor
        cov = torch.sum(h_product, dim=1) # (w.size(1) - 1)
        # had normalization code commented out
        denom = self.t0_norm * t_norm

        ncc = cov / denom
        ncc = ncc - self.start_ncc
        return ncc