networks.py 文件源码

def forward(self, x):
        n = x.size(2) * x.size(3)
        t = x.view(x.size(0), x.size(1), n)
        mean = torch.mean(t, 2).unsqueeze(2).expand_as(x)
        # Calculate the biased var. torch.var returns unbiased var
        var = torch.var(t, 2).unsqueeze(2).expand_as(x) * ((n - 1) / float(n))
        scale_broadcast = self.weight.unsqueeze(1).unsqueeze(1).unsqueeze(0)
        scale_broadcast = scale_broadcast.expand_as(x)
        shift_broadcast = self.bias.unsqueeze(1).unsqueeze(1).unsqueeze(0)
        shift_broadcast = shift_broadcast.expand_as(x)
        out = (x - mean) / torch.sqrt(var + self.eps)
        out = out * scale_broadcast + shift_broadcast
        return out