model.py 文件源码

def forward(self, input, sigma=None):
        res = F.linear(input, self.weight, self.bias)
        if sigma is None:
            return res
        if self.rand_buf is None or self.rand_buf.size() != res.size():
            self.rand_buf = torch.FloatTensor(res.size())
            if input.is_cuda:
                self.rand_buf = self.rand_buf.cuda()
        torch.randn(self.rand_buf.size(), out=self.rand_buf)
#        print(m.size(), res.size())
        return res + torch.mul(sigma, Variable(self.rand_buf))