SpatialConvolutionMap.py 文件源码

def reset(self, stdv=None):
        if stdv is not None:
            stdv = stdv * math.sqrt(3)
            self.weight.uniform_(-stdv, stdv)
            self.bias.uniform_(-stdv, stdv)
        else:
            ninp = torch.Tensor(self.nOutputPlane).zero_()
            for i in range(self.connTable.size(0)):
                idx = int(self.connTable[i, 1])
                ninp[idx] += 1
            for k in range(self.connTable.size(0)):
                idx = int(self.connTable[k, 1])
                stdv = 1. / math.sqrt(self.kW * self.kH * ninp[idx])
                self.weight.select(0, k).uniform_(-stdv, stdv)
            for k in range(self.bias.size(0)):
                stdv = 1. / math.sqrt(self.kW * self.kH * ninp[k])
                # TODO: torch.uniform
                self.bias[k] = random.uniform(-stdv, stdv)