models.py 文件源码

def forward(self, x):
        nBatch = x.size(0)

        x = F.max_pool2d(self.conv1(x), 2)
        x = F.max_pool2d(self.conv2(x), 2)
        x = x.view(nBatch, -1)

        L = self.M*self.L
        Q = L.mm(L.t()) + self.eps*Variable(torch.eye(self.nHidden)).cuda()
        Q = Q.unsqueeze(0).expand(nBatch, self.nHidden, self.nHidden)
        G = self.G.unsqueeze(0).expand(nBatch, self.nineq, self.nHidden)
        z0 = self.qp_z0(x)
        s0 = self.qp_s0(x)
        h = z0.mm(self.G.t())+s0
        e = Variable(torch.Tensor())
        inputs = self.qp_o(x)
        x = QPFunction()(Q, inputs, G, h, e, e)
        x = x[:,:10]

        return F.log_softmax(x)