task_net.py 文件源码

def forward(self, y):
        nBatch, k = y.size()

        Q_scale = torch.cat([torch.diag(torch.cat(
            [self.one, y[i], y[i]])).unsqueeze(0) for i in range(nBatch)], 0)
        Q = self.Q.unsqueeze(0).expand_as(Q_scale).mul(Q_scale)
        p_scale = torch.cat([Variable(torch.ones(nBatch,1).cuda()), y, y], 1)
        p = self.p.unsqueeze(0).expand_as(p_scale).mul(p_scale)
        G = self.G.unsqueeze(0).expand(nBatch, self.G.size(0), self.G.size(1))
        h = self.h.unsqueeze(0).expand(nBatch, self.h.size(0))
        e = Variable(torch.Tensor().cuda()).double()

        out = QPFunction(verbose=False)\
            (Q.double(), p.double(), G.double(), h.double(), e, e).float()

        return out[:,:1]