model.py 文件源码

def forward(self, img, qst):
        x = self.conv(img) ## x = (64 x 24 x 5 x 5)

        """g"""
        mb = x.size()[0]
        n_channels = x.size()[1]
        d = x.size()[2]
        # x_flat = (64 x 25 x 24)
        x_flat = x.view(mb,n_channels,d*d).permute(0,2,1)

        # add coordinates
        x_flat = torch.cat([x_flat, self.coord_tensor],2)

        # add question everywhere
        qst = torch.unsqueeze(qst, 1)
        qst = qst.repeat(1,25,1)
        qst = torch.unsqueeze(qst, 2)

        # cast all pairs against each other
        x_i = torch.unsqueeze(x_flat,1) # (64x1x25x26+11)
        x_i = x_i.repeat(1,25,1,1) # (64x25x25x26+11)
        x_j = torch.unsqueeze(x_flat,2) # (64x25x1x26+11)
        x_j = torch.cat([x_j,qst],3)
        x_j = x_j.repeat(1,1,25,1) # (64x25x25x26+11)

        # concatenate all together
        x_full = torch.cat([x_i,x_j],3) # (64x25x25x2*26+11)

        # reshape for passing through network
        x_ = x_full.view(mb*d*d*d*d,63)
        x_ = self.g_fc1(x_)
        x_ = F.relu(x_)
        x_ = self.g_fc2(x_)
        x_ = F.relu(x_)
        x_ = self.g_fc3(x_)
        x_ = F.relu(x_)
        x_ = self.g_fc4(x_)
        x_ = F.relu(x_)

        # reshape again and sum
        x_g = x_.view(mb,d*d*d*d,256)
        x_g = x_g.sum(1).squeeze()

        """f"""
        x_f = self.f_fc1(x_g)
        x_f = F.relu(x_f)

        return self.fcout(x_f)