ModelBuider()

    def __init__(self):
        super(ModelBuilder, self).__init__()

        # build backbone
        self.backbone = get_backbone(cfg.BACKBONE.TYPE,
                                     **cfg.BACKBONE.KWARGS)

        # build car head
        self.car_head = CARHead(cfg, 256)

        # build response map
        self.attention = Graph_Attention_Union(256, 256)

在CARHead()中进行分类和回归子网的包围盒预测

在Graph_Attention_Union()中

class Graph_Attention_Union(nn.Module):
    def __init__(self, in_channel, out_channel):
        super(Graph_Attention_Union, self).__init__()

        # search region nodes linear transformation
        self.support = nn.Conv2d(in_channel, in_channel, 1, 1)

        # target template nodes linear transformation
        self.query = nn.Conv2d(in_channel, in_channel, 1, 1)

        # linear transformation for message passing
        self.g = nn.Sequential(
            nn.Conv2d(in_channel, in_channel, 1, 1),
            nn.BatchNorm2d(in_channel),
            nn.ReLU(inplace=True),
        )

        # aggregated feature
        self.fi = nn.Sequential(
            nn.Conv2d(in_channel*2, out_channel, 1, 1),
            nn.BatchNorm2d(out_channel),
            nn.ReLU(inplace=True),
        )

    def forward(self, zf, xf):
        # linear transformation
        xf_trans = self.query(xf)
        zf_trans = self.support(zf)

        # linear transformation for message passing
        xf_g = self.g(xf)
        zf_g = self.g(zf)

        # calculate similarity
        shape_x = xf_trans.shape
        shape_z = zf_trans.shape

        zf_trans_plain = zf_trans.view(-1, shape_z[1], shape_z[2] * shape_z[3])
        zf_g_plain = zf_g.view(-1, shape_z[1], shape_z[2] * shape_z[3]).permute(0, 2, 1)
        xf_trans_plain = xf_trans.view(-1, shape_x[1], shape_x[2] * shape_x[3]).permute(0, 2, 1)

        similar = torch.matmul(xf_trans_plain, zf_trans_plain)
        similar = F.softmax(similar, dim=2)

 以上代码对应于论文中

 这里用softmax函数规划华是为了平衡发送到搜索区域的信息量。

        embedding = torch.matmul(similar, zf_g_plain).permute(0, 2, 1)
        embedding = embedding.view(-1, shape_x[1], shape_x[2], shape_x[3])

 对应论文中

得到Gt中所有结点传递到Gs中的第i个节点的注意 

        # aggregated feature
        output = torch.cat([embedding, xf_g], 1)
        output = self.fi(output)
        return output

对应论文中

 将聚合特征与节点特征hi融合，以获得目标信息赋予的更强大的特征表示