dgllife.model.gnn与torch_geometric.nn.GraphConv如何互相转换使用

dgllife和torch_geometric都是流行的图神经网络框架，其中dgllife提供了一个名为gnn的模块，而torch_geometric则提供了一个名为GraphConv的模块。这两个模块的主要作用都是对图数据进行卷积操作。

如果你想在dgllife中使用torch_geometric的GraphConv，可以通过将GraphConv转换为一个DGL GraphConv来实现。具体地说，你需要将GraphConv的权重矩阵转换为DGL GraphConv中的权重张量，然后再将其传递给DGL GraphConv。示例代码如下：

import torch
from dgllife.model import GCN

# 转换torch_geometric的GraphConv为DGL GraphConv
class GraphConv(torch.nn.Module):
    def __init__(self, in_channels, out_channels):
        super(GraphConv, self).__init__()
        self.conv = torch_geometric.nn.GraphConv(in_channels, out_channels)

    def forward(self, g, feat):
        # 将权重矩阵转换为权重张量
        weight = torch.transpose(self.conv.weight, 0, 1)
        weight = torch.unsqueeze(weight, dim=0)
        weight = weight.repeat(g.batch_size, 1, 1)

        # 传递给DGL GraphConv
        return torch.relu(dgl.nn.GraphConv(out_channels, out_channels)(g, feat, weight))
        
# 创建一个包含GraphConv的GCN模型
class GCN(torch.nn.Module):
    def __init__(self, in_feats, hidden_feats, out_feats):
        super(GCN, self).__init__()

        self.gcn_layers = torch.nn.ModuleList()
        self.gcn_layers.append(GraphConv(in_feats, hidden_feats))
        self.gcn_layers.append(GraphConv(hidden_feats, out_feats))

    def forward(self, g, feat):
        for i, layer in enumerate(self.gcn_layers):
            if i != 0:
                feat = torch.relu(feat)
            feat = layer(g, feat)
        return feat

如果你想在torch_geometric中使用dgllife的gnn，可以通过将gnn转换为一个torch_geometric GraphConv来实现。具体地说，你需要将gnn的权重张量转换为GraphConv中的权重矩阵，然后再将其传递给GraphConv。示例代码如下：

import torch_geometric.nn as pyg_nn

# 转换dgllife的gnn为torch_geometric GraphConv
class GNN(torch.nn.Module):
    def __init__(self, in_feats, hidden_feats, out_feats):
        super(GNN, self).__init__()
        self.gnn = dgllife.model.gnn.AttentiveFPGNN(in_feats=in_feats,
                                                    hidden_feats=hidden_feats,
                                                    out_feats=out_feats,
                                                    num_layers=2)

    def forward(self, g, feat):
        # 将权重张量转换为权重矩阵
        weight = torch.transpose(self.gnn.layers[0].fc.weight, 0, 1)
        weight = torch.unsqueeze(weight, dim=0)

        # 传递给torch_geometric GraphConv
        return pyg_nn.GraphConv(in_channels=self.gnn.layers[0].in_feats,
                                out_channels=self.gnn.layers[0].out_feats,
                                bias=True)(g, feat, weight)

# 创建一个包含GraphConv的模型
class PyGCN(torch.nn.Module):
    def __init__(self, in_feats, hidden_feats, out_feats):
        super(PyGCN, self).__init__()

        self.gcn_layers = torch.nn.ModuleList()
        self.gcn_layers.append(GNN(in_feats, hidden_feats, out_feats))
        self.gcn_layers.append(pyg_nn.GraphConv(hidden_feats, out_feats))

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        for i, layer in enumerate(self.gcn_layers):
            if i != 0:
                x = torch.relu(x)
            x = layer(edge_index, x)
        return x

需要注意的是，这两种转换方式可能会对模型的性能产生一定的影响，因此在使用时应该进行适当的调整和比较。