用分子图来写D-mpnn的代码
时间: 2023-11-27 20:54:08 浏览: 48
以下是一个简单的D-MPNN模型的代码示例:
```
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
class DMPNN(MessagePassing):
def __init__(self, in_channels, out_channels, edge_channels, dropout=0.5):
super(DMPNN, self).__init__(aggr='mean')
self.in_channels = in_channels
self.out_channels = out_channels
self.edge_channels = edge_channels
self.lin_node = nn.Linear(in_channels, out_channels)
self.lin_edge = nn.Linear(edge_channels, out_channels)
self.lin_update = nn.Linear(2 * out_channels, out_channels)
self.dropout = nn.Dropout(dropout)
def forward(self, x, edge_index, edge_attr):
edge_index, _ = add_self_loops(edge_index, num_nodes=x.size(0))
row, col = edge_index
node_rep = self.lin_node(x)
edge_rep = self.lin_edge(edge_attr)
# Message passing
out = self.propagate(edge_index, x=node_rep, edge=edge_rep)
out = F.relu(out)
# Node update
out = torch.cat([node_rep, out], dim=-1)
out = self.lin_update(out)
out = F.relu(out)
out = self.dropout(out)
return out
def message(self, x_j, edge):
return x_j * edge.unsqueeze(-1)
def update(self, aggr_out):
return aggr_out
class DMPNNModel(nn.Module):
def __init__(self, num_node_features, num_edge_features, hidden_dim, num_classes, dropout=0.5):
super(DMPNNModel, self).__init__()
self.num_node_features = num_node_features
self.num_edge_features = num_edge_features
self.hidden_dim = hidden_dim
self.num_classes = num_classes
self.dropout = dropout
self.dmpnn1 = DMPNN(num_node_features, hidden_dim, num_edge_features, dropout)
self.dmpnn2 = DMPNN(hidden_dim, hidden_dim, num_edge_features, dropout)
self.lin = nn.Linear(hidden_dim, num_classes)
def forward(self, x, edge_index, edge_attr):
x = self.dmpnn1(x, edge_index, edge_attr)
x = self.dmpnn2(x, edge_index, edge_attr)
x = global_mean_pool(x, torch.zeros(x.size(0), dtype=torch.long))
x = self.lin(x)
return F.log_softmax(x, dim=-1)
```
这个D-MPNN模型有两个`DMPNN`层和一个线性层,用于分类问题。`DMPNN`层采用了`MessagePassing`类,并且实现了`message`和`update`函数来定义信息传递和节点更新过程。在`forward`中,模型首先调用两个`DMPNN`层来进行节点嵌入的计算,然后使用全局平均池化将节点嵌入转换为图嵌入,最后通过线性层和softmax层来计算类别概率。
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