class CellTrack_GNN(EedgePath_MPNN): def __init__(self, in_channels: int, hidden_channels: int, in_edge_channels: int, hidden_edge_channels_linear: int, hidden_edge_channels_conv: int, num_layers: int, num_nodes_features: int, dropout: float = 0.0, act: Optional[Callable] = ReLU(inplace=True), norm: Optional[torch.nn.Module] = None, jk: str = 'last', **kwargs): super().__init__(in_channels, hidden_channels, in_edge_channels, hidden_edge_channels_linear, num_layers, dropout, act, norm, jk) assert in_edge_channels == hidden_edge_channels_linear[-1] in_edge_dims = in_edge_channels + num_nodes_features * in_channels + 1 self.convs.append(PDNConv(in_channels, hidden_channels, in_edge_channels, hidden_edge_channels_conv, **kwargs)) self.fcs.append(MLP(in_edge_dims, hidden_edge_channels_linear, dropout_p=dropout)) for _ in range(1, num_layers): self.convs.append( PDNConv(hidden_channels, hidden_channels, in_edge_channels, hidden_edge_channels_conv, **kwargs)) self.fcs.append(MLP(in_edge_dims, hidden_edge_channels_linear, dropout_p=dropout))
时间: 2024-02-14 08:28:51 浏览: 29
这段代码定义了一个名为CellTrack_GNN的类,该类继承自EedgePath_MPNN类。在类的构造函数`__init__`中,有一系列参数用于初始化模型的各个组件。
- `in_channels`、`hidden_channels`、`in_edge_channels`、`hidden_edge_channels_linear`、`hidden_edge_channels_conv`、`num_layers`、`num_nodes_features`、`dropout`、`act`、`norm`和`jk`等是构建图神经网络所需的参数。
- `super().__init__(in_channels, hidden_channels, in_edge_channels, hidden_edge_channels_linear, num_layers, dropout, act, norm, jk)`调用了父类EedgePath_MPNN的构造函数,初始化了一些基本的组件。
- `in_edge_dims = in_edge_channels + num_nodes_features * in_channels + 1`计算了输入边特征的维度。
- `self.convs.append(PDNConv(in_channels, hidden_channels, in_edge_channels, hidden_edge_channels_conv, **kwargs))`将一个PDNConv层对象添加到self.convs列表中,用于对节点特征进行卷积操作。
- `self.fcs.append(MLP(in_edge_dims, hidden_edge_channels_linear, dropout_p=dropout))`将一个MLP层对象添加到self.fcs列表中,用于对输入边特征进行全连接操作。
- 然后使用循环,根据num_layers的值,依次添加PDNConv和MLP层对象到self.convs和self.fcs列表中,构建图神经网络的层数。
通过这些组件的初始化,CellTrack_GNN类可以进行图神经网络的前向传播操作。
相关推荐
![pptx](https://img-home.csdnimg.cn/images/20210720083543.png)
![application/octet-stream](https://img-home.csdnimg.cn/images/20210720083646.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://img-home.csdnimg.cn/images/20210720083646.png)
![](https://img-home.csdnimg.cn/images/20210720083646.png)
![pptx](https://img-home.csdnimg.cn/images/20210720083543.png)
![pptx](https://img-home.csdnimg.cn/images/20210720083543.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![xlsx](https://img-home.csdnimg.cn/images/20210720083732.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)