gnn_model.py

import torch
import torch.nn as nn
import dgl

class WindGNN(nn.Module):
    def __init__(self, in_feats=5, hidden_size=128, out_feats=3, num_layers=3):
        super(WindGNN, self).__init__()
        self.layers = nn.ModuleList()
        self.input_proj = nn.Linear(in_feats, hidden_size)
        
        for _ in range(num_layers):
            self.layers.append(dgl.nn.GraphConv(hidden_size, hidden_size))
        
        self.velocity_head = nn.Sequential(
            nn.Linear(hidden_size, hidden_size // 4),
            nn.LayerNorm(hidden_size // 4),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(hidden_size // 4, 1),
            nn.Sigmoid()
        )
        
        self.direction_head = nn.Sequential(
            nn.Linear(hidden_size, hidden_size // 4),
            nn.LayerNorm(hidden_size // 4),
            nn.ReLU(),
            nn.Dropout(0.2),
            nn.Linear(hidden_size // 4, 2)
        )
        
        self.layer_norms = nn.ModuleList([
            nn.LayerNorm(hidden_size) for _ in range(num_layers)
        ])
        
        self.dropout = nn.Dropout(0.2)

    def forward(self, g, features):
        h = self.input_proj(features)
        for i, (conv, norm) in enumerate(zip(self.layers, self.layer_norms)):
            h_new = conv(g, h)
            h_new = norm(h_new)
            h_new = nn.functional.relu(h_new)
            h_new = self.dropout(h_new)
            h = h + h_new if i > 0 else h_new
        velocity = self.velocity_head(h)
        direction = self.direction_head(h)
        direction = nn.functional.normalize(direction, dim=1)
        return torch.cat([velocity, direction], dim=1)