import torch import torch.nn as nn from torch.nn.modules.rnn import GRU from models.STDEN.ode_func import ODEFunc from models.STDEN.diffeq_solver import DiffeqSolver from models.STDEN import utils from data.graph_loader import load_graph class EncoderAttrs: """编码器属性配置类""" def __init__(self, config, adj_mx): self.adj_mx = adj_mx self.num_nodes = adj_mx.shape[0] self.num_edges = (adj_mx > 0.).sum() self.gcn_step = int(config.get('gcn_step', 2)) self.filter_type = config.get('filter_type', 'default') self.num_rnn_layers = int(config.get('num_rnn_layers', 1)) self.rnn_units = int(config.get('rnn_units')) self.latent_dim = int(config.get('latent_dim', 4)) class STDENModel(nn.Module, EncoderAttrs): """STDEN主模型：时空微分方程网络""" def __init__(self, config): nn.Module.__init__(self) adj_mx = load_graph(config) EncoderAttrs.__init__(self, config['model'], adj_mx) # 输入输出维度配置 self.input_dim = int(config['model'].get('input_dim', 1)) self.output_dim = int(config['model'].get('output_dim', 1)) # Node到Edge的转换层 self.node_to_edge = nn.Linear(self.num_nodes * self.input_dim, self.num_edges * self.input_dim) # Edge到Node的转换层 self.edge_to_node = nn.Linear(self.num_edges * self.output_dim, self.num_nodes * self.output_dim) # 初始化转换层权重 utils.init_network_weights(self.node_to_edge) utils.init_network_weights(self.edge_to_node) # 识别网络 self.encoder_z0 = Encoder_z0_RNN(config['model'], adj_mx) model_kwargs = config['model'] # ODE求解器配置 self.n_traj_samples = int(model_kwargs.get('n_traj_samples', 1)) self.ode_method = model_kwargs.get('ode_method', 'dopri5') self.atol = float(model_kwargs.get('odeint_atol', 1e-4)) self.rtol = float(model_kwargs.get('odeint_rtol', 1e-3)) self.num_gen_layer = int(model_kwargs.get('gen_layers', 1)) self.ode_gen_dim = int(model_kwargs.get('gen_dim', 64)) # 创建ODE函数和求解器 odefunc = ODEFunc( self.ode_gen_dim, self.latent_dim, adj_mx, self.gcn_step, self.num_nodes, filter_type=self.filter_type ) self.diffeq_solver = DiffeqSolver( odefunc, self.ode_method, self.latent_dim, odeint_rtol=self.rtol, odeint_atol=self.atol ) # 潜在特征保存设置 self.save_latent = bool(model_kwargs.get('save_latent', False)) self.latent_feat = None # 解码器 self.horizon = int(model_kwargs.get('horizon', 1)) self.out_feat = int(model_kwargs.get('output_dim', 1)) self.decoder = Decoder( self.out_feat, adj_mx, self.num_nodes, self.num_edges ) def forward(self, inputs, labels=None, batches_seen=None): """ seq2seq前向传播 :param inputs: (batch_size, seq_len, num_nodes, input_dim) - 节点格式输入 :param labels: (batch_size, horizon, num_nodes, output_dim) - 节点格式标签 :param batches_seen: 已见批次数量 :return: outputs: (batch_size, horizon, num_nodes, output_dim) - 节点格式输出 """ # 输入格式转换：从node格式转换为edge格式 B, T, N, C = inputs.shape inputs_node = inputs.view(T, B, N * C) # (T, B, N*C) # 将node格式转换为edge格式 inputs_edge = self.node_to_edge(inputs_node) # (T, B, E*C) # 编码初始潜在状态 first_point_mu, first_point_std = self.encoder_z0(inputs_edge) # 采样轨迹 means_z0 = first_point_mu.repeat(self.n_traj_samples, 1, 1) sigma_z0 = first_point_std.repeat(self.n_traj_samples, 1, 1) first_point_enc = utils.sample_standard_gaussian(means_z0, sigma_z0) # 时间步预测 time_steps_to_predict = torch.arange(start=0, end=self.horizon, step=1).float() time_steps_to_predict = time_steps_to_predict / len(time_steps_to_predict) # ODE求解 sol_ys, fe = self.diffeq_solver(first_point_enc, time_steps_to_predict) if self.save_latent: self.latent_feat = torch.mean(sol_ys.detach(), axis=1) # 解码输出（edge格式） outputs_edge = self.decoder(sol_ys) # (horizon, B, E*output_dim) # 将edge格式转换回node格式 outputs_node = self.edge_to_node(outputs_edge) # (horizon, B, N*output_dim) # 重塑为最终输出格式 outputs = outputs_node.view(self.horizon, B, N, self.output_dim) outputs = outputs.transpose(0, 1) # (B, horizon, N, output_dim) return outputs, fe class Encoder_z0_RNN(nn.Module, EncoderAttrs): """RNN编码器：将输入序列编码为初始潜在状态""" def __init__(self, config, adj_mx): nn.Module.__init__(self) EncoderAttrs.__init__(self, config, adj_mx) self.recg_type = config.get('recg_type', 'gru') self.input_dim = int(config.get('input_dim', 1)) if self.recg_type == 'gru': self.gru_rnn = GRU(self.input_dim, self.rnn_units) else: raise NotImplementedError("只支持'gru'识别网络") # 隐藏状态到z0的映射 self.inv_grad = utils.graph_grad(adj_mx).transpose(-2, -1) self.inv_grad[self.inv_grad != 0.] = 0.5 self.hiddens_to_z0 = nn.Sequential( nn.Linear(self.rnn_units, 50), nn.Tanh(), nn.Linear(50, self.latent_dim * 2) ) utils.init_network_weights(self.hiddens_to_z0) def forward(self, inputs): """ 编码器前向传播 :param inputs: (seq_len, batch_size, num_edges * input_dim) :return: mean, std: (1, batch_size, latent_dim) """ seq_len, batch_size = inputs.size(0), inputs.size(1) # 重塑输入并处理 - 现在输入是edge格式 inputs = inputs.reshape(seq_len, batch_size, self.num_edges, self.input_dim) inputs = inputs.reshape(seq_len, batch_size * self.num_edges, self.input_dim) # GRU处理 outputs, _ = self.gru_rnn(inputs) last_output = outputs[-1] # 重塑并转换维度 - 从edge格式转换回node格式 last_output = torch.reshape(last_output, (batch_size, self.num_edges, -1)) last_output = torch.transpose(last_output, -2, -1) last_output = torch.matmul(last_output, self.inv_grad).transpose(-2, -1) # 生成均值和标准差 mean, std = utils.split_last_dim(self.hiddens_to_z0(last_output)) mean = mean.reshape(batch_size, -1) std = std.reshape(batch_size, -1).abs() return mean.unsqueeze(0), std.unsqueeze(0) class Decoder(nn.Module): """解码器：将潜在状态解码为输出""" def __init__(self, output_dim, adj_mx, num_nodes, num_edges): super(Decoder, self).__init__() self.num_nodes = num_nodes self.num_edges = num_edges self.grap_grad = utils.graph_grad(adj_mx) self.output_dim = output_dim def forward(self, inputs): """ :param inputs: (horizon, n_traj_samples, batch_size, num_nodes * latent_dim) :return: outputs: (horizon, batch_size, num_edges * output_dim) """ horizon, n_traj_samples, batch_size = inputs.size()[:3] # 重塑输入 inputs = inputs.reshape(horizon, n_traj_samples, batch_size, self.num_nodes, -1).transpose(-2, -1) latent_dim = inputs.size(-2) # 图梯度变换：从节点到边 outputs = torch.matmul(inputs, self.grap_grad) # 重塑并平均采样轨迹 outputs = outputs.reshape(horizon, n_traj_samples, batch_size, latent_dim, self.num_edges, self.output_dim) outputs = torch.mean(torch.mean(outputs, axis=3), axis=1) outputs = outputs.reshape(horizon, batch_size, -1) return outputs