更新项目文件：添加数据选择器、模型选择器、训练器选择器和ODE训练器

.gitignore

@@ -160,3 +160,4 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+STDEN/

.idea/.gitignore

@@ -0,0 +1,8 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/Project-I.iml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="TS" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,27 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="DuplicatedCode" enabled="true" level="WEAK WARNING" enabled_by_default="true">
+      <Languages>
+        <language minSize="136" name="Python" />
+      </Languages>
+    </inspection_tool>
+    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="8">
+            <item index="0" class="java.lang.String" itemvalue="argparse" />
+            <item index="1" class="java.lang.String" itemvalue="torch_summary" />
+            <item index="2" class="java.lang.String" itemvalue="positional_encodings" />
+            <item index="3" class="java.lang.String" itemvalue="scikit_learn" />
+            <item index="4" class="java.lang.String" itemvalue="easy_torch" />
+            <item index="5" class="java.lang.String" itemvalue="setuptools" />
+            <item index="6" class="java.lang.String" itemvalue="numpy" />
+            <item index="7" class="java.lang.String" itemvalue="openpyxl" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.10" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="TS" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/Project-I.iml" filepath="$PROJECT_DIR$/.idea/Project-I.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/STDEN" vcs="Git" />
+  </component>
+</project>

data/PEMS08/PEMS08.csv

@@ -0,0 +1,296 @@
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data/data_selector.py

@@ -2,6 +2,7 @@ import numpy as np
 import os
 
 def load_dataset(config):
+    
     dataset_name = config['basic']['dataset']
     node_num = config['data']['num_nodes']
     input_dim = config['data']['input_dim']
@@ -10,4 +11,8 @@ def load_dataset(config):
         case 'EcoSolar':
             data_path = os.path.join('./data/EcoSolar.npy')
             data = np.load(data_path)[:, :node_num, :input_dim]
+        case 'PEMS08':
+            data_path = os.path.join('./data/PEMS08/PEMS08.npz')
+            data = np.load(data_path)['data'][:, :node_num, :input_dim]
+
     return data

data/graph_loader.py

@@ -0,0 +1,9 @@
+import numpy as np
+
+def load_graph(config):
+    dataset_path = config['data']['graph_pkl_filename']
+    graph = np.load(dataset_path)
+    # 将inf值填充为0
+    graph = np.nan_to_num(graph, nan=0.0, posinf=0.0, neginf=0.0)
+    
+    return graph

exp/PEMS08_STDEN_2025/09/03-00:53:52/config.yaml

@@ -0,0 +1,62 @@
+basic:
+  dataset: PEMS08
+  device: cuda:0
+  mode: train
+  model: STDEN
+  seed: 2025
+data:
+  add_day_in_week: true
+  add_time_in_day: true
+  batch_size: 32
+  column_wise: false
+  dataset_dir: data/PEMS08
+  days_per_week: 7
+  default_graph: true
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  horizon: 24
+  input_dim: 1
+  lag: 24
+  normalizer: std
+  num_nodes: 100
+  steps_per_day: 24
+  test_ratio: 0.2
+  tod: false
+  val_batch_size: 32
+  val_ratio: 0.2
+model:
+  filter_type: default
+  gcn_step: 2
+  horizon: 12
+  input_dim: 1
+  l1_decay: 0
+  latent_dim: 4
+  n_traj_samples: 3
+  nfe: false
+  num_rnn_layers: 1
+  ode_method: dopri5
+  odeint_atol: 1.0e-05
+  odeint_rtol: 1.0e-05
+  output_dim: 1
+  recg_type: gru
+  rnn_units: 64
+  save_latent: false
+  seq_len: 12
+train:
+  batch_size: 64
+  debug: false
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-00:53:52
+  loss: mae
+  lr_decay: false
+  lr_decay_rate: 0.3
+  lr_decay_step: 5,20,40,70
+  lr_init: 0.003
+  mae_thresh: None
+  mape_thresh: 0.001
+  max_grad_norm: 5
+  output_dim: 1
+  real_value: true
+  weight_decay: 0

exp/PEMS08_STDEN_2025/09/03-00:56:54/config.yaml

@@ -0,0 +1,62 @@
+basic:
+  dataset: PEMS08
+  device: cuda:0
+  mode: train
+  model: STDEN
+  seed: 2025
+data:
+  add_day_in_week: true
+  add_time_in_day: true
+  batch_size: 32
+  column_wise: false
+  dataset_dir: data/PEMS08
+  days_per_week: 7
+  default_graph: true
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  horizon: 24
+  input_dim: 1
+  lag: 24
+  normalizer: std
+  num_nodes: 100
+  steps_per_day: 24
+  test_ratio: 0.2
+  tod: false
+  val_batch_size: 32
+  val_ratio: 0.2
+model:
+  filter_type: default
+  gcn_step: 2
+  horizon: 12
+  input_dim: 1
+  l1_decay: 0
+  latent_dim: 4
+  n_traj_samples: 3
+  nfe: false
+  num_rnn_layers: 1
+  ode_method: dopri5
+  odeint_atol: 1.0e-05
+  odeint_rtol: 1.0e-05
+  output_dim: 1
+  recg_type: gru
+  rnn_units: 64
+  save_latent: false
+  seq_len: 12
+train:
+  batch_size: 64
+  debug: false
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-00:56:54
+  loss: mae
+  lr_decay: false
+  lr_decay_rate: 0.3
+  lr_decay_step: 5,20,40,70
+  lr_init: 0.003
+  mae_thresh: None
+  mape_thresh: 0.001
+  max_grad_norm: 5
+  output_dim: 1
+  real_value: true
+  weight_decay: 0

exp/PEMS08_STDEN_2025/09/03-01:07:23/config.yaml

@@ -0,0 +1,62 @@
+basic:
+  dataset: PEMS08
+  device: cuda:0
+  mode: train
+  model: STDEN
+  seed: 2025
+data:
+  add_day_in_week: true
+  add_time_in_day: true
+  batch_size: 32
+  column_wise: false
+  dataset_dir: data/PEMS08
+  days_per_week: 7
+  default_graph: true
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  horizon: 24
+  input_dim: 1
+  lag: 24
+  normalizer: std
+  num_nodes: 170
+  steps_per_day: 24
+  test_ratio: 0.2
+  tod: false
+  val_batch_size: 32
+  val_ratio: 0.2
+model:
+  filter_type: default
+  gcn_step: 2
+  horizon: 12
+  input_dim: 1
+  l1_decay: 0
+  latent_dim: 4
+  n_traj_samples: 3
+  nfe: false
+  num_rnn_layers: 1
+  ode_method: dopri5
+  odeint_atol: 1.0e-05
+  odeint_rtol: 1.0e-05
+  output_dim: 1
+  recg_type: gru
+  rnn_units: 64
+  save_latent: false
+  seq_len: 12
+train:
+  batch_size: 64
+  debug: false
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:07:23
+  loss: mae
+  lr_decay: false
+  lr_decay_rate: 0.3
+  lr_decay_step: 5,20,40,70
+  lr_init: 0.003
+  mae_thresh: None
+  mape_thresh: 0.001
+  max_grad_norm: 5
+  output_dim: 1
+  real_value: true
+  weight_decay: 0

exp/PEMS08_STDEN_2025/09/03-01:08:24/config.yaml

@@ -0,0 +1,62 @@
+basic:
+  dataset: PEMS08
+  device: cuda:0
+  mode: train
+  model: STDEN
+  seed: 2025
+data:
+  add_day_in_week: true
+  add_time_in_day: true
+  batch_size: 32
+  column_wise: false
+  dataset_dir: data/PEMS08
+  days_per_week: 7
+  default_graph: true
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  horizon: 24
+  input_dim: 1
+  lag: 24
+  normalizer: std
+  num_nodes: 170
+  steps_per_day: 24
+  test_ratio: 0.2
+  tod: false
+  val_batch_size: 32
+  val_ratio: 0.2
+model:
+  filter_type: default
+  gcn_step: 2
+  horizon: 12
+  input_dim: 1
+  l1_decay: 0
+  latent_dim: 4
+  n_traj_samples: 3
+  nfe: false
+  num_rnn_layers: 1
+  ode_method: dopri5
+  odeint_atol: 1.0e-05
+  odeint_rtol: 1.0e-05
+  output_dim: 1
+  recg_type: gru
+  rnn_units: 64
+  save_latent: false
+  seq_len: 12
+train:
+  batch_size: 64
+  debug: false
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:08:24
+  loss: mae
+  lr_decay: false
+  lr_decay_rate: 0.3
+  lr_decay_step: 5,20,40,70
+  lr_init: 0.003
+  mae_thresh: None
+  mape_thresh: 0.001
+  max_grad_norm: 5
+  output_dim: 1
+  real_value: true
+  weight_decay: 0

exp/PEMS08_STDEN_2025/09/03-01:09:13/config.yaml

@@ -0,0 +1,62 @@
+basic:
+  dataset: PEMS08
+  device: cuda:0
+  mode: train
+  model: STDEN
+  seed: 2025
+data:
+  add_day_in_week: true
+  add_time_in_day: true
+  batch_size: 32
+  column_wise: false
+  dataset_dir: data/PEMS08
+  days_per_week: 7
+  default_graph: true
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  horizon: 24
+  input_dim: 1
+  lag: 24
+  normalizer: std
+  num_nodes: 170
+  steps_per_day: 24
+  test_ratio: 0.2
+  tod: false
+  val_batch_size: 32
+  val_ratio: 0.2
+model:
+  filter_type: default
+  gcn_step: 2
+  horizon: 12
+  input_dim: 1
+  l1_decay: 0
+  latent_dim: 4
+  n_traj_samples: 3
+  nfe: false
+  num_rnn_layers: 1
+  ode_method: dopri5
+  odeint_atol: 1.0e-05
+  odeint_rtol: 1.0e-05
+  output_dim: 1
+  recg_type: gru
+  rnn_units: 64
+  save_latent: false
+  seq_len: 12
+train:
+  batch_size: 64
+  debug: false
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:09:13
+  loss: mae
+  lr_decay: false
+  lr_decay_rate: 0.3
+  lr_decay_step: 5,20,40,70
+  lr_init: 0.003
+  mae_thresh: None
+  mape_thresh: 0.001
+  max_grad_norm: 5
+  output_dim: 1
+  real_value: true
+  weight_decay: 0

exp/PEMS08_STDEN_2025/09/03-01:09:53/config.yaml

@@ -0,0 +1,62 @@
+basic:
+  dataset: PEMS08
+  device: cuda:0
+  mode: train
+  model: STDEN
+  seed: 2025
+data:
+  add_day_in_week: true
+  add_time_in_day: true
+  batch_size: 32
+  column_wise: false
+  dataset_dir: data/PEMS08
+  days_per_week: 7
+  default_graph: true
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  horizon: 24
+  input_dim: 1
+  lag: 24
+  normalizer: std
+  num_nodes: 170
+  steps_per_day: 24
+  test_ratio: 0.2
+  tod: false
+  val_batch_size: 32
+  val_ratio: 0.2
+model:
+  filter_type: default
+  gcn_step: 2
+  horizon: 12
+  input_dim: 1
+  l1_decay: 0
+  latent_dim: 4
+  n_traj_samples: 3
+  nfe: false
+  num_rnn_layers: 1
+  ode_method: dopri5
+  odeint_atol: 1.0e-05
+  odeint_rtol: 1.0e-05
+  output_dim: 1
+  recg_type: gru
+  rnn_units: 64
+  save_latent: false
+  seq_len: 12
+train:
+  batch_size: 64
+  debug: false
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:09:53
+  loss: mae
+  lr_decay: false
+  lr_decay_rate: 0.3
+  lr_decay_step: 5,20,40,70
+  lr_init: 0.003
+  mae_thresh: None
+  mape_thresh: 0.001
+  max_grad_norm: 5
+  output_dim: 1
+  real_value: true
+  weight_decay: 0

main.py

@@ -3,8 +3,6 @@
 时空数据深度学习预测项目主程序
 专门处理时空数据格式 (batch_size, seq_len, num_nodes, features)
 """
-
-import os
 from utils.args_reader import config_loader
 import utils.init as init
 import torch

models/model_selector.py

@@ -1,6 +1,8 @@
-
+from models.STDEN.stden_model import STDENModel
 
 def model_selector(config):
     model_name = config['basic']['model']
     model = None
+    match model_name:
+        case 'STDEN': model = STDENModel(config)
     return model

trainer/ode_trainer.py

@@ -2,6 +2,8 @@ import math
 import os
 import time
 import copy
+import pandas as pd
+import numpy as np
 from tqdm import tqdm
 
 import torch
@@ -25,6 +27,10 @@ class Trainer:
         self.best_path = os.path.join(logger.dir_path, 'best_model.pth')
         self.best_test_path = os.path.join(logger.dir_path, 'best_test_model.pth')
         self.loss_figure_path = os.path.join(logger.dir_path, 'loss.png')
+        
+        # 用于收集nfe数据
+        self.c = []
+        self.res, self.keys = [], []
 
     def _run_epoch(self, epoch, dataloader, mode):
         if mode == 'train':
@@ -36,18 +42,29 @@ class Trainer:
 
         total_loss = 0
         epoch_time = time.time()
+        
+        # 清空nfe数据收集
+        if mode == 'train':
+            self.c.clear()
 
         with torch.set_grad_enabled(optimizer_step):
             with tqdm(total=len(dataloader), desc=f'{mode.capitalize()} Epoch {epoch}') as pbar:
                 for batch_idx, (data, target) in enumerate(dataloader):
                     label = target[..., :self.args['output_dim']]
-                    output = self.model(data).to(self.args['device'])
+                    output, fe = self.model(data)
 
                     if self.args['real_value']:
                         # 只对输出维度进行反归一化
                         output = self._inverse_transform_output(output)
 
                     loss = self.loss(output, label)
+                    
+                    # 收集nfe数据（仅在训练模式下）
+                    if mode == 'train':
+                        self.c.append([*fe, loss.item()])
+                        # 记录FE信息
+                        self.logger.logger.debug("FE: number - {}, time - {:.3f} s, err - {:.3f}".format(*fe, loss.item()))
+                    
                     if optimizer_step and self.optimizer is not None:
                         self.optimizer.zero_grad()
                         loss.backward()
@@ -69,6 +86,12 @@ class Trainer:
         avg_loss = total_loss / len(dataloader)
         self.logger.logger.info(
             f'{mode.capitalize()} Epoch {epoch}: average Loss: {avg_loss:.6f}, time: {time.time() - epoch_time:.2f} s')
+        
+        # 收集nfe数据（仅在训练模式下）
+        if mode == 'train':
+            self.res.append(pd.DataFrame(self.c, columns=['nfe', 'time', 'err']))
+            self.keys.append(epoch)
+            
         return avg_loss
 
     def _inverse_transform_output(self, output):
@@ -143,6 +166,10 @@ class Trainer:
                 best_test_model = copy.deepcopy(self.model.state_dict())
                 torch.save(best_test_model, self.best_test_path)
 
+        # 保存nfe数据（如果启用）
+        if hasattr(self.args, 'nfe') and bool(self.args.get('nfe', False)):
+            self._save_nfe_data()
+
         if not self.args['debug']:
             torch.save(best_model, self.best_path)
             torch.save(best_test_model, self.best_test_path)
@@ -164,17 +191,23 @@ class Trainer:
         if path:
             checkpoint = torch.load(path)
             model.load_state_dict(checkpoint['state_dict'])
-            model.to(args.device)
+            model.to(args['device'])
 
         model.eval()
         y_pred, y_true = [], []
+        
+        # 用于收集nfe数据
+        c = []
 
         with torch.no_grad():
             for data, target in data_loader:
                 label = target[..., :args['output_dim']]
-                output = model(data)
+                output, fe = model(data)
                 y_pred.append(output)
                 y_true.append(label)
+                
+                # 收集nfe数据
+                c.append([*fe, 0.0])  # 测试时没有loss，设为0
 
         if args['real_value']:
             # 只对输出维度进行反归一化
@@ -192,6 +225,10 @@ class Trainer:
         mae, rmse, mape = logger.all_metrics(y_pred, y_true, args['mae_thresh'], args['mape_thresh'])
         logger.logger.info(f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
 
+        # 保存nfe数据（如果启用）
+        if hasattr(args, 'nfe') and bool(args.get('nfe', False)):
+            Trainer._save_nfe_data_static(c, model, logger)
+
         # 只在需要时生成可视化图片
         if generate_viz:
             save_dir = logger.dir_path if hasattr(logger, 'dir_path') else './logs'
@@ -514,6 +551,26 @@ class Trainer:
         plt.savefig(summary_path, dpi=300, bbox_inches='tight')
         plt.close()
 
+    def _save_nfe_data(self):
+        """保存nfe数据到文件"""
+        if not self.res:
+            return
+            
+        res = pd.concat(self.res, keys=self.keys)
+        res.index.names = ['epoch', 'iter']
+        
+        # 获取模型配置参数
+        filter_type = getattr(self.model, 'filter_type', 'unknown')
+        atol = getattr(self.model, 'atol', 1e-5)
+        rtol = getattr(self.model, 'rtol', 1e-5)
+        
+        # 保存nfe数据
+        nfe_file = os.path.join(
+            self.logger.dir_path, 
+            'nfe_{}_a{}_r{}.pkl'.format(filter_type, int(atol*1e5), int(rtol*1e5)))
+        res.to_pickle(nfe_file)
+        self.logger.logger.info(f"NFE data saved to {nfe_file}")
+
     @staticmethod
     def _compute_sampling_threshold(global_step, k):
         return k / (k + math.exp(global_step / k))

trainer/trainer_selector.py

@@ -1,11 +1,14 @@
 from trainer.trainer import Trainer
+from trainer.ode_trainer import Trainer as ode_trainer
 
 def select_trainer(config, model, loss, optimizer, train_loader, val_loader, test_loader, scaler,
                    lr_scheduler, kwargs):
     model_name = config['basic']['model']
     selected_Trainer = None
     match model_name:
+        case 'STDEN': selected_Trainer = ode_trainer(config, model, loss, optimizer,
+                               train_loader, val_loader, test_loader, scaler, lr_scheduler)
         case _: selected_Trainer = Trainer(config, model, loss, optimizer,
-                               train_loader, val_loader, test_loader, scaler,lr_scheduler)
+                               train_loader, val_loader, test_loader, scaler, lr_scheduler)
     if selected_Trainer is None: raise NotImplementedError
     return selected_Trainer