添加models/gpt2/到.gitignore以屏蔽GPT2模型文件

.gitignore

@@ -160,3 +160,8 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
 
+.STDEN/
+.data/PEMS08/
+exp/
+STDEN/
+models/gpt2/

.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>

.idea/workspace.xml

@@ -0,0 +1,216 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="AutoImportSettings">
+    <option name="autoReloadType" value="SELECTIVE" />
+  </component>
+  <component name="ChangeListManager">
+    <list default="true" id="8b1aea27-342c-41a7-b776-2aba4fceda0d" name="更改" comment="">
+      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/STDEN" beforeDir="false" afterPath="$PROJECT_DIR$/STDEN" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/STDEN/lib/utils.py" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/STDEN/stden_eval.py" beforeDir="false" afterPath="$PROJECT_DIR$/STDEN/stden_eval.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/STDEN/stden_train.py" beforeDir="false" afterPath="$PROJECT_DIR$/STDEN/stden_train.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/models/STGODE/STGODE.py" beforeDir="false" afterPath="$PROJECT_DIR$/models/STGODE/STGODE.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/models/STGODE/adj.py" beforeDir="false" afterPath="$PROJECT_DIR$/models/STGODE/adj.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/models/model_selector.py" beforeDir="false" afterPath="$PROJECT_DIR$/models/model_selector.py" afterDir="false" />
+    </list>
+    <option name="SHOW_DIALOG" value="false" />
+    <option name="HIGHLIGHT_CONFLICTS" value="true" />
+    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
+    <option name="LAST_RESOLUTION" value="IGNORE" />
+  </component>
+  <component name="FileTemplateManagerImpl">
+    <option name="RECENT_TEMPLATES">
+      <list>
+        <option value="Python Script" />
+      </list>
+    </option>
+  </component>
+  <component name="Git.Settings">
+    <excluded-from-favorite>
+      <branch-storage>
+        <map>
+          <entry type="LOCAL">
+            <value>
+              <list>
+                <branch-info repo="$PROJECT_DIR$" source="main" />
+              </list>
+            </value>
+          </entry>
+        </map>
+      </branch-storage>
+    </excluded-from-favorite>
+    <option name="RECENT_BRANCH_BY_REPOSITORY">
+      <map>
+        <entry key="$PROJECT_DIR$" value="main" />
+      </map>
+    </option>
+    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
+    <option name="ROOT_SYNC" value="DONT_SYNC" />
+  </component>
+  <component name="MarkdownSettingsMigration">
+    <option name="stateVersion" value="1" />
+  </component>
+  <component name="ProjectColorInfo">{
+  &quot;associatedIndex&quot;: 3
+}</component>
+  <component name="ProjectId" id="3264JlB7seHXuXCCcdmTyEsXI45" />
+  <component name="ProjectViewState">
+    <option name="hideEmptyMiddlePackages" value="true" />
+    <option name="showLibraryContents" value="true" />
+  </component>
+  <component name="PropertiesComponent"><![CDATA[{
+  "keyToString": {
+    "Python.STDEN.executor": "Debug",
+    "Python.STGODE.executor": "Run",
+    "Python.main.executor": "Run",
+    "RunOnceActivity.OpenProjectViewOnStart": "true",
+    "RunOnceActivity.ShowReadmeOnStart": "true",
+    "git-widget-placeholder": "STGODE",
+    "last_opened_file_path": "/home/czzhangheng/code/Project-I/main.py",
+    "node.js.detected.package.eslint": "true",
+    "node.js.detected.package.tslint": "true",
+    "node.js.selected.package.eslint": "(autodetect)",
+    "node.js.selected.package.tslint": "(autodetect)",
+    "nodejs_package_manager_path": "npm",
+    "vue.rearranger.settings.migration": "true"
+  }
+}]]></component>
+  <component name="RdControllerToolWindowsLayoutState" isNewUi="true">
+    <layout>
+      <window_info id="Space Code Reviews" show_stripe_button="false" />
+      <window_info id="Bookmarks" show_stripe_button="false" side_tool="true" />
+      <window_info id="Merge Requests" show_stripe_button="false" />
+      <window_info id="Commit_Guest" show_stripe_button="false" />
+      <window_info id="Pull Requests" show_stripe_button="false" />
+      <window_info id="Learn" show_stripe_button="false" />
+      <window_info active="true" content_ui="combo" id="Project" order="0" visible="true" weight="0.27326387" />
+      <window_info id="Commit" order="1" weight="0.25" />
+      <window_info id="Structure" order="2" side_tool="true" weight="0.25" />
+      <window_info anchor="bottom" id="Database Changes" show_stripe_button="false" />
+      <window_info anchor="bottom" id="TypeScript" show_stripe_button="false" />
+      <window_info anchor="bottom" id="Debug" weight="0.32989067" />
+      <window_info anchor="bottom" id="TODO" show_stripe_button="false" />
+      <window_info anchor="bottom" id="File Transfer" show_stripe_button="false" />
+      <window_info active="true" anchor="bottom" id="Run" visible="true" weight="0.32989067" />
+      <window_info anchor="bottom" id="Version Control" order="0" />
+      <window_info anchor="bottom" id="Problems" order="1" />
+      <window_info anchor="bottom" id="Problems View" order="2" weight="0.33686176" />
+      <window_info anchor="bottom" id="Terminal" order="3" weight="0.32989067" />
+      <window_info anchor="bottom" id="Services" order="4" />
+      <window_info anchor="bottom" id="Python Packages" order="5" weight="0.1" />
+      <window_info anchor="bottom" id="Python Console" order="6" weight="0.1" />
+      <window_info anchor="right" id="Endpoints" show_stripe_button="false" />
+      <window_info anchor="right" id="SciView" show_stripe_button="false" />
+      <window_info anchor="right" content_ui="combo" id="Notifications" order="0" weight="0.25" />
+      <window_info anchor="right" id="AIAssistant" order="1" weight="0.25" />
+      <window_info anchor="right" id="Database" order="2" weight="0.25" />
+      <window_info anchor="right" id="Gradle" order="3" weight="0.25" />
+      <window_info anchor="right" id="Maven" order="4" weight="0.25" />
+      <window_info anchor="right" id="Plots" order="5" weight="0.1" />
+    </layout>
+  </component>
+  <component name="RecentsManager">
+    <key name="CopyFile.RECENT_KEYS">
+      <recent name="$PROJECT_DIR$/trainer" />
+      <recent name="$PROJECT_DIR$/configs/STDEN" />
+      <recent name="$PROJECT_DIR$/models/STDEN" />
+    </key>
+    <key name="MoveFile.RECENT_KEYS">
+      <recent name="$PROJECT_DIR$/models/STDEN" />
+    </key>
+  </component>
+  <component name="RunManager" selected="Python.STGODE">
+    <configuration name="STDEN" type="PythonConfigurationType" factoryName="Python">
+      <module name="Project-I" />
+      <option name="ENV_FILES" value="" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="SDK_NAME" value="TS" />
+      <option name="WORKING_DIRECTORY" value="" />
+      <option name="IS_MODULE_SDK" value="false" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/main.py" />
+      <option name="PARAMETERS" value="--config ./configs/STDEN/PEMS08.yaml" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <configuration name="STGODE" type="PythonConfigurationType" factoryName="Python">
+      <module name="Project-I" />
+      <option name="ENV_FILES" value="" />
+      <option name="INTERPRETER_OPTIONS" value="" />
+      <option name="PARENT_ENVS" value="true" />
+      <envs>
+        <env name="PYTHONUNBUFFERED" value="1" />
+      </envs>
+      <option name="SDK_HOME" value="" />
+      <option name="SDK_NAME" value="TS" />
+      <option name="WORKING_DIRECTORY" value="" />
+      <option name="IS_MODULE_SDK" value="false" />
+      <option name="ADD_CONTENT_ROOTS" value="true" />
+      <option name="ADD_SOURCE_ROOTS" value="true" />
+      <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+      <option name="SCRIPT_NAME" value="$PROJECT_DIR$/main.py" />
+      <option name="PARAMETERS" value="--config ./configs/STGODE/PEMS08.yaml" />
+      <option name="SHOW_COMMAND_LINE" value="false" />
+      <option name="EMULATE_TERMINAL" value="false" />
+      <option name="MODULE_MODE" value="false" />
+      <option name="REDIRECT_INPUT" value="false" />
+      <option name="INPUT_FILE" value="" />
+      <method v="2" />
+    </configuration>
+    <list>
+      <item itemvalue="Python.STDEN" />
+      <item itemvalue="Python.STGODE" />
+    </list>
+  </component>
+  <component name="SharedIndexes">
+    <attachedChunks>
+      <set>
+        <option value="bundled-python-sdk-eebebe6c2be4-b11f5e8da5ad-com.jetbrains.pycharm.pro.sharedIndexes.bundled-PY-233.15325.20" />
+      </set>
+    </attachedChunks>
+  </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="应用程序级" UseSingleDictionary="true" transferred="true" />
+  <component name="TaskManager">
+    <task active="true" id="Default" summary="默认任务">
+      <changelist id="8b1aea27-342c-41a7-b776-2aba4fceda0d" name="更改" comment="" />
+      <created>1756727620810</created>
+      <option name="number" value="Default" />
+      <option name="presentableId" value="Default" />
+      <updated>1756727620810</updated>
+      <workItem from="1756727623101" duration="4721000" />
+      <workItem from="1756856673845" duration="652000" />
+      <workItem from="1756864144998" duration="1063000" />
+    </task>
+    <servers />
+  </component>
+  <component name="TypeScriptGeneratedFilesManager">
+    <option name="version" value="3" />
+  </component>
+  <component name="XDebuggerManager">
+    <breakpoint-manager>
+      <breakpoints>
+        <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
+          <url>file://$PROJECT_DIR$/models/STDEN/stden_model.py</url>
+          <line>131</line>
+          <option name="timeStamp" value="5" />
+        </line-breakpoint>
+      </breakpoints>
+    </breakpoint-manager>
+  </component>
+  <component name="com.intellij.coverage.CoverageDataManagerImpl">
+    <SUITE FILE_PATH="coverage/Project_I$main.coverage" NAME="STDEN 覆盖结果" MODIFIED="1756832980407" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="" />
+    <SUITE FILE_PATH="coverage/Project_I$STGODE.coverage" NAME="STGODE 覆盖结果" MODIFIED="1756864828915" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="" />
+  </component>
+</project>

STDEN

@@ -0,0 +1 @@
+Subproject commit e50a1ba6d70528b3e684c85f316aed05bb5085f2

configs/STDEN/PEMS08.yaml

@@ -0,0 +1,65 @@
+basic:
+  device: cuda:0
+  dataset: PEMS08
+  model: STDEN
+  mode: train
+  seed: 2025
+
+data:
+  dataset_dir: data/PEMS08
+  val_batch_size: 32
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  num_nodes: 170
+  batch_size: 32
+  input_dim: 1
+  lag: 24
+  horizon: 24
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 24
+  days_per_week: 7
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1 
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5 
+  odeint_atol: 0.00001 
+  odeint_rtol: 0.00001 
+  rnn_units: 64 
+  num_rnn_layers: 1
+  gcn_step: 2
+  filter_type: default # unkP IncP default
+  recg_type: gru 
+  save_latent: false
+  nfe: false
+
+train:
+  loss: mae
+  batch_size: 64
+  epochs: 100
+  lr_init: 0.003
+  mape_thresh: 0.001
+  mae_thresh: None
+  debug: False
+  output_dim: 1
+  weight_decay: 0
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+  

configs/STDEN/stde_gt.yaml

@@ -0,0 +1,44 @@
+---
+log_base_dir: logs/BJ_GM
+log_level: INFO
+
+data:
+  batch_size: 32
+  dataset_dir: data/BJ_GM
+  val_batch_size: 32
+  graph_pkl_filename: data/sensor_graph/adj_GM.npy
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1 
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5 
+  odeint_atol: 0.00001 
+  odeint_rtol: 0.00001 
+  rnn_units: 64 
+  num_rnn_layers: 1
+  gcn_step: 2
+  filter_type: default # unkP IncP default
+  recg_type: gru 
+  save_latent: false
+  nfe: false
+
+train:
+  base_lr: 0.01
+  dropout: 0
+  load: 0 
+  epoch: 0 
+  epochs: 100
+  epsilon: 1.0e-3
+  lr_decay_ratio: 0.1
+  max_grad_norm: 5
+  min_learning_rate: 2.0e-06
+  optimizer: adam
+  patience: 20
+  steps: [20, 30, 40, 50]
+  test_every_n_epochs: 5
+  

configs/STDEN/stde_wrs.yaml

@@ -0,0 +1,44 @@
+---
+log_base_dir: logs/BJ_RM
+log_level: INFO
+
+data:
+  batch_size: 32
+  dataset_dir: data/BJ_RM
+  val_batch_size: 32
+  graph_pkl_filename: data/sensor_graph/adj_RM.npy
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5
+  odeint_atol: 0.00001
+  odeint_rtol: 0.00001
+  rnn_units: 64 # for recognition
+  num_rnn_layers: 1 
+  gcn_step: 2 
+  filter_type: default # unkP IncP default
+  recg_type: gru
+  save_latent: false
+  nfe: false
+
+train:
+  base_lr: 0.01
+  dropout: 0
+  load: 0 # 0 for not load
+  epoch: 0
+  epochs: 100
+  epsilon: 1.0e-3
+  lr_decay_ratio: 0.1
+  max_grad_norm: 5
+  min_learning_rate: 2.0e-06
+  optimizer: adam
+  patience: 20
+  steps: [20, 30, 40, 50]
+  test_every_n_epochs: 5
+  

configs/STDEN/stde_zgc.yaml

@@ -0,0 +1,44 @@
+---
+log_base_dir: logs/BJ_XZ
+log_level: INFO
+
+data:
+  batch_size: 32
+  dataset_dir: data/BJ_XZ
+  val_batch_size: 32
+  graph_pkl_filename: data/sensor_graph/adj_XZ.npy
+
+model:
+  l1_decay: 0
+  seq_len: 12
+  horizon: 12
+  input_dim: 1
+  output_dim: 1
+  latent_dim: 4
+  n_traj_samples: 3
+  ode_method: dopri5
+  odeint_atol: 0.00001
+  odeint_rtol: 0.00001
+  rnn_units: 64
+  num_rnn_layers: 1
+  gcn_step: 2
+  filter_type: default # unkP IncP default
+  recg_type: gru
+  save_latent: false
+  nfe: false
+
+train:
+  base_lr: 0.01
+  dropout: 0
+  load: 0 # 0 for not load
+  epoch: 0 
+  epochs: 100
+  epsilon: 1.0e-3
+  lr_decay_ratio: 0.1
+  max_grad_norm: 5
+  min_learning_rate: 2.0e-06
+  optimizer: adam
+  patience: 20
+  steps: [20, 30, 40, 50]
+  test_every_n_epochs: 5
+  

configs/STGODE/PEMS08.yaml

@@ -0,0 +1,60 @@
+basic:
+  device: cuda:0
+  dataset: PEMS08
+  model: STGODE
+  mode: train
+  seed: 2025
+
+data:
+  dataset_dir: data/PEMS08
+  val_batch_size: 32
+  graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
+  num_nodes: 170
+  batch_size: 64
+  input_dim: 1
+  lag: 12
+  horizon: 12
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 24
+  days_per_week: 7
+
+model:
+  input_dim: 1
+  output_dim: 1
+  history: 12
+  horizon: 12
+  num_features: 1
+  rnn_units: 64
+  sigma1: 0.1
+  sigma2: 10
+  thres1: 0.6
+  thres2: 0.5
+
+
+train:
+  loss: mae
+  batch_size: 64
+  epochs: 100
+  lr_init: 0.003
+  mape_thresh: 0.001
+  mae_thresh: None
+  debug: False
+  output_dim: 1
+  weight_decay: 0
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+  log_step: 3000
+  

data/PEMS08/PEMS08.csv

@@ -0,0 +1,296 @@
+from,to,cost
+9,153,310.6
+153,62,330.9
+62,111,332.9
+111,11,324.2
+11,28,336.0
+28,169,133.7
+138,135,354.7
+135,133,387.9
+133,163,337.1
+163,20,352.0
+20,19,420.8
+19,14,351.3
+14,39,340.2
+39,164,350.3
+164,167,365.2
+167,70,359.0
+70,59,388.2
+59,58,305.7
+58,67,294.4
+67,66,299.5
+66,55,313.3
+55,53,332.1
+53,150,278.9
+150,61,308.4
+61,64,311.4
+64,63,243.6
+47,65,372.8
+65,48,319.4
+48,49,309.7
+49,54,320.5
+54,56,318.3
+56,57,297.9
+57,68,293.5
+68,69,342.5
+69,60,318.0
+60,17,305.9
+17,5,321.4
+5,18,402.2
+18,22,447.4
+22,30,377.5
+30,29,417.7
+29,21,360.8
+21,132,407.6
+132,134,386.9
+134,136,350.2
+123,121,326.3
+121,140,385.2
+140,118,393.0
+118,96,296.7
+96,94,398.2
+94,86,337.1
+86,78,473.8
+78,46,353.4
+46,152,385.7
+152,157,350.0
+157,35,354.4
+35,77,356.1
+77,52,354.2
+52,3,357.8
+3,16,382.4
+16,0,55.7
+42,12,335.1
+12,139,328.8
+139,168,412.6
+168,154,337.3
+154,143,370.7
+143,10,6.3
+107,105,354.6
+105,104,386.9
+104,148,362.1
+148,97,316.3
+97,101,380.7
+101,137,361.4
+137,102,365.5
+102,24,375.5
+24,166,312.2
+129,156,256.1
+156,33,329.1
+33,32,356.5
+91,89,405.6
+89,147,347.0
+147,15,351.7
+15,44,339.5
+44,41,350.8
+41,43,322.6
+43,100,338.9
+100,83,347.9
+83,87,327.2
+87,88,321.0
+88,75,335.8
+75,51,384.8
+51,73,391.1
+73,71,289.3
+31,155,260.0
+155,34,320.4
+34,128,393.3
+145,115,399.4
+115,112,328.1
+112,8,469.4
+8,117,816.2
+117,125,397.1
+125,127,372.7
+127,109,380.5
+109,161,355.5
+161,110,367.7
+110,160,102.0
+72,159,342.9
+159,50,383.3
+50,74,354.1
+74,82,350.2
+82,81,335.4
+81,99,391.6
+99,84,354.9
+84,13,306.4
+13,40,327.4
+40,162,413.9
+162,108,301.9
+108,146,317.8
+146,85,376.6
+85,90,347.0
+26,27,341.6
+27,6,359.4
+6,149,417.8
+149,126,388.0
+126,124,384.3
+124,7,763.3
+7,114,323.1
+114,113,351.6
+113,116,411.9
+116,144,262.0
+25,103,350.2
+103,23,376.3
+23,165,396.4
+165,38,381.0
+38,92,368.0
+92,37,336.3
+37,130,357.8
+130,106,532.3
+106,131,166.5
+1,2,371.6
+2,4,338.1
+4,76,429.0
+76,36,366.1
+36,158,344.5
+158,151,350.1
+151,45,358.8
+45,93,340.9
+93,80,329.9
+80,79,384.1
+79,95,335.7
+95,98,320.9
+98,119,340.3
+119,120,376.8
+120,122,393.1
+122,141,428.7
+141,142,359.3
+30,165,379.6
+165,29,41.7
+29,38,343.3
+65,72,297.9
+72,48,21.5
+17,153,375.6
+153,5,256.3
+153,62,330.9
+18,6,499.4
+6,22,254.0
+22,149,185.4
+22,4,257.9
+4,30,236.8
+30,76,307.0
+95,98,320.9
+98,144,45.1
+45,93,340.9
+93,106,112.2
+162,151,113.6
+151,108,192.9
+108,45,359.8
+146,92,311.2
+92,85,343.9
+85,37,373.2
+13,169,326.2
+169,40,96.1
+124,13,460.7
+13,7,305.5
+7,40,624.1
+124,169,145.2
+169,7,631.5
+90,132,152.2
+26,32,106.7
+9,129,148.3
+129,153,219.6
+31,26,116.0
+26,155,270.7
+9,128,142.2
+128,153,215.0
+153,167,269.7
+167,62,64.8
+62,70,332.6
+124,169,145.2
+169,7,631.5
+44,169,397.8
+169,41,124.0
+44,124,375.7
+124,41,243.9
+41,7,519.4
+6,14,289.3
+14,149,259.0
+149,39,206.9
+144,98,45.1
+19,4,326.8
+4,14,178.6
+14,76,299.0
+15,151,136.4
+151,44,203.1
+45,106,260.6
+106,93,112.2
+20,165,132.5
+165,19,289.2
+89,92,323.2
+92,147,321.9
+147,37,48.2
+133,91,152.8
+91,163,313.6
+150,71,221.1
+71,61,89.6
+78,107,143.9
+107,46,236.3
+104,147,277.5
+147,148,84.7
+20,101,201.2
+101,19,534.4
+19,137,245.5
+8,42,759.5
+42,117,58.9
+44,42,342.3
+42,41,102.5
+44,8,789.1
+8,41,657.4
+41,117,160.5
+168,167,172.4
+167,154,165.2
+143,128,81.9
+128,10,88.2
+118,145,250.6
+145,96,85.1
+15,152,135.0
+152,44,204.6
+19,77,320.7
+77,14,299.8
+14,52,127.6
+14,127,314.8
+127,39,280.4
+39,109,237.0
+31,160,116.5
+160,155,272.4
+133,91,152.8
+91,163,313.6
+150,71,221.1
+71,61,89.6
+32,160,107.7
+72,162,3274.4
+162,13,554.5
+162,40,413.9
+65,72,297.9
+72,48,21.5
+13,42,319.8
+42,40,40.7
+8,42,759.5
+42,117,58.9
+8,13,450.3
+13,117,378.5
+117,40,64.0
+46,162,391.6
+162,152,115.3
+152,108,191.4
+104,108,375.9
+108,148,311.6
+148,146,80.0
+21,90,396.9
+90,132,152.2
+101,29,252.3
+29,137,110.7
+77,22,353.8
+22,52,227.8
+52,30,186.6
+127,18,425.2
+18,109,439.1
+109,22,135.5
+168,17,232.7
+17,154,294.2
+154,5,166.3
+78,107,143.9
+107,46,236.3
+118,145,250.6
+145,96,85.1

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

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/STDEN/STDEN_modules.md

@@ -0,0 +1,88 @@
+### STDEN 模块与执行流（缩进层级表）
+
+模块 | 类/函数 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | STDENModel.forward | inputs: (seq_len, batch_size, num_edges x input_dim) | outputs: (horizon, batch_size, num_edges x output_dim); fe: (nfe:int, time:float)
+1.1 | Encoder_z0_RNN.forward | (seq_len, batch_size, num_edges x input_dim) | mean: (1, batch_size, num_nodes x latent_dim); std: (1, batch_size, num_nodes x latent_dim)
+1.1.1 | utils.sample_standard_gaussian | mu: (n_traj, batch, num_nodes x latent_dim); sigma: 同形状 | z0: (n_traj, batch, num_nodes x latent_dim)
+1.2 | DiffeqSolver.forward | first_point: (n_traj, batch, num_nodes x latent_dim); t: (horizon,) | sol_ys: (horizon, n_traj, batch, num_nodes x latent_dim); fe: (nfe:int, time:float)
+1.2.1 | ODEFunc.forward | t_local: 标量/1D; y: (B, num_nodes x latent_dim) | dy/dt: (B, num_nodes x latent_dim)
+1.3 | Decoder.forward | (horizon, n_traj, batch, num_nodes x latent_dim) | (horizon, batch, num_edges x output_dim)
+
+---
+
+### 细节模块 — Encoder_z0_RNN
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 重塑到边批 | (seq_len, batch, num_edges x input_dim) | (seq_len, batch, num_edges, input_dim)
+2 | 合并边到批 | (seq_len, batch, num_edges, input_dim) | (seq_len, batch x num_edges, input_dim)
+3 | GRU 序列编码 | 同上 | (seq_len, batch x num_edges, rnn_units)
+4 | 取最后时间步 | 同上 | (batch x num_edges, rnn_units)
+5 | 还原边维 | (batch x num_edges, rnn_units) | (batch, num_edges, rnn_units)
+6 | 转置 + 边→节点映射 | (batch, num_edges, rnn_units) 经 inv_grad | (batch, num_nodes, rnn_units)
+7 | 全连接映射到 2x latent | (batch, num_nodes, rnn_units) | (batch, num_nodes, 2 x latent_dim)
+8 | 拆分均值/标准差 | 同上 | mean/std: (batch, num_nodes, latent_dim)
+9 | 展平并加时间维 | (batch, num_nodes, latent_dim) | (1, batch, num_nodes x latent_dim)
+
+备注：inv_grad 来源于 `utils.graph_grad(adj).T` 并做缩放；`hiddens_to_z0` 为两层 MLP + Tanh 后线性映射至 2 x latent_dim。
+
+---
+
+### 细节模块 — 采样（utils.sample_standard_gaussian）
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 重复到 n_traj | mean/std: (1, batch, N·Z) → 重复 | (n_traj, batch, N·Z)
+2 | 重参数化采样 | mu, sigma | z0: (n_traj, batch, N·Z)
+
+其中 N·Z = num_nodes x latent_dim。
+
+---
+
+### 细节模块 — DiffeqSolver（含 ODEFunc 调用）
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 合并样本维度 | first_point: (n_traj, batch, N·Z) | (n_traj x batch, N·Z)
+2 | ODE 积分 | t: (horizon,), y0 | pred_y: (horizon, n_traj x batch, N·Z)
+3 | 还原维度 | 同上 | (horizon, n_traj, batch, N·Z)
+4 | 统计代价 | odefunc.nfe, elapsed_time | fe: (nfe:int, time:float)
+
+ODEFunc 默认（filter_type="default"）为扩散过程：随机游走支持 + 多阶图卷积门控。
+
+---
+
+### 细节模块 — ODEFunc（默认扩散过程）
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 形状整理 | y: (B, N·Z) → (B, N, Z) | (B, N, Z)
+2 | 多阶图卷积 _gconv | (B, N, Z) | (B, N, Z') 按需设置 Z'（通常保持 Z）
+3 | 门控 θ | _gconv(..., output=latent_dim) → Sigmoid | θ: (B, N·Z)
+4 | 生成场 ode_func_net | 堆叠 _gconv + 激活 | f(y): (B, N·Z)
+5 | 右端梯度 | - θ ⊙ f(y) | dy/dt: (B, N·Z)
+
+说明：
+- 支撑矩阵来自 `utils.calculate_random_walk_matrix(adj)`（正向/反向）并构造稀疏 Chebyshev 递推的多阶通道。
+- 若 `filter_type="unkP"`，则使用 `create_net` 的全连接网络在节点域逐点计算梯度。
+
+---
+
+### 细节模块 — Decoder
+
+步骤 | 操作 | 输入 (shape) | 输出 (shape)
+--- | --- | --- | ---
+1 | 重塑到节点域 | (T, S, B, N·Z) → (T, S, B, N, Z) | (T, S, B, N, Z)
+2 | 节点→边映射 | 乘以 graph_grad (N, E) | (T, S, B, Z, E)
+3 | 轨迹与通道均值 | 对 S 和 Z 维做均值 | (T, B, E)
+4 | 展平到输出维 | 考虑 output_dim（通常为 1） | (T, B, E x output_dim)
+
+符号：T=horizon，S=n_traj_samples，N=num_nodes，E=num_edges，Z=latent_dim，B=batch。
+
+---
+
+### 备注与约定
+- 内部采用边展平后的时序输入：`(seq_len, batch, num_edges x input_dim)`。
+- 图算子：`utils.graph_grad(adj)` 形状 `(N, E)`；`utils.calculate_random_walk_matrix(adj)` 生成随机游走稀疏矩阵用于图卷积。
+- 关键超参数（由配置传入）：`latent_dim`, `rnn_units`, `gcn_step`, `n_traj_samples`, `ode_method`, `horizon`, `input_dim`, `output_dim`。

models/STDEN/diffeq_solver.py

@@ -0,0 +1,49 @@
+import torch
+import torch.nn as nn
+import time 
+
+from torchdiffeq import odeint
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class DiffeqSolver(nn.Module):
+    def __init__(self, odefunc, method, latent_dim, 
+            odeint_rtol = 1e-4, odeint_atol = 1e-5):
+        nn.Module.__init__(self)
+
+        self.ode_method = method
+        self.odefunc = odefunc
+        self.latent_dim = latent_dim
+
+        self.rtol = odeint_rtol
+        self.atol = odeint_atol
+
+    def forward(self, first_point, time_steps_to_pred):
+        """
+        Decoder the trajectory through the ODE Solver.
+
+        :param time_steps_to_pred: horizon
+        :param first_point: (n_traj_samples, batch_size, num_nodes * latent_dim)
+        :return: pred_y: # shape (horizon, n_traj_samples, batch_size, self.num_nodes * self.output_dim)
+        """
+        n_traj_samples, batch_size = first_point.size()[0], first_point.size()[1] 
+        first_point = first_point.reshape(n_traj_samples * batch_size, -1) # reduce the complexity by merging dimension
+        
+        # pred_y shape: (horizon, n_traj_samples * batch_size, num_nodes * latent_dim)
+        start_time = time.time()
+        self.odefunc.nfe = 0
+        pred_y = odeint(self.odefunc, 
+                            first_point, 
+                            time_steps_to_pred, 
+                            rtol=self.rtol, 
+                            atol=self.atol,
+                            method=self.ode_method)
+        time_fe = time.time() - start_time
+        
+        # pred_y shape: (horizon, n_traj_samples, batch_size, num_nodes * latent_dim)
+        pred_y = pred_y.reshape(pred_y.size()[0], n_traj_samples, batch_size, -1)
+        # assert(pred_y.size()[1] == n_traj_samples)
+        # assert(pred_y.size()[2] == batch_size)
+        
+        return pred_y, (self.odefunc.nfe, time_fe)
+        

models/STDEN/ode_func.py

@@ -0,0 +1,165 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+from models.STDEN import utils
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class LayerParams:
+    def __init__(self, rnn_network: nn.Module, layer_type: str):
+        self._rnn_network = rnn_network
+        self._params_dict = {}
+        self._biases_dict = {}
+        self._type = layer_type
+
+    def get_weights(self, shape):
+        if shape not in self._params_dict:
+            nn_param = nn.Parameter(torch.empty(*shape, device=device))
+            nn.init.xavier_normal_(nn_param)
+            self._params_dict[shape] = nn_param
+            self._rnn_network.register_parameter('{}_weight_{}'.format(self._type, str(shape)),
+                                                 nn_param)
+        return self._params_dict[shape]
+
+    def get_biases(self, length, bias_start=0.0):
+        if length not in self._biases_dict:
+            biases = nn.Parameter(torch.empty(length, device=device))
+            nn.init.constant_(biases, bias_start)
+            self._biases_dict[length] = biases
+            self._rnn_network.register_parameter('{}_biases_{}'.format(self._type, str(length)),
+                                                 biases)
+
+        return self._biases_dict[length]
+
+class ODEFunc(nn.Module):
+    def __init__(self, num_units, latent_dim, adj_mx, gcn_step, num_nodes, 
+                gen_layers=1, nonlinearity='tanh', filter_type="default"):
+        """
+        :param num_units: dimensionality of the hidden layers 
+        :param latent_dim: dimensionality used for ODE (input and output). Analog of a continous latent state
+        :param adj_mx:
+        :param gcn_step:
+        :param num_nodes:
+        :param gen_layers: hidden layers in each ode func. 
+        :param nonlinearity:
+        :param filter_type: default
+        :param use_gc_for_ru: whether to use Graph convolution to calculate the reset and update gates.
+        """
+        super(ODEFunc, self).__init__()
+        self._activation = torch.tanh if nonlinearity == 'tanh' else torch.relu
+        
+        self._num_nodes = num_nodes
+        self._num_units = num_units # hidden dimension
+        self._latent_dim = latent_dim
+        self._gen_layers = gen_layers
+        self.nfe = 0
+        
+        self._filter_type = filter_type
+        if(self._filter_type == "unkP"):
+            ode_func_net = utils.create_net(latent_dim, latent_dim, n_units=num_units)
+            utils.init_network_weights(ode_func_net)
+            self.gradient_net = ode_func_net
+        else:
+            self._gcn_step = gcn_step
+            self._gconv_params = LayerParams(self, 'gconv')
+            self._supports = []
+            supports = []
+            supports.append(utils.calculate_random_walk_matrix(adj_mx).T)
+            supports.append(utils.calculate_random_walk_matrix(adj_mx.T).T)
+            
+            for support in supports:
+                self._supports.append(self._build_sparse_matrix(support))
+    
+    @staticmethod
+    def _build_sparse_matrix(L):
+        L = L.tocoo()
+        indices = np.column_stack((L.row, L.col))
+        # this is to ensure row-major ordering to equal torch.sparse.sparse_reorder(L)
+        indices = indices[np.lexsort((indices[:, 0], indices[:, 1]))]
+        L = torch.sparse_coo_tensor(indices.T, L.data, L.shape, device=device)
+        return L
+    
+    def forward(self, t_local, y, backwards = False):
+        """
+		Perform one step in solving ODE. Given current data point y and current time point t_local, returns gradient dy/dt at this time point
+
+		t_local: current time point
+		y: value at the current time point, shape (B, num_nodes * latent_dim)
+
+        :return
+        - Output: A `2-D` tensor with shape `(B, num_nodes * latent_dim)`.
+        """
+        self.nfe += 1
+        grad = self.get_ode_gradient_nn(t_local, y)
+        if backwards:
+            grad = -grad
+        return grad
+    
+    def get_ode_gradient_nn(self, t_local, inputs):
+        if(self._filter_type == "unkP"):
+            grad = self._fc(inputs)
+        elif (self._filter_type == "IncP"):
+            grad = - self.ode_func_net(inputs)
+        else: # default is diffusion process
+            # theta shape: (B, num_nodes * latent_dim)
+            theta = torch.sigmoid(self._gconv(inputs, self._latent_dim, bias_start=1.0)) 
+            grad = - theta * self.ode_func_net(inputs)
+        return grad
+
+    def ode_func_net(self, inputs):
+        c = inputs
+        for i in range(self._gen_layers):
+            c = self._gconv(c, self._num_units)
+            c = self._activation(c)
+        c = self._gconv(c, self._latent_dim)
+        c = self._activation(c)
+        return c
+    
+    def _fc(self, inputs):
+        batch_size = inputs.size()[0]
+        grad = self.gradient_net(inputs.view(batch_size * self._num_nodes, self._latent_dim))
+        return grad.reshape(batch_size, self._num_nodes * self._latent_dim) # (batch_size, num_nodes, latent_dim)
+    
+    @staticmethod
+    def _concat(x, x_):
+        x_ = x_.unsqueeze(0)
+        return torch.cat([x, x_], dim=0)
+
+    def _gconv(self, inputs, output_size, bias_start=0.0):
+        # Reshape input and state to (batch_size, num_nodes, input_dim/state_dim)
+        batch_size = inputs.shape[0]
+        inputs = torch.reshape(inputs, (batch_size, self._num_nodes, -1))
+        # state = torch.reshape(state, (batch_size, self._num_nodes, -1))
+        # inputs_and_state = torch.cat([inputs, state], dim=2)
+        input_size = inputs.size(2)
+
+        x = inputs
+        x0 = x.permute(1, 2, 0)  # (num_nodes, total_arg_size, batch_size)
+        x0 = torch.reshape(x0, shape=[self._num_nodes, input_size * batch_size])
+        x = torch.unsqueeze(x0, 0)
+
+        if self._gcn_step == 0:
+            pass
+        else:
+            for support in self._supports:
+                x1 = torch.sparse.mm(support, x0)
+                x = self._concat(x, x1)
+
+                for k in range(2, self._gcn_step + 1):
+                    x2 = 2 * torch.sparse.mm(support, x1) - x0
+                    x = self._concat(x, x2)
+                    x1, x0 = x2, x1
+
+        num_matrices = len(self._supports) * self._gcn_step + 1  # Adds for x itself.
+        x = torch.reshape(x, shape=[num_matrices, self._num_nodes, input_size, batch_size])
+        x = x.permute(3, 1, 2, 0)  # (batch_size, num_nodes, input_size, order)
+        x = torch.reshape(x, shape=[batch_size * self._num_nodes, input_size * num_matrices])
+
+        weights = self._gconv_params.get_weights((input_size * num_matrices, output_size))
+        x = torch.matmul(x, weights)  # (batch_size * self._num_nodes, output_size)
+
+        biases = self._gconv_params.get_biases(output_size, bias_start)
+        x += biases
+        # Reshape res back to 2D: (batch_size, num_node, state_dim) -> (batch_size, num_node * state_dim)
+        return torch.reshape(x, [batch_size, self._num_nodes * output_size])

models/STDEN/stden_model.py

@@ -0,0 +1,181 @@
+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)
+
+        # 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: (seq_len, batch_size, num_edges * input_dim)
+        :param labels: (horizon, batch_size, num_edges * output_dim)
+        :param batches_seen: 已见批次数量
+        :return: outputs: (horizon, batch_size, num_edges * output_dim)
+        """
+        # 编码初始潜在状态
+        B, T, N, C = inputs.shape
+        inputs = inputs.view(T, B, N * C)
+        first_point_mu, first_point_std = self.encoder_z0(inputs)
+
+        # 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
+        outputs = self.decoder(sol_ys)
+
+        outputs = outputs.view(B, T, N, C)
+
+        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
+        inputs = inputs.reshape(seq_len, batch_size, self.num_nodes, self.input_dim)
+        inputs = inputs.reshape(seq_len, batch_size * self.num_nodes, self.input_dim)
+        
+        # GRU处理
+        outputs, _ = self.gru_rnn(inputs)
+        last_output = outputs[-1]
+        
+        # 重塑并转换维度
+        last_output = torch.reshape(last_output, (batch_size, self.num_nodes, -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_nodes, self.output_dim)
+        outputs = torch.mean(torch.mean(outputs, axis=3), axis=1)
+        outputs = outputs.reshape(horizon, batch_size, -1)
+        
+        return outputs
+

models/STDEN/utils.py

@@ -0,0 +1,234 @@
+import logging
+import numpy as np
+import os
+import time
+import scipy.sparse as sp
+import sys
+import torch
+import torch.nn as nn
+
+
+class DataLoader(object):
+    def __init__(self, xs, ys, batch_size, pad_with_last_sample=True, shuffle=False):
+        """
+
+        :param xs:
+        :param ys:
+        :param batch_size:
+        :param pad_with_last_sample: pad with the last sample to make number of samples divisible to batch_size.
+        """
+        self.batch_size = batch_size
+        self.current_ind = 0
+        if pad_with_last_sample:
+            num_padding = (batch_size - (len(xs) % batch_size)) % batch_size
+            x_padding = np.repeat(xs[-1:], num_padding, axis=0)
+            y_padding = np.repeat(ys[-1:], num_padding, axis=0)
+            xs = np.concatenate([xs, x_padding], axis=0)
+            ys = np.concatenate([ys, y_padding], axis=0)
+        self.size = len(xs)
+        self.num_batch = int(self.size // self.batch_size)
+        if shuffle:
+            permutation = np.random.permutation(self.size)
+            xs, ys = xs[permutation], ys[permutation]
+        self.xs = xs
+        self.ys = ys
+
+    def get_iterator(self):
+        self.current_ind = 0
+
+        def _wrapper():
+            while self.current_ind < self.num_batch:
+                start_ind = self.batch_size * self.current_ind
+                end_ind = min(self.size, self.batch_size * (self.current_ind + 1))
+                x_i = self.xs[start_ind: end_ind, ...]
+                y_i = self.ys[start_ind: end_ind, ...]
+                yield (x_i, y_i)
+                self.current_ind += 1
+
+        return _wrapper()
+
+
+class StandardScaler:
+    """
+    Standard the input
+    """
+
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def transform(self, data):
+        return (data - self.mean) / self.std
+
+    def inverse_transform(self, data):
+        return (data * self.std) + self.mean
+
+
+def calculate_random_walk_matrix(adj_mx):
+    adj_mx = sp.coo_matrix(adj_mx)
+    d = np.array(adj_mx.sum(1))
+    d_inv = np.power(d, -1).flatten()
+    d_inv[np.isinf(d_inv)] = 0.
+    d_mat_inv = sp.diags(d_inv)
+    random_walk_mx = d_mat_inv.dot(adj_mx).tocoo()
+    return random_walk_mx
+
+
+def config_logging(log_dir, log_filename='info.log', level=logging.INFO):
+    # Add file handler and stdout handler
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    # Create the log directory if necessary.
+    try:
+        os.makedirs(log_dir)
+    except OSError:
+        pass
+    file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
+    file_handler.setFormatter(formatter)
+    file_handler.setLevel(level=level)
+    # Add console handler.
+    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(console_formatter)
+    console_handler.setLevel(level=level)
+    logging.basicConfig(handlers=[file_handler, console_handler], level=level)
+
+
+def get_logger(log_dir, name, log_filename='info.log', level=logging.INFO):
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    # Add file handler and stdout handler
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    file_handler = logging.FileHandler(os.path.join(log_dir, log_filename))
+    file_handler.setFormatter(formatter)
+    # Add console handler.
+    console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setFormatter(console_formatter)
+    logger.addHandler(file_handler)
+    logger.addHandler(console_handler)
+    # Add google cloud log handler
+    logger.info('Log directory: %s', log_dir)
+    return logger
+
+
+def get_log_dir(kwargs):
+    log_dir = kwargs['train'].get('log_dir')
+    if log_dir is None:
+        batch_size = kwargs['data'].get('batch_size')
+
+        filter_type = kwargs['model'].get('filter_type')
+        gcn_step = kwargs['model'].get('gcn_step')
+        horizon = kwargs['model'].get('horizon')
+        latent_dim = kwargs['model'].get('latent_dim')
+        n_traj_samples = kwargs['model'].get('n_traj_samples')
+        ode_method = kwargs['model'].get('ode_method')
+
+        seq_len = kwargs['model'].get('seq_len')
+        rnn_units = kwargs['model'].get('rnn_units')
+        recg_type = kwargs['model'].get('recg_type')
+
+        if filter_type == 'unkP':
+            filter_type_abbr = 'UP'
+        elif filter_type == 'IncP':
+            filter_type_abbr = 'NV'
+        else:
+            filter_type_abbr = 'DF'
+
+        run_id = 'STDEN_%s-%d_%s-%d_L-%d_N-%d_M-%s_bs-%d_%d-%d_%s/' % (
+            recg_type, rnn_units, filter_type_abbr, gcn_step, latent_dim, n_traj_samples, ode_method, batch_size,
+            seq_len, horizon, time.strftime('%m%d%H%M%S'))
+        base_dir = kwargs.get('log_base_dir')
+        log_dir = os.path.join(base_dir, run_id)
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    return log_dir
+
+
+def load_dataset(dataset_dir, batch_size, val_batch_size=None, **kwargs):
+    if ('BJ' in dataset_dir):
+        data = dict(np.load(os.path.join(dataset_dir, 'flow.npz')))  # convert readonly NpzFile to writable dict Object
+        for category in ['train', 'val', 'test']:
+            data['x_' + category] = data['x_' + category]  # [..., :4] # ignore the time index
+    else:
+        data = {}
+        for category in ['train', 'val', 'test']:
+            cat_data = np.load(os.path.join(dataset_dir, category + '.npz'))
+            data['x_' + category] = cat_data['x']
+            data['y_' + category] = cat_data['y']
+    scaler = StandardScaler(mean=data['x_train'].mean(), std=data['x_train'].std())
+    # Data format
+    for category in ['train', 'val', 'test']:
+        data['x_' + category] = scaler.transform(data['x_' + category])
+        data['y_' + category] = scaler.transform(data['y_' + category])
+    data['train_loader'] = DataLoader(data['x_train'], data['y_train'], batch_size, shuffle=True)
+    data['val_loader'] = DataLoader(data['x_val'], data['y_val'], val_batch_size, shuffle=False)
+    data['test_loader'] = DataLoader(data['x_test'], data['y_test'], val_batch_size, shuffle=False)
+    data['scaler'] = scaler
+
+    return data
+
+
+def load_graph_data(pkl_filename):
+    adj_mx = np.load(pkl_filename)
+    return adj_mx
+
+
+def graph_grad(adj_mx):
+    """Fetch the graph gradient operator."""
+    num_nodes = adj_mx.shape[0]
+
+    num_edges = (adj_mx > 0.).sum()
+    grad = torch.zeros(num_nodes, num_edges)
+    e = 0
+    for i in range(num_nodes):
+        for j in range(num_nodes):
+            if adj_mx[i, j] == 0:
+                continue
+            grad[i, e] = 1.
+            grad[j, e] = -1.
+            e += 1
+    return grad
+
+
+def init_network_weights(net, std=0.1):
+    """
+    Just for nn.Linear net.
+    """
+    for m in net.modules():
+        if isinstance(m, nn.Linear):
+            nn.init.normal_(m.weight, mean=0, std=std)
+            nn.init.constant_(m.bias, val=0)
+
+
+def split_last_dim(data):
+    last_dim = data.size()[-1]
+    last_dim = last_dim // 2
+
+    res = data[..., :last_dim], data[..., last_dim:]
+    return res
+
+
+def get_device(tensor):
+    device = torch.device("cpu")
+    if tensor.is_cuda:
+        device = tensor.get_device()
+    return device
+
+
+def sample_standard_gaussian(mu, sigma):
+    device = get_device(mu)
+
+    d = torch.distributions.normal.Normal(torch.Tensor([0.]).to(device), torch.Tensor([1.]).to(device))
+    r = d.sample(mu.size()).squeeze(-1)
+    return r * sigma.float() + mu.float()
+
+
+def create_net(n_inputs, n_outputs, n_layers=0, n_units=100, nonlinear=nn.Tanh):
+    layers = [nn.Linear(n_inputs, n_units)]
+    for i in range(n_layers):
+        layers.append(nonlinear())
+        layers.append(nn.Linear(n_units, n_units))
+
+    layers.append(nonlinear())
+    layers.append(nn.Linear(n_units, n_outputs))
+    return nn.Sequential(*layers)

models/STGODE/STGODE.py

@@ -0,0 +1,180 @@
+import torch
+import math
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.STGODE.odegcn import ODEG
+from models.STGODE.adj import get_A_hat
+
+class Chomp1d(nn.Module):
+    """
+    extra dimension will be added by padding, remove it
+    """
+
+    def __init__(self, chomp_size):
+        super(Chomp1d, self).__init__()
+        self.chomp_size = chomp_size
+
+    def forward(self, x):
+        return x[:, :, :, :-self.chomp_size].contiguous()
+
+
+class TemporalConvNet(nn.Module):
+    """
+    time dilation convolution
+    """
+
+    def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
+        """
+        Args:
+            num_inputs : channel's number of input data's feature
+            num_channels : numbers of data feature tranform channels, the last is the output channel
+            kernel_size : using 1d convolution, so the real kernel is (1, kernel_size)
+        """
+        super(TemporalConvNet, self).__init__()
+        layers = []
+        num_levels = len(num_channels)
+        for i in range(num_levels):
+            dilation_size = 2 ** i
+            in_channels = num_inputs if i == 0 else num_channels[i - 1]
+            out_channels = num_channels[i]
+            padding = (kernel_size - 1) * dilation_size
+            self.conv = nn.Conv2d(in_channels, out_channels, (1, kernel_size), dilation=(1, dilation_size),
+                                  padding=(0, padding))
+            self.conv.weight.data.normal_(0, 0.01)
+            self.chomp = Chomp1d(padding)
+            self.relu = nn.ReLU()
+            self.dropout = nn.Dropout(dropout)
+
+            layers += [nn.Sequential(self.conv, self.chomp, self.relu, self.dropout)]
+
+        self.network = nn.Sequential(*layers)
+        self.downsample = nn.Conv2d(num_inputs, num_channels[-1], (1, 1)) if num_inputs != num_channels[-1] else None
+        if self.downsample:
+            self.downsample.weight.data.normal_(0, 0.01)
+
+    def forward(self, x):
+        """
+        like ResNet
+        Args:
+            X : input data of shape (B, N, T, F)
+        """
+        # permute shape to (B, F, N, T)
+        y = x.permute(0, 3, 1, 2)
+        y = F.relu(self.network(y) + self.downsample(y) if self.downsample else y)
+        y = y.permute(0, 2, 3, 1)
+        return y
+
+
+class GCN(nn.Module):
+    def __init__(self, A_hat, in_channels, out_channels, ):
+        super(GCN, self).__init__()
+        self.A_hat = A_hat
+        self.theta = nn.Parameter(torch.FloatTensor(in_channels, out_channels))
+        self.reset()
+
+    def reset(self):
+        stdv = 1. / math.sqrt(self.theta.shape[1])
+        self.theta.data.uniform_(-stdv, stdv)
+
+    def forward(self, X):
+        y = torch.einsum('ij, kjlm-> kilm', self.A_hat, X)
+        return F.relu(torch.einsum('kjlm, mn->kjln', y, self.theta))
+
+
+class STGCNBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, num_nodes, A_hat):
+        """
+        Args:
+            in_channels: Number of input features at each node in each time step.
+            out_channels: a list of feature channels in timeblock, the last is output feature channel
+            num_nodes: Number of nodes in the graph
+            A_hat: the normalized adjacency matrix
+        """
+        super(STGCNBlock, self).__init__()
+        self.A_hat = A_hat
+        self.temporal1 = TemporalConvNet(num_inputs=in_channels,
+                                         num_channels=out_channels)
+        self.odeg = ODEG(out_channels[-1], 12, A_hat, time=6)
+        self.temporal2 = TemporalConvNet(num_inputs=out_channels[-1],
+                                         num_channels=out_channels)
+        self.batch_norm = nn.BatchNorm2d(num_nodes)
+
+    def forward(self, X):
+        """
+        Args:
+            X: Input data of shape (batch_size, num_nodes, num_timesteps, num_features)
+        Return:
+            Output data of shape(batch_size, num_nodes, num_timesteps, out_channels[-1])
+        """
+        t = self.temporal1(X)
+        t = self.odeg(t)
+        t = self.temporal2(F.relu(t))
+
+        return self.batch_norm(t)
+
+
+class ODEGCN(nn.Module):
+    """ the overall network framework """
+
+    def __init__(self, config):
+        """
+        Args:
+            num_nodes : number of nodes in the graph
+            num_features : number of features at each node in each time step
+            num_timesteps_input : number of past time steps fed into the network
+            num_timesteps_output : desired number of future time steps output by the network
+            A_sp_hat : nomarlized adjacency spatial matrix
+            A_se_hat : nomarlized adjacency semantic matrix
+        """
+
+        super(ODEGCN, self).__init__()
+        args = config['model']
+        num_nodes = config['data']['num_nodes']
+        num_features = args['num_features']
+        num_timesteps_input = args['history']
+        num_timesteps_output = args['horizon']
+        A_sp_hat, A_se_hat = get_A_hat(config)
+
+        # spatial graph
+        self.sp_blocks = nn.ModuleList(
+            [nn.Sequential(
+                STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64],
+                           num_nodes=num_nodes, A_hat=A_sp_hat),
+                STGCNBlock(in_channels=64, out_channels=[64, 32, 64],
+                           num_nodes=num_nodes, A_hat=A_sp_hat)) for _ in range(3)
+            ])
+        # semantic graph
+        self.se_blocks = nn.ModuleList([nn.Sequential(
+            STGCNBlock(in_channels=num_features, out_channels=[64, 32, 64],
+                       num_nodes=num_nodes, A_hat=A_se_hat),
+            STGCNBlock(in_channels=64, out_channels=[64, 32, 64],
+                       num_nodes=num_nodes, A_hat=A_se_hat)) for _ in range(3)
+        ])
+
+        self.pred = nn.Sequential(
+            nn.Linear(num_timesteps_input * 64, num_timesteps_output * 32),
+            nn.ReLU(),
+            nn.Linear(num_timesteps_output * 32, num_timesteps_output)
+        )
+
+    def forward(self, x):
+        """
+        Args:
+            x : input data of shape (batch_size, num_nodes, num_timesteps, num_features) == (B, N, T, F)
+        Returns:
+            prediction for future of shape (batch_size, num_nodes, num_timesteps_output)
+        """
+        x = x[..., 0:1].permute(0, 2, 1, 3)
+        outs = []
+        # spatial graph
+        for blk in self.sp_blocks:
+            outs.append(blk(x))
+        # semantic graph
+        for blk in self.se_blocks:
+            outs.append(blk(x))
+        outs = torch.stack(outs)
+        x = torch.max(outs, dim=0)[0]
+        x = x.reshape((x.shape[0], x.shape[1], -1))
+
+        return self.pred(x).permute(0,2,1).unsqueeze(dim=-1)

models/STGODE/adj.py

@@ -0,0 +1,132 @@
+import csv
+import os
+import pandas as pd
+import numpy as np
+from fastdtw import fastdtw
+from tqdm import tqdm
+import torch
+
+files = {
+    358: ['PEMS03/PEMS03.npz', 'PEMS03/PEMS03.csv'],
+    307: ['PEMS04/PEMS04.npz', 'PEMS04/PEMS04.csv'],
+    883: ['PEMS07/PEMS07.npz', 'PEMS07/PEMS07.csv'],
+    170: ['PEMS08/PEMS08.npz', 'PEMS08/PEMS08.csv'],
+    # 'pemsbay': ['PEMSBAY/pems_bay.npz', 'PEMSBAY/distance.csv'],
+    # 'pemsD7M': ['PEMSD7M/PEMSD7M.npz', 'PEMSD7M/distance.csv'],
+    # 'pemsD7L': ['PEMSD7L/PEMSD7L.npz', 'PEMSD7L/distance.csv']
+}
+
+
+def get_A_hat(config):
+    """read data, generate spatial adjacency matrix and semantic adjacency matrix by dtw
+
+    Args:
+        sigma1: float, default=0.1, sigma for the semantic matrix
+        sigma2: float, default=10, sigma for the spatial matrix
+        thres1: float, default=0.6, the threshold for the semantic matrix
+        thres2: float, default=0.5, the threshold for the spatial matrix
+
+    Returns:
+        data: tensor, T * N * 1
+        dtw_matrix: array, semantic adjacency matrix
+        sp_matrix: array, spatial adjacency matrix
+    """
+    file_path = config['data']['graph_pkl_filename']
+    filename = config['basic']['dataset']
+    dataset_path = config['data']['dataset_dir']
+    args = config['model']
+
+    data = np.load(file_path)
+    data = np.nan_to_num(data, nan=0.0, posinf=0.0, neginf=0.0)
+    num_node = data.shape[1]
+    mean_value = np.mean(data, axis=(0, 1)).reshape(1, 1, -1)
+    std_value = np.std(data, axis=(0, 1)).reshape(1, 1, -1)
+    data = (data - mean_value) / std_value
+
+    # 计算dtw_distance, 如果存在缓存则直接读取缓存
+    if not os.path.exists(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy'):
+        data_mean = np.mean([data[:, :, 0][24 * 12 * i: 24 * 12 * (i + 1)] for i in range(data.shape[0] // (24 * 12))],
+                            axis=0)
+        data_mean = data_mean.squeeze().T
+        dtw_distance = np.zeros((num_node, num_node))
+        for i in tqdm(range(num_node)):
+            for j in range(i, num_node):
+                dtw_distance[i][j] = fastdtw(data_mean[i], data_mean[j], radius=6)[0]
+        for i in range(num_node):
+            for j in range(i):
+                dtw_distance[i][j] = dtw_distance[j][i]
+        np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy', dtw_distance)
+
+    dist_matrix = np.load(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_dtw_distance.npy')
+
+    mean = np.mean(dist_matrix)
+    std = np.std(dist_matrix)
+    dist_matrix = (dist_matrix - mean) / std
+    sigma = args['sigma1']
+    dist_matrix = np.exp(-dist_matrix ** 2 / sigma ** 2)
+    dtw_matrix = np.zeros_like(dist_matrix)
+    dtw_matrix[dist_matrix > args['thres1']] = 1
+
+    # 计算spatial_distance, 如果存在缓存则直接读取缓存
+    if not os.path.exists(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy'):
+        if num_node == 358:
+            with open(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}.txt', 'r') as f:
+                id_dict = {int(i): idx for idx, i in enumerate(f.read().strip().split('\n'))}  # 建立映射列表
+                # 使用 pandas 读取 CSV 文件，跳过标题行
+                df = pd.read_csv(f'{dataset_path}/{filename}.csv', skiprows=1, header=None)
+                dist_matrix = np.zeros((num_node, num_node)) + float('inf')
+                for _, row in df.iterrows():
+                    start = int(id_dict[row[0]])
+                    end = int(id_dict[row[1]])
+                    dist_matrix[start][end] = float(row[2])
+                    dist_matrix[end][start] = float(row[2])
+                np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix)
+        else:
+            # 使用 pandas 读取 CSV 文件，跳过标题行
+            df = pd.read_csv(f'{dataset_path}/{filename}.csv', skiprows=1, header=None)
+            dist_matrix = np.zeros((num_node, num_node)) + float('inf')
+            for _, row in df.iterrows():
+                start = int(row[0])
+                end = int(row[1])
+                dist_matrix[start][end] = float(row[2])
+                dist_matrix[end][start] = float(row[2])
+            np.save(f'data/PEMS0{filename[-1]}/PEMS0{filename[-1]}_spatial_distance.npy', dist_matrix)
+    # normalization
+    std = np.std(dist_matrix[dist_matrix != float('inf')])
+    mean = np.mean(dist_matrix[dist_matrix != float('inf')])
+    dist_matrix = (dist_matrix - mean) / std
+    sigma = args['sigma2']
+    sp_matrix = np.exp(- dist_matrix ** 2 / sigma ** 2)
+    sp_matrix[sp_matrix < args['thres2']] = 0
+
+    return (get_normalized_adj(dtw_matrix).to(config['basic']['device']),
+            get_normalized_adj(sp_matrix).to(config['basic']['device']))
+
+
+def get_normalized_adj(A):
+    """
+    Returns a tensor, the degree normalized adjacency matrix.
+    """
+    alpha = 0.8
+    D = np.array(np.sum(A, axis=1)).reshape((-1,))
+    D[D <= 10e-5] = 10e-5  # Prevent infs
+    diag = np.reciprocal(np.sqrt(D))
+    A_wave = np.multiply(np.multiply(diag.reshape((-1, 1)), A),
+                         diag.reshape((1, -1)))
+    A_reg = alpha / 2 * (np.eye(A.shape[0]) + A_wave)
+    return torch.from_numpy(A_reg.astype(np.float32))
+
+
+
+if __name__ == '__main__':
+    config = {
+        'sigma1': 0.1,
+        'sigma2': 10,
+        'thres1': 0.6,
+        'thres2': 0.5,
+        'device': 'cuda:0' if torch.cuda.is_available() else 'cpu'
+    }
+
+    for nodes in [358, 170, 883]:
+        args = {'num_nodes': nodes, **config}
+        get_A_hat(args)

models/STGODE/odegcn.py

@@ -0,0 +1,74 @@
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+# Whether use adjoint method or not.
+adjoint = False
+if adjoint:
+    from torchdiffeq import odeint_adjoint as odeint
+else:
+    from torchdiffeq import odeint
+
+
+# Define the ODE function.
+# Input:
+# --- t: A tensor with shape [], meaning the current time.
+# --- x: A tensor with shape [#batches, dims], meaning the value of x at t.
+# Output:
+# --- dx/dt: A tensor with shape [#batches, dims], meaning the derivative of x at t.
+class ODEFunc(nn.Module):
+
+    def __init__(self, feature_dim, temporal_dim, adj):
+        super(ODEFunc, self).__init__()
+        self.adj = adj
+        self.x0 = None
+        self.alpha = nn.Parameter(0.8 * torch.ones(adj.shape[1]))
+        self.beta = 0.6
+        self.w = nn.Parameter(torch.eye(feature_dim))
+        self.d = nn.Parameter(torch.zeros(feature_dim) + 1)
+        self.w2 = nn.Parameter(torch.eye(temporal_dim))
+        self.d2 = nn.Parameter(torch.zeros(temporal_dim) + 1)
+
+    def forward(self, t, x):
+        alpha = torch.sigmoid(self.alpha).unsqueeze(-1).unsqueeze(-1).unsqueeze(0)
+        xa = torch.einsum('ij, kjlm->kilm', self.adj, x)
+
+        # ensure the eigenvalues to be less than 1
+        d = torch.clamp(self.d, min=0, max=1)
+        w = torch.mm(self.w * d, torch.t(self.w))
+        xw = torch.einsum('ijkl, lm->ijkm', x, w)
+
+        d2 = torch.clamp(self.d2, min=0, max=1)
+        w2 = torch.mm(self.w2 * d2, torch.t(self.w2))
+        xw2 = torch.einsum('ijkl, km->ijml', x, w2)
+
+        f = alpha / 2 * xa - x + xw - x + xw2 - x + self.x0
+        return f
+
+
+class ODEblock(nn.Module):
+    def __init__(self, odefunc, t=torch.tensor([0,1])):
+        super(ODEblock, self).__init__()
+        self.t = t
+        self.odefunc = odefunc
+
+    def set_x0(self, x0):
+        self.odefunc.x0 = x0.clone().detach()
+
+    def forward(self, x):
+        t = self.t.type_as(x)
+        z = odeint(self.odefunc, x, t, method='euler')[1]
+        return z
+
+
+# Define the ODEGCN model.
+class ODEG(nn.Module):
+    def __init__(self, feature_dim, temporal_dim, adj, time):
+        super(ODEG, self).__init__()
+        self.odeblock = ODEblock(ODEFunc(feature_dim, temporal_dim, adj), t=torch.tensor([0, time]))
+
+    def forward(self, x):
+        self.odeblock.set_x0(x)
+        z = self.odeblock(x)
+        return F.relu(z)

models/model_selector.py

@@ -1,6 +1,12 @@
-
+from models.STDEN.stden_model import STDENModel
+from models.STGODE.STGODE import ODEGCN
 
 def model_selector(config):
     model_name = config['basic']['model']
     model = None
+    match model_name:
+        case 'STDEN': 
+            model = STDENModel(config)
+        case 'STGODE':
+            model = ODEGCN(config)
     return model

trainer/ode_trainer.py

@@ -0,0 +1,576 @@
+import math
+import os
+import time
+import copy
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+import torch
+
+class Trainer:
+    def __init__(self, config, model, loss, optimizer, train_loader, val_loader, test_loader,
+                 scalers, logger, lr_scheduler=None):
+        self.model = model
+        self.loss = loss
+        self.optimizer = optimizer
+        self.train_loader = train_loader
+        self.val_loader = val_loader
+        self.test_loader = test_loader
+        self.scalers = scalers  # 现在是多个标准化器的列表
+        self.args = config['train']
+        self.logger = logger
+        self.args['device'] = config['basic']['device']
+        self.lr_scheduler = lr_scheduler
+        self.train_per_epoch = len(train_loader)
+        self.val_per_epoch = len(val_loader) if val_loader else 0
+        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':
+            self.model.train()
+            optimizer_step = True
+        else:
+            self.model.eval()
+            optimizer_step = False
+
+        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, 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()
+
+                        if self.args['grad_norm']:
+                            torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args['max_grad_norm'])
+                        self.optimizer.step()
+
+                    total_loss += loss.item()
+
+                    if mode == 'train' and (batch_idx + 1) % self.args['log_step'] == 0:
+                        self.logger.info(
+                            f'Train Epoch {epoch}: {batch_idx + 1}/{len(dataloader)} Loss: {loss.item():.6f}')
+
+                    # 更新 tqdm 的进度
+                    pbar.update(1)
+                    pbar.set_postfix(loss=loss.item())
+
+        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):
+        """
+        只对输出维度进行反归一化
+        假设输出数据形状为 [batch, horizon, nodes, features]
+        只对前output_dim个特征进行反归一化
+        """
+        if not self.args['real_value']:
+            return output
+            
+        # 获取输出维度的数量
+        output_dim = self.args['output_dim']
+        
+        # 如果输出特征数小于等于标准化器数量，直接使用对应的标准化器
+        if output_dim <= len(self.scalers):
+            # 对每个输出特征分别进行反归一化
+            for feature_idx in range(output_dim):
+                if feature_idx < len(self.scalers):
+                    output[..., feature_idx:feature_idx+1] = self.scalers[feature_idx].inverse_transform(
+                        output[..., feature_idx:feature_idx+1]
+                    )
+        else:
+            # 如果输出特征数大于标准化器数量，只对前len(scalers)个特征进行反归一化
+            for feature_idx in range(len(self.scalers)):
+                output[..., feature_idx:feature_idx+1] = self.scalers[feature_idx].inverse_transform(
+                    output[..., feature_idx:feature_idx+1]
+                )
+        
+        return output
+
+    def train_epoch(self, epoch):
+        return self._run_epoch(epoch, self.train_loader, 'train')
+
+    def val_epoch(self, epoch):
+        return self._run_epoch(epoch, self.val_loader or self.test_loader, 'val')
+
+    def test_epoch(self, epoch):
+        return self._run_epoch(epoch, self.test_loader, 'test')
+
+    def train(self):
+        best_model, best_test_model = None, None
+        best_loss, best_test_loss = float('inf'), float('inf')
+        not_improved_count = 0
+
+        self.logger.logger.info("Training process started")
+        for epoch in range(1, self.args['epochs'] + 1):
+            train_epoch_loss = self.train_epoch(epoch)
+            val_epoch_loss = self.val_epoch(epoch)
+            test_epoch_loss = self.test_epoch(epoch)
+
+            if train_epoch_loss > 1e6:
+                self.logger.logger.warning('Gradient explosion detected. Ending...')
+                break
+
+            if val_epoch_loss < best_loss:
+                best_loss = val_epoch_loss
+                not_improved_count = 0
+                best_model = copy.deepcopy(self.model.state_dict())
+                torch.save(best_model, self.best_path)
+                self.logger.logger.info('Best validation model saved!')
+            else:
+                not_improved_count += 1
+
+            if self.args['early_stop'] and not_improved_count == self.args['early_stop_patience']:
+                self.logger.logger.info(
+                    f"Validation performance didn't improve for {self.args['early_stop_patience']} epochs. Training stops.")
+                break
+
+            if test_epoch_loss < best_test_loss:
+                best_test_loss = test_epoch_loss
+                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)
+            self.logger.logger.info(f"Best models saved at {self.best_path} and {self.best_test_path}")
+
+        self._finalize_training(best_model, best_test_model)
+
+    def _finalize_training(self, best_model, best_test_model):
+        self.model.load_state_dict(best_model)
+        self.logger.logger.info("Testing on best validation model")
+        self.test(self.model, self.args, self.test_loader, self.scalers, self.logger, generate_viz=False)
+
+        self.model.load_state_dict(best_test_model)
+        self.logger.logger.info("Testing on best test model")
+        self.test(self.model, self.args, self.test_loader, self.scalers, self.logger, generate_viz=True)
+
+    @staticmethod
+    def test(model, args, data_loader, scalers, logger, path=None, generate_viz=True):
+        if path:
+            checkpoint = torch.load(path)
+            model.load_state_dict(checkpoint['state_dict'])
+            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, 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
+            y_pred = Trainer._inverse_transform_output_static(torch.cat(y_pred, dim=0), args, scalers)
+        else:
+            y_pred = torch.cat(y_pred, dim=0)
+        y_true = torch.cat(y_true, dim=0)
+
+        # 计算每个时间步的指标
+        for t in range(y_true.shape[1]):
+            mae, rmse, mape = logger.all_metrics(y_pred[:, t, ...], y_true[:, t, ...],
+                                                 args['mae_thresh'], args['mape_thresh'])
+            logger.logger.info(f"Horizon {t + 1:02d}, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
+
+        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'
+            Trainer._generate_node_visualizations(y_pred, y_true, logger, save_dir)
+            Trainer._generate_input_output_comparison(y_pred, y_true, data_loader, logger, save_dir, 
+                                                   target_node=1, num_samples=10, scalers=scalers)
+
+    @staticmethod
+    def _inverse_transform_output_static(output, args, scalers):
+        """
+        静态方法：只对输出维度进行反归一化
+        """
+        if not args['real_value']:
+            return output
+            
+        # 获取输出维度的数量
+        output_dim = args['output_dim']
+        
+        # 如果输出特征数小于等于标准化器数量，直接使用对应的标准化器
+        if output_dim <= len(scalers):
+            # 对每个输出特征分别进行反归一化
+            for feature_idx in range(output_dim):
+                if feature_idx < len(scalers):
+                    output[..., feature_idx:feature_idx+1] = scalers[feature_idx].inverse_transform(
+                        output[..., feature_idx:feature_idx+1]
+                    )
+        else:
+            # 如果输出特征数大于标准化器数量，只对前len(scalers)个特征进行反归一化
+            for feature_idx in range(len(scalers)):
+                output[..., feature_idx:feature_idx+1] = scalers[feature_idx].inverse_transform(
+                    output[..., feature_idx:feature_idx+1]
+                )
+        
+        return output
+
+    @staticmethod
+    def _generate_node_visualizations(y_pred, y_true, logger, save_dir):
+        """
+        生成节点预测可视化图片
+
+        Args:
+            y_pred: 预测值
+            y_true: 真实值
+            logger: 日志记录器
+            save_dir: 保存目录
+        """
+        import matplotlib.pyplot as plt
+        import numpy as np
+        import os
+        import matplotlib
+        from tqdm import tqdm
+
+        # 设置matplotlib配置，减少字体查找输出
+        matplotlib.set_loglevel('error')  # 只显示错误信息
+        plt.rcParams['font.family'] = 'DejaVu Sans'  # 使用默认字体
+
+        # 检查数据有效性
+        if y_pred is None or y_true is None:
+            return
+
+        # 创建pic文件夹
+        pic_dir = os.path.join(save_dir, 'pic')
+        os.makedirs(pic_dir, exist_ok=True)
+
+        # 固定生成10张图片
+        num_nodes_to_plot = 10
+
+        # 生成单个节点的详细图
+        with tqdm(total=num_nodes_to_plot, desc="Generating node visualizations") as pbar:
+            for node_id in range(num_nodes_to_plot):
+                # 获取对应节点的数据
+                if len(y_pred.shape) > 2 and y_pred.shape[-2] > node_id:
+                    # 数据格式: [time_step, seq_len, num_node, dim]
+                    node_pred = y_pred[:, 12, node_id, 0].cpu().numpy()  # t=1时刻，指定节点，第一个特征
+                    node_true = y_true[:, 12, node_id, 0].cpu().numpy()
+                else:
+                    # 如果数据不足10个节点，只处理实际存在的节点
+                    if node_id >= y_pred.shape[-2]:
+                        pbar.update(1)
+                        continue
+                    else:
+                        node_pred = y_pred[:, 0, node_id, 0].cpu().numpy()
+                        node_true = y_true[:, 0, node_id, 0].cpu().numpy()
+
+                # 检查数据有效性
+                if np.isnan(node_pred).any() or np.isnan(node_true).any():
+                    pbar.update(1)
+                    continue
+
+                # 取前500个时间步
+                max_steps = min(500, len(node_pred))
+                if max_steps <= 0:
+                    pbar.update(1)
+                    continue
+
+                node_pred_500 = node_pred[:max_steps]
+                node_true_500 = node_true[:max_steps]
+
+                # 创建时间轴
+                time_steps = np.arange(max_steps)
+
+                # 绘制对比图
+                plt.figure(figsize=(12, 6))
+                plt.plot(time_steps, node_true_500, 'b-', label='True Values', linewidth=2, alpha=0.8)
+                plt.plot(time_steps, node_pred_500, 'r-', label='Predictions', linewidth=2, alpha=0.8)
+                plt.xlabel('Time Steps')
+                plt.ylabel('Values')
+                plt.title(f'Node {node_id + 1}: True vs Predicted Values (First {max_steps} Time Steps)')
+                plt.legend()
+                plt.grid(True, alpha=0.3)
+
+                # 保存图片，使用不同的命名
+                save_path = os.path.join(pic_dir, f'node{node_id + 1:02d}_prediction_first500.png')
+                plt.savefig(save_path, dpi=300, bbox_inches='tight')
+                plt.close()
+
+                pbar.update(1)
+
+        # 生成所有节点的对比图（前100个时间步，便于观察）
+        # 选择前100个时间步
+        plot_steps = min(100, y_pred.shape[0])
+        if plot_steps <= 0:
+            return
+
+        # 创建子图
+        fig, axes = plt.subplots(2, 5, figsize=(20, 8))
+        axes = axes.flatten()
+
+        for node_id in range(num_nodes_to_plot):
+            if len(y_pred.shape) > 2 and y_pred.shape[-2] > node_id:
+                # 数据格式: [time_step, seq_len, num_node, dim]
+                node_pred = y_pred[:plot_steps, 0, node_id, 0].cpu().numpy()
+                node_true = y_true[:plot_steps, 0, node_id, 0].cpu().numpy()
+            else:
+                # 如果数据不足10个节点，只处理实际存在的节点
+                if node_id >= y_pred.shape[-2]:
+                    axes[node_id].text(0.5, 0.5, f'Node {node_id + 1}\nNo Data',
+                                       ha='center', va='center', transform=axes[node_id].transAxes)
+                    continue
+                else:
+                    node_pred = y_pred[:plot_steps, 0, node_id, 0].cpu().numpy()
+                    node_true = y_true[:plot_steps, 0, node_id, 0].cpu().numpy()
+
+            # 检查数据有效性
+            if np.isnan(node_pred).any() or np.isnan(node_true).any():
+                axes[node_id].text(0.5, 0.5, f'Node {node_id + 1}\nNo Data',
+                                   ha='center', va='center', transform=axes[node_id].transAxes)
+                continue
+
+            time_steps = np.arange(plot_steps)
+
+            axes[node_id].plot(time_steps, node_true, 'b-', label='True', linewidth=1.5, alpha=0.8)
+            axes[node_id].plot(time_steps, node_pred, 'r-', label='Pred', linewidth=1.5, alpha=0.8)
+            axes[node_id].set_title(f'Node {node_id + 1}')
+            axes[node_id].grid(True, alpha=0.3)
+            axes[node_id].legend(fontsize=8)
+
+            if node_id >= 5:  # 下面一行添加x轴标签
+                axes[node_id].set_xlabel('Time Steps')
+            if node_id % 5 == 0:  # 左边一列添加y轴标签
+                axes[node_id].set_ylabel('Values')
+
+        plt.tight_layout()
+        summary_path = os.path.join(pic_dir, 'all_nodes_summary.png')
+        plt.savefig(summary_path, dpi=300, bbox_inches='tight')
+        plt.close()
+
+    @staticmethod
+    def _generate_input_output_comparison(y_pred, y_true, data_loader, logger, save_dir, 
+                                        target_node=1, num_samples=10, scalers=None):
+        """
+        生成输入-输出样本比较图
+        
+        Args:
+            y_pred: 预测值
+            y_true: 真实值
+            data_loader: 数据加载器，用于获取输入数据
+            logger: 日志记录器
+            save_dir: 保存目录
+            target_node: 目标节点ID（从1开始）
+            num_samples: 要比较的样本数量
+            scalers: 标准化器列表，用于反归一化输入数据
+        """
+        import matplotlib.pyplot as plt
+        import numpy as np
+        import os
+        import matplotlib
+        from tqdm import tqdm
+
+        # 设置matplotlib配置
+        matplotlib.set_loglevel('error')
+        plt.rcParams['font.family'] = 'DejaVu Sans'
+
+        # 创建compare文件夹
+        compare_dir = os.path.join(save_dir, 'pic', 'compare')
+        os.makedirs(compare_dir, exist_ok=True)
+
+        # 获取输入数据
+        input_data = []
+        for batch_idx, (data, target) in enumerate(data_loader):
+            if batch_idx >= num_samples:
+                break
+            input_data.append(data.cpu().numpy())
+        
+        if not input_data:
+            return
+
+        # 获取目标节点的索引（从0开始）
+        node_idx = target_node - 1
+        
+        # 检查节点索引是否有效
+        if node_idx >= y_pred.shape[-2]:
+            return
+
+        # 为每个样本生成比较图
+        with tqdm(total=min(num_samples, len(input_data)), desc="Generating input-output comparisons") as pbar:
+            for sample_idx in range(min(num_samples, len(input_data))):
+                # 获取输入序列（假设输入形状为 [batch, seq_len, nodes, features]）
+                input_seq = input_data[sample_idx][0, :, node_idx, 0]  # 第一个batch，所有时间步，目标节点，第一个特征
+                
+                # 对输入数据进行反归一化
+                if scalers is not None and len(scalers) > 0:
+                    # 使用第一个标准化器对输入进行反归一化（假设输入特征使用第一个标准化器）
+                    input_seq = scalers[0].inverse_transform(input_seq.reshape(-1, 1)).flatten()
+                
+                # 获取对应的预测值和真实值
+                pred_seq = y_pred[sample_idx, :, node_idx, 0].cpu().numpy()  # 所有horizon，目标节点，第一个特征
+                true_seq = y_true[sample_idx, :, node_idx, 0].cpu().numpy()
+                
+                # 检查数据有效性
+                if (np.isnan(input_seq).any() or np.isnan(pred_seq).any() or np.isnan(true_seq).any()):
+                    pbar.update(1)
+                    continue
+                
+                # 创建时间轴 - 输入和输出连续
+                total_time = np.arange(len(input_seq) + len(pred_seq))
+                
+                # 创建合并的图形 - 输入和输出在同一个图中
+                plt.figure(figsize=(14, 8))
+                
+                # 绘制完整的真实值曲线（输入 + 真实输出）
+                true_combined = np.concatenate([input_seq, true_seq])
+                plt.plot(total_time, true_combined, 'b', label='True Values (Input + Output)', 
+                        linewidth=2.5, alpha=0.9, linestyle='-')
+                
+                # 绘制预测值曲线（只绘制输出部分）
+                output_time = np.arange(len(input_seq), len(input_seq) + len(pred_seq))
+                plt.plot(output_time, pred_seq, 'r', label='Predicted Values', 
+                        linewidth=2, alpha=0.8, linestyle='-')
+                
+                # 添加垂直线分隔输入和输出
+                plt.axvline(x=len(input_seq)-0.5, color='gray', linestyle=':', alpha=0.7, 
+                           label='Input/Output Boundary')
+                
+                # 设置图形属性
+                plt.xlabel('Time Steps')
+                plt.ylabel('Values')
+                plt.title(f'Sample {sample_idx + 1}: Input-Output Comparison (Node {target_node})')
+                plt.legend()
+                plt.grid(True, alpha=0.3)
+                
+                # 调整布局
+                plt.tight_layout()
+                
+                # 保存图片
+                save_path = os.path.join(compare_dir, f'sample{sample_idx + 1:02d}_node{target_node:02d}_comparison.png')
+                plt.savefig(save_path, dpi=300, bbox_inches='tight')
+                plt.close()
+                
+                pbar.update(1)
+        
+        # 生成汇总图（所有样本的预测值对比）
+        
+        fig, axes = plt.subplots(2, 5, figsize=(20, 8))
+        axes = axes.flatten()
+        
+        for sample_idx in range(min(num_samples, len(input_data))):
+            if sample_idx >= 10:  # 最多显示10个子图
+                break
+                
+            ax = axes[sample_idx]
+            
+            # 获取输入序列和预测值、真实值
+            input_seq = input_data[sample_idx][0, :, node_idx, 0]
+            if scalers is not None and len(scalers) > 0:
+                input_seq = scalers[0].inverse_transform(input_seq.reshape(-1, 1)).flatten()
+            
+            pred_seq = y_pred[sample_idx, :, node_idx, 0].cpu().numpy()
+            true_seq = y_true[sample_idx, :, node_idx, 0].cpu().numpy()
+            
+            # 检查数据有效性
+            if np.isnan(input_seq).any() or np.isnan(pred_seq).any() or np.isnan(true_seq).any():
+                ax.text(0.5, 0.5, f'Sample {sample_idx + 1}\nNo Data',
+                       ha='center', va='center', transform=ax.transAxes)
+                continue
+            
+            # 绘制对比图 - 输入和输出连续显示
+            total_time = np.arange(len(input_seq) + len(pred_seq))
+            true_combined = np.concatenate([input_seq, true_seq])
+            output_time = np.arange(len(input_seq), len(input_seq) + len(pred_seq))
+            
+            ax.plot(total_time, true_combined, 'b', label='True', linewidth=2, alpha=0.9, linestyle='-')
+            ax.plot(output_time, pred_seq, 'r', label='Pred', linewidth=1.5, alpha=0.8, linestyle='-')
+            ax.axvline(x=len(input_seq)-0.5, color='gray', linestyle=':', alpha=0.5)
+            ax.set_title(f'Sample {sample_idx + 1}')
+            ax.grid(True, alpha=0.3)
+            ax.legend(fontsize=8)
+            
+            if sample_idx >= 5:  # 下面一行添加x轴标签
+                ax.set_xlabel('Time Steps')
+            if sample_idx % 5 == 0:  # 左边一列添加y轴标签
+                ax.set_ylabel('Values')
+        
+        # 隐藏多余的子图
+        for i in range(min(num_samples, len(input_data)), 10):
+            axes[i].set_visible(False)
+        
+        plt.tight_layout()
+        summary_path = os.path.join(compare_dir, f'all_samples_node{target_node:02d}_summary.png')
+        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