更新项目文件：添加数据选择器、模型选择器、训练器选择器和ODE训练器
This commit is contained in:
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commit
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@ -160,3 +160,4 @@ cython_debug/
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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STDEN/
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@ -0,0 +1,8 @@
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# 默认忽略的文件
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/shelf/
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/workspace.xml
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# 基于编辑器的 HTTP 客户端请求
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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@ -0,0 +1,12 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="jdk" jdkName="TS" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PyDocumentationSettings">
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<option name="format" value="PLAIN" />
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<option name="myDocStringFormat" value="Plain" />
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</component>
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</module>
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@ -0,0 +1,27 @@
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<component name="InspectionProjectProfileManager">
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<profile version="1.0">
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<option name="myName" value="Project Default" />
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<inspection_tool class="DuplicatedCode" enabled="true" level="WEAK WARNING" enabled_by_default="true">
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<Languages>
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<language minSize="136" name="Python" />
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</Languages>
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</inspection_tool>
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<inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
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<inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
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<option name="ignoredPackages">
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<value>
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<list size="8">
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<item index="0" class="java.lang.String" itemvalue="argparse" />
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<item index="1" class="java.lang.String" itemvalue="torch_summary" />
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<item index="2" class="java.lang.String" itemvalue="positional_encodings" />
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<item index="3" class="java.lang.String" itemvalue="scikit_learn" />
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<item index="4" class="java.lang.String" itemvalue="easy_torch" />
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<item index="5" class="java.lang.String" itemvalue="setuptools" />
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<item index="6" class="java.lang.String" itemvalue="numpy" />
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<item index="7" class="java.lang.String" itemvalue="openpyxl" />
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</list>
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</value>
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</option>
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</inspection_tool>
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</profile>
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</component>
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@ -0,0 +1,6 @@
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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@ -0,0 +1,7 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="Black">
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<option name="sdkName" value="Python 3.10" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="TS" project-jdk-type="Python SDK" />
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</project>
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@ -0,0 +1,8 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/Project-I.iml" filepath="$PROJECT_DIR$/.idea/Project-I.iml" />
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</modules>
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</component>
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</project>
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@ -0,0 +1,7 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="" vcs="Git" />
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<mapping directory="$PROJECT_DIR$/STDEN" vcs="Git" />
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</component>
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</project>
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@ -0,0 +1,296 @@
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from,to,cost
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9,153,310.6
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153,62,330.9
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62,111,332.9
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111,11,324.2
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11,28,336.0
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28,169,133.7
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138,135,354.7
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135,133,387.9
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133,163,337.1
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163,20,352.0
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20,19,420.8
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19,14,351.3
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14,39,340.2
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39,164,350.3
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164,167,365.2
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167,70,359.0
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70,59,388.2
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59,58,305.7
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58,67,294.4
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67,66,299.5
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66,55,313.3
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55,53,332.1
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53,150,278.9
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150,61,308.4
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61,64,311.4
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64,63,243.6
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47,65,372.8
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65,48,319.4
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48,49,309.7
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49,54,320.5
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54,56,318.3
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56,57,297.9
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57,68,293.5
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68,69,342.5
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69,60,318.0
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60,17,305.9
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17,5,321.4
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5,18,402.2
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18,22,447.4
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22,30,377.5
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30,29,417.7
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29,21,360.8
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21,132,407.6
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132,134,386.9
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134,136,350.2
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123,121,326.3
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121,140,385.2
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140,118,393.0
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118,96,296.7
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96,94,398.2
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94,86,337.1
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86,78,473.8
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78,46,353.4
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46,152,385.7
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152,157,350.0
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157,35,354.4
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35,77,356.1
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77,52,354.2
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52,3,357.8
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3,16,382.4
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16,0,55.7
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42,12,335.1
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12,139,328.8
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139,168,412.6
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168,154,337.3
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154,143,370.7
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143,10,6.3
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107,105,354.6
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105,104,386.9
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104,148,362.1
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148,97,316.3
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97,101,380.7
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101,137,361.4
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137,102,365.5
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102,24,375.5
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24,166,312.2
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129,156,256.1
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156,33,329.1
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33,32,356.5
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91,89,405.6
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89,147,347.0
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147,15,351.7
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||||
15,44,339.5
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44,41,350.8
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41,43,322.6
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43,100,338.9
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100,83,347.9
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83,87,327.2
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87,88,321.0
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88,75,335.8
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75,51,384.8
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51,73,391.1
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73,71,289.3
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31,155,260.0
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155,34,320.4
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34,128,393.3
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145,115,399.4
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115,112,328.1
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112,8,469.4
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8,117,816.2
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117,125,397.1
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125,127,372.7
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127,109,380.5
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109,161,355.5
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161,110,367.7
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110,160,102.0
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72,159,342.9
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159,50,383.3
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50,74,354.1
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74,82,350.2
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82,81,335.4
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81,99,391.6
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99,84,354.9
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84,13,306.4
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13,40,327.4
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40,162,413.9
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162,108,301.9
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108,146,317.8
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146,85,376.6
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85,90,347.0
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26,27,341.6
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27,6,359.4
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6,149,417.8
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149,126,388.0
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126,124,384.3
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124,7,763.3
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7,114,323.1
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114,113,351.6
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113,116,411.9
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116,144,262.0
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25,103,350.2
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103,23,376.3
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23,165,396.4
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165,38,381.0
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38,92,368.0
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92,37,336.3
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37,130,357.8
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130,106,532.3
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106,131,166.5
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1,2,371.6
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2,4,338.1
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4,76,429.0
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76,36,366.1
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36,158,344.5
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158,151,350.1
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151,45,358.8
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45,93,340.9
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93,80,329.9
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80,79,384.1
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79,95,335.7
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95,98,320.9
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98,119,340.3
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119,120,376.8
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120,122,393.1
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122,141,428.7
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141,142,359.3
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30,165,379.6
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165,29,41.7
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29,38,343.3
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65,72,297.9
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||||
72,48,21.5
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17,153,375.6
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153,5,256.3
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153,62,330.9
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18,6,499.4
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6,22,254.0
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22,149,185.4
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22,4,257.9
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4,30,236.8
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30,76,307.0
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95,98,320.9
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98,144,45.1
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45,93,340.9
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93,106,112.2
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162,151,113.6
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151,108,192.9
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108,45,359.8
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146,92,311.2
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92,85,343.9
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85,37,373.2
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13,169,326.2
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169,40,96.1
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124,13,460.7
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||||
13,7,305.5
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||||
7,40,624.1
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||||
124,169,145.2
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||||
169,7,631.5
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90,132,152.2
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||||
26,32,106.7
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9,129,148.3
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||||
129,153,219.6
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||||
31,26,116.0
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||||
26,155,270.7
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||||
9,128,142.2
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||||
128,153,215.0
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||||
153,167,269.7
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||||
167,62,64.8
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||||
62,70,332.6
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||||
124,169,145.2
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169,7,631.5
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||||
44,169,397.8
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||||
169,41,124.0
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||||
44,124,375.7
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||||
124,41,243.9
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||||
41,7,519.4
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||||
6,14,289.3
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||||
14,149,259.0
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||||
149,39,206.9
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||||
144,98,45.1
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||||
19,4,326.8
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||||
4,14,178.6
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14,76,299.0
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||||
15,151,136.4
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151,44,203.1
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||||
45,106,260.6
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||||
106,93,112.2
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||||
20,165,132.5
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||||
165,19,289.2
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||||
89,92,323.2
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||||
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||||
147,37,48.2
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133,91,152.8
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91,163,313.6
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150,71,221.1
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78,107,143.9
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107,46,236.3
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104,147,277.5
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||||
147,148,84.7
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20,101,201.2
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||||
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||||
19,137,245.5
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8,42,759.5
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42,117,58.9
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44,8,789.1
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8,41,657.4
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||||
41,117,160.5
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||||
168,167,172.4
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167,154,165.2
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143,128,81.9
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128,10,88.2
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15,152,135.0
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152,44,204.6
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19,77,320.7
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14,52,127.6
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14,127,314.8
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127,39,280.4
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39,109,237.0
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31,160,116.5
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160,155,272.4
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133,91,152.8
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91,163,313.6
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150,71,221.1
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71,61,89.6
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72,162,3274.4
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162,13,554.5
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162,40,413.9
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||||
65,72,297.9
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72,48,21.5
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13,42,319.8
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42,40,40.7
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8,42,759.5
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8,13,450.3
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13,117,378.5
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||||
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||||
108,148,311.6
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||||
148,146,80.0
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21,90,396.9
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||||
90,132,152.2
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||||
52,30,186.6
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||||
127,18,425.2
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||||
18,109,439.1
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17,154,294.2
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154,5,166.3
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107,46,236.3
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118,145,250.6
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145,96,85.1
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@ -2,6 +2,7 @@ import numpy as np
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import os
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def load_dataset(config):
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dataset_name = config['basic']['dataset']
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node_num = config['data']['num_nodes']
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input_dim = config['data']['input_dim']
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@ -10,4 +11,8 @@ def load_dataset(config):
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case 'EcoSolar':
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data_path = os.path.join('./data/EcoSolar.npy')
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data = np.load(data_path)[:, :node_num, :input_dim]
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case 'PEMS08':
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data_path = os.path.join('./data/PEMS08/PEMS08.npz')
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data = np.load(data_path)['data'][:, :node_num, :input_dim]
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return data
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import numpy as np
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def load_graph(config):
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dataset_path = config['data']['graph_pkl_filename']
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graph = np.load(dataset_path)
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# 将inf值填充为0
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graph = np.nan_to_num(graph, nan=0.0, posinf=0.0, neginf=0.0)
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return graph
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basic:
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dataset: PEMS08
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device: cuda:0
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mode: train
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model: STDEN
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seed: 2025
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data:
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add_day_in_week: true
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add_time_in_day: true
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batch_size: 32
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column_wise: false
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dataset_dir: data/PEMS08
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days_per_week: 7
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default_graph: true
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graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
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horizon: 24
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input_dim: 1
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lag: 24
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normalizer: std
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num_nodes: 100
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steps_per_day: 24
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test_ratio: 0.2
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tod: false
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val_batch_size: 32
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val_ratio: 0.2
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model:
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filter_type: default
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gcn_step: 2
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horizon: 12
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input_dim: 1
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l1_decay: 0
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latent_dim: 4
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n_traj_samples: 3
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nfe: false
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num_rnn_layers: 1
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ode_method: dopri5
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odeint_atol: 1.0e-05
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odeint_rtol: 1.0e-05
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output_dim: 1
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recg_type: gru
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rnn_units: 64
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save_latent: false
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seq_len: 12
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train:
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batch_size: 64
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debug: false
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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grad_norm: false
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log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-00:53:52
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loss: mae
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lr_decay: false
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lr_decay_rate: 0.3
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lr_decay_step: 5,20,40,70
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lr_init: 0.003
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mae_thresh: None
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mape_thresh: 0.001
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max_grad_norm: 5
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output_dim: 1
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real_value: true
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weight_decay: 0
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@ -0,0 +1,62 @@
|
|||
basic:
|
||||
dataset: PEMS08
|
||||
device: cuda:0
|
||||
mode: train
|
||||
model: STDEN
|
||||
seed: 2025
|
||||
data:
|
||||
add_day_in_week: true
|
||||
add_time_in_day: true
|
||||
batch_size: 32
|
||||
column_wise: false
|
||||
dataset_dir: data/PEMS08
|
||||
days_per_week: 7
|
||||
default_graph: true
|
||||
graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
|
||||
horizon: 24
|
||||
input_dim: 1
|
||||
lag: 24
|
||||
normalizer: std
|
||||
num_nodes: 100
|
||||
steps_per_day: 24
|
||||
test_ratio: 0.2
|
||||
tod: false
|
||||
val_batch_size: 32
|
||||
val_ratio: 0.2
|
||||
model:
|
||||
filter_type: default
|
||||
gcn_step: 2
|
||||
horizon: 12
|
||||
input_dim: 1
|
||||
l1_decay: 0
|
||||
latent_dim: 4
|
||||
n_traj_samples: 3
|
||||
nfe: false
|
||||
num_rnn_layers: 1
|
||||
ode_method: dopri5
|
||||
odeint_atol: 1.0e-05
|
||||
odeint_rtol: 1.0e-05
|
||||
output_dim: 1
|
||||
recg_type: gru
|
||||
rnn_units: 64
|
||||
save_latent: false
|
||||
seq_len: 12
|
||||
train:
|
||||
batch_size: 64
|
||||
debug: false
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
grad_norm: false
|
||||
log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-00:56:54
|
||||
loss: mae
|
||||
lr_decay: false
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: 5,20,40,70
|
||||
lr_init: 0.003
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
max_grad_norm: 5
|
||||
output_dim: 1
|
||||
real_value: true
|
||||
weight_decay: 0
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
basic:
|
||||
dataset: PEMS08
|
||||
device: cuda:0
|
||||
mode: train
|
||||
model: STDEN
|
||||
seed: 2025
|
||||
data:
|
||||
add_day_in_week: true
|
||||
add_time_in_day: true
|
||||
batch_size: 32
|
||||
column_wise: false
|
||||
dataset_dir: data/PEMS08
|
||||
days_per_week: 7
|
||||
default_graph: true
|
||||
graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
|
||||
horizon: 24
|
||||
input_dim: 1
|
||||
lag: 24
|
||||
normalizer: std
|
||||
num_nodes: 170
|
||||
steps_per_day: 24
|
||||
test_ratio: 0.2
|
||||
tod: false
|
||||
val_batch_size: 32
|
||||
val_ratio: 0.2
|
||||
model:
|
||||
filter_type: default
|
||||
gcn_step: 2
|
||||
horizon: 12
|
||||
input_dim: 1
|
||||
l1_decay: 0
|
||||
latent_dim: 4
|
||||
n_traj_samples: 3
|
||||
nfe: false
|
||||
num_rnn_layers: 1
|
||||
ode_method: dopri5
|
||||
odeint_atol: 1.0e-05
|
||||
odeint_rtol: 1.0e-05
|
||||
output_dim: 1
|
||||
recg_type: gru
|
||||
rnn_units: 64
|
||||
save_latent: false
|
||||
seq_len: 12
|
||||
train:
|
||||
batch_size: 64
|
||||
debug: false
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
grad_norm: false
|
||||
log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:07:23
|
||||
loss: mae
|
||||
lr_decay: false
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: 5,20,40,70
|
||||
lr_init: 0.003
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
max_grad_norm: 5
|
||||
output_dim: 1
|
||||
real_value: true
|
||||
weight_decay: 0
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
basic:
|
||||
dataset: PEMS08
|
||||
device: cuda:0
|
||||
mode: train
|
||||
model: STDEN
|
||||
seed: 2025
|
||||
data:
|
||||
add_day_in_week: true
|
||||
add_time_in_day: true
|
||||
batch_size: 32
|
||||
column_wise: false
|
||||
dataset_dir: data/PEMS08
|
||||
days_per_week: 7
|
||||
default_graph: true
|
||||
graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
|
||||
horizon: 24
|
||||
input_dim: 1
|
||||
lag: 24
|
||||
normalizer: std
|
||||
num_nodes: 170
|
||||
steps_per_day: 24
|
||||
test_ratio: 0.2
|
||||
tod: false
|
||||
val_batch_size: 32
|
||||
val_ratio: 0.2
|
||||
model:
|
||||
filter_type: default
|
||||
gcn_step: 2
|
||||
horizon: 12
|
||||
input_dim: 1
|
||||
l1_decay: 0
|
||||
latent_dim: 4
|
||||
n_traj_samples: 3
|
||||
nfe: false
|
||||
num_rnn_layers: 1
|
||||
ode_method: dopri5
|
||||
odeint_atol: 1.0e-05
|
||||
odeint_rtol: 1.0e-05
|
||||
output_dim: 1
|
||||
recg_type: gru
|
||||
rnn_units: 64
|
||||
save_latent: false
|
||||
seq_len: 12
|
||||
train:
|
||||
batch_size: 64
|
||||
debug: false
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
grad_norm: false
|
||||
log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:08:24
|
||||
loss: mae
|
||||
lr_decay: false
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: 5,20,40,70
|
||||
lr_init: 0.003
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
max_grad_norm: 5
|
||||
output_dim: 1
|
||||
real_value: true
|
||||
weight_decay: 0
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
basic:
|
||||
dataset: PEMS08
|
||||
device: cuda:0
|
||||
mode: train
|
||||
model: STDEN
|
||||
seed: 2025
|
||||
data:
|
||||
add_day_in_week: true
|
||||
add_time_in_day: true
|
||||
batch_size: 32
|
||||
column_wise: false
|
||||
dataset_dir: data/PEMS08
|
||||
days_per_week: 7
|
||||
default_graph: true
|
||||
graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
|
||||
horizon: 24
|
||||
input_dim: 1
|
||||
lag: 24
|
||||
normalizer: std
|
||||
num_nodes: 170
|
||||
steps_per_day: 24
|
||||
test_ratio: 0.2
|
||||
tod: false
|
||||
val_batch_size: 32
|
||||
val_ratio: 0.2
|
||||
model:
|
||||
filter_type: default
|
||||
gcn_step: 2
|
||||
horizon: 12
|
||||
input_dim: 1
|
||||
l1_decay: 0
|
||||
latent_dim: 4
|
||||
n_traj_samples: 3
|
||||
nfe: false
|
||||
num_rnn_layers: 1
|
||||
ode_method: dopri5
|
||||
odeint_atol: 1.0e-05
|
||||
odeint_rtol: 1.0e-05
|
||||
output_dim: 1
|
||||
recg_type: gru
|
||||
rnn_units: 64
|
||||
save_latent: false
|
||||
seq_len: 12
|
||||
train:
|
||||
batch_size: 64
|
||||
debug: false
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
grad_norm: false
|
||||
log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:09:13
|
||||
loss: mae
|
||||
lr_decay: false
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: 5,20,40,70
|
||||
lr_init: 0.003
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
max_grad_norm: 5
|
||||
output_dim: 1
|
||||
real_value: true
|
||||
weight_decay: 0
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
basic:
|
||||
dataset: PEMS08
|
||||
device: cuda:0
|
||||
mode: train
|
||||
model: STDEN
|
||||
seed: 2025
|
||||
data:
|
||||
add_day_in_week: true
|
||||
add_time_in_day: true
|
||||
batch_size: 32
|
||||
column_wise: false
|
||||
dataset_dir: data/PEMS08
|
||||
days_per_week: 7
|
||||
default_graph: true
|
||||
graph_pkl_filename: data/PEMS08/PEMS08_spatial_distance.npy
|
||||
horizon: 24
|
||||
input_dim: 1
|
||||
lag: 24
|
||||
normalizer: std
|
||||
num_nodes: 170
|
||||
steps_per_day: 24
|
||||
test_ratio: 0.2
|
||||
tod: false
|
||||
val_batch_size: 32
|
||||
val_ratio: 0.2
|
||||
model:
|
||||
filter_type: default
|
||||
gcn_step: 2
|
||||
horizon: 12
|
||||
input_dim: 1
|
||||
l1_decay: 0
|
||||
latent_dim: 4
|
||||
n_traj_samples: 3
|
||||
nfe: false
|
||||
num_rnn_layers: 1
|
||||
ode_method: dopri5
|
||||
odeint_atol: 1.0e-05
|
||||
odeint_rtol: 1.0e-05
|
||||
output_dim: 1
|
||||
recg_type: gru
|
||||
rnn_units: 64
|
||||
save_latent: false
|
||||
seq_len: 12
|
||||
train:
|
||||
batch_size: 64
|
||||
debug: false
|
||||
early_stop: true
|
||||
early_stop_patience: 15
|
||||
epochs: 100
|
||||
grad_norm: false
|
||||
log_dir: /home/czzhangheng/code/Project-I/exp/PEMS08_STDEN_2025/09/03-01:09:53
|
||||
loss: mae
|
||||
lr_decay: false
|
||||
lr_decay_rate: 0.3
|
||||
lr_decay_step: 5,20,40,70
|
||||
lr_init: 0.003
|
||||
mae_thresh: None
|
||||
mape_thresh: 0.001
|
||||
max_grad_norm: 5
|
||||
output_dim: 1
|
||||
real_value: true
|
||||
weight_decay: 0
|
||||
2
main.py
2
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
|
||||
|
|
|
|||
|
|
@ -1,6 +1,8 @@
|
|||
|
||||
from models.STDEN.stden_model import STDENModel
|
||||
|
||||
def model_selector(config):
|
||||
model_name = config['basic']['model']
|
||||
model = None
|
||||
match model_name:
|
||||
case 'STDEN': model = STDENModel(config)
|
||||
return model
|
||||
|
|
@ -2,6 +2,8 @@ import math
|
|||
import os
|
||||
import time
|
||||
import copy
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
import torch
|
||||
|
|
@ -26,6 +28,10 @@ class Trainer:
|
|||
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()
|
||||
|
|
@ -37,17 +43,28 @@ class Trainer:
|
|||
total_loss = 0
|
||||
epoch_time = time.time()
|
||||
|
||||
# 清空nfe数据收集
|
||||
if mode == 'train':
|
||||
self.c.clear()
|
||||
|
||||
with torch.set_grad_enabled(optimizer_step):
|
||||
with tqdm(total=len(dataloader), desc=f'{mode.capitalize()} Epoch {epoch}') as pbar:
|
||||
for batch_idx, (data, target) in enumerate(dataloader):
|
||||
label = target[..., :self.args['output_dim']]
|
||||
output = self.model(data).to(self.args['device'])
|
||||
output, fe = self.model(data)
|
||||
|
||||
if self.args['real_value']:
|
||||
# 只对输出维度进行反归一化
|
||||
output = self._inverse_transform_output(output)
|
||||
|
||||
loss = self.loss(output, label)
|
||||
|
||||
# 收集nfe数据（仅在训练模式下）
|
||||
if mode == 'train':
|
||||
self.c.append([*fe, loss.item()])
|
||||
# 记录FE信息
|
||||
self.logger.logger.debug("FE: number - {}, time - {:.3f} s, err - {:.3f}".format(*fe, loss.item()))
|
||||
|
||||
if optimizer_step and self.optimizer is not None:
|
||||
self.optimizer.zero_grad()
|
||||
loss.backward()
|
||||
|
|
@ -69,6 +86,12 @@ class Trainer:
|
|||
avg_loss = total_loss / len(dataloader)
|
||||
self.logger.logger.info(
|
||||
f'{mode.capitalize()} Epoch {epoch}: average Loss: {avg_loss:.6f}, time: {time.time() - epoch_time:.2f} s')
|
||||
|
||||
# 收集nfe数据（仅在训练模式下）
|
||||
if mode == 'train':
|
||||
self.res.append(pd.DataFrame(self.c, columns=['nfe', 'time', 'err']))
|
||||
self.keys.append(epoch)
|
||||
|
||||
return avg_loss
|
||||
|
||||
def _inverse_transform_output(self, output):
|
||||
|
|
@ -143,6 +166,10 @@ class Trainer:
|
|||
best_test_model = copy.deepcopy(self.model.state_dict())
|
||||
torch.save(best_test_model, self.best_test_path)
|
||||
|
||||
# 保存nfe数据（如果启用）
|
||||
if hasattr(self.args, 'nfe') and bool(self.args.get('nfe', False)):
|
||||
self._save_nfe_data()
|
||||
|
||||
if not self.args['debug']:
|
||||
torch.save(best_model, self.best_path)
|
||||
torch.save(best_test_model, self.best_test_path)
|
||||
|
|
@ -164,18 +191,24 @@ class Trainer:
|
|||
if path:
|
||||
checkpoint = torch.load(path)
|
||||
model.load_state_dict(checkpoint['state_dict'])
|
||||
model.to(args.device)
|
||||
model.to(args['device'])
|
||||
|
||||
model.eval()
|
||||
y_pred, y_true = [], []
|
||||
|
||||
# 用于收集nfe数据
|
||||
c = []
|
||||
|
||||
with torch.no_grad():
|
||||
for data, target in data_loader:
|
||||
label = target[..., :args['output_dim']]
|
||||
output = model(data)
|
||||
output, fe = model(data)
|
||||
y_pred.append(output)
|
||||
y_true.append(label)
|
||||
|
||||
# 收集nfe数据
|
||||
c.append([*fe, 0.0]) # 测试时没有loss，设为0
|
||||
|
||||
if args['real_value']:
|
||||
# 只对输出维度进行反归一化
|
||||
y_pred = Trainer._inverse_transform_output_static(torch.cat(y_pred, dim=0), args, scalers)
|
||||
|
|
@ -192,6 +225,10 @@ class Trainer:
|
|||
mae, rmse, mape = logger.all_metrics(y_pred, y_true, args['mae_thresh'], args['mape_thresh'])
|
||||
logger.logger.info(f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
|
||||
|
||||
# 保存nfe数据（如果启用）
|
||||
if hasattr(args, 'nfe') and bool(args.get('nfe', False)):
|
||||
Trainer._save_nfe_data_static(c, model, logger)
|
||||
|
||||
# 只在需要时生成可视化图片
|
||||
if generate_viz:
|
||||
save_dir = logger.dir_path if hasattr(logger, 'dir_path') else './logs'
|
||||
|
|
@ -514,6 +551,26 @@ class Trainer:
|
|||
plt.savefig(summary_path, dpi=300, bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
def _save_nfe_data(self):
|
||||
"""保存nfe数据到文件"""
|
||||
if not self.res:
|
||||
return
|
||||
|
||||
res = pd.concat(self.res, keys=self.keys)
|
||||
res.index.names = ['epoch', 'iter']
|
||||
|
||||
# 获取模型配置参数
|
||||
filter_type = getattr(self.model, 'filter_type', 'unknown')
|
||||
atol = getattr(self.model, 'atol', 1e-5)
|
||||
rtol = getattr(self.model, 'rtol', 1e-5)
|
||||
|
||||
# 保存nfe数据
|
||||
nfe_file = os.path.join(
|
||||
self.logger.dir_path,
|
||||
'nfe_{}_a{}_r{}.pkl'.format(filter_type, int(atol*1e5), int(rtol*1e5)))
|
||||
res.to_pickle(nfe_file)
|
||||
self.logger.logger.info(f"NFE data saved to {nfe_file}")
|
||||
|
||||
@staticmethod
|
||||
def _compute_sampling_threshold(global_step, k):
|
||||
return k / (k + math.exp(global_step / k))
|
||||
|
|
|
|||
|
|
@ -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
|
||||
Loading…
Reference in New Issue