{
  "public": {
    "netWorkError": "网络或后端服务错误，请检查。",
    "browserWarning": "您当前的浏览器可能导致部分功能失效或不可使用，建议使用Chrome 65版本以上的浏览器。",
    "timeout": "超时，请重新请求。",
    "noData": "暂无数据",
    "reset": "重置",
    "tagFilterPlaceHolder": "请输入需要的标签(支持正则表达式)",
    "dataError": "获取到的数据异常",
    "regIllegal": "请输入正确的检索条件",
    "stayTuned": "敬请期待",
    "select": "请选择",
    "search": "请搜索",
    "enter": "请输入",
    "remark": "备注",
    "zhLanguage": "中",
    "enLanguage": "EN",
    "sure": "确定",
    "clear": "清空",
    "cancel": "取消",
    "selectAll": "全选",
    "deselectAll": "取消全选",
    "dataLoading": "数据加载中",
    "notice": "提示"
  },
  "symbols": {
    "leftbracket": "(",
    "rightbracket": ")",
    "point": "·",
    "slashes": "/",
    "colon": "：",
    "fullStop": "。"
  },
  "header": {
    "refreshData": "刷新数据",
    "refreshingData": "正在刷新数据",
    "timeReload": "自动刷新",
    "timeSecond": "秒",
    "timeReloadScope": "自动刷新时长范围为[3, 300]秒"
  },
  "summaryManage": {
    "summaryList": "训练列表",
    "currentFolder": "训练日志根路径：",
    "sorting": "序号",
    "summaryPath": "训练日志路径",
    "createTime": "创建时间",
    "updateTime": "更新时间",
    "operation": "操作",
    "viewDashboard": "训练看板",
    "viewProfiler": "性能分析",
    "modelTraceback": "模型溯源",
    "dataTraceback": "数据溯源",
    "comparePlate": "对比看板",
    "disableProfilerTip": "无profiler日志，无法查看性能分析",
    "disableDashboardTip": "无summary日志或pb文件，无法查看训练看板",
    "disableParameterTip": "无lineage日志，无法查看参数详情",
    "openNewTab": "打开新页签",
    "paramDetails": "参数详情",
    "trainingParamDetails": "训练参数详情",
    "tracebackAnalysis": "溯源分析",
    "compareAnalysis": "对比分析",
    "guidTipFirst": "请在训练脚本中",
    "guidTipSecond": "收集summary",
    "guidTipThird": "，保存",
    "guidTipForth": "数据。",
    "scalarTipFirst": "标量",
    "histogramGuidTipFirst": "参数分布",
    "tensorTipFirst": "张量",
    "graphTipFirst": "计算图",
    "graphTipSecond": "结构数据。",
    "dataProcessTipFirst": "数据处理过程",
    "imageTipFirst": "指定图片",
    "guidUrl": "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/summary_record.html",
    "scalarUrl": "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id3",
    "histogramUrl": "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id4",
    "tensorUrl": "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id8",
    "graphUrl": "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id5",
    "dataProcessUrl": "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id6",
    "imageUrl": "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/dashboard.html#id7"
  },
  "modelTraceback": {
    "summaryPath": "训练日志路径",
    "trainSetPath": "训练集路径",
    "testSetPath": "测试集路径",
    "trainingSampleNum": "训练样本数量",
    "testSampleNum": "测试样本数量",
    "showAllData": "显示全部",
    "showSelected": "确认筛选",
    "hideSelected": "隐藏所选",
    "network": "网络",
    "optimizer": "优化器",
    "lossFunc": "损失函数",
    "learningRate": "学习率",
    "modelSize": "模型大小",
    "dataProcess": "数据处理",
    "noDataFound": "暂无满足筛选条件的数据",
    "allHide": "数据已全部隐藏",
    "userDefined": "自定义数据",
    "metric": "度量指标",
    "deviceNum": "device数目",
    "mixedItemMessage": "该参数含有多种类型数据，无法筛选展示",
    "notSupportSelected": "该参数类型暂不支持筛选",
    "displayColumn": "请选择展示列",
    "mustExist": "必选项",
    "remarkValidation": "备注为英文字母、数字、中文、下划线、中划线、点的组合，长度范围为[1,128]字符",
    "changeSuccess": "修改成功",
    "metricLabel": "Metric",
    "userDefinedLabel": "User Defined",
    "hyperLabel": "Hyper",
    "otherLabel": "其他",
    "remarkTips": "提示：终止服务后备注及tag将被清除",
    "optimizationObject": "优化目标",
    "targetDistribution": "优化目标分布",
    "parameterImportance": "参数重要性",
    "optimizationTitle": "参数和优化目标",
    "viewBigImage": "查看大图",
    "mustOptions": "必选",
    "customOptions": "自定义",
    "targetTips": "可以通过上述参数的柱状图来切换参数和优化目标的散点图",
    "reasonCode": {
      "1": "该参数含有非数字值",
      "2": "该参数和优化目标的有效条目≤2,无法计算重要性",
      "3": "重要性为NaN"
    },
    "explan": "说明"
  },
  "dataTraceback": {
    "details": "详情",
    "key": "KEY",
    "value": "VALUE",
    "dataTraceTips": "该数据涉及合并操作",
    "noDataFound": "暂无满足筛选条件的数据"
  },
  "trainingDashboard": {
    "trainingDashboardTitle": "训练看板",
    "calculationChart": "计算图",
    "dataMap": "数据图",
    "trainingScalar": "训练标量信息",
    "samplingData": "数据抽样",
    "imagesampleSwitch": "切换标签",
    "invalidId": "无效的作业",
    "summaryDirPath": "训练日志路径：",
    "loadingTip": "加载中",
    "waitLoading": "待加载",
    "switchTitle": "计算图和数据图模块不支持自动刷新"
  },
  "scalar": {
    "titleText": "标量",
    "tagSelectTitle": "标签选择",
    "xAxisTitle": "水平轴",
    "smoothness": "平滑度",
    "step": "步骤",
    "relativeTime": "相对时间",
    "absoluteTime": "绝对时间",
    "fullScreen": "切换全屏",
    "stepBack": "分步回退",
    "openOrCloseSelection": "开启/关闭框选",
    "toggleYaxisScale": "切换Y轴比例",
    "charTipHeadName": "名称",
    "charTipTagName": "标签",
    "charTipHeadValue": "值",
    "charSmoothedValue": "平滑值",
    "comparison": "标量合成",
    "compareCancel": "取消合成",
    "open": "展开",
    "close": "折叠",
    "invalidData": "存在无效数据",
    "restore": "还原",
    "currentThreshold": "当前阈值",
    "deleteThreshold": "删除阈值",
    "setThreshold": "设置阈值",
    "currentTag": "当前标签",
    "filterCriteria": "筛选条件",
    "placeHolderThreshold": "请输入阈值",
    "or": "或",
    "and": "且",
    "greaterThan": "大于",
    "lessThan": "小于",
    "applyAllSelectTag": "应用到当前所选标签",
    "placeHolderNumber": "请输入数值",
    "noSpace": "请勿输入空格",
    "sameCompare": "不能有相同的比较运算符",
    "unreasonable": "逻辑不合理",
    "info": "提示",
    "isDelete": "是否删除当前阈值"
  },
  "images": {
    "titleText": "图像",
    "tagSelectTitle": "标签选择",
    "open": "展开",
    "close": "折叠",
    "step": "步骤：",
    "setBright": "亮度调整",
    "setContrast": "对比度调整"
  },
  "histogram": {
    "titleText": "参数分布图",
    "xAxisTitle": "纵轴",
    "viewType": "视角",
    "centerValue": "中心值",
    "step": "步骤",
    "relativeTime": "相对时间",
    "absoluteTime": "绝对时间",
    "overlay": "正视",
    "offset": "俯视",
    "fullScreen": "切换全屏"
  },
  "dataMap": {
    "titleText": "数据图"
  },
  "tensors": {
    "titleText": "张量",
    "dimension": "形状：",
    "tensorType": "数据类型：",
    "viewTypeTitle": "视图",
    "chartViewType": "表格",
    "histogramViewType": "直方图",
    "tensorDashboardLimitErrorMsg": "请求的数据过大，请进入张量页使用其他维度重试。"
  },
  "graph": {
    "titleText": "计算图",
    "downloadPic": "下载",
    "fitScreen": "适应屏幕",
    "nodeInfo": "节点信息",
    "legend": "图例",
    "nameSpace": "命名空间",
    "operatorNode": "算子节点",
    "virtualNode": "虚拟节点",
    "constantNode": "常量节点",
    "polymetric": "聚合节点",
    "dataFlowEdge": "数据流向边",
    "controllDepEdge": "控制依赖边",
    "name": "名称",
    "count": "子节点数",
    "type": "类型",
    "attr": "属性",
    "inputs": "输入",
    "outputs": "输出",
    "outputs_i": "Outputs_i",
    "controlDependencies": "控制边",
    "searchLoading": "正在定位节点，请稍候。定位速度与节点数量有关，节点数量多时，速度会变慢。",
    "queryLoading": "正在加载图，请稍候。",
    "fullScreen": "全屏",
    "tooManyNodes": "节点太多，打开失败",
    "inputNodeName": "请输入节点名称",
    "guide": "用户指南",
    "guideTitle1": "1/3 主要功能介绍",
    "guideTitle2": "2/3 节点类型介绍",
    "guideTitle3": "3/3 边介绍",
    "guideContent11": "1. 在计算图展示区域可以查看计算图，可以通过滚轮对计算图放大和缩小，可以鼠标点击后拖拽。",
    "guideContent12": "2. 支持全屏展示计算图，或者将当前页面保存成SVG图片文件。",
    "guideContent13": "3. 在右边的功能区中，可以切换查看不同文件的计算图，或者在计算图中进行节点搜索。",
    "guideContent14": "4. 在节点信息的输入和输出中，选择并点击一个节点后，可以跳转到选择的节点。",
    "guideContent2": "计算图中有命名空间节点，算子节点，虚拟节点，聚合节点，常量节点等几种类型节点。其中 \"Default\" 表示正向传播，\"Gradients\" 表示反向传播。",
    "guideContent3": "计算图中存在数据边和控制边。其中数据边表示数据的输入，控制边表示节点之间执行依赖关系。",
    "next": "下一步",
    "finish": "完成",
    "dataTooLarge": "节点和边的数量过多，导致打开失败",
    "tooManyChain": "直接子节点的深度超过70，不支持展开"
  },
  "operator": {
    "titleText": "性能分析",
    "currentCard": "当前卡片",
    "pie": "饼图",
    "bar": "柱状图",
    "allOperator": "全部",
    "classificationOperator": "分类",
    "card": "卡",
    "searchByType": "请输入算子类型搜索",
    "searchByName": "请输入算子名称搜索",
    "searchByCoreName": "请输入内核名称搜索",
    "searchByCoreFullName": "请输入算子全名搜索",
    "operatorInfo": "算子信息",
    "kernelInfo": "内核信息"
  },
  "profiling": {
    "profilingDashboard": "性能看板",
    "showAverage": "展示平均值",
    "iterationGapTime": "迭代间隙时间",
    "fpBpTime": "前向+反向时间",
    "tailTime": "迭代拖尾时间",
    "time": "时间",
    "operatorTimeConAnalysis": "算子耗时分析",
    "avgCost": "平均总耗时：",
    "getCost": "平均取数据耗时：",
    "pushCost": "平均推送数据耗时：",
    "lterationGap": "迭代间隙",
    "lterationTail": "迭代拖尾",
    "minddataTitle": "数据准备详情",
    "title": "迭代间隙流程分析",
    "dataQueue": "数据队列",
    "smartHelper": "小助手",
    "suggestions": "优化建议",
    "common-profiler_tutorial": {
      "desc": "如何使用Profiler进行性能分析",
      "anchor": ["desc"],
      "url": [
        "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/performance_profiling.html"
      ],
      "gpuUrl": [
        "https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/performance_profiling_gpu.html"
      ]
    },
    "step_trace-proposer_type_label": {
      "desc": "迭代轨迹性能优化参考建议"
    },
    "step_trace-iter_interval": {
      "desc": "在开启图模式和数据集下沉模式后，如果平均迭代间隙时间大于{n1}ms，则数据处理至计算图执行之间的流程可能存在优化空间。"
    },
    "common-proposer_type_label": {
      "desc": "性能分析与优化指导"
    },
    "minddata_pipeline-proposer_type_label": {
      "desc": "数据处理性能优化参考建议"
    },
    "minddata_pipeline-general": {
      "desc": "Pipeline中的算子{n1}可能存在性能瓶颈，请用户重点关注。"
    },
    "minddata_pipeline-dataset_op": {
      "desc": "对于算子{n1}，用户可以尝试调整num_parallel_workers参数。"
    },
    "minddata_pipeline-generator_op": {
      "desc": "对于算子{n1}，用户可以尝试调整num_parallel_workers参数或优化训练脚本，如果性能没有得到优化，可以尝试替换为MindRecordDataset算子。"
    },
    "minddata_pipeline-map_op": {
      "desc": "对于算子{n1}，用户可以尝试调整num_parallel_workers参数,如果使用的是Python的算子，可以尝试优化训练脚本。"
    },
    "minddata_pipeline-batch_op": {
      "desc": "对于算子{n1}，用户可以尝试增加prefetch_size大小。"
    },
    "minddata_warning_op": {
      "desc": "经过上述判断，算子{n1}可能存在优化空间。"
    },
    "minddata-proposer_type_label": {
      "desc": "迭代间隙性能分析"
    },
    "minddata_device_queue": {
      "desc": "主机侧队列为空比例{n1}/{n2}，为满比例{n3}/{n4}。"
    },
    "minddata_get_next_queue": {
      "desc": "芯片侧队列为空比例{n1}/{n2}。"
    },
    "millisecond": "ms",
    "curCard": "当前卡片",
    "stepTrace": "迭代轨迹",
    "mindData": "数据准备",
    "timeLine": "时间线",
    "rankOfOperator": "算子耗时统计排名",
    "stepTraceDetail": "迭代轨迹详情",
    "viewDetail": "查看详情",
    "stepNum": "耗时step数",
    "iterGapTimeLabel": "迭代间隙时长",
    "iterGapRateLabel": "迭代间隙占比",
    "fpBpTimeLabel": "前向+反向时长",
    "fpBpRateLabel": "前向+反向占比",
    "tailTimeLabel": "迭代拖尾时长",
    "tailRateLabel": "迭代拖尾占比",
    "operatorDetail": "算子详情",
    "times": "次",
    "queueStep": "队列step分布图",
    "queueInfo": "迭代间隙",
    "pipeline": "数据处理",
    "pipelineTopTitle": "算子间队列平均使用率",
    "pipelineMiddleTitle": "算子间队列关系",
    "deviceQueueOp": "数据发送",
    "deviceQueueOpTip": "前向+反向",
    "getNext": "取数据算子",
    "connectorQuene": "主机队列",
    "getData": "数据获取",
    "opTotalTime": "算子执行总时间：",
    "streamNum": "执行流数量：",
    "opNum": "算子数目：",
    "opTimes": "算子执行总次数：",
    "features": "功能介绍：",
    "iterationInfo": "迭代轨迹展示的是每个step从上个迭代开始至该step结束的耗时信息，主体时间分为3部分：迭代间隙、前向反向、迭代拖尾。",
    "iterationGapInfo": "主要负责从数据队列中读取数据，如果该部分耗时较长，建议前往数据处理部分进一步分析；",
    "fpbpTitle": "前向反向",
    "fpbpInfo": "执行网络中的前向算子以及反向算子，承载了一个step主要的计算工作，如果该部分耗时较长，建议前往算子统计或时间线中进一步分析；",
    "iterativeTailingTitle": "迭代拖尾",
    "iterativeTailingInfo": "主要在多卡场景下执行参数聚合、参数更新操作，如果该部分耗时较长，建议查看all_reduce耗时以及并行情况。",
    "statistics": "统计信息：",
    "totalTime": "总耗时：",
    "totalSteps": "总step数：",
    "fpbpTimeRatio": "前向反向耗时占比：",
    "iterationGapTimeRatio": "迭代间隙耗时占比：",
    "iterativeTailingTimeRatio": "迭代拖尾耗时占比：",
    "dataProcess": "该图展示了数据处理阶段的流程，数据通过数据处理阶段存入主机队列，再通过数据传输阶段存入芯片侧的数据队列，最终由数据传输算子get_next发送给前向训练使用。",
    "dataProcessInfo": "综合分析该阶段的流程，通过判断主机队列和数据队列为空的情况就可以初步判断可能出现性能异常的阶段。",
    "analysisOne": "1、如果迭代间隙较长，并且芯片侧的数据队列部分batch为空，那么可能由于数据处理和数据传输阶段导致的性能异常，参考2，反之则定位数据传输算子get_next内部问题；",
    "analysisTwo": "2、如果通过1定位为数据处理、数据传输阶段异常，则查看主机队列情况，如果大概率为空，则可能为数据处理阶段导致异常，如果大概率不为空，则可能数据传输阶段异常；",
    "higherAnalysis": "注：可结合下方算子耗时进行高阶分析",
    "chipInfo": "芯片侧数据队列为空比例：",
    "hostIsEmpty": "主机侧队列为空比例：",
    "hostIsFull": "主机侧队列为满比例：",
    "operatorInfo": "{msg1}、{msg2}算子信息",
    "workersNum": "使用线程数",
    "queueDeepChartTitle": "{msg}队列深度折线图",
    "sampleInterval": "采样间隔",
    "queueTip1": "队列为满比例：",
    "queueTip2": "队列为空比例：",
    "totalCapacity": "总容量",
    "averageCapacity": "平均使用容量",
    "FPMessage": "前向起始算子：",
    "BPMessage": "反向终止算子：",
    "approximateTime": "总时长 ≈ ",
    "stepInputTip": "请输入step值(1~{max}的正整数，值为空时展示平均值)",
    "inputError": "输入参数异常，请输入一个1~{max}的正整数",
    "defaultTip": "默认展示平均值",
    "downloadTimeline": "下载",
    "timelineTips": {
      "title1": "时间线功能可以帮您对训练过程进行分析，它可以展示：",
      "content11": "- 算子分配到哪个设备 (AI CPU/AI Core) 执行；",
      "content12": "- MindSpore对该网络的流切分策略；",
      "content13": "- 算子在Device上的执行序列和执行时长。",
      "title2": "如何查看时间线：",
      "content21": {
        "part1": "要查看时间线的详细信息，您可以点击 \"",
        "part2": "下载",
        "part3": "\" 按钮将带有时间线信息的文件保存到本地，再通过工具查看。"
      },
      "content22": "我们推荐您使用谷歌插件：chrome://tracing，或 Perfetto工具：https://ui.perfetto.dev/#!/viewer",
      "content23": {
        "part1": "选择一个工具，在浏览器地址栏输入地址并",
        "part2": "回车",
        "part3": "，进入页面点击按钮加载文件查看时间线（tracing工具点击左上角 \"",
        "part4": "Load",
        "part5": "\"，Perfetto工具点击左侧栏 \"",
        "part6": "Open trace file",
        "part7": "\"）。"
      },
      "title3": "如何使用时间线：",
      "content31": "您可以通过时间线信息分析流切分方法是否合理、迭代间隙和拖尾时间是否过长等；",
      "content32": "也可以具体定位到某个算子，查看分析它的执行时间。"
    },
    "countUnit": "次",
    "unit": "ms/次",
    "gpuunit": "us/次",
    "chartTitle": "平均耗时排名"
  },
  "profilingGPU": {
    "minddata_get_next_queue": {
      "desc": "数据队列为空比例{n1}/{n2}。"
    },
    "dataProcess": "该图展示了数据处理阶段的流程，数据通过数据处理阶段存入主机队列，再通过数据传输阶段存入数据队列，最终由数据传输算子get_next发送给前向训练使用。",
    "dataProcessInfo": "综合分析该阶段的流程，通过判断主机队列和数据队列为空的情况就可以初步判断可能出现性能异常的阶段。",
    "analysisOne": "1、如果迭代间隙较长，并且数据队列部分batch为空，那么可能由于数据处理和数据传输阶段导致的性能异常，参考2，反之则定位数据传输算子get_next内部问题；",
    "analysisTwo": "2、如果通过1定位为数据处理、数据传输阶段异常，则查看主机队列情况，如果大概率为空，则可能为数据处理阶段导致异常，如果大概率不为空，则可能数据传输阶段异常；",
    "chipInfo": "数据队列为空比例："
  },
  "components": {
    "summaryTitle": "训练选择",
    "tagSelectTitle": "标签选择",
    "selectAll": "全选",
    "tagFilterPlaceHolder": "请输入需要的标签(支持正则表达式)",
    "open": "展开",
    "close": "折叠",
    "gridIncorrectDataError": "当前只支持最多二维数组的展示",
    "gridAccuracy": "保留小数位",
    "inCorrectInput": "无效输入",
    "gridTableNoData": "表格无数据",
    "value": "数值",
    "dimsFilterInputTitle": "维度选择",
    "dimsFilterInputTip": "维度输入值可以是具体的索引（和Python的索引含义一致,支持负号）或者冒号\":\"，其中冒号\":\"表示当前维度的所有值"
  },
  "debugger": {
    "debugger": "调试器",
    "nodeList": "节点列表",
    "watchList": "监测点列表",
    "watchPoint": "监测点",
    "continue": "继续",
    "pause": "暂停",
    "terminate": "结束",
    "selectCondition": "请选择条件",
    "inputStep": "请输入轮次值（大于0的整数）",
    "curHitNode": "命中的监测点",
    "backstageStatus": "后台运行状态是",
    "view": "查看",
    "deleteWatchpointConfirm": "此操作将删除当前监测点，是否继续？",
    "ternimateConfirm": "此操作将结束当前状态，是否继续？",
    "successCreateWP": "新建监测点成功",
    "successDeleteWP": "删除监测点成功",
    "pendingTips": "等待训练连接...",
    "nextNodeTip": "当前已经是最后一个节点",
    "previousNodeTip": "当前已经是第一个节点",
    "serviceError": "连接后端服务失败，请检查后刷新页面",
    "debuggerError": "连接后端服务失败，请检查服务是否正确启动",
    "tolerance": "容忍度",
    "curValue": "当前值",
    "compareToPre": "上一步对比",
    "stepTip": "(0表示训练初始状态)",
    "toSummeryList": "返回训练列表",
    "clientIp": "训练端地址",
    "deviceId": "卡号",
    "currentStep": "当前轮次",
    "step": "轮次",
    "currentNode": "当前节点",
    "previousNode": "上一个节点",
    "nextNode": "下一个节点",
    "tensorMsg": "张量信息",
    "dType": "类型",
    "shape": "形状",
    "value": "数值",
    "largeDataTip": "请求的数据过大，请使用其他维度重试。",
    "continueTo": "运行到该节点"
  },
  "explain": {
    "explain": "模型解释",
    "explainSummary": "解释列表",
    "explainSummaryCurrentFolder": "解释日志根路径：",
    "summaryPath": "解释日志路径",
    "title": "显著图可视化",
    "explainMethod": "解释方法",
    "viewScore": "查看评分",
    "fetch": "筛选",
    "minConfidence": "概率阈值",
    "imgSort": "图片排序",
    "default": "默认",
    "byProbability": "概率值降序",
    "superposeImg": "叠加于原图",
    "originalPicture": "原始图片",
    "forecastTag": "预测标签",
    "tag": "标签",
    "confidence": "概率",
    "forecastTagTip": "当推理图片带有正确标签时，标签行会显示下列四种旗标",
    "TP": "TP：代表Ture Positive，标签为正样本，分类为正样本；",
    "FN": "FN：代表False Negative，标签为正样本，分类为负样本；",
    "FP": "FP：代表Fasle Positive，标签为负样本，分类为正样本；",
    "TN": "TN：代表Ture Negative，标签为负样本，分类为负样本；",
    "mainTipTitle": "功能说明:",
    "mainTipPartOne": "本功能对模型分类的依据进行可视化。选定待解释模型、图片和标签后，解释方法计算得到原始图像中每个像素对选定标签的贡献度，以类似热力图的显著图进行可视。显著图颜色越亮，表示对应区域对于模型预测选定标签的贡献越多；颜色越暗，该区域对选定标签的贡献越小。",
    "mainTipPartTwo": "显著图可以帮助我们了解深度神经网络推理时，和指定标签有关系的特征。当模型推理依据和期望不同时，可以参考显著图对模型进行调试，让模型依据合理的特征进行推理。",
    "mainTipPartThree": "主流生成显著图的解释方法可以参考论文3.2节Local Methods:",
    "mainTipPartFour": "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9050829",
    "noExplainer": "请选择解释方法"
  },
  "metric": {
    "scoreSystem": "评分体系",
    "comprehensive": "综合评估",
    "classify": "分类评估",
    "singleMethod": "单方法多指标",
    "multiMethod": "单指标多方法",
    "interpretation": "解释方法",
    "measurement": "度量方法",
    "seeInterpretation": "您正在查看解释方法 ",
    "seeMeasurement": "您正在查看度量方法 ",
    "showGrade": " 在不同标签类型上的分数",
    "evaluationScore": "评价分数",
    "weightAllocatgion": "权重配置",
    "compositeScore": "综合得分",
    "metric": "评分维度",
    "inputWeightScore": "请输入权重分数(0-1)",
    "weightSumNotNull": "权重总和不能为空",
    "weightError": "权重计算出错",
    "weightSum": "权重总和必须为1",
    "radarChart": "解释方法对比雷达图",
    "comprehensiveTooltip": "综合评估页面支持对不同评分维度配置权重，用于计算解释方法的综合评分，以便对解释方法进行排序筛选。解释方法在不同评分维度上各有优势，可以参考各个维度的介绍，根据实际需求对权重进行调整，有侧重地评估解释方法。本功能默认对各个维度设置相同的权重。",
    "classifyTooltip": "分类评估将数据集按照标签进行分组，在不同标签的数据上分别度量解释方法。",
    "scoreSystemtooltipOne": "功能说明：",
    "scoreSystemtooltiptwo": "评分体系提供多个维度对解释方法进行评分。",
    "scoreSystemtooltipthree": "评分维度：",
    "scoreSystemtooltipfour": "  -Faithfulness: 该维度评估解释结果对模型的忠实度，即显著图是否正确反映模型分类依据。给定显著图，按照一定规则（如从亮到暗）对原始图像中的相应位置进行修改，将修改后的图像传给模型依次进行推理，如果显著图越亮的区域对选定标签影响越大，则解释结果忠实度越高；反之，解释结果可能无法反映模型的推理依据。当前有三种主流方案度量解释的faithfulness：Naive Faithfulness，Insertion AUC，Deletion AUC。",
    "scoreSystemtooltipfive": "  -Localization: 该维度借助数据集的bounding box对显著图高亮区域的定位准确性进行评估，选定一组目标和相关标签，如果解释方法准确高亮了图像中和选定标签相关的区域，高亮区域和选定目标的bounding box应当有较大重合度。Localization通过评估高亮区域和bounding box的重合度，对解释结果的定位能力进行评估。"
  },
  "error": {
    "50540000": "系统错误",
    "50540001": "参数类型错误，请检查请求参数类型是否都符合要求",
    "50540002": "参数值错误，请检查请求参数值是否都符合要求",
    "50540003": "参数缺失必需的参数，请检查请求参数是否必填项都满足",
    "50545001": "API 路由资源不存在",
    "50545002": "请求API的HTTP方法错误",
    "50545005": "训练作业不存在",
    "50545007": "Summary数据正在被加载，请等待Summary数据加载结束",
    "50545009": "查询的节点不在图中，请刷新",
    "5054500A": "训练作业ID进行URL解码失败",
    "5054500C": "计算图不存在，请刷新",
    "5054500D": "图片数据不存在，请刷新",
    "5054500E": "标量数据不存在，请刷新",
    "5054500F": "参数分布图数据不存在，请刷新",
    "50545010": "请求的数据不在缓存中，请刷新",
    "50542082": "模型名称缺失",
    "50542085": "模型名称不合法",
    "50542215": "查询参数错误",
    "50542216": "Summary日志文件未找到",
    "50542217": "Summary日志路径错误",
    "50542218": "筛选参数错误",
    "50545012": "张量数据不存在，请刷新。",
    "50545013": "请求的数据过大，请使用其他维度重试。",
    "50545014": "查询的张量数据已被新数据替换，请刷新。",
    "50546083": "性能数据目录不存在",
    "50548001": "昇腾AI处理器信息查询超时",
    "5054B080": "参数类型错误，请检查输入参数类型",
    "5054B081": "参数值错误，请检查输入参数",
    "5054B180": "监测点创建失败，请暂停训练后重试",
    "5054B181": "监测点更新失败，请暂停训练后重试",
    "5054B182": "监测点删除失败，请暂停训练后重试",
    "5054B183": "后台训练运行中，请稍后重试",
    "5054B184": "操作过快，后台服务已暂停。"
  }
}