diff --git a/mindinsight/ui/src/locales/en-us.json b/mindinsight/ui/src/locales/en-us.json index c545e687..ce0c4b5e 100644 --- a/mindinsight/ui/src/locales/en-us.json +++ b/mindinsight/ui/src/locales/en-us.json @@ -479,7 +479,9 @@ "backstageStatus": "The backend running status is ", "view": "View", "deleteWatchpointConfirm": "Are you sure you want to delete the watchpoint?", + "clearWatchpointConfirm": "All watchpoints will be deleted. Are you sure you want to continue? ", "ternimateConfirm": "Are you sure you want to end the status?", + "createWP": "Create Watchpoint", "successCreateWP": "The watchpoint is created.", "successDeleteWP": "The watchpoint is deleted.", "pendingTips": "Waiting for training connection...", @@ -514,7 +516,188 @@ "zero": "0:", "positiveNum": "Positive number:", "negativeNum": "Negative number:", - "all": "All" + "all": "All", + "tensorTip": "tensor", + "recheck": "Recheck", + "clearWatchpoint": "Clear Watchpoint", + "nodeType": { + "all": "All", + "weight": "Weight", + "gradient": "Gradient", + "activation": "Activation" + }, + "recheckSuccess": "Rechecked.", + "curStatisticsLabel": "Current Step:", + "preStatisticsLabel": "Previous Step:", + "diffStatisticsLabel": "Comparison Result:", + "graphFile": "Graph File", + "tensorDiagram": "Tensor Relationship Diagram", + "selectDetail": "Select a tensor and double-click it to view details.", + "operator": "Operator", + "jumpFailInformation": "Redirection fails because the tensor information is empty.", + "optimizationOrientation": "Optimization Guide", + "tuningAdvice": "Optimization Suggestions", + "tensorTuningRule": { + "operator_real_data_validation": "Real data validation using single operator", + "loss_overflow": "Loss overflow (NAN,INF)", + "weight_condition_collection": "Weight check", + "weight_initialization": "Initial weight value", + "weight_overflow": "Weight overflow", + "weight_too_large": "Weight above threshold", + "weight_too_small": "Weight below threshold", + "weight_not_changed": "Unchanged weight", + "weight_change_too_large": "Weight change above threshold", + "weight_change_too_small": "Weight change below threshold", + "gradient_condition_collection": "Gradient check", + "gradient_vanishing": "Gradient disappearance", + "gradient_too_large": "Gradient above threshold", + "gradient_exploding": "Gradient explosion", + "activation_condition_collection": "Activation value check", + "activation_range": "Activation value range", + "tensor_condition_collection": "Tensor check", + "tensor_overflow": "Tensor overflow", + "operator_overflow": "Operator overflow", + "tensor_initialization": "Initial tensor value", + "tensor_too_large": "Tensor above threshold", + "tensor_too_small": "Tensor below threshold", + "tensor_range": "Tensor value range", + "tensor_all_zero": "Whether tensor values are all 0", + "tensor_change_too_large": "Tensor change above threshold", + "tensor_change_too_small": "Tensor change below threshold", + "tensor_not_changed": "Unchanged tensor" + }, + "tensorTuningAdvice": { + "operator_real_data_validation": [ + "Real data validation using single operator", + "The operator may be faulty. You can perform the following operations to locate the fault:", + ["1.Check whether the operator implementation is correct."] + ], + "loss_overflow": [ + "Loss overflow (NAN,INF)", + "Loss overflow occurs. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether a tensor overflow occurs on a network. Find the first overflow tensor for further analysis." + ] + ], + "weight_initialization": [ + "Initial weight value", + "The initial weight value may be incorrect. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether the weight initialization algorithm in the script is correct." + ] + ], + "weight_overflow": [ + "Weight overflow", + "Weight value overflow occurs. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether the learning rate is too high.", + "2.Check whether the gradient is too large.", + "3.Check whether the optimizer algorithm is correct." + ] + ], + "weight_too_large": [ + "Weight above threshold", + "The weight value is greater than the threshold. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether the learning rate is correct.", + "2.Check whether the gradient is correct.", + "3.Check whether the optimizer algorithm is correct.", + "4.Check whether the weight before the update is too large." + ] + ], + "weight_too_small": [ + "Weight below threshold", + "The weight value is less than the threshold. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether the learning rate is correct.", + "2.Check whether the gradient is correct.", + "3.Check whether the optimizer algorithm is correct.", + "4.Check whether the weight before the update is too small." + ] + ], + "gradient_vanishing": [ + "Gradient disappearance", + "Gradient disappears. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "gradient_too_large": [ + "Gradient above threshold", + "The gradient value is greater than the threshold. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "gradient_exploding": [ + "Gradient explosion", + "Gradient value overflows. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "activation_range": [ + "Activation value range", + "The activation value range is not within the specified range. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "tensor_overflow": [ + "Tensor overflow", + "Tensor overflows. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "operator_overflow": [ + "Operator overflow", + "Overflow occurs during operator computation. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "tensor_too_large": [ + "Tensor above threshold", + "The tensor is greater than the threshold. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "tensor_too_small": [ + "Tensor below threshold", + "The tensor is less than the threshold. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "tensor_range": [ + "Tensor value range", + "The tensor value range is not within the configured range. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "tensor_all_zero": [ + "Whether tensor values are all 0", + "There are too many 0 values in a tensor. This may be an anomaly. You can perform the following operations to locate the fault:", + ["1.Check whether the input tensor of the operator is normal."] + ], + "weight_not_changed": [ + "Unchanged weight", + "The weight is not updated. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether the learning rate is too low.", + "2.Check whether the gradient is too small.", + "3.Check whether the optimizer algorithm is correct.", + "4.Check whether the weight is fixed." + ] + ], + "weight_change_too_large": [ + "Weight change above threshold", + "The weight change is greater than the threshold. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether the learning rate is correct.", + "2.Check whether the gradient is correct.", + "3.Check whether the optimizer algorithm is correct." + ] + ], + "weight_change_too_small": [ + "Weight change below threshold", + "The weight change is less than the threshold. This may be an anomaly. You can perform the following operations to locate the fault:", + [ + "1.Check whether the learning rate is correct.", + "2.Check whether the gradient is correct.", + "3.Check whether the optimizer algorithm is correct." + ] + ] + }, + "noAdvice": "No suggestions available", + "curStep": "Current Step", + "preStep": "Previous Step", + "compareResult": "Comparison Result" }, "explain": { "explain": "Model Explanation", diff --git a/mindinsight/ui/src/locales/zh-cn.json b/mindinsight/ui/src/locales/zh-cn.json index 7746331e..307b17d7 100644 --- a/mindinsight/ui/src/locales/zh-cn.json +++ b/mindinsight/ui/src/locales/zh-cn.json @@ -567,6 +567,131 @@ "tensor_change_too_small": "检查张量变化过小", "tensor_not_changed": "检查未变化张量" }, + "tensorTuningAdvice": { + "operator_real_data_validation": [ + "真实数据单算子验证", + "算子可能存在问题。您可以参考以下思路进行检查：", + ["1.请检查算子实现是否存在错误。"] + ], + "loss_overflow": [ + "检查loss值溢出（NAN，INF）", + "loss存在溢出现象。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.检查网络中的张量是否存在溢出，找到第一个溢出的张量后进行进一步分析"] + ], + "weight_initialization": [ + "检查权重初始值", + "权重初始值可能存在问题。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查脚本中的权重初始化算法是否存在问题"] + ], + "weight_overflow": [ + "检查权重溢出", + "权重值存在溢出现象。这可能是一个异常现象。您可以参考以下思路进行检查：", + [ + "1.请检查学习率是否过大", + "2.请检查梯度是否过大", + "3.请检查优化器算法是否正确" + ] + ], + "weight_too_large": [ + "检查过大权重", + "权重值大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：", + [ + "1.请检查学习率是否正确", + "2.请检查梯度是否正确", + "3.请检查优化器算法是否正确", + "4.请检查更新前的权重是否过大" + ] + ], + "weight_too_small": [ + "检查过小权重", + "权重值小于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：", + [ + "1.请检查学习率是否正确", + "2.请检查梯度是否正确", + "3.请检查优化器算法是否正确", + "4.请检查更新前的权重是否过小" + ] + ], + "gradient_vanishing": [ + "检查梯度消失", + "存在梯度消失现象。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "gradient_too_large": [ + "检查梯度过大", + "梯度值大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "gradient_exploding": [ + "检查梯度爆炸", + "梯度值溢出。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "activation_range": [ + "检查激活值范围", + "激活值范围不在设置的范围中。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "tensor_overflow": [ + "检查张量溢出", + "张量溢出。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "operator_overflow": [ + "检查计算过程溢出", + "算子计算过程中发生了溢出。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "tensor_too_large": [ + "检查过大张量", + "张量大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "tensor_too_small": [ + "检查过小张量", + "张量小于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "tensor_range": [ + "检查张量值范围", + "张量值范围不在设置的范围中。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "tensor_all_zero": [ + "检查张量是否全为0", + "张量中有太多0。这可能是一个异常现象。您可以参考以下思路进行检查：", + ["1.请检查算子的输入张量是否正常"] + ], + "weight_not_changed": [ + "检查未变化权重", + "权重未更新。这可能是一个异常现象。您可以参考以下思路进行检查：", + [ + "1.请检查学习率是否过小", + "2.请检查梯度是否过小", + "3.请检查优化器算法是否正确", + "4.请检查是否固定了此权重" + ] + ], + "weight_change_too_large": [ + "检查权重变化过大", + "权重变化大于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：", + [ + "1.请检查学习率是否正确", + "2.请检查梯度是否正确", + "3.请检查优化器算法是否正确" + ] + ], + "weight_change_too_small": [ + "检查权重变化过小", + "权重变化小于设置的阈值。这可能是一个异常现象。您可以参考以下思路进行检查：", + [ + "1.请检查学习率是否正确", + "2.请检查梯度是否正确", + "3.请检查优化器算法是否正确" + ] + ] + }, + "noAdvice": "没有可用的建议", "curStep": "显示当前step", "preStep": "显示上一step", "compareResult": "显示对比结果" diff --git a/mindinsight/ui/src/mixins/debuggerMixin.vue b/mindinsight/ui/src/mixins/debuggerMixin.vue index d6681657..f79ee6c8 100644 --- a/mindinsight/ui/src/mixins/debuggerMixin.vue +++ b/mindinsight/ui/src/mixins/debuggerMixin.vue @@ -98,6 +98,12 @@ export default { this.showFilterInput = true; } const tensorValue = res.data.tensor_value; + const statistics = tensorValue.statistics || {}; + this.statisticsArr = [ + tensorValue.curr_step_statistics || {}, + tensorValue.prev_step_statistics || {}, + tensorValue.statistics || {}, + ]; if (tensorValue.diff === 'Too large to show.') { this.tensorValue = []; this.$nextTick(() => { @@ -118,12 +124,7 @@ export default { ) { this.tensorValue = [this.tensorValue]; } - const statistics = tensorValue.statistics || {}; - this.statisticsArr = [ - tensorValue.curr_step_statistics || {}, - tensorValue.prev_step_statistics || {}, - tensorValue.statistics || {}, - ]; + this.$nextTick(() => { this.$refs.tensorValue.updateGridData( this.tensorValue, @@ -219,6 +220,9 @@ export default { if (this.step === 0) { this.step = 1; } + if (this.step >= 1000000000) { + this.step = 999999999; + } }, /** * Query current node info @@ -595,6 +599,8 @@ export default { } if (res.data.tensor_value) { const value = res.data.tensor_value.value; + const statistics = res.data.tensor_value.statistics || {}; + this.statisticsArr = [statistics]; if (value === 'Too large to show.') { this.tensorValue = []; this.$nextTick(() => { @@ -608,8 +614,6 @@ export default { return; } this.tensorValue = value instanceof Array ? value : [value]; - const statistics = res.data.tensor_value.statistics || {}; - this.statisticsArr = [statistics]; this.$nextTick(() => { this.$refs.tensorValue.updateGridData( this.tensorValue, @@ -657,14 +661,22 @@ export default { this.metadata.graph_name = metadata.graph_name ? metadata.graph_name : this.metadata.graph_name; - let graphName = this.graphFiles.value; - if (this.graphFiles.value === this.$t('debugger.all')) { + let graphName = + this.graphFiles.value === this.$t('debugger.all') + ? '' + : this.graphFiles.value; + if ( + this.graphFiles.value === this.$t('debugger.all') && + this.selectedNode.name + ) { graphName = this.selectedNode.name.split('/')[0]; } if (metadata.graph_name) { graphName = metadata.graph_name; } - this.queryAllTreeData(this.nodeName, true, graphName); + if (this.nodeName) { + this.queryAllTreeData(this.nodeName, true, graphName); + } } } if (metadata.step && metadata.step > this.metadata.step) { @@ -717,32 +729,6 @@ export default { this.radio1 = 'hit'; this.dealWatchpointHits(res.data.watch_point_hits); } - if ( - res.data.tensor_history && - this.metadata.step <= res.data.metadata.step - ) { - setTimeout(() => { - const tableData = res.data.tensor_history; - - if (this.tableData.length) { - this.tableData.forEach((val, key, arr) => { - tableData.forEach((value) => { - if (val.name === value.name) { - val.dtype = value.dtype; - val.shape = value.shape; - val.step = value.step; - val.value = value.value; - } - }); - }); - } else { - this.tableData = tableData; - } - - this.dealTableData(this.tableData); - this.tableData = JSON.parse(JSON.stringify(this.tableData)); - }, 200); - } this.pollData(); } }, @@ -1029,6 +1015,7 @@ export default { */ dealCheckPro(childNodes, check) { childNodes.forEach((val) => { + val.indeterminate = false; val.checked = check; if (val.childNodes) { this.dealCheckPro(val.childNodes, check); @@ -1039,8 +1026,6 @@ export default { * Add watchpoint */ addWatchPoint() { - this.searchWord = ''; - this.treeFlag = true; this.createWatchPointArr.push({ collection: { selectedId: this.conditionCollections[0].id, @@ -1075,7 +1060,7 @@ export default { * Function to be executed after the search value changes */ filterChange() { - if (this.searchWord === '') { + if (this.searchWord === '' && this.nodeTypes.value === 'all') { this.treeFlag = true; this.$nextTick(() => { setTimeout(() => { @@ -1093,6 +1078,7 @@ export default { * Filter tree data by node name */ filter() { + this.treeFlag = this.searchWord === '' && this.nodeTypes.value === 'all'; if (this.searchWord || this.nodeTypes.value !== 'all') { const params = { name: this.searchWord, @@ -1108,12 +1094,10 @@ export default { RequestService.search(params).then( (res) => { if (res.data && res.data.nodes) { - this.treeFlag = false; this.searchTreeData = res.data.nodes; this.searchCheckedArr = []; this.dealSearchResult(this.searchTreeData); this.defaultCheckedArr = this.searchCheckedArr; - this.treeFlag = false; this.searchNode.childNodes = []; const data = res.data.nodes.map((val) => { return { @@ -1439,12 +1423,15 @@ export default { selected: false, }; }); + if (focusLast) { - this.loadOriginalTree(); - this.$refs.tree.getCheckedKeys().forEach((val) => { - this.$refs.tree.setChecked(val, false); - }); - this.defaultCheckedArr = []; + if (this.nodeTypes.value === 'all' && !this.searchWord) { + this.loadOriginalTree(); + this.$refs.tree.getCheckedKeys().forEach((val) => { + this.$refs.tree.setChecked(val, false); + }); + this.defaultCheckedArr = []; + } const obj = this.watchPointArr[this.watchPointArr.length - 1]; obj.selected = true; @@ -1595,37 +1582,40 @@ export default { } }, focusWatchpointHit() { - let selectedNodeName = this.selectedNode.name; - if (this.graphFiles.value === this.$t('debugger.all')) { - selectedNodeName = selectedNodeName.replace( - `${selectedNodeName.split('/')[0]}/`, - '', - ); - } - this.expandKeys = []; - this.watchPointHits.forEach((val) => { - if (val.name === selectedNodeName) { - val.selected = true; - this.expandKeys.push(val.id); - } else { - val.selected = false; + if (this.selectedNode.name) { + let selectedNodeName = this.selectedNode.name; + if (this.graphFiles.value === this.$t('debugger.all')) { + selectedNodeName = selectedNodeName.replace( + `${selectedNodeName.split('/')[0]}/`, + '', + ); } - }); - this.$nextTick(() => { - setTimeout(() => { - const dom = document.querySelector('.hit-item.selected'); - if (dom) { - dom.scrollIntoView(); + this.expandKeys = []; + this.watchPointHits.forEach((val) => { + if (val.name === selectedNodeName) { + val.selected = true; + this.expandKeys.push(val.id); + } else { + val.selected = false; } - }, 200); - }); + }); + this.$nextTick(() => { + setTimeout(() => { + const dom = document.querySelector('.hit-item.selected'); + if (dom) { + dom.scrollIntoView(); + } + }, 200); + }); + } }, /** * Update tensor value * @param {number} key The index of the node of the watchPointHits currently clicked */ updateTensorValue(key) { - const name = this.watchPointHits[key].name; + const currentHit = this.watchPointHits[key]; + const name = currentHit.name; const temName = this.nodeName; this.nodeName = name; this.isHitIntoView = false; @@ -1635,14 +1625,12 @@ export default { name, single_node: true, watch_point_id: this.curWatchPointId ? this.curWatchPointId : 0, - graph_name: this.watchPointHits[key].graph_name, + graph_name: currentHit.graph_name, }, }; if (this.graphFiles.value === this.$t('debugger.all')) { delete params.params.graph_name; - params.params.name = `${this.watchPointHits[key].graph_name}/${name}`; - } else { - this.graphFiles.value = this.watchPointHits[key].graph_name; + params.params.name = `${currentHit.graph_name}/${name}`; } this.watchPointHits.forEach((val, index) => { if (key === index) { @@ -1659,19 +1647,28 @@ export default { } this.retrieveTensorHistory( {name: this.nodeName}, - this.watchPointHits[key].graph_name, + currentHit.graph_name, ); if (res.data && res.data.graph) { const graph = res.data.graph; + + if ( + this.graphFiles.value !== currentHit.graph_name && + this.graphFiles.value !== this.$t('debugger.all') + ) { + this.graphFiles.value = currentHit.graph_name; + this.resetAllData(graph, params.params.name); + } else { + this.querySingleNode( + JSON.parse(JSON.stringify(graph)), + params.params.name, + true, + ); + } if (graph.children) { this.dealTreeData(graph.children, name); this.defaultCheckedArr = this.$refs.tree.getCheckedKeys(); } - this.querySingleNode( - JSON.parse(JSON.stringify(graph)), - params.params.name, - true, - ); } }, (err) => { @@ -1696,11 +1693,13 @@ export default { watch_point_id: this.curWatchPointId ? this.curWatchPointId : 0, }, }; - if (this.graphFiles.value === this.$t('debugger.all')) { - if (graphName !== name) { - name = `${graphName}/${name}`; - params.params.name = name; - } + if ( + this.graphFiles.value === this.$t('debugger.all') && + graphName && + name + ) { + name = `${graphName}/${name}`; + params.params.name = name; } else { params.params.graph_name = graphName; } @@ -1712,42 +1711,8 @@ export default { if (res.data && res.data.graph) { const graph = res.data.graph; if (graph.nodes && !this.isCurrentGraph) { - this.node.childNodes = []; - this.origialTree = graph.nodes.map((val) => { - return { - label: val.name.split('/').pop(), - leaf: - val.type === 'name_scope' || - val.type === 'aggregation_scope' - ? false - : true, - ...val, - }; - }); - this.resolve(this.origialTree); - this.node.childNodes.forEach((val) => { - if (val.data.watched === 2) { - val.checked = true; - } - if (val.data.watched === 1) { - val.indeterminate = true; - } - }); + this.resetAllData(graph, name); this.isCurrentGraph = true; - this.firstFloorNodes = []; - this.allGraphData = {}; - d3.select('#graph svg').remove(); - this.selectedNode.name = ''; - this.packageDataToObject( - '', - true, - JSON.parse(JSON.stringify(graph.nodes)), - ); - this.querySingleNode( - JSON.parse(JSON.stringify(graph)), - name, - true, - ); } else { this.querySingleNode( JSON.parse(JSON.stringify(graph)), @@ -1827,6 +1792,41 @@ export default { this.dealTreeData(children.children, name); } }, + resetAllData(graph, name) { + this.node.childNodes = []; + this.origialTree = graph.nodes.map((val) => { + return { + label: val.name.split('/').pop(), + leaf: + val.type === 'name_scope' || val.type === 'aggregation_scope' + ? false + : true, + ...val, + }; + }); + this.resolve(this.origialTree); + this.node.childNodes.forEach((val) => { + if (val.data.watched === 2) { + val.checked = true; + } + if (val.data.watched === 1) { + val.indeterminate = true; + } + }); + this.firstFloorNodes = []; + this.allGraphData = {}; + d3.select('#graph svg').remove(); + this.packageDataToObject( + '', + true, + JSON.parse(JSON.stringify(graph.nodes)), + ); + if (name) { + this.querySingleNode(JSON.parse(JSON.stringify(graph)), name, true); + } else { + this.resetGraph(); + } + }, }, }; diff --git a/mindinsight/ui/src/views/debugger/debugger.vue b/mindinsight/ui/src/views/debugger/debugger.vue index 35424013..62cb8c1d 100644 --- a/mindinsight/ui/src/views/debugger/debugger.vue +++ b/mindinsight/ui/src/views/debugger/debugger.vue @@ -69,7 +69,7 @@ limitations under the License.
+ v-if="curWatchPointId && treeFlag">
{{scope.row.name}}
+ @click="updateTensorValue(scope.$index)"> + {{scope.row.graph_name}}/{{scope.row.name}} +
@@ -217,11 +219,17 @@ limitations under the License.
- - + + + + + {{ $t('tensors.dimension') }} {{ curRowObj.shape }}
- - + + {{ $t('debugger.max') }} {{ statistics.overall_max }} {{ $t('debugger.min') }} {{ statistics.overall_min }} @@ -822,7 +830,11 @@ export default { ); }, selectAllFiles(type) { - if (!type && !this.$refs.tree.getCheckedKeys().length) { + if ( + !type && + !this.$refs.tree.getCheckedKeys().length && + !this.$refs.tree.getHalfCheckedKeys().length + ) { return; } if (type && !this.node.childNodes.find((val) => val.checked === false)) { @@ -830,9 +842,10 @@ export default { } const watchNodes = []; this.node.childNodes.forEach((val) => { - if (type !== val.checked) { + if (type !== val.checked || (!type && val.indeterminate)) { watchNodes.push(val.data.name); } + val.indeterminate = false; val.checked = type; if (val.childNodes) { @@ -1214,10 +1227,7 @@ export default { if (response && response.data && response.data.graph) { const graph = response.data.graph; const nodes = JSON.parse(JSON.stringify(graph.nodes)); - if (this.treeFlag) { - this.nodeExpandLinkage(nodes, name); - } - + this.nodeExpandLinkage(nodes, name); this.dealGraphData(nodes, name); } },