我正在尝试为 React 应用程序中的 blazeface 人脸检测模型设置 WASM 后端。尽管带有 vanillajs 的演示可以运行它几个小时而没有任何错误，但在将凸轮打开超过 3-5 分钟后，它会抛出“未处理的拒绝（运行时错误）：索引越界错误”。

整个应用程序因此错误而崩溃。从下面的错误日志来看，也许它与我猜测的disposeData()或disposeTensor()函数有关，它们与垃圾收集有关。但我不知道这是否是 WASM 库本身的错误。你知道为什么会发生这种情况吗？

下面我也提供了我的渲染预测功能。

  renderPrediction = async () => {
    const model = await blazeface.load({ maxFaces: 1, scoreThreshold: 0.95 });
    if (this.play) {
      const canvas = this.refCanvas.current;
      const ctx = canvas.getContext("2d");
      const returnTensors = false;
      const flipHorizontal = true;
      const annotateBoxes = true;
      const predictions = await model.estimateFaces(
        this.refVideo.current,
        returnTensors,
        flipHorizontal,
        annotateBoxes
      );

      if (predictions.length > 0) {
        ctx.clearRect(0, 0, canvas.width, canvas.height);
        for (let i = 0; i < predictions.length; i++) {
          if (returnTensors) {
            predictions[i].topLeft = predictions[i].topLeft.arraySync();
            predictions[i].bottomRight = predictions[i].bottomRight.arraySync();
            if (annotateBoxes) {
              predictions[i].landmarks = predictions[i].landmarks.arraySync();
            }
          }
          try {
          } catch (err) {
            console.log(err.message);
          }
          const start = predictions[i].topLeft;
          const end = predictions[i].bottomRight;
          const size = [end[0] - start[0], end[1] - start[1]];

      


          if (annotateBoxes) {
            const landmarks = predictions[i].landmarks;

            ctx.fillStyle = "blue";
            for (let j = 0; j < landmarks.length; j++) {
              const x = landmarks[j][0];
              //console.log(typeof x) // number
              const y = landmarks[j][1];
              ctx.fillRect(x, y, 5, 5);
            }
          }
        }
      }
      requestAnimationFrame(this.renderPrediction);
    }
  };

错误的完整日志：

Unhandled Rejection (RuntimeError): index out of bounds
(anonymous function)
unknown
./node_modules/@tensorflow/tfjs-backend-wasm/dist/tf-backend-wasm.esm.js/</tt</r</r._dispose_data
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/wasm-out/tfjs-backend-wasm.js:9



disposeData
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/backend_wasm.ts:115

  112 | 
  113 | disposeData(dataId: DataId) {
  114 |   const data = this.dataIdMap.get(dataId);
> 115 |   this.wasm._free(data.memoryOffset);
      | ^  116 |   this.wasm.tfjs.disposeData(data.id);
  117 |   this.dataIdMap.delete(dataId);
  118 | }

disposeTensor
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/engine.ts:838

  835 |     'tensors');
  836 | let res;
  837 | const inputMap = {};
> 838 | inputs.forEach((input, i) => {
      | ^  839 |     inputMap[i] = input;
  840 | });
  841 | return this.runKernelFunc((_, save) => {

dispose
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/tensor.ts:388
endScope
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/engine.ts:983
tidy/<
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/engine.ts:431

  428 | if (kernel != null) {
  429 |     kernelFunc = () => {
  430 |         const numDataIdsBefore = this.backend.numDataIds();
> 431 |         out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });
      | ^  432 |         const outInfos = Array.isArray(out) ? out : [out];
  433 |         if (this.shouldCheckForMemLeaks()) {
  434 |             this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);

scopedRun
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/engine.ts:448

  445 | // inputsToSave and outputsToSave. Currently this is the set of ops
  446 | // with kernel support in the WASM backend. Once those ops and
  447 | // respective gradients are modularised we can remove this path.
> 448 | if (outputsToSave == null) {
      | ^  449 |     outputsToSave = [];
  450 | }
  451 | const outsToSave = outTensors.filter((_, i) => outputsToSave[i]);

tidy
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/engine.ts:431

  428 | if (kernel != null) {
  429 |     kernelFunc = () => {
  430 |         const numDataIdsBefore = this.backend.numDataIds();
> 431 |         out = kernel.kernelFunc({ inputs, attrs, backend: this.backend });
      | ^  432 |         const outInfos = Array.isArray(out) ? out : [out];
  433 |         if (this.shouldCheckForMemLeaks()) {
  434 |             this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);

tidy
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/src/globals.ts:190

  187 |     const tensors = getTensorsInContainer(container);
  188 |     tensors.forEach(tensor => tensor.dispose());
  189 | }
> 190 | /**
  191 |  * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed
  192 |  * automatically.
  193 |  */

estimateFaces
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/blazeface_reactjs/node_modules/@tensorflow-models/blazeface/dist/blazeface.esm.js:17
Camera/this.renderPrediction
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/blazeface_reactjs/src/Camera.js:148

  145 | const returnTensors = false;
  146 | const flipHorizontal = true;
  147 | const annotateBoxes = true;
> 148 | const predictions = await model.estimateFaces(
      | ^  149 |   this.refVideo.current,
  150 |   returnTensors,
  151 |   flipHorizontal,

async*Camera/this.renderPrediction
C:/Users/osman.cakir/Documents/osmancakirio/deepLearning/blazeface_reactjs/src/Camera.js:399

  396 |         // }
  397 |       }
  398 |     }
> 399 |     requestAnimationFrame(this.renderPrediction);
      | ^  400 |   }
  401 | };
  402 |