本文以花朵识别项目为例

一、训练花朵识别模型

使用Colab训练TensorFlow Lite模型。训练后下载相应的模型文件（model.tflite）、标签文件（labels.txt）。

二、配置项目

implementation('org.tensorflow:tensorflow-lite:0.0.0-nightly') { 
    changing = true } implementation('org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly') { 
    changing = true } implementation('org.tensorflow:tensorflow-lite-support:0.0.0-nightly') { 
    changing = true } 

为防止编译项目时TensorFlow Lite模型文件被编译，android{}代码块中添加

讯享网aaptOptions { 
    noCompress "tflite" } 

三、编写代码

1、初始化解释器，加载模型、标签文件，指定运行代理、线程数量

讯享网

/ * 初始化分类器 * @param activity * @param device * @param numThreads * @throws IOException */ protected Classifier(Activity activity, Device device, int numThreads) throws IOException { 
    tfliteModel = FileUtil.loadMappedFile(activity, getModelPath()); switch (device) { 
    case NNAPI: nnApiDelegate = new NnApiDelegate(); tfliteOptions.addDelegate(nnApiDelegate); break; case GPU: gpuDelegate = new GpuDelegate(); tfliteOptions.addDelegate(gpuDelegate); break; case CPU: break; } tfliteOptions.setNumThreads(numThreads); tflite = new Interpreter(tfliteModel, tfliteOptions); // Loads labels out from the label file. labels = FileUtil.loadLabels(activity, getLabelPath()); // Reads type and shape of input and output tensors, respectively. int imageTensorIndex = 0; int[] imageShape = tflite.getInputTensor(imageTensorIndex).shape(); // { 
   1, height, width, 3} imageSizeY = imageShape[1]; imageSizeX = imageShape[2]; DataType imageDataType = tflite.getInputTensor(imageTensorIndex).dataType(); int probabilityTensorIndex = 0; int[] probabilityShape = tflite.getOutputTensor(probabilityTensorIndex).shape(); // { 
   1, NUM_CLASSES} DataType probabilityDataType = tflite.getOutputTensor(probabilityTensorIndex).dataType(); // Creates the input tensor. inputImageBuffer = new TensorImage(imageDataType); // Creates the output tensor and its processor. outputProbabilityBuffer = TensorBuffer.createFixedSize(probabilityShape, probabilityDataType); // Creates the post processor for the output probability. probabilityProcessor = new TensorProcessor.Builder().add(getPostprocessNormalizeOp()).build(); LOGGER.d("Created a Tensorflow Lite Image Classifier."); } 

2、运行解释器，根据图片获取相应的预测结果。

讯享网/ * 运行解释器并返回结果 * @param bitmap * @param sensorOrientation * @return */ public List<Recognition> recognizeImage(final Bitmap bitmap, int sensorOrientation) { 
    // Logs this method so that it can be analyzed with systrace. Trace.beginSection("recognizeImage"); Trace.beginSection("loadImage"); long startTimeForLoadImage = SystemClock.uptimeMillis(); inputImageBuffer = loadImage(bitmap, sensorOrientation); long endTimeForLoadImage = SystemClock.uptimeMillis(); Trace.endSection(); LOGGER.v("Timecost to load the image: " + (endTimeForLoadImage - startTimeForLoadImage)); // Runs the inference call. Trace.beginSection("runInference"); long startTimeForReference = SystemClock.uptimeMillis(); tflite.run(inputImageBuffer.getBuffer(), outputProbabilityBuffer.getBuffer().rewind()); long endTimeForReference = SystemClock.uptimeMillis(); Trace.endSection(); LOGGER.v("Timecost to run model inference: " + (endTimeForReference - startTimeForReference)); // Gets the map of label and probability. Map<String, Float> labeledProbability = new TensorLabel(labels, probabilityProcessor.process(outputProbabilityBuffer)) .getMapWithFloatValue(); Trace.endSection(); // Gets top-k results. return getTopKProbability(labeledProbability); } 

3、关闭解释器、释放资源

 public void close() { 
    if (tflite != null) { 
    tflite.close(); tflite = null; } if (gpuDelegate != null) { 
    gpuDelegate.close(); gpuDelegate = null; } if (nnApiDelegate != null) { 
    nnApiDelegate.close(); nnApiDelegate = null; } tfliteModel = null; } 

四、关键类解释

讯享网try (Interpreter interpreter = new Interpreter(tensorflow_lite_model_file)) { 
    interpreter.run(input, output); } 

GpuDelegate delegate = new GpuDelegate(); Interpreter.Options options = (new Interpreter.Options()).addDelegate(delegate); Interpreter interpreter = new Interpreter(tensorflow_lite_model_file, options); try { 
    interpreter.run(input, output); }