APP开发
build.gradle导入库
//implementation 'org.tensorflow:tensorflow-android:+'
implementation 'org.tensorflow:tensorflow-lite:2.4.0'
implementation 'org.tensorflow:tensorflow-lite-support:0.3.1
implementation 'org.tensorflow:tensorflow-lite-metadata:0.3.1'
加载模型
try {
tfLiteClassificationUtil = new TFLiteClassificationUtil(CONST.downPath + "/zjym.tflite");
Toast.makeText(MainActTflite.this, "模型加载成功！", Toast.LENGTH_SHORT).show();
} catch (Exception e) {
Toast.makeText(MainActTflite.this, "模型加载失败！", Toast.LENGTH_SHORT).show();
e.printStackTrace();
finish();
}
模型一般在assets目录下，在编译时会集成到APP中，不利于模型的迭代，这里模型保存在内部存储目录下。
分类预测
try { // 预测图像
FileInputStream fis = new FileInputStream(image_path);
imageView.setImageBitmap(BitmapFactory.decodeStream(fis));
long start = System.currentTimeMillis();
int\[\]\[\] res2Arr = tfLiteClassificationUtil.predictImage(image_path);
long end = System.currentTimeMillis();
String show_text = "预测结果标签：" + (int) res2Arrres2Arr.length-10 +
"\n名称：" + classNames.get((int) res2Arrres2Arr.length-10) +"概率：" + (float) res2Arrres2Arr.length - 11 / 256 +
"\n名称：" + classNames.get((int) res2Arrres2Arr.length-20) +"概率：" + (float) res2Arrres2Arr.length - 21 / 256 +
"\n名称：" + classNames.get((int) res2Arrres2Arr.length-30) +"概率：" + (float) res2Arrres2Arr.length - 31 / 256 +
"\n时间：" + (end - start) + "ms";
textView.setText(show_text);
} catch (Exception e) {
e.printStackTrace();
}
res2Arrres2Arr.length - 11 / 256，两个整数相除显示为0，添加(float)显示字符串
TFLiteClassificationUtil类功能模块
public TFLiteClassificationUtil(String modelPath) throws Exception {
File file = new File(modelPath);
if (!file.exists()) {
throw new Exception("model file is not exists!");
}
try {
Interpreter.Options options = new Interpreter.Options();
options.setNumThreads(NUM_THREADS);// 使用多线程预测
NnApiDelegate delegate = new NnApiDelegate();// 使用Android自带的API或者GPU加速
// GpuDelegate delegate = new GpuDelegate();
options.addDelegate(delegate);
tflite = new Interpreter(file, options);
// 获取输入，shape为{1, height, width, 3}
int\[\] imageShape = tflite.getInputTensor(tflite.getInputIndex("input_1")).shape();
DataType imageDataType = tflite.getInputTensor(tflite.getInputIndex("input_1")).dataType();
inputImageBuffer = new TensorImage(imageDataType);
// 获取输入，shape为{1, NUM_CLASSES}
int\[\] probabilityShape = tflite.getOutputTensor(tflite.getOutputIndex("Identity")).shape();
DataType probabilityDataType = tflite.getOutputTensor(tflite.getOutputIndex("Identity")).dataType();
//outputProbabilityBuffer = TensorBuffer.createFixedSize(probabilityShape, probabilityDataType);
outputProbabilityBuffer = TensorBuffer.createFixedSize(tflite.getOutputTensor(0).shape(), DataType.UINT8);
// 添加图像预处理方式
imageProcessor = new ImageProcessor.Builder()
.add(new ResizeOp(224, 224, ResizeOp.ResizeMethod.NEAREST_NEIGHBOR))
.add(new NormalizeOp(new float\[\] {0.0f}, new float\[\] {255.0f}))
.add(new QuantizeOp(0f, 0.003921569f))
.add(new CastOp(DataType.UINT8))
.build();
TensorProcessor probabilityPostProcessor = new TensorProcessor.Builder()
.add(new DequantizeOp((float) 0, (float) 0.00390625))
.add(new NormalizeOp(new float\[\]{0.0f}, new float\[\]{1.0f}))
.build();
} catch (Exception e) {
e.printStackTrace();
throw new Exception("load model fail!");
}
}
public int\[\]\[\] predictImage(String image_path) throws Exception {
if (!new File(image_path).exists()) {
throw new Exception("image file is not exists!");
}
FileInputStream fis = new FileInputStream(image_path);
Bitmap bitmap = BitmapFactory.decodeStream(fis);
int\[\]\[\] result = predictImage(bitmap);
if (bitmap.isRecycled()) {
bitmap.recycle();
}
return result;
}
// 重载方法，直接使用Bitmap预测
public int\[\]\[\] predictImage(Bitmap bitmap) throws Exception {
return predict(bitmap);
}
private int\[\]\[\] predict(Bitmap bmp) throws Exception {
inputImageBuffer = loadImage(bmp);
try {
tflite.run(inputImageBuffer.getBuffer(), outputProbabilityBuffer.getBuffer().rewind());
} catch (Exception e) {
throw new Exception("predict image fail! log:" + e);
}
int\[\] results = outputProbabilityBuffer.getIntArray();
Log.d("results", Arrays.toString(results));
int\[\]\[\] arr = new intresults.length2;
for (int i=0;i<results.length;i++) {
arri0 = i;
arri1 = resultsi;
}
Arrays.sort(arr, Comparator.comparingInt(e -> e1));
//int l = getMaxResult(results);
return arr;//new float\[\]{l, resultsl};
}
tflite默认保存格式为UINT8，如果不加add(new CastOp(DataType.UINT8))可能显示
Cannot copy to a TensorFlowLite tensor (input_1) with 150528 bytes from a Java Buffer with 602112 bytes
默认的预训练模型是 EfficientNet-Lite0，如果为其他模型，其输入参数等也要修改。可通过下述方法查看。
Android Studio ->File ->open ->other ->tflite，打开tflite模型，build ->Make Project 会自动生成模型接口类，并移动模型到ml目录，查看类中模型参数。