無法在類型為 UINT8 的 TensorFlowLite 張量和類型為 Java 的對象之間轉換

Question

我是從圖像數據集創建模型，我的數據集是 Tensorflow Lite 版本“tflite”。 當我嘗試使用 android 對象檢測時，出現此錯誤：無法在 UINT8 類型的 TensorFlowLite 張量和 [[[F 類型的 Java 對象之間轉換（與 TensorFlowLite 類型 FLOAT32 兼容）。

注意：我的模型是量化的，我用 python 中的 tensorflow tflite-model-maker 構建了它。

我的 tflite 模型輸入：

[{'name': 'input_1', 'index': 178, 'shape': array([  1, 224, 224,   3], dtype=int32), 'shape_signature': array([ -1, 224, 224,   3], dtype=int32), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.003921568859368563, 0), 'quantization_parameters': {'scales': array([0.00392157], dtype=float32), 'zero_points': array([0], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]

模型輸出：

[{'name': 'Identity', 'index': 179, 'shape': array([  1, 131], dtype=int32), 'shape_signature': array([ -1, 131], dtype=int32), 'dtype': <class 'numpy.uint8'>, 'quantization': (0.00390625, 0), 'quantization_parameters': {'scales': array([0.00390625], dtype=float32), 'zero_points': array([0], dtype=int32), 'quantized_dimension': 0}, 'sparsity_parameters': {}}]

我嘗試使用以下代碼（Android Java）：

 private static final String TAG = "TFLiteObjectDetectionAPIModelWithInterpreter";

 // Only return this many results.
 private static final int NUM_DETECTIONS = 131;
 // Float model
 private static final float IMAGE_MEAN = 127.5f;
 private static final float IMAGE_STD = 127.5f;
 // Number of threads in the java app
 private static final int NUM_THREADS = 4;
 private boolean isModelQuantized;
 // Config values.
 private int inputSize;
 // Pre-allocated buffers.
 private final List<String> labels = new ArrayList<>();
 private int[] intValues;
 // outputLocations: array of shape [Batchsize, NUM_DETECTIONS,4]
 // contains the location of detected boxes
 private float[][][] outputLocations;
 //private float[][][] outputLocations;
 // outputClasses: array of shape [Batchsize, NUM_DETECTIONS]
 // contains the classes of detected boxes
 private float[][] outputClasses;
 // outputScores: array of shape [Batchsize, NUM_DETECTIONS]
 // contains the scores of detected boxes
 private float[][] outputScores;
 // numDetections: array of shape [Batchsize]
 // contains the number of detected boxes
 private float[] numDetections;

 private ByteBuffer imgData;

 private MappedByteBuffer tfLiteModel;
 private Interpreter.Options tfLiteOptions;
 private Interpreter tfLite;

 private TFLiteObjectDetectionAPIModel() {}

 /** Memory-map the model file in Assets. */
 private static MappedByteBuffer loadModelFile(AssetManager assets, String modelFilename)
     throws IOException {
   AssetFileDescriptor fileDescriptor = assets.openFd(modelFilename);
   FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
   FileChannel fileChannel = inputStream.getChannel();
   long startOffset = fileDescriptor.getStartOffset();
   long declaredLength = fileDescriptor.getDeclaredLength();
   return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
 }

 /**
  * Initializes a native TensorFlow session for classifying images.
  *
  * @param modelFilename The model file path relative to the assets folder
  * @param labelFilename The label file path relative to the assets folder
  * @param inputSize The size of image input
  * @param isQuantized Boolean representing model is quantized or not
  */
 public static Detector create(
     final Context context,
     final String modelFilename,
     final String labelFilename,
     final int inputSize,
     final boolean isQuantized)
     throws IOException {
   final TFLiteObjectDetectionAPIModel d = new TFLiteObjectDetectionAPIModel();

   MappedByteBuffer modelFile = loadModelFile(context.getAssets(), modelFilename);
   MetadataExtractor metadata = new MetadataExtractor(modelFile);
   try (BufferedReader br =
       new BufferedReader(
           new InputStreamReader(
               metadata.getAssociatedFile(labelFilename), Charset.defaultCharset()))) {
     String line;
     while ((line = br.readLine()) != null) {
       Log.w(TAG, line);
       d.labels.add(line);
     }
   }

   d.inputSize = inputSize;

   try {
     Interpreter.Options options = new Interpreter.Options();
     options.setNumThreads(NUM_THREADS);
     d.tfLite = new Interpreter(modelFile, options);
     d.tfLiteModel = modelFile;
     d.tfLiteOptions = options;
   } catch (Exception e) {
     throw new RuntimeException(e);
   }

   d.isModelQuantized = isQuantized;
   // Pre-allocate buffers.
   int numBytesPerChannel;
   if (isQuantized) {
     numBytesPerChannel = 1; // Quantized
   } else {
     numBytesPerChannel = 4; // Floating point
   }

   d.imgData = ByteBuffer.allocateDirect(1 * d.inputSize * d.inputSize * 3 * numBytesPerChannel);
  // d.imgData = ByteBuffer.allocateDirect(1 * d.inputSize * d.inputSize * 3 * 1);
   d.imgData.order(ByteOrder.nativeOrder());
   d.intValues = new int[d.inputSize * d.inputSize];

   d.outputLocations = new float[1][NUM_DETECTIONS][4];
   d.outputClasses = new float[1][NUM_DETECTIONS];
   d.outputScores = new float[1][NUM_DETECTIONS];
   d.numDetections = new float[1];
   return d;
 }

 @Override
 public List<Recognition> recognizeImage(final Bitmap bitmap) {
   // Log this method so that it can be analyzed with systrace.
   Trace.beginSection("recognizeImage");

   Trace.beginSection("preprocessBitmap");
   // Preprocess the image data from 0-255 int to normalized float based
   // on the provided parameters.
   bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());

   imgData.rewind();
   for (int i = 0; i < inputSize; ++i) {
     for (int j = 0; j < inputSize; ++j) {
       int pixelValue = intValues[i * inputSize + j];
       if (isModelQuantized) {
         // Quantized model
         imgData.put((byte) ((pixelValue >> 16) & 0xFF));
         imgData.put((byte) ((pixelValue >> 8) & 0xFF));
         imgData.put((byte) (pixelValue & 0xFF));

       } else { // Float model
         imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
         imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
         imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
       }
     }
   }

   Trace.endSection(); // preprocessBitmap

   // Copy the input data into TensorFlow.
   Trace.beginSection("feed");
   outputLocations = new float[1][NUM_DETECTIONS][4];
   outputClasses = new float[1][NUM_DETECTIONS];
   outputScores = new float[1][NUM_DETECTIONS];
   numDetections = new float[1];

   Object[] inputArray = {imgData};
   Map<Integer, Object> outputMap = new HashMap<>();
   outputMap.put(0, outputLocations);
   outputMap.put(1, outputClasses);
   outputMap.put(2, outputScores);
   outputMap.put(3, numDetections);
   Trace.endSection();

   // Run the inference call.
   Trace.beginSection("run");
   tfLite.runForMultipleInputsOutputs(inputArray, outputMap);
   Trace.endSection();

   // Show the best detections.
   // after scaling them back to the input size.
   // You need to use the number of detections from the output and not the NUM_DETECTONS variable
   // declared on top
   // because on some models, they don't always output the same total number of detections
   // For example, your model's NUM_DETECTIONS = 20, but sometimes it only outputs 16 predictions
   // If you don't use the output's numDetections, you'll get nonsensical data
   int numDetectionsOutput =
       min(
           NUM_DETECTIONS,
           (int) numDetections[0]); // cast from float to integer, use min for safety

   final ArrayList<Recognition> recognitions = new ArrayList<>(numDetectionsOutput);
   for (int i = 0; i < numDetectionsOutput; ++i) {
     final RectF detection =
         new RectF(
             outputLocations[0][i][1] * inputSize,
             outputLocations[0][i][0] * inputSize,
             outputLocations[0][i][3] * inputSize,
             outputLocations[0][i][2] * inputSize);


     recognitions.add(
         new Recognition(
             "" + i, labels.get((int) outputClasses[0][i]), outputScores[0][i], detection));
   }
   Trace.endSection(); // "recognizeImage"
   return recognitions;
 }

 @Override
 public void enableStatLogging(final boolean logStats) {}

 @Override
 public String getStatString() {
   return "";
 }

 @Override
 public void close() {
   if (tfLite != null) {
     tfLite.close();
     tfLite = null;
   }
 }

 @Override
 public void setNumThreads(int numThreads) {
   if (tfLite != null) {
     tfLiteOptions.setNumThreads(numThreads);
     recreateInterpreter();
   }
 }

 @Override
 public void setUseNNAPI(boolean isChecked) {
   if (tfLite != null) {
     tfLiteOptions.setUseNNAPI(isChecked);
     recreateInterpreter();
   }
 }

 private void recreateInterpreter() {
   tfLite.close();
   tfLite = new Interpreter(tfLiteModel, tfLiteOptions);
 }
}

Answer 1

當模型完全量化輸出也是字節大小時。 您嘗試將輸出加載到浮點數組中：

outputLocations = new float[1][NUM_DETECTIONS][4];
outputClasses = new float[1][NUM_DETECTIONS];
outputScores = new float[1][NUM_DETECTIONS];
numDetections = new float[1];

將它們更改為byte ，事情應該可以工作。 檢查您是否需要正確地對輸出進行反量化。

改進點：使用tflite 支持庫：這將簡化您的前處理、量化-反量化、輸入-輸出數據管理

Answer 2

在閱讀了一些文檔后，模型的默認導出是 32。TensorFlow Lite 模型應該有一個訓練后量化成為 float16。 請參閱此處使用 TensorFlow Lite 進行圖像分類<\/a>

運行此命令以定義量化配置。

然后使用配置導出 TensorFlow Lite 模型。

使用配置導出 TensorFlow Lite 模型標簽

model.export(export_dir='.', quantization_config=config,export_format=ExportFormat.LABEL)