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Cant convert between a TensorFlowLite tensor with type UINT8 and a Java object of type [[F (which is compatible with the TensorFlowLite type FLOAT32)

I’m trying to run a Tflite model on android, with flutter but i’m getting this error-

E/AndroidRuntime(18461): Caused by: java.lang.IllegalArgumentException: Cannot copy to a TensorFlowLite tensor (serving_default_sequential_5_input:0) with 150528 bytes from a Java Buffer with 602112 bytes. E/AndroidRuntime(18461): at org.tensorflow.lite.TensorImpl.throwIfSrcShapeIsIncompatible(TensorImpl.java:418) E/AndroidRuntime(18461): at org.tensorflow.lite.TensorImpl.setTo(TensorImpl.java:139) E/AndroidRuntime(18461): at org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:237) E/AndroidRuntime(18461): at org.tensorflow.lite.InterpreterImpl.runForMultipleInputsOutputs(InterpreterImpl.java:135) E/AndroidRuntime(18461): at org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:80) E/AndroidRuntime(18461): at org.tensorflow.lite.InterpreterImpl.run(InterpreterImpl.java:128) E/AndroidRuntime(18461): at org.tensorflow.lite.Interpreter.run(Interpreter.java:80) E/AndroidRuntime(18461): at sq.flutter.tflite.TflitePlugin$RunModelOnBinary.runTflite(TflitePlugin.java:530) E/AndroidRuntime(18461): at sq.flutter.tflite.TflitePlugin$TfliteTask.doInBackground(TflitePlugin.java:471) E/AndroidRuntime(18461): at sq.flutter.tflite.TflitePlugin$TfliteTask.doInBackground(TflitePlugin.java:445) E/AndroidRuntime(18461): at android.os.AsyncTask$3.call(AsyncTask.java:378) E/AndroidRuntime(18461): at java.util.concurrent.FutureTask.run(FutureTask.java:266) E/AndroidRuntime(18461): … 4 more I/Process (18461): Sending signal. PID: 18461 SIG: 9

And this line stood out to me-

Caused by: java.lang.IllegalArgumentException: Cannot copy to a TensorFlowLite tensor (serving_default_sequential_5_input:0) with 150528 bytes from a Java Buffer with 602112 bytes.

What am I doing wrong? Here’s my code-

Future<List<dynamic>> runModel(Uint8List image) async {
    print("Loadin gmodel");
    String? res = await Tflite.loadModel(
      model: "assets/model.tflite",
      labels: "assets/labels.txt",
    );
    print("model loaded and loading running predictin");
    img.Image? Image = img.decodeJpg(image);
    var recognitions = await Tflite.runModelOnBinary(
        binary: imageToByteListFloat32(Image!, 224),
        numResults: 2, // get this value to be the number of classes you have
        threshold: 0.05, // defaults to 0.1, or put whatever you want here
        asynch: true // defaults to true
        );
    print(recognitions);
    await Tflite.close();
    return [];
  }

  Uint8List imageToByteListFloat32(img.Image image, int inputSize) {
    var convertedBytes = Float32List(1 * inputSize * inputSize * 3);
    var buffer = Float32List.view(convertedBytes.buffer);
    int pixelIndex = 0;
    for (var i = 0; i < inputSize; i++) {
      for (var j = 0; j < inputSize; j++) {
        var pixel = image.getPixel(j, i);
        buffer[pixelIndex++] = img.getRed(pixel) / 255.0;
        buffer[pixelIndex++] = img.getGreen(pixel) / 255.0;
        buffer[pixelIndex++] = img.getBlue(pixel) / 255.0;
      }
    }
    return convertedBytes.buffer.asUint8List();
  }

Answer

The model seems to be requesting a UINT8 (unsigned 8-bits integer) tensor.

I think you can simplify your code a bit:

  • Prepare a UInt8 buffer instead of Float32
  • You don’t need to divide the value by 255.0

Then it should work.

(As a side note, using ByteBuffer will be much more efficient than array/list)

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