Cannot convert between a TensorFlowLite tensor with type UINT8 and a Java object of type

Question

Main Error is

Cannot convert between a TensorFlowLite tensor with type UINT8 and a Java object of type [[F (which is compatible with the TensorFlowLite type FLOAT32).

Model used from https://www.tensorflow.org/lite/models/image_classification/overview

Model load code

      String res = await Tflite.loadModel(
        model: "assets/mobilenet_v1.tflite",
        labels: "assets/mobilenet_v1.txt",
        numThreads: 1, // defaults to 1
        isAsset: true,
        useGpuDelegate:
            false, // defaults to false, set to true to use GPU delegate
      );

Running model on image

    var recognitions = await Tflite.runModelOnImage(
      path: image.path,
      imageMean: 0.0,
      imageStd: 255.0, // defaults to 1.0
      threshold: 0.2, // defaults to 0.1
    );

    setState(() {
      _recognitions = recognitions;
      print('Got ${_recognitions.length} recognitions');
    });

Error stack

I/flutter (14229): Working on /data/user/0/com.example.safety_app/cache/image_picker2866004901851985262.jpg
W/com.example.safety_app(14229): type=1400 audit(0.0:118930): avc: denied { read } for comm=4173796E635461736B202332 name="u:object_r:vendor_default_prop:s0" dev="tmpfs" ino=18666 scontext=u:r:untrusted_app:s0:c170,c258,c512,c768 tcontext=u:object_r:vendor_default_prop:s0 tclass=file permissive=0
E/libc    (14229): Access denied finding property "ro.hardware.chipname"
E/AndroidRuntime(14229): FATAL EXCEPTION: AsyncTask #2
E/AndroidRuntime(14229): Process: com.example.safety_app, PID: 14229
E/AndroidRuntime(14229): java.lang.RuntimeException: An error occurred while executing doInBackground()
E/AndroidRuntime(14229):    at android.os.AsyncTask$3.done(AsyncTask.java:354)
E/AndroidRuntime(14229):    at java.util.concurrent.FutureTask.finishCompletion(FutureTask.java:383)
E/AndroidRuntime(14229):    at java.util.concurrent.FutureTask.setException(FutureTask.java:252)
E/AndroidRuntime(14229):    at java.util.concurrent.FutureTask.run(FutureTask.java:271)
E/AndroidRuntime(14229):    at android.os.AsyncTask$SerialExecutor$1.run(AsyncTask.java:245)
E/AndroidRuntime(14229):    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1167)
E/AndroidRuntime(14229):    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:641)
E/AndroidRuntime(14229):    at java.lang.Thread.run(Thread.java:764)
E/AndroidRuntime(14229): Caused by: java.lang.IllegalArgumentException: Cannot convert between a TensorFlowLite tensor with type UINT8 and a Java object of type [[F (which is compatible with the TensorFlowLite type FLOAT32).
E/AndroidRuntime(14229):    at org.tensorflow.lite.Tensor.throwIfTypeIsIncompatible(Tensor.java:406)
E/AndroidRuntime(14229):    at org.tensorflow.lite.Tensor.copyTo(Tensor.java:251)
E/AndroidRuntime(14229):    at org.tensorflow.lite.NativeInterpreterWrapper.run(NativeInterpreterWrapper.java:170)
E/AndroidRuntime(14229):    at org.tensorflow.lite.Interpreter.runForMultipleInputsOutputs(Interpreter.java:343)
E/AndroidRuntime(14229):    at org.tensorflow.lite.Interpreter.run(Interpreter.java:304)
E/AndroidRuntime(14229):    at sq.flutter.tflite.TflitePlugin$RunModelOnImage.runTflite(TflitePlugin.java:481)
E/AndroidRuntime(14229):    at sq.flutter.tflite.TflitePlugin$TfliteTask.doInBackground(TflitePlugin.java:448)
E/AndroidRuntime(14229):    at sq.flutter.tflite.TflitePlugin$TfliteTask.doInBackground(TflitePlugin.java:422)
E/AndroidRuntime(14229):    at android.os.AsyncTask$2.call(AsyncTask.java:333)
E/AndroidRuntime(14229):    at java.util.concurrent.FutureTask.run(FutureTask.java:266)
E/AndroidRuntime(14229):    ... 4 more
W/System  (14229): A resource failed to call close.
I/Process (14229): Sending signal. PID: 14229 SIG: 9
Lost connection to device.
Exited (sigterm)

Alex K. Alex K. · Accepted Answer · 2020-08-25T07:37:22

Let me start with something obvious: you are trying to feed floating point image to integer-type model input.

According to your link there is only 1 model: Mobilenet_V1_1.0_224_quant. This is quantized which performs all operations in integer numbers and requires integer on input:

All of the models require three color channels per pixel (red, green, and blue). Quantized models require 1 byte per channel, and float models require 4 bytes per channel.

If described above is real case I see few solutions here:

Use the same model, manage correctly your input. Quantized models performs better on CPU but have lower accuracy
Check in detail hosted models and pick floating point one

Cannot convert between a TensorFlowLite tensor with type UINT8 and a Java object of type

1 Answers