How do I make sure that my tflite model is functioning properly and is ready to be used in a mobile app?

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I'm working on a real-time object detection mobile application using kotlin. I trained a yolo v5 model on a custom dataset of 4 classes, then converted the model to a .tflite file.
I used this line to convert the yolo model to tflite:

!python /content/yolov5/export.py --weights best.pt --include tflite --nms --img 640 --data data.yaml

Output Tensor Details:
======================
Name: StatefulPartitionedCall:0
Shape: [ 1 100 4]
Type: <class 'numpy.float32'="">
the problem is the mobile app keeps generating errors and crashes once the splash screen is viewed, I don't know if the problem is coming from the app or the model.

What I have tried:

I used this sample kotlin code provided with the model:

val model = ObjectDetection.newInstance(context)

// Creates inputs for reference.
val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 640, 640, 3), DataType.FLOAT32)
inputFeature0.loadBuffer(byteBuffer)

// Runs model inference and gets result.
val outputs = model.process(inputFeature0)
val outputFeature0 = outputs.outputFeature0AsTensorBuffer
val outputFeature1 = outputs.outputFeature1AsTensorBuffer
val outputFeature2 = outputs.outputFeature2AsTensorBuffer
val outputFeature3 = outputs.outputFeature3AsTensorBuffer

// Releases model resources if no longer used.
model.close()

The error generated in the mobile app now is:
java.lang.AssertionError: TensorBuffer does not support data type: INT32

this error is generated from this line:

val outputs = model.process(inputFeature0)

however, I traced the model's output and found that it's of datatype: float32 so I don't get why do i get this error?

Posted 18-Mar-23 3:49am

Fatema Shawki

Updated 20-Mar-23 4:02am

Richard MacCutchan

v2

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Member 15950766 20-Mar-23 9:45am

Can your provide more information about the steps you followed for converting the YOLOv5 model to TFlite?

Fatema Shawki 20-Mar-23 19:43pm

I used the export function to convert from yolo to tensor flow and then converted the tensor flow saved model to tensor flow lite using tf.convertor
1)
!python /content/yolov5/export.py --weights /content/yolov5/runs/train/exp/weights/best.pt --include tflite --img 640 --data data.yaml
2)
converter = tf.lite.TFLiteConverter.from_saved_model('/content/yolov5/runs/train/exp/weights/best_saved_model')
tflite_model = converter.convert()
with open('model.tflite', 'wb') as f:
f.write(tflite_model)

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Richard MacCutchan · Answer 1 · 2023-03-20T04:01:00

I suspect the problem may be the use of intArrayOf in the followibng line:

Kotlin

val inputFeature0 = TensorBuffer.createFixedSize(intArrayOf(1, 640, 640, 3), DataType.FLOAT32)

which suggests that you are passing an array of INT32 values (1, 640, 640 and 3), even though you have declared the content to be FLOAT32.
See TensorBuffer | TensorFlow Lite[^] for the correct usage.