Hello... there is no channel for Kotlin-DL, so if ...

# server

[Saher Al-Sous]

Hello...
there is no channel for Kotlin-DL, so if there anyone that can help with this in Spring boot, i will be really thankful...
I'm trying to find a way to use `tensorflow`model in Spring Boot, I loaded the model successfully, and created the needed tensor, but I can't make a call to get the result from the model because of this error:

Caused by: org.tensorflow.exceptions.TFInvalidArgumentException: Expects arg[0] to be float but uint8 is provided

I checked the model signature and it was like this:

Method: "tensorflow/serving/predict"
    Inputs:
        "input_1": dtype=DT_FLOAT, shape=(-1, 299, 299, 3)
    Outputs:
        "dense_3": dtype=DT_FLOAT, shape=(-1, 41)

Signature for "__saved_model_init_op":
    Outputs:
        "__saved_model_init_op": dtype=DT_INVALID, shape=()

my tensor details are DT_UINT8 tensor with shape [299, 299, 3].
When I changed my tensor data type into float like this:

val imageShape = TFloat32.tensorOf(runner.fetch(decodeImage).run()[0].shape())
        val reshape = tf.reshape(
            decodeImage,
            tf.array(
                -1.0f,
                imageShape[0].getFloat(),
                imageShape[1].getFloat(),
                imageShape[2].getFloat())
            )

I got this error:

org.tensorflow.exceptions.TFInvalidArgumentException: Value for attr 'Tshape' of float is not in the list of allowed values: int32, int64

how can I fix this problem?
if someone is curious how I loaded the model, created the tensor and called it, here is the code below
Loading the model in `TFServices`:

fun model(): SavedModelBundle  {
        return SavedModelBundle
            .loader("/home/saher/kotlin/Spring/machinelearning/src/main/resources/pd/")
            .withRunOptions(RunOptions.getDefaultInstance())
            .load()
    }

Building the Tensor and calling the model

val graph = Graph()
        val session = Session(graph)
        val tf = Ops.create(graph)
        val fileName = tf.constant("/home/***/src/main/resources/keyframe_1294.jpg")
        val readFile = tf.io.readFile(fileName)
        val runner = session.runner()
        val decodingOptions = DecodeJpeg.channels(3)
        val decodeImage = tf.image.decodeJpeg(readFile.contents(), decodingOptions)
        val imageShape = runner.fetch(decodeImage).run()[0].shape()
        val reshape = tf.reshape(
            decodeImage,
            tf.array(
                -1,
                imageShape.asArray()[0],
                imageShape.asArray()[1],
                imageShape.asArray()[2])
            )
        val tensor = runner.fetch(reshape).run()[0]
        val inputMap = mutableMapOf("input_tensor" to tensor)
        println(tensor.shape())
        println(tensor.dataType())
        println(tensor.asRawTensor())
        val result = tfService.model().function("serving_default").call(inputMap)

regards

[Saher Al-Sous]

Update:
I changed the whole code, and used the Kotlin Tensorflow dependencies

implementation("org.jetbrains.kotlinx:kotlin-deeplearning-api:0.5.2")
implementation("org.jetbrains.kotlinx:kotlin-deeplearning-tensorflow:0.5.2")

I loaded the model:

fun myModel(): SavedModel {
        return SavedModel.load("/home/***/src/main/resources/pd/")
    }

and used called for the prediction:

val file = File("/home/***/src/main/resources/keyframe_1294.jpg")
        val byteArray = ImageIO.read(file)
        val floatArray = ImageConverter.toRawFloatArray(byteArray)
        val myResult = tfService.myModel().predictSoftly(floatArray, "dense_3")
        println(myResult)

but i got this error:

Caused by: org.tensorflow.TensorFlowException: Op type not registered 'DisableCopyOnRead' in binary running on My Computer. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) tf.contrib.resampler should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.

[Filipp Zhinkin]

@Saher Al-Sous there's #kotlindl channel, maybe someone there can help you

[Saher Al-Sous]

didn't know about it... thank you