Tensor type issue

[SaherAlSous]

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template

System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Tensorflow for Java (using Korlin)
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04 x86_64): I have Ubuntu 20.
• TensorFlow installed from (source or binary): implementation("org.tensorflow:tensorflow-core-platform:0.5.0")
• TensorFlow version (use command below): TF 2
• Java version (i.e., the output of java -version): 8
• Java command line flags (e.g., GC parameters):
• Python version (if transferring a model trained in Python): 3.11
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory:

Describe the current behavior I'm trying to find a way to use tensorflowmodel 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: Signature for "serving_default":

` 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

Describe the expected behavior Doing inference / expectation succesffuly.

Code to reproduce the issue Provide a reproducible test case that is the bare minimum necessary to generate the problem. Loading the model in TFServices: fun model(): SavedModelBundle { return SavedModelBundle .loader("/home/***/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)

Other info / logs Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

[Craigacp]

The reshape command you're using isn't a cast, you'll need to cast it from uint8 to float32 assuming that's what the model wants.

[karllessard]

+1 to @Craigacp comment, use tf.dtypes.cast to convert your Uint8 decoded image to Float.

Also it looks like you are trying to run TF eagerly but using a graph Session? You might want to look at EagerSession instead, especially that the way you are currently doing will leak, as you need close the output tensors (nor the inputs).

[SaherAlSous]

Thank you.