0.27调用tensorflow的pb模型崩溃（可以加载模型，推理时崩溃）

[LEEay]

Description

0.27调用tensorflow的pb模型崩溃（可以加载模型，推理时崩溃）
Java frames: (J=compiled Java code, j=interpreted, Vv=VM code)
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j ai.djl.tensorflow.engine.javacpp.JavacppUtils.runSession(Lorg/tensorflow/internal/c_api/TF_Session;Lorg/tensorflow/proto/framework/RunOptions;[Lorg/tensorflow/internal/c_api/TF_Tensor;[Lorg/tensorflow/internal/c_api/TF_Operation;[I[Lorg/tensorflow/internal/c_api/TF_Operation;[I[Lorg/tensorflow/internal/c_api/TF_Operation;)[Lorg/tensorflow/internal/c_api/TF_Tensor;+270
j ai.djl.tensorflow.engine.TfSymbolBlock.forwardInternal(Lai/djl/training/ParameterStore;Lai/djl/ndarray/NDList;ZLai/djl/util/PairList;)Lai/djl/ndarray/NDList;+202
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j ai.djl.nn.Block.forward(Lai/djl/training/ParameterStore;Lai/djl/ndarray/NDList;Z)Lai/djl/ndarray/NDList;+5
j ai.djl.inference.Predictor.predictInternal(Lai/djl/translate/TranslatorContext;Lai/djl/ndarray/NDList;)Lai/djl/ndarray/NDList;+21
j ai.djl.inference.Predictor.batchPredict(Ljava/util/List;)Ljava/util/List;+133
j ai.djl.inference.Predictor.predict(Ljava/lang/Object;)Ljava/lang/Object;+5
j com.fly.ai.wdtag.WdTagService.detect(Lai/djl/modality/cv/Image;)Lai/djl/modality/Classifications;+17
j com.fly.ai.wdtag.WdTagService.tag(Lai/djl/modality/cv/Image;)Lai/djl/modality/Classifications;+2
j com.fly.ai.wdtag.WdTagService.tag(Ljava/io/InputStream;)Lai/djl/modality/Classifications;+10
j com.fly.ai.wdtag.WdTagController.tag(Lorg/springframework/web/multipart/MultipartFile;)Ljava/util/List;+10
v ~StubRoutines::call_stub
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v ~StubRoutines::call_stub

.h5文件和.pb 转化后的文件在python里调用正常
使用tensorflow的java代码，也是正常的

python 3.10.4
tensorflow 2.16.1

[frankfliu]

Can you provide a mini-reproduce project?

Does it wither older version?

Do you have code that works for TFJava?

[LEEay]

使用的是KichangKim/DeepDanbooru github上的项目，tags v3-20211112-sgd-e28, 然后根据djl文档的转化成pb模型
我的tfjava代码：
Session session = SavedModelBundle.load("E:\Download\mymodel\deepdanbooru-v3-20211112",
"serve").session();

    float[][] input = {
            {2.6327686f, -9.201903f},
            {-1.3209248f, 8.569574f},
            {-5.6642127f, 3.3681698f},
            {9.604832f, 5.9664965f},
            {-0.8812313f, -6.76733f}
    };
    LongNdArray matrix3d = NdArrays.ofLongs(Shape.of(1, 100, 9));
    TInt64 rank3Tensor = TInt64.tensorOf(matrix3d);
    Tensor resultTensor = session.runner()
            .feed("serving_default_inputs:0", rank3Tensor)
            .fetch("StatefulPartitionedCall:0")
            .run().get(0);

    resultTensor.shape();
    session.close();

模型参数不是正确的，是我测试的，但是tfjava会提醒我参数错误，djl这样推理就会程序崩溃

因为参数是python代码，我还没有完全掌握转成djl 的写法，所有使用了前置后置使用python代码实现，模型使用djl实现
代码：
PythonTranslator；
@OverRide
public void prepare(TranslatorContext ctx) throws Exception {
if (predictor == null) {
Criteria<Input, Output> criteria =
Criteria.builder()
.setTypes(Input.class, Output.class)
.optModelUrls(getRealUrl("/model/wdtag"))
.optEngine("Python")
.build();
model = criteria.loadModel();
predictor = model.newPredictor();
}
}

@Override
public Classifications processOutput(TranslatorContext translatorContext, NDList ndList) throws Exception {
    System.out.println("##################################");
    return null;
}

@Override
public NDList processInput(TranslatorContext translatorContext, String s) throws Exception {
    Input input = new Input();
    input.add("data", s);
    input.addProperty("Content-Type", "text/plain");
    input.addProperty("handler", "preprocess");
    Output output = predictor.predict(input);
    if (output.getCode() != 200) {
        throw new TranslateException("Python preprocess() failed: " + output.getMessage());
    }

// NDList list=output.getDataAsNDList(translatorContext.getNDManager());
NDArray ndArray = output.getDataAsNDList(translatorContext.getNDManager()).get(0);
ndArray.setName("serving_default_inputs:0");
System.out.println(new NDList(ndArray).toString());
return new NDList(ndArray);
// return output.getDataAsNDList(translatorContext.getNDManager());
}

加载模型代码：
PythonTranslator translator = new PythonTranslator();
Criteria<String, Classifications> criteria = Criteria.builder()
.optEngine(ENGINE_TENSORFLOW)
.optDevice(device)
.optModelUrls(getRealUrl(prop.getDetectUrl()))
.setTypes(String.class, Classifications.class)
// .optModelName(prop.getModelName())
.optProgress(new ProgressBar())
// .optOption("Tags", "")
// .optOption("SignatureDefKey", "serving_default")
.optTranslator(translator)
.build();
// Engine.getEngine("TensorFlow");
detectionModel = criteria.loadModel();

python的图片前置后置操作是按照KichangKim/DeepDanbooru 代码写的