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）
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.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

[LEEay]

有人能帮忙看看吗？或者有社区地址什么的吗？微信群或者qq群？

[frankfliu]

Can you provide your .pb or converted saved model bundle file?

[LEEay]

模型地址https://github.com/KichangKim/DeepDanbooru/tags, 使用的v3-20211112-sgd-e28 这个tag，然后使用djl官方的代码转成的.pb
代码：
loaded_model = tf.keras.models.load_model(r"E:\Download\model-resnet_custom_v3.h5", compile=False)
tf.saved_model.save(loaded_model, r"E:\Download\1")

[frankfliu]

@LEEay

I looked into this model, it seems broken after converted to SavedModelBunndle, I use the following TFJava code to run it:

        Path path = Paths.get("resnet").toRealPath();
        Session session = SavedModelBundle.load(path.toString(), "serve").session();

        FloatNdArray matrix3d = NdArrays.ofFloats(org.tensorflow.ndarray.Shape.of(1, 255, 255, 3));
        TFloat32 rank3Tensor = TFloat32.tensorOf(matrix3d);
        Tensor resultTensor = session.runner()
                .feed("serving_default_inputs:0", rank3Tensor)
                .fetch("StatefulPartitionedCall:0")
                .run().get(0);
        System.out.println(resultTensor.shape());
        session.close();

I got the following error:

org.tensorflow.exceptions.TFFailedPreconditionException: Could not find variable batch_normalization_111/moving_mean. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status error message=Resource localhost/batch_normalization_111/moving_mean/N10tensorflow3VarE does not exist.
         [[{{function_node __inference_serving_default_11824}}{{node resnet_custom_v3_1/batch_normalization_111_1/Cast/ReadVariableOp}}]]
        at app//org.tensorflow.internal.c_api.AbstractTF_Status.throwExceptionIfNotOK(AbstractTF_Status.java:95)
        at app//org.tensorflow.Session.run(Session.java:835)
        at app//org.tensorflow.Session$Runner.runHelper(Session.java:558)
        at app//org.tensorflow.Session$Runner.run(Session.java:485)

You might need work with TFJava team to troubleshoot this issue

I use the following DJL code, it running as expect (the same error as TFJava):

        Criteria<NDList, NDList> criteria = Criteria.builder()
                .optEngine("TensorFlow")
                .optModelPath(path)
                .setTypes(NDList.class, NDList.class)
                .build();

        try (ZooModel<NDList, NDList> model = criteria.loadModel();
             Predictor<NDList, NDList> predictor = model.newPredictor()) {
            NDManager manager = model.getNDManager();
            NDArray array = manager.create(new Shape(1, 512, 512, 3));
            NDList input = new NDList(array);
            NDList result = predictor.predict(input);
            System.out.println(result.get(0).getShape());
        }

[frankfliu]

@LEEay

By the way, you are using image classification model the following code should work for you if you fix your model issue:

        Path path = Paths.get("resnet").toRealPath();
        Criteria<Image, Classifications> criteria =
                Criteria.builder()
                        .setTypes(Image.class, Classifications.class)
                        .optModelPath(path)
                        .optEngine("TensorFlow")
                        .optArgument("width", "512")
                        .optArgument("height", "512")
                        .optArgument("resize", "true")
                        .optArgument("toTensor", "false")
                        .optTranslatorFactory(new ImageClassificationTranslatorFactory())
                        .build();

        Path file = Paths.get("../../../examples/src/test/resources/kitten.jpg");
        Image img = ImageFactory.getInstance().fromFile(file);
        try (ZooModel<Image, Classifications> model = criteria.loadModel();
                Predictor<Image, Classifications> predictor = model.newPredictor()) {
            Classifications result = predictor.predict(img);
            System.out.println(result.best().getClassName());
        }

[LEEay]

#3173 (comment)
使用python的代码可以执行转换后的模型文件
import tensorflow as tf
import tensorflow_io as tfio
import skimage

model = tf.keras.models.load_model(r"E:\Download\model-resnet_custom_v3.h5", compile=False)

model = tf.saved_model.load(r"E:\Download\deepdanbooru-v3-20211112")
def evaluate_image(
image_input: str ,model:object, threshold: float
) :
# width = model.input_shape[2]
# height = model.input_shape[1]
width = 512
height = 512

image = load_image_for_evaluate(image_input, width=width, height=height)

image_shape = image.shape
image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
print(image)
size = image.shape
print("数组大小:", size)
# y = model.predict(image)[0]
infer = model.signatures["serving_default"]
print(infer.inputs[0])
output = infer(inputs=tf.constant(image))
print(output)
# print(y)
result_dict = {}

# for i, tag in enumerate(tags):
#     result_dict[tag] = y[i]

# for tag in tags:
#     if result_dict[tag] >= threshold:
#         yield tag, result_dict[tag]

def load_image_for_evaluate(
input_ , width: int, height: int, normalize: bool = True
) :
print(width,height)
if True:
image_raw = tf.io.read_file(input_)
try:
image = tf.io.decode_png(image_raw, channels=3)
except:
image = tfio.image.decode_webp(image_raw)
image = tfio.experimental.color.rgba_to_rgb(image)

image = tf.image.resize(
    image,
    size=(height, width),
    method=tf.image.ResizeMethod.AREA,
    preserve_aspect_ratio=True,
)
image = image.numpy()  # EagerTensor to np.array
# print("image-up",image)
image = transform_and_pad_image(image, width, height)
# print("image-down",image)
if normalize:
    image = image / 255.0

return image

def transform_and_pad_image(
image,
target_width,
target_height,
scale=None,
rotation=None,
shift=None,
order=1,
mode="edge",
):
"""
Transform image and pad by edge pixles.
"""
image_width = image.shape[1]
image_height = image.shape[0]
image_array = image

# centerize
t = skimage.transform.AffineTransform(
    translation=(-image_width * 0.5, -image_height * 0.5)
)

if scale:
    t += skimage.transform.AffineTransform(scale=(scale, scale))

if rotation:
    radian = (rotation / 180.0) * math.pi
    t += skimage.transform.AffineTransform(rotation=radian)

t += skimage.transform.AffineTransform(
    translation=(target_width * 0.5, target_height * 0.5)
)

if shift:
    t += skimage.transform.AffineTransform(
        translation=(target_width * shift[0], target_height * shift[1])
    )

warp_shape = (target_height, target_width)

image_array = skimage.transform.warp(
    image_array, (t).inverse, output_shape=warp_shape, order=order, mode=mode
)

return image_array

yz : int =0.5 #生成的标签可信度阈值
evaluate_image(r"E:\Download\111111111111.png",model,yz)

[LEEay]

#3173 (comment)
因为模型的示例里的转化图片的方法，在djl里我不会转化，所以我现在使用djl 调用python的前置操作来转化图片，然后用djl调用模型文件来推理。
这个是我的model.py，转化图片代码
`import tensorflow as tf
import tensorflow_io as tfio
import skimage
import logging

from typing import Optional, Any
from djl_python import Input
from djl_python import Output
from djl_python.np_util import to_nd_list

class Processing(object):

def __init__(self):
    self.topK = 5
    self.image_processing = None
    self.mapping = None
    self.initialized = False

def preprocess(self, inputs: Input) -> Output:
    outputs = Output()
    try:
        width = 512
        height = 512
        imageinput = "test"
        batch = inputs.get_batches()
        for i, item in enumerate(batch):
            imageinput = item.get_as_string()
        image = self.load_image_for_evaluate(imageinput, 512, 512)
    
        image_shape = image.shape
        image = image.reshape((1, image_shape[0], image_shape[1], image_shape[2]))
        #print(image)
        #print(dsd)
        outputs.add_as_numpy(image)
        outputs.add_property("content-type", "tensor/ndlist")
        #print(outputs.)
    except Exception as e:
        logging.exception("pre-process failed")
        # error handling
        outputs = Output().error(str(e))

    return outputs

def postprocess(self, inputs: Input) -> Output:
    outputs = Output()
    try:
        data = inputs.get_as_numpy(0)[0]
        print(data)
    except Exception as e:
        logging.exception("post-process failed")
        # error handling
        outputs = Output().error(str(e))

    return outputs

def load_image_for_evaluate(self,
    input_ , width: int, height: int, normalize: bool = True
    )  :
        print(input_)
        if True:
            image_raw = tf.io.read_file(r"E:\Download\111111111111.png")
        try:
            image = tf.io.decode_png(image_raw, channels=3)
        except:
            image = tfio.image.decode_webp(image_raw)
            image = tfio.experimental.color.rgba_to_rgb(image)
        image = tf.image.resize(
            image,
            size=(height, width),
            method=tf.image.ResizeMethod.AREA,
            preserve_aspect_ratio=True,
        )
        image = image.numpy()  # EagerTensor to np.array
        # print("image-up",image)
        image = self.transform_and_pad_image(image, width, height)
        # print("image-down",image)
        if normalize:
            image = image / 255.0
    
        return image
        
def transform_and_pad_image(self,
        image,
        target_width,
        target_height,
        scale=None,
        rotation=None,
        shift=None,
        order=1,
        mode="edge",
    ):
        """
        Transform image and pad by edge pixles.
        """
        image_width = image.shape[1]
        image_height = image.shape[0]
        image_array = image
    
        # centerize
        t = skimage.transform.AffineTransform(
            translation=(-image_width * 0.5, -image_height * 0.5)
        )
    
        if scale:
            t += skimage.transform.AffineTransform(scale=(scale, scale))
    
        if rotation:
            radian = (rotation / 180.0) * math.pi
            t += skimage.transform.AffineTransform(rotation=radian)
    
        t += skimage.transform.AffineTransform(
            translation=(target_width * 0.5, target_height * 0.5)
        )
    
        if shift:
            t += skimage.transform.AffineTransform(
                translation=(target_width * shift[0], target_height * shift[1])
            )
    
        warp_shape = (target_height, target_width)
    
        image_array = skimage.transform.warp(
            image_array, (t).inverse, output_shape=warp_shape, order=order, mode=mode
        )
    
        return image_array

_service = Processing()

def preprocess(inputs: Input) -> Output:
return _service.preprocess(inputs)

def postprocess(inputs: Input) -> Output:
return _service.postprocess(inputs)

def handle(inputs: Input) -> Optional[Output]:
return None`

djl调用python处理图片代码
`public class PythonTranslator implements NoBatchifyTranslator<String, Classifications> {

private ZooModel<Input, Output> model;
private Predictor<Input, Output> predictor;

@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());
}
}`

推理代码
Device device = Device.Type.CPU.equalsIgnoreCase(prop.getDeviceType()) ? Device.cpu() : Device.gpu(); PythonTranslator translator = new PythonTranslator(); // Translator<Image, Classifications> translator = ImageClassificationTranslator.builder().optFlag(Image.Flag.GRAYSCALE).setPipeline(new Pipeline(new ToTensor())).optApplySoftmax(true).build(); //加载神经网络模型，文字检测和识别都使用百度的PaddlePaddle 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();

[LEEay]

这个是加载模型的代码
Device device = Device.Type.CPU.equalsIgnoreCase(prop.getDeviceType()) ? Device.cpu() : Device.gpu();
PythonTranslator translator = new PythonTranslator();
Criteria<String, Classifications> criteria = Criteria.builder()
.optEngine(ENGINE_TENSORFLOW)
.optDevice(device)
.optModelUrls(getRealUrl(prop.getDetectUrl()))
.setTypes(String.class, Classifications.class)
.optProgress(new ProgressBar())
.optTranslator(translator)
.build();
detectionModel = criteria.loadModel();

[frankfliu]

The exception comes from TFJava code:

org.tensorflow.exceptions.TFFailedPreconditionException: Could not find variable batch_normalization_111/moving_mean. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status error message=Resource localhost/batch_normalization_111/moving_mean/N10tensorflow3VarE does not exist.
         [[{{function_node __inference_serving_default_11824}}{{node resnet_custom_v3_1/batch_normalization_111_1/Cast/ReadVariableOp}}]]
        at app//org.tensorflow.internal.c_api.AbstractTF_Status.throwExceptionIfNotOK(AbstractTF_Status.java:95)
        at app//org.tensorflow.Session.run(Session.java:835)
        at app//org.tensorflow.Session$Runner.runHelper(Session.java:558)
        at app//org.tensorflow.Session$Runner.run(Session.java:485)

I think you'd better ask TFjava github to solve this issue first.