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Caused by: java.lang.UnsatisfiedLinkError: C:\Users\liwan\.javacpp\cache\tensorflow-core-native-1.0.0-rc.1-windows-x86_64.jar\org\tensorflow\internal\c_api\windows-x86_64\jnitensorflow.dll: Can't find dependent libraries #543
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Please follow the instructions at https://github.com/bytedeco/javacpp-presets/wiki/Debugging-UnsatisfiedLinkError-on-Windows
Reacted by LEEayReacted by LEEay根据工具解决了dll缺失问题，但是运行还是报错，ptions.TFFailedPreconditionException: Could not find variable batch_normalization_170/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_170/moving_mean/class tensorflow::Var does not exist.
在python代码里可以正常推理，模型使用的是：https://github.com/KichangKim/DeepDanbooru，
这个模型的.h5 和 .pb 在python里 都能正常推理，但是tfjava 就是报错TF-Java doesn't support h5 files. If you load the model in in TF Python then save it as a TF SavedModel then it should work.
我是转成了.pb模型，
`import tensorflow as tf
import tf2onnx
loaded_model = tf.keras.models.load_model(r"E:\Download\model-resnet_custom_v3.h5", compile=False)保存模型为.pb
tf.saved_model.save(loaded_model, r"E:\Download\mymodel\deepdanbooru-v3-20211112")
加载已保存的模型
loaded_model = tf.saved_model.load(r"E:\Download\mymodel\deepdanbooru-v3-20211112\saved_model.pb")`
使用的是https://github.com/KichangKim/DeepDanbooru/tags 里面的 v3-20211112-sgd-e28
转化后在tfjava里运行报错
Session session = SavedModelBundle.load("E:\\Download\\mymodel\\deepdanbooru-v3-20211112", "serve").session(); FloatNdArray matrix3d = NdArrays.ofFloats(org.tensorflow.ndarray.Shape.of(1, 512, 512, 3)); TFloat32 rank3Tensor = TFloat32.tensorOf(matrix3d); System.out.println(rank3Tensor.toString()); Tensor resultTensor = session.runner() .feed("serving_default_inputs:0", rank3Tensor) .fetch("StatefulPartitionedCall:0") .run().get(0); System.out.println(resultTensor.shape()); session.close();It has the same error as your earlier comment?
是的
2024-06-14 11:31:35.282952: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variableTF_ENABLE_ONEDNN_OPTS=0.
2024-06-14 11:31:35.477578: I tensorflow/cc/saved_model/reader.cc:83] Reading SavedModel from: E:\Download\mymodel\deepdanbooru-v3-20211112
2024-06-14 11:31:35.533223: I tensorflow/cc/saved_model/reader.cc:51] Reading meta graph with tags { serve }
2024-06-14 11:31:35.533251: I tensorflow/cc/saved_model/reader.cc:146] Reading SavedModel debug info (if present) from: E:\Download\mymodel\deepdanbooru-v3-20211112
2024-06-14 11:31:35.533351: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE SSE2 SSE3 SSE4.1 SSE4.2 AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-14 11:31:36.092973: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:388] MLIR V1 optimization pass is not enabled
2024-06-14 11:31:36.136289: I tensorflow/cc/saved_model/loader.cc:234] Restoring SavedModel bundle.
2024-06-14 11:31:37.619728: I tensorflow/cc/saved_model/loader.cc:218] Running initialization op on SavedModel bundle at path: E:\Download\mymodel\deepdanbooru-v3-20211112
2024-06-14 11:31:38.008834: I tensorflow/cc/saved_model/loader.cc:317] SavedModel load for tags { serve }; Status: success: OK. Took 2531231 microseconds.
DenseTFloat32(shape=[1, 512, 512, 3])
2024-06-14 11:31:40.720244: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: FAILED_PRECONDITION: Could not find variable batch_normalization_89/beta. 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_89/beta/class tensorflow::Var does not exist.
[[{{function_node __inference_serving_default_11824}}{{node resnet_custom_v3_1/batch_normalization_89_1/Cast_3/ReadVariableOp}}]]
Exception in thread "main" org.tensorflow.exceptions.TFFailedPreconditionException: Could not find variable batch_normalization_89/beta. 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_89/beta/class tensorflow::Var does not exist.
[[{{function_node __inference_serving_default_11824}}{{node resnet_custom_v3_1/batch_normalization_89_1/Cast_3/ReadVariableOp}}]]
at org.tensorflow.internal.c_api.AbstractTF_Status.throwExceptionIfNotOK(AbstractTF_Status.java:84)
at org.tensorflow.Session.run(Session.java:835)
at org.tensorflow.Session$Runner.runHelper(Session.java:558)
at org.tensorflow.Session$Runner.run(Session.java:485)
at com.drsle3.douzhanpro.qingfeng.common.PredictNN.main(PredictNN.java:26)Batch norm can be a pain in Keras, can you try setting the model so it's not trainable before saving out the SavedModel in Python? I think it's failing to find a variable that should be fixed after training, but it might have saved the training version of the model. Alternatively, it looks like it might also behave a little differently saved on GPU vs CPU, so you could try loading it directly into a GPU with the config options.
我使用官方方法 把模型转成onnx后，用onnx代码，可以推理并获得结果
onnx代码try { // 初始化 ONNX Runtime 环境 OrtEnvironment env = OrtEnvironment.getEnvironment(); // 加载 ONNX 模型 String modelPath = "E:\\Download\\mymodel\\onnxmodel1.onnx"; OrtSession.SessionOptions sessionOptions = new OrtSession.SessionOptions(); OrtSession session = env.createSession(modelPath, sessionOptions); // 创建 TensorFlow FloatNdArray FloatNdArray matrix3d = NdArrays.ofFloats(Shape.of(1, 512, 512, 3)); // 填充数据到 FloatNdArray（此处仅为示例，实际使用时应填充实际数据） // for (int i = 0; i < 512 * 512 * 3; i++) { // matrix3d.setFloat(1.0f, 0, i / (512 * 512), (i % (512 * 512)) / 512, i % 512); // } // 将 FloatNdArray 转换为 FloatBuffer FloatBuffer floatBuffer = FloatBuffer.allocate((int) matrix3d.size()); matrix3d.scalars().forEachIndexed((coords, scalar) -> floatBuffer.put(scalar.getFloat())); floatBuffer.flip(); // 创建 ONNX Tensor long[] inputShape = new long[]{1, 512, 512, 3}; OnnxTensor inputTensor = OnnxTensor.createTensor(env, floatBuffer, inputShape); // 构建输入映射 Map<String, OnnxTensor> inputs = new HashMap<>(); inputs.put("inputs", inputTensor); // 输入节点名称需要与你的模型匹配 // 运行推理 OrtSession.Result results = session.run(inputs); // 获取输出 (根据你的模型调整输出节点名称) OnnxValue outputValue = results.get(0); float[] outputData = ((OnnxTensor) outputValue).getFloatBuffer().array(); // 打印输出 System.out.println("Model output:"); for (float value : outputData) { System.out.println(value); } // 释放资源 inputTensor.close(); results.close(); session.close(); env.close(); } catch (OrtException e) { e.printStackTrace(); }
So it didn't change if you exported it after setting it into eval mode, but tf2onnx made a ONNX model which worked?
#543 (comment)
转成pb格式后
python -m tf2onnx.convert --saved-model mymodel/deepdanbooru-v3-20211112/ --output mymodel/onnxmodel1.onnx --opset 9
使用命令转成onnx，然后用onnx的java代码简单推理可以正常使用
Caused by: java.lang.UnsatisfiedLinkError: C:\Users\liwan.javacpp\cache\tensorflow-core-native-1.0.0-rc.1-windows-x86_64.jar\org\tensorflow\internal\c_api\windows-x86_64\jnitensorflow.dll: Can't find dependent libraries
java 17