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 |
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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. |
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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. |
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我是转成了.pb模型， 保存模型为.pbtf.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 |
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It has the same error as your earlier comment? |
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是的 |
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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. |
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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 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();
} |
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So it didn't change if you exported it after setting it into eval mode, but tf2onnx made a ONNX model which worked? |
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#543 (comment) |
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
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