chore: import upstream snapshot with attribution

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+# Paddle Inference java API
+
+Paddle Inference java API 基于 [capi](../capi_exp) 和 jni 实现，需要您提前准备好C预测库。
+
+## 安装(Linux)
+
+##### 1.下载C预测库
+
+您可以选择直接下载[paddle_inference_c](https://github.com/PaddlePaddle/Paddle-Inference-Demo/blob/master/docs/user_guides/download_lib.md)预测库，或通过源码编译的方式安装，源码编译方式参考官网文档，注意这里cmake编译时打开`-DON_INFER=ON`,在编译目录下得到`paddle_inference_c_install_dir`。
+
+##### 2.准备预测部署模型
+
+下载 [resnet50](https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/resnet50.tgz) 模型后解压，得到 Paddle Combined 形式的模型。
+
+```
+wget https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/resnet50.tgz
+tar zxf resnet50.tgz
+
+# 获得 resnet50 目录结构如下
+resnet50/
+├── inference.pdmodel
+├── inference.pdiparams
+└── inference.pdiparams.info
+```
+
+##### 3.准备预测执行目录
+
+```
+git clone github.com/paddlepaddle/paddle/paddle/fluid/inference/javaapi
+```
+
+##### 3. 编译动态链接库和jar包
+
+```bash
+在javaapi目录下执行
+
+./build_gpu.sh {c预测库目录} {jni头文件目录} {jni系统头文件目录}
+
+以笔者的目录结构为例
+./build.sh /root/paddle_c/paddle_inference_c_2.2/paddle_inference_c /usr/lib/jvm/java-8-openjdk-amd64/include /usr/lib/jvm/java-8-openjdk-amd64/include/linux
+
+执行完成后，会在当前目录下生成JavaInference.jar和libpaddle_inference.so
+```
+
+##### 5.运行单测，验证
+
+```
+在javaapi目录下执行
+
+./test.sh {c预测库目录} {.pdmodel文件目录} {.pdiparams文件目录}
+
+以笔者的目录结构为例
+./test.sh "/root/paddle_c/paddle_inference_c_2.2/paddle_inference_c"  "/root/paddle_c/resnet50/inference.pdmodel" "/root/paddle_c/resnet50/inference.pdiparams"
+```
+
+## 在Java中使用Paddle预测
+
+首先创建预测配置
+```java
+Config config = new Config();
+config.setCppModel(model_file, params_file);
+```
+
+创建predictor
+```java
+Predictor predictor = Predictor.createPaddlePredictor(config);
+```
+
+获取输入Tensor
+```java
+String inNames = predictor.getInputNameById(0);
+Tensor inHandle = predictor.getInputHandle(inNames);
+```
+
+设置输入数据(假设只有一个输入)
+```java
+inHandle.Reshape(4, new int[]{1, 3, 224, 224});
+float[] inData = new float[1*3*224*224];
+inHandle.CopyFromCpu(inData);
+```
+
+运行预测
+```java
+predictor.Run();
+```
+
+获取输出Tensor
+```java
+String outNames = predictor.getOutputNameById(0);
+Tensor outHandle = predictor.getOutputHandle(outNames);
+float[] outData = new float[outHandle.GetSize()];
+outHandle.CopyToCpu(outData);
+```