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94 lines
2.6 KiB
Markdown
94 lines
2.6 KiB
Markdown
# Paddle Inference java API
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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。
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## 安装(Linux)
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##### 1.下载C预测库
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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`。
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##### 2.准备预测部署模型
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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。
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```
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wget https://paddle-inference-dist.bj.bcebos.com/Paddle-Inference-Demo/resnet50.tgz
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tar zxf resnet50.tgz
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# 获得 resnet50 目录结构如下
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resnet50/
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├── inference.pdmodel
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├── inference.pdiparams
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└── inference.pdiparams.info
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```
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##### 3.准备预测执行目录
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```
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git clone github.com/paddlepaddle/paddle/paddle/fluid/inference/javaapi
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```
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##### 3. 编译动态链接库和jar包
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```bash
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在javaapi目录下执行
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./build_gpu.sh {c预测库目录} {jni头文件目录} {jni系统头文件目录}
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以笔者的目录结构为例
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./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
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执行完成后，会在当前目录下生成JavaInference.jar和libpaddle_inference.so
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```
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##### 5.运行单测，验证
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```
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在javaapi目录下执行
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./test.sh {c预测库目录} {.pdmodel文件目录} {.pdiparams文件目录}
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以笔者的目录结构为例
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./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"
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```
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## 在Java中使用Paddle预测
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首先创建预测配置
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```java
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Config config = new Config();
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config.setCppModel(model_file, params_file);
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```
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创建predictor
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```java
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Predictor predictor = Predictor.createPaddlePredictor(config);
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```
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获取输入Tensor
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```java
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String inNames = predictor.getInputNameById(0);
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Tensor inHandle = predictor.getInputHandle(inNames);
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```
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设置输入数据(假设只有一个输入)
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```java
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inHandle.Reshape(4, new int[]{1, 3, 224, 224});
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float[] inData = new float[1*3*224*224];
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inHandle.CopyFromCpu(inData);
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```
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运行预测
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```java
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predictor.Run();
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```
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获取输出Tensor
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```java
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String outNames = predictor.getOutputNameById(0);
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Tensor outHandle = predictor.getOutputHandle(outNames);
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float[] outData = new float[outHandle.GetSize()];
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outHandle.CopyToCpu(outData);
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```
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