wehub-resource-sync
68 lines
2.2 KiB
Java
68 lines
2.2 KiB
Java
import com.baidu.paddle.inference.Predictor;
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import com.baidu.paddle.inference.Config;
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import com.baidu.paddle.inference.Tensor;
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public class test {
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static {
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System.loadLibrary("paddle_inference");
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}
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public static void main(String[] args) {
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Config config = new Config();
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config.setCppModel(args[0], args[1]);
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config.enableMemoryOptim(true);
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config.enableProfile();
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config.enableMKLDNN();
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System.out.println("summary:\n" + config.summary());
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System.out.println("model dir:\n" + config.getCppModelDir());
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System.out.println("prog file:\n" + config.getProgFile());
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System.out.println("params file:\n" + config.getCppParamsFile());
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config.getCpuMathLibraryNumThreads();
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config.getFractionOfGpuMemoryForPool();
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config.switchIrDebug(false);
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System.out.println(config.summary());
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Predictor predictor = Predictor.createPaddlePredictor(config);
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long n = predictor.getInputNum();
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String inNames = predictor.getInputNameById(0);
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Tensor inHandle = predictor.getInputHandle(inNames);
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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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predictor.run();
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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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predictor.tryShrinkMemory();
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predictor.clearIntermediateTensor();
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System.out.println(outData[0]);
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System.out.println(outData.length);
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outHandle.destroyNativeTensor();
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inHandle.destroyNativeTensor();
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predictor.destroyNativePredictor();
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Config newConfig = new Config();
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newConfig.setCppModelDir("/model_dir");
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newConfig.setCppProgFile("/prog_file");
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newConfig.setCppParamsFile("/param");
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System.out.println("model dir:\n" + newConfig.getCppModelDir());
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System.out.println("prog file:\n" + newConfig.getProgFile());
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System.out.println("params file:\n" + newConfig.getCppParamsFile());
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config.destroyNativeConfig();
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}
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}
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