第一时间测试TensorflowLite Android(安卓)Demo，性能平均200ms

Google发布了tensorflow lite，致力于移动端智能计算，第一时间下载来尝试，平均性能200ms，感谢谷歌！！！

这里我上传了我的工程（http://download.csdn.net/download/houwenbin1986/10120816）本压缩包是Android的AS工程，解压导入就能轻松编译测试！！！

我贴一下主要的步骤：

app/build.gradle

apply plugin: 'com.android.application'android {    compileSdkVersion 26    buildToolsVersion "26.0.1"    defaultConfig {        applicationId "com.example.android.tflitecamerademo"        minSdkVersion 21        targetSdkVersion 26        versionCode 1        versionName "1.0"        testInstrumentationRunner "android.support.test.runner.AndroidJUnitRunner"    }    buildTypes {        release {            minifyEnabled false            proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'        }    }    aaptOptions {        noCompress "tflite"//这个好重要啊！！！不然不能初始化成功！！！    }}dependencies {    compile fileTree(dir: 'libs', include: ['*.jar'])    androidTestCompile('com.android.support.test.espresso:espresso-core:2.2.2', {        exclude group: 'com.android.support', module: 'support-annotations'    })    compile 'com.android.support:appcompat-v7:26.+'    compile 'com.android.support:support-v13:26.+'    testCompile 'junit:junit:4.12'}

TF加载类：

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.Licensed under the Apache License, Version 2.0 (the "License");you may not use this file except in compliance with the License.You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.==============================================================================*/package org.tensorflow.lite;/** Static utility methods loading the TensorFlowLite runtime. */public final class TensorFlowLite {  private static final String LIBNAME = "tensorflowlite_jni";  private TensorFlowLite() {}  /** Returns the version of the underlying TensorFlowLite runtime. */  public static native String version();  /**   * Load the TensorFlowLite runtime C library.   */  static boolean init() {    try {      System.loadLibrary(LIBNAME);//加载TensorFlowLite库      return true;    } catch (UnsatisfiedLinkError e) {      System.err.println("TensorFlowLite: failed to load native library: " + e.getMessage());      return false;    }  }  static {    init();  }}

Native接口封装类

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.Licensed under the Apache License, Version 2.0 (the "License");you may not use this file except in compliance with the License.You may obtain a copy of the License at    http://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.==============================================================================*/package org.tensorflow.lite;import java.lang.reflect.Array;import java.nio.ByteBuffer;import java.nio.ByteOrder;import java.nio.MappedByteBuffer;import java.util.HashMap;import java.util.Map;/** * A wrapper wraps native interpreter and controls model execution. * * WARNING: Resources consumed by the {@code NativeInterpreterWrapper} object must be * explicitly freed by invoking the {@link #close()} method when the {@code * NativeInterpreterWrapper} object is no longer needed. */final class NativeInterpreterWrapper implements AutoCloseable {  NativeInterpreterWrapper(String modelPath) {    errorHandle = createErrorReporter(ERROR_BUFFER_SIZE);    modelHandle = createModel(modelPath, errorHandle);    interpreterHandle = createInterpreter(modelHandle);  }  /**   * Initializes a {@code NativeInterpreterWrapper} with a {@code MappedByteBuffer}. The   * MappedByteBuffer should not be modified after the construction of a {@code   * NativeInterpreterWrapper}.   */  NativeInterpreterWrapper(MappedByteBuffer mappedByteBuffer) {    modelByteBuffer = mappedByteBuffer;    errorHandle = createErrorReporter(ERROR_BUFFER_SIZE);    modelHandle = createModelWithBuffer(modelByteBuffer, errorHandle);    interpreterHandle = createInterpreter(modelHandle);  }  /** Releases resources associated with this {@code NativeInterpreterWrapper}. */  @Override  public void close() {    delete(errorHandle, modelHandle, interpreterHandle);    errorHandle = 0;    modelHandle = 0;    interpreterHandle = 0;    modelByteBuffer = null;    inputsIndexes = null;    outputsIndexes = null;  }  /** Sets inputs, runs model inference and returns outputs. */  Tensor[] run(Object[] inputs) {    if (inputs == null || inputs.length == 0) {      throw new IllegalArgumentException("Invalid inputs. Inputs should not be null or empty.");    }    int[] dataTypes = new int[inputs.length];    Object[] sizes = new Object[inputs.length];    int[] numsOfBytes = new int[inputs.length];    for (int i = 0; i < inputs.length; ++i) {      DataType dataType = dataTypeOf(inputs[i]);      dataTypes[i] = dataType.getNumber();      if (dataType == DataType.BYTEBUFFER) {        ByteBuffer buffer = (ByteBuffer) inputs[i];        if (buffer.order() != ByteOrder.nativeOrder()) {          throw new IllegalArgumentException(              "Invalid ByteBuffer. It shoud use ByteOrder.nativeOrder().");        }        numsOfBytes[i] = buffer.limit();        sizes[i] = getInputDims(interpreterHandle, i, numsOfBytes[i]);      } else if (isNonEmptyArray(inputs[i])) {        int[] dims = shapeOf(inputs[i]);        sizes[i] = dims;        numsOfBytes[i] = dataType.elemByteSize() * numElements(dims);      } else {        throw new IllegalArgumentException(            String.format(                "%d-th element of the %d inputs is not an array or a ByteBuffer.",                i, inputs.length));      }    }    long[] outputsHandles =        run(interpreterHandle, errorHandle, sizes, dataTypes, numsOfBytes, inputs);    if (outputsHandles == null || outputsHandles.length == 0) {      throw new IllegalStateException("Interpreter has no outputs.");    }    Tensor[] outputs = new Tensor[outputsHandles.length];    for (int i = 0; i < outputsHandles.length; ++i) {      outputs[i] = Tensor.fromHandle(outputsHandles[i]);    }    return outputs;  }  /** Resizes dimensions of a specific input. */  void resizeInput(int idx, int[] dims) {    resizeInput(interpreterHandle, errorHandle, idx, dims);  }  void setUseNNAPI(boolean useNNAPI) {    useNNAPI(interpreterHandle, useNNAPI);  }  /** Gets index of an input given its name. */  int getInputIndex(String name) {    if (inputsIndexes == null) {      String[] names = getInputNames(interpreterHandle);      inputsIndexes = new HashMap<>();      if (names != null) {        for (int i = 0; i < names.length; ++i) {          inputsIndexes.put(names[i], i);        }      }    }    if (inputsIndexes.containsKey(name)) {      return inputsIndexes.get(name);    } else {      throw new IllegalArgumentException(          String.format(              "%s is not a valid name for any input. The indexes of the inputs are %s",              name, inputsIndexes.toString()));    }  }  /** Gets index of an output given its name. */  int getOutputIndex(String name) {    if (outputsIndexes == null) {      String[] names = getOutputNames(interpreterHandle);      outputsIndexes = new HashMap<>();      if (names != null) {        for (int i = 0; i < names.length; ++i) {          outputsIndexes.put(names[i], i);        }      }    }    if (outputsIndexes.containsKey(name)) {      return outputsIndexes.get(name);    } else {      throw new IllegalArgumentException(          String.format(              "%s is not a valid name for any output. The indexes of the outputs are %s",              name, outputsIndexes.toString()));    }  }  static int numElements(int[] shape) {    if (shape == null) {      return 0;    }    int n = 1;    for (int i = 0; i < shape.length; i++) {      n *= shape[i];    }    return n;  }  static boolean isNonEmptyArray(Object o) {    return (o != null && o.getClass().isArray() && Array.getLength(o) != 0);  }  /** Returns the type of the data. */  static DataType dataTypeOf(Object o) {    if (o != null) {      Class<?> c = o.getClass();      while (c.isArray()) {        c = c.getComponentType();      }      if (float.class.equals(c)) {        return DataType.FLOAT32;      } else if (int.class.equals(c)) {        return DataType.INT32;      } else if (byte.class.equals(c)) {        return DataType.UINT8;      } else if (long.class.equals(c)) {        return DataType.INT64;      } else if (ByteBuffer.class.isInstance(o)) {        return DataType.BYTEBUFFER;      }    }    throw new IllegalArgumentException("cannot resolve DataType of " + o.getClass().getName());  }  /** Returns the shape of an object as an int array. */  static int[] shapeOf(Object o) {    int size = numDimensions(o);    int[] dimensions = new int[size];    fillShape(o, 0, dimensions);    return dimensions;  }  static int numDimensions(Object o) {    if (o == null || !o.getClass().isArray()) {      return 0;    }    if (Array.getLength(o) == 0) {      throw new IllegalArgumentException("array lengths cannot be 0.");    }    return 1 + numDimensions(Array.get(o, 0));  }  static void fillShape(Object o, int dim, int[] shape) {    if (shape == null || dim == shape.length) {      return;    }    final int len = Array.getLength(o);    if (shape[dim] == 0) {      shape[dim] = len;    } else if (shape[dim] != len) {      throw new IllegalArgumentException(          String.format("mismatched lengths (%d and %d) in dimension %d", shape[dim], len, dim));    }    for (int i = 0; i < len; ++i) {      fillShape(Array.get(o, i), dim + 1, shape);    }  }  private static final int ERROR_BUFFER_SIZE = 512;  private long errorHandle;  private long interpreterHandle;  private long modelHandle;  private int inputSize;  private MappedByteBuffer modelByteBuffer;  private Map inputsIndexes;  private Map outputsIndexes;  private static native String[] getInputNames(long interpreterHandle);//  private static native String[] getOutputNames(long interpreterHandle);//  private static native void resizeInput(      long interpreterHandle, long errorHandle, int inputIdx, int[] dims);//  private static native void useNNAPI(long interpreterHandle, boolean state);  private static native long createErrorReporter(int size);//1、设置错误报告器的大小  private static native long createModel(String modelPathOrBuffer, long errorHandle);//2、由文件创建网络模型  private static native long createModelWithBuffer(MappedByteBuffer modelBuffer, long errorHandle);//2.2、由内存模型数据创建  private static native long createInterpreter(long modelHandle);//3、创建tf实例  private static native long[] run(      long interpreterHandle,      long errorHandle,      Object[] sizes,      int[] dtypes,      int[] numsOfBytes,      Object[] values);//4、运行网络预测  private static native void delete(long errorHandle, long modelHandle, long interpreterHandle);//5、删除实例、模型等  private static native int[] getInputDims(long interpreterHandle, int inputIdx, int numBytes);//  static {    TensorFlowLite.init();  }}

工程文件：

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