如何通过ARM Compute Library写深度学习模型

在上一篇博客中已经讲了挺多关于arm compute library的情况。
传送们在这里！！！

http://blog.csdn.net/u014432647/article/details/73613642

这篇薄荷主要是想将一下如何用arm compute library写深度学习模型。这里以vgg16为例子，再一次放上我自己写的vgg16的github！！！（vgg16的代码放在example这个文件中，文件名是： vgg16_model_arm_compute_library_NEON.cpp vgg16_model_arm_compute_library_NEON.cpp）

https://github.com/ppplinday/vgg16-by-ARM-Compute-Library

1. 初始化Tensor
   tensor是一个多维矩阵，用来存放某一个操作的数据。例如输入的tensor通过激活操作或者卷积操作之后，把新的数据放入一个输出tensor，这个很重要，基本整个arm compute library就是围绕这个tensor运行的。放出一点定的代码：

   //init_input_tensor    Tensor input;    //init_conv_1_tensor    Tensor weights_1_1;    Tensor biases_1_1;    Tensor out_1_1;    Tensor act_1_1;    Tensor weights_1_2;    Tensor biases_1_2;    Tensor out_1_2;    Tensor act_1_2;    Tensor pool_1;

这里分别定义了输入，和vgg前两层卷积层的tensor

TensorShape：用来描述一个tensor是有多少维，分别是多大。有方法x()，y()，z()相当于数组中的[0][1][2]，如果有更高的维度就要用数组的表达方式
TensorInfo：用来提取TensorShape的维度信息

    //init_tensor    constexpr unsigned int input_width  = 224;    constexpr unsigned int input_height = 224;    constexpr unsigned int input_fm     = 3;    const TensorShape input_shape(input_width, input_height, input_fm);    input.allocator() -> init(TensorInfo(input_shape, 1, DataType::F32));    //init_conv_1_1    constexpr unsigned int conv_1_1_kernel_x = 3;    constexpr unsigned int conv_1_1_kernel_y = 3;    constexpr unsigned int conv_1_1_fm       = 64;    const TensorShape conv_1_1_weights_shape(conv_1_1_kernel_x, conv_1_1_kernel_y, input_shape.z(), conv_1_1_fm);    const TensorShape conv_1_1_biases_shape(conv_1_1_weights_shape[3]);    const TensorShape conv_1_1_out_shape(input_shape.x(), input_shape.y(), conv_1_1_weights_shape[3]);    weights_1_1.allocator() -> init(TensorInfo(conv_1_1_weights_shape, 1, DataType::F32));    biases_1_1.allocator() -> init(TensorInfo(conv_1_1_biases_shape, 1, DataType::F32));    out_1_1.allocator() -> init(TensorInfo(conv_1_1_out_shape, 1, DataType::F32));    act_1_1.allocator() -> init(TensorInfo(conv_1_1_out_shape, 1, DataType::F32));

首先先看一下input这个tensor。vgg的input是224，224，3，所以在input的tensorshape定义了它的维度：const TensorShape input_shape(input_width, input_height, input_fm);
然后通过tensor的allocator方法进行初始化：input.allocator() -> init(TensorInfo(input_shape, 1, DataType::F32));
在init方法中是输入tensorinfo的信息。在卷积核的时候先定义大小，然后根据模型调用allocator方法，之后的tensor初始化都是这样。

1. Configure Functions
   这个的意思是定义操作，定义不同功能的层，然后tensor在这些层中流进进行操作然后流出

    NEConvolutionLayer    conv_1_1;    NEConvolutionLayer    conv_1_2;    NEConvolutionLayer    conv_2_1;

这是定义层的。

    //conv_1    //in: 224 * 224 * 3, kernel: 3 * 3 * 3 * 64, out: 224 * 224 * 64    conv_1_1.configure(&input, &weights_1_1, &biases_1_1, &out_1_1, PadStrideInfo(1, 1, 1, 1));    //in: 224 * 224 * 64, out: 224 * 224 * 64    Nact_1_1.configure(&out_1_1, &act_1_1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 224 × 224 × 64, kernel: 3 * 3 * 64 * 64, out: 224 * 224 * 64    conv_1_2.configure(&act_1_1, &weights_1_2, &biases_1_2, &out_1_2, PadStrideInfo(1, 1, 1, 1));    //in: 224 * 224 * 64, out: 224 * 224 * 64    Nact_1_2.configure(&out_1_2, &act_1_2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 224 * 224 * 64, out: 112 * 112 * 64    Npool_1.configure(&act_1_2, &pool_1, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2)));

然后这个是层的configure方法。例如第一个卷积层，流入是input，通过weight和biase流出out，之后的是padstrideinfo，体现strides和padding。卷积核的大小是体现与weight的维度中。

1. Allocate tensors
   相当于内存分配，这一步用掉手机的大量内存，最好可用内存有2g以上吧，这步简单容易理解。

    //input    input.allocator() -> allocate();    //conv_1    weights_1_1.allocator() -> allocate();    biases_1_1.allocator() -> allocate();    out_1_1.allocator() -> allocate();    act_1_1.allocator() -> allocate();    weights_1_2.allocator() -> allocate();    biases_1_2.allocator() -> allocate();    out_1_2.allocator() -> allocate();    act_1_2.allocator() -> allocate();    pool_1.allocator() -> allocate();

1. Load the weights
   比较重要的一部分，input，weight和biase的数据都是这样子加载进去，最后的结果也是用这样的方法提取出来。先上例子代码：

    //conv_1_1    //W: 3 * 3 * 3 * 64    x = y = z = k = 0;    std::ifstream conv_1_1_Wfile("/data/local/tmp/conv1_1_W.csv");    line = "";    while(getline(conv_1_1_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_1_1.buffer() + weights_1_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_1_1_weights_shape.x()) ++ y, x = 0;            if(y == conv_1_1_weights_shape.y()) ++ z, y = 0;            if(z == conv_1_1_weights_shape.z()) ++ k, z = 0;        }    }    conv_1_1_Wfile.close();

这里以conv11中的weight为例子。首先是读取文件中的权重，通过getline在getline得到没一个权重的string值。然后通过这句：

*reinterpret_cast<float *>(weights_1_1.buffer() + weights_1_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;

这句中的weights_1_1.buffer()是找到weights的首地址，接着weights_1_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))是找到现在相应的地址。它不是像数据那样连续的（这样读取或出错，有些数据读不进去），下面在更新xyzk就行了（就像用了4个for循环这样更新）

1. Execute the functions
   这步就是把代码给跑起来啦！！！

    //conv_1    conv_1_1.run();    Nact_1_1.run();    conv_1_2.run();    Nact_1_2.run();    Npool_1.run();

跑完之后就可以用上面看个方法提取出最后output的内存，就可以知道概率啦！！！

大约整个流程是这样子，以上的代码基本都是给出了卷积层1作为参考，其他也是大同小异，最后放出整个完整的vgg16的代码作为参考吧，如果有什么问题可以尽管留言哈哈哈！！！

#include "arm_compute/runtime/NEON/NEFunctions.h"#include "arm_compute/runtime/CL/CLFunctions.h"#include "arm_compute/core/Types.h"#include "test_helpers/Utils.h"#include <iostream>#include <sstream>#include <fstream>#include <ostream>#include <sys/time.h>#include <map>using namespace arm_compute;using namespace test_helpers;static float StringToFloat(const std::string & str){    std::istringstream iss(str);    float number;    iss >> number;    return number;}void main_neon_dnn(int argc, const char **argv){    /*----------------------------------[init_model_vgg16]-----------------------------------*/    /*----------------------------------BEGIN:[init_Tensor]----------------------------------*/    //init_input_tensor    Tensor input;    //init_conv_1_tensor    Tensor weights_1_1;    Tensor biases_1_1;    Tensor out_1_1;    Tensor act_1_1;    Tensor weights_1_2;    Tensor biases_1_2;    Tensor out_1_2;    Tensor act_1_2;    Tensor pool_1;    //init_conv_2_tensor    Tensor weights_2_1;    Tensor biases_2_1;    Tensor out_2_1;    Tensor act_2_1;    Tensor weights_2_2;    Tensor biases_2_2;    Tensor out_2_2;    Tensor act_2_2;    Tensor pool_2;    //init_conv_3_tensor    Tensor weights_3_1;    Tensor biases_3_1;    Tensor out_3_1;    Tensor act_3_1;    Tensor weights_3_2;    Tensor biases_3_2;    Tensor out_3_2;    Tensor act_3_2;    Tensor weights_3_3;    Tensor biases_3_3;    Tensor out_3_3;    Tensor act_3_3;    Tensor pool_3;    //init_conv_4_tensor    Tensor weights_4_1;    Tensor biases_4_1;    Tensor out_4_1;    Tensor act_4_1;    Tensor weights_4_2;    Tensor biases_4_2;    Tensor out_4_2;    Tensor act_4_2;    Tensor weights_4_3;    Tensor biases_4_3;    Tensor out_4_3;    Tensor act_4_3;    Tensor pool_4;    //init_conv_5_tensor    Tensor weights_5_1;    Tensor biases_5_1;    Tensor out_5_1;    Tensor act_5_1;    Tensor weights_5_2;    Tensor biases_5_2;    Tensor out_5_2;    Tensor act_5_2;    Tensor weights_5_3;    Tensor biases_5_3;    Tensor out_5_3;    Tensor act_5_3;    Tensor pool_5;    //init_fc_6    Tensor weights_6;    Tensor biases_6;    Tensor out_6;    Tensor act_6;    //init_fc_7    Tensor weights_7;    Tensor biases_7;    Tensor out_7;    Tensor act_7;    //init_fc_8    Tensor weights_8;    Tensor biases_8;    Tensor out_8;    Tensor softmax_tensor;    //init_tensor    constexpr unsigned int input_width  = 224;    constexpr unsigned int input_height = 224;    constexpr unsigned int input_fm     = 3;    const TensorShape input_shape(input_width, input_height, input_fm);    input.allocator() -> init(TensorInfo(input_shape, 1, DataType::F32));    //init_conv_1_1    constexpr unsigned int conv_1_1_kernel_x = 3;    constexpr unsigned int conv_1_1_kernel_y = 3;    constexpr unsigned int conv_1_1_fm       = 64;    const TensorShape conv_1_1_weights_shape(conv_1_1_kernel_x, conv_1_1_kernel_y, input_shape.z(), conv_1_1_fm);    const TensorShape conv_1_1_biases_shape(conv_1_1_weights_shape[3]);    const TensorShape conv_1_1_out_shape(input_shape.x(), input_shape.y(), conv_1_1_weights_shape[3]);    weights_1_1.allocator() -> init(TensorInfo(conv_1_1_weights_shape, 1, DataType::F32));    biases_1_1.allocator() -> init(TensorInfo(conv_1_1_biases_shape, 1, DataType::F32));    out_1_1.allocator() -> init(TensorInfo(conv_1_1_out_shape, 1, DataType::F32));    act_1_1.allocator() -> init(TensorInfo(conv_1_1_out_shape, 1, DataType::F32));    //init_conv_1_2    constexpr unsigned int conv_1_2_kernel_x = 3;    constexpr unsigned int conv_1_2_kernel_y = 3;    constexpr unsigned int conv_1_2_fm       = 64;    const TensorShape conv_1_2_weights_shape(conv_1_2_kernel_x, conv_1_2_kernel_y, conv_1_1_out_shape.z(), conv_1_2_fm);    const TensorShape conv_1_2_biases_shape(conv_1_2_weights_shape[3]);    const TensorShape conv_1_2_out_shape(conv_1_1_out_shape.x(), conv_1_1_out_shape.y(), conv_1_1_weights_shape[3]);    weights_1_2.allocator() -> init(TensorInfo(conv_1_2_weights_shape, 1, DataType::F32));    biases_1_2.allocator() -> init(TensorInfo(conv_1_2_biases_shape, 1, DataType::F32));    out_1_2.allocator() -> init(TensorInfo(conv_1_2_out_shape, 1, DataType::F32));    act_1_2.allocator() -> init(TensorInfo(conv_1_2_out_shape, 1, DataType::F32));    TensorShape conv_1_pool = conv_1_2_out_shape;    conv_1_pool.set(0, conv_1_pool.x() / 2);    conv_1_pool.set(1, conv_1_pool.y() / 2);    pool_1.allocator() -> init(TensorInfo(conv_1_pool, 1, DataType::F32));    //init_conv_2_1    constexpr unsigned int conv_2_1_kernel_x = 3;    constexpr unsigned int conv_2_1_kernel_y = 3;    constexpr unsigned int conv_2_1_fm       = 128;    const TensorShape conv_2_1_weights_shape(conv_2_1_kernel_x, conv_2_1_kernel_y, conv_1_pool.z(), conv_2_1_fm);    const TensorShape conv_2_1_biases_shape(conv_2_1_weights_shape[3]);    const TensorShape conv_2_1_out_shape(conv_1_pool.x(), conv_1_pool.y(), conv_2_1_weights_shape[3]);    weights_2_1.allocator() -> init(TensorInfo(conv_2_1_weights_shape, 1, DataType::F32));    biases_2_1.allocator() -> init(TensorInfo(conv_2_1_biases_shape, 1, DataType::F32));    out_2_1.allocator() -> init(TensorInfo(conv_2_1_out_shape, 1, DataType::F32));    act_2_1.allocator() -> init(TensorInfo(conv_2_1_out_shape, 1, DataType::F32));    //init_conv_2_2    constexpr unsigned int conv_2_2_kernel_x = 3;    constexpr unsigned int conv_2_2_kernel_y = 3;    constexpr unsigned int conv_2_2_fm       = 128;    const TensorShape conv_2_2_weights_shape(conv_2_2_kernel_x, conv_2_2_kernel_y, conv_2_1_out_shape.z(), conv_2_2_fm);    const TensorShape conv_2_2_biases_shape(conv_2_2_weights_shape[3]);    const TensorShape conv_2_2_out_shape(conv_2_1_out_shape.x(), conv_2_1_out_shape.y(), conv_2_2_weights_shape[3]);    weights_2_2.allocator() -> init(TensorInfo(conv_2_2_weights_shape, 1, DataType::F32));    biases_2_2.allocator() -> init(TensorInfo(conv_2_2_biases_shape, 1, DataType::F32));    out_2_2.allocator() -> init(TensorInfo(conv_2_2_out_shape, 1, DataType::F32));    act_2_2.allocator() -> init(TensorInfo(conv_2_2_out_shape, 1, DataType::F32));    TensorShape conv_2_pool = conv_2_2_out_shape;    conv_2_pool.set(0, conv_2_pool.x() / 2);    conv_2_pool.set(1, conv_2_pool.y() / 2);    pool_2.allocator() -> init(TensorInfo(conv_2_pool, 1, DataType::F32));    //init_conv_3_1    constexpr unsigned int conv_3_1_kernel_x = 3;    constexpr unsigned int conv_3_1_kernel_y = 3;    constexpr unsigned int conv_3_1_fm       = 256;    const TensorShape conv_3_1_weights_shape(conv_3_1_kernel_x, conv_3_1_kernel_y, conv_2_pool.z(), conv_3_1_fm);    const TensorShape conv_3_1_biases_shape(conv_3_1_weights_shape[3]);    const TensorShape conv_3_1_out_shape(conv_2_pool.x(), conv_2_pool.y(), conv_3_1_weights_shape[3]);    weights_3_1.allocator() -> init(TensorInfo(conv_3_1_weights_shape, 1, DataType::F32));    biases_3_1.allocator() -> init(TensorInfo(conv_3_1_biases_shape, 1, DataType::F32));    out_3_1.allocator() -> init(TensorInfo(conv_3_1_out_shape, 1, DataType::F32));    act_3_1.allocator() -> init(TensorInfo(conv_3_1_out_shape, 1, DataType::F32));    //init_conv_3_2    constexpr unsigned int conv_3_2_kernel_x = 3;    constexpr unsigned int conv_3_2_kernel_y = 3;    constexpr unsigned int conv_3_2_fm       = 256;    const TensorShape conv_3_2_weights_shape(conv_3_2_kernel_x, conv_3_2_kernel_y, conv_3_1_out_shape.z(), conv_3_2_fm);    const TensorShape conv_3_2_biases_shape(conv_3_2_weights_shape[3]);    const TensorShape conv_3_2_out_shape(conv_3_1_out_shape.x(), conv_3_1_out_shape.y(), conv_3_2_weights_shape[3]);    weights_3_2.allocator() -> init(TensorInfo(conv_3_2_weights_shape, 1, DataType::F32));    biases_3_2.allocator() -> init(TensorInfo(conv_3_2_biases_shape, 1, DataType::F32));    out_3_2.allocator() -> init(TensorInfo(conv_3_2_out_shape, 1, DataType::F32));    act_3_2.allocator() -> init(TensorInfo(conv_3_2_out_shape, 1, DataType::F32));    //init_conv_3_3    constexpr unsigned int conv_3_3_kernel_x = 3;    constexpr unsigned int conv_3_3_kernel_y = 3;    constexpr unsigned int conv_3_3_fm       = 256;    const TensorShape conv_3_3_weights_shape(conv_3_3_kernel_x, conv_3_3_kernel_y, conv_3_2_out_shape.z(), conv_3_3_fm);    const TensorShape conv_3_3_biases_shape(conv_3_3_weights_shape[3]);    const TensorShape conv_3_3_out_shape(conv_3_2_out_shape.x(), conv_3_2_out_shape.y(), conv_3_3_weights_shape[3]);    weights_3_3.allocator() -> init(TensorInfo(conv_3_3_weights_shape, 1, DataType::F32));    biases_3_3.allocator() -> init(TensorInfo(conv_3_3_biases_shape, 1, DataType::F32));    out_3_3.allocator() -> init(TensorInfo(conv_3_3_out_shape, 1, DataType::F32));    act_3_3.allocator() -> init(TensorInfo(conv_3_3_out_shape, 1, DataType::F32));    TensorShape conv_3_pool = conv_3_3_out_shape;    conv_3_pool.set(0, conv_3_pool.x() / 2);    conv_3_pool.set(1, conv_3_pool.y() / 2);    pool_3.allocator() -> init(TensorInfo(conv_3_pool, 1, DataType::F32));    //init_conv_4_1    constexpr unsigned int conv_4_1_kernel_x = 3;    constexpr unsigned int conv_4_1_kernel_y = 3;    constexpr unsigned int conv_4_1_fm       = 512;    const TensorShape conv_4_1_weights_shape(conv_4_1_kernel_x, conv_4_1_kernel_y, conv_3_pool.z(), conv_4_1_fm);    const TensorShape conv_4_1_biases_shape(conv_4_1_weights_shape[3]);    const TensorShape conv_4_1_out_shape(conv_3_pool.x(), conv_3_pool.y(), conv_4_1_weights_shape[3]);    weights_4_1.allocator() -> init(TensorInfo(conv_4_1_weights_shape, 1, DataType::F32));    biases_4_1.allocator() -> init(TensorInfo(conv_4_1_biases_shape, 1, DataType::F32));    out_4_1.allocator() -> init(TensorInfo(conv_4_1_out_shape, 1, DataType::F32));    act_4_1.allocator() -> init(TensorInfo(conv_4_1_out_shape, 1, DataType::F32));    //init_conv_4_2    constexpr unsigned int conv_4_2_kernel_x = 3;    constexpr unsigned int conv_4_2_kernel_y = 3;    constexpr unsigned int conv_4_2_fm       = 512;    const TensorShape conv_4_2_weights_shape(conv_4_2_kernel_x, conv_4_2_kernel_y, conv_4_1_out_shape.z(), conv_4_2_fm);    const TensorShape conv_4_2_biases_shape(conv_4_2_weights_shape[3]);    const TensorShape conv_4_2_out_shape(conv_4_1_out_shape.x(), conv_4_1_out_shape.y(), conv_4_2_weights_shape[3]);    weights_4_2.allocator() -> init(TensorInfo(conv_4_2_weights_shape, 1, DataType::F32));    biases_4_2.allocator() -> init(TensorInfo(conv_4_2_biases_shape, 1, DataType::F32));    out_4_2.allocator() -> init(TensorInfo(conv_4_2_out_shape, 1, DataType::F32));    act_4_2.allocator() -> init(TensorInfo(conv_4_2_out_shape, 1, DataType::F32));    //init_conv_4_3    constexpr unsigned int conv_4_3_kernel_x = 3;    constexpr unsigned int conv_4_3_kernel_y = 3;    constexpr unsigned int conv_4_3_fm       = 512;    const TensorShape conv_4_3_weights_shape(conv_4_3_kernel_x, conv_4_3_kernel_y, conv_4_2_out_shape.z(), conv_4_3_fm);    const TensorShape conv_4_3_biases_shape(conv_4_3_weights_shape[3]);    const TensorShape conv_4_3_out_shape(conv_4_2_out_shape.x(), conv_4_2_out_shape.y(), conv_4_3_weights_shape[3]);    weights_4_3.allocator() -> init(TensorInfo(conv_4_3_weights_shape, 1, DataType::F32));    biases_4_3.allocator() -> init(TensorInfo(conv_4_3_biases_shape, 1, DataType::F32));    out_4_3.allocator() -> init(TensorInfo(conv_4_3_out_shape, 1, DataType::F32));    act_4_3.allocator() -> init(TensorInfo(conv_4_3_out_shape, 1, DataType::F32));    TensorShape conv_4_pool = conv_4_3_out_shape;    conv_4_pool.set(0, conv_4_pool.x() / 2);    conv_4_pool.set(1, conv_4_pool.y() / 2);    pool_4.allocator() -> init(TensorInfo(conv_4_pool, 1, DataType::F32));    //init_conv_5_1    constexpr unsigned int conv_5_1_kernel_x = 3;    constexpr unsigned int conv_5_1_kernel_y = 3;    constexpr unsigned int conv_5_1_fm       = 512;    const TensorShape conv_5_1_weights_shape(conv_5_1_kernel_x, conv_5_1_kernel_y, conv_4_pool.z(), conv_5_1_fm);    const TensorShape conv_5_1_biases_shape(conv_5_1_weights_shape[3]);    const TensorShape conv_5_1_out_shape(conv_4_pool.x(), conv_4_pool.y(), conv_5_1_weights_shape[3]);    weights_5_1.allocator() -> init(TensorInfo(conv_5_1_weights_shape, 1, DataType::F32));    biases_5_1.allocator() -> init(TensorInfo(conv_5_1_biases_shape, 1, DataType::F32));    out_5_1.allocator() -> init(TensorInfo(conv_5_1_out_shape, 1, DataType::F32));    act_5_1.allocator() -> init(TensorInfo(conv_5_1_out_shape, 1, DataType::F32));    //init_conv_5_2    constexpr unsigned int conv_5_2_kernel_x = 3;    constexpr unsigned int conv_5_2_kernel_y = 3;    constexpr unsigned int conv_5_2_fm       = 512;    const TensorShape conv_5_2_weights_shape(conv_5_2_kernel_x, conv_5_2_kernel_y, conv_5_1_out_shape.z(), conv_5_2_fm);    const TensorShape conv_5_2_biases_shape(conv_5_2_weights_shape[3]);    const TensorShape conv_5_2_out_shape(conv_5_1_out_shape.x(), conv_5_1_out_shape.y(), conv_5_2_weights_shape[3]);    weights_5_2.allocator() -> init(TensorInfo(conv_5_2_weights_shape, 1, DataType::F32));    biases_5_2.allocator() -> init(TensorInfo(conv_5_2_biases_shape, 1, DataType::F32));    out_5_2.allocator() -> init(TensorInfo(conv_5_2_out_shape, 1, DataType::F32));    act_5_2.allocator() -> init(TensorInfo(conv_5_2_out_shape, 1, DataType::F32));    //init_conv_5_3    constexpr unsigned int conv_5_3_kernel_x = 3;    constexpr unsigned int conv_5_3_kernel_y = 3;    constexpr unsigned int conv_5_3_fm       = 512;    const TensorShape conv_5_3_weights_shape(conv_5_3_kernel_x, conv_5_3_kernel_y, conv_5_2_out_shape.z(), conv_5_3_fm);    const TensorShape conv_5_3_biases_shape(conv_5_3_weights_shape[3]);    const TensorShape conv_5_3_out_shape(conv_5_2_out_shape.x(), conv_5_2_out_shape.y(), conv_5_3_weights_shape[3]);    weights_5_3.allocator() -> init(TensorInfo(conv_5_3_weights_shape, 1, DataType::F32));    biases_5_3.allocator() -> init(TensorInfo(conv_5_3_biases_shape, 1, DataType::F32));    out_5_3.allocator() -> init(TensorInfo(conv_5_3_out_shape, 1, DataType::F32));    act_5_3.allocator() -> init(TensorInfo(conv_5_3_out_shape, 1, DataType::F32));    TensorShape conv_5_pool = conv_5_3_out_shape;    conv_5_pool.set(0, conv_5_pool.x() / 2);    conv_5_pool.set(1, conv_5_pool.y() / 2);    pool_5.allocator() -> init(TensorInfo(conv_5_pool, 1, DataType::F32));    //init_fc_6    constexpr unsigned int fc_6_numoflabel = 4096;    const TensorShape fc_6_weights_shape(conv_5_pool.x() * conv_5_pool.y() * conv_5_pool.z(), fc_6_numoflabel);    const TensorShape fc_6_biases_shape(fc_6_numoflabel);    const TensorShape fc_6_out_shape(fc_6_numoflabel);    weights_6.allocator() -> init(TensorInfo(fc_6_weights_shape, 1, DataType::F32));    biases_6.allocator() -> init(TensorInfo(fc_6_biases_shape, 1, DataType::F32));    out_6.allocator() -> init(TensorInfo(fc_6_out_shape, 1, DataType::F32));    act_6.allocator() -> init(TensorInfo(fc_6_out_shape, 1, DataType::F32));    //init_fc_7    constexpr unsigned int fc_7_numoflabel = 4096;    const TensorShape fc_7_weights_shape(fc_6_out_shape.x(), fc_7_numoflabel);    const TensorShape fc_7_biases_shape(fc_7_numoflabel);    const TensorShape fc_7_out_shape(fc_7_numoflabel);    weights_7.allocator() -> init(TensorInfo(fc_7_weights_shape, 1, DataType::F32));    biases_7.allocator() -> init(TensorInfo(fc_7_biases_shape, 1, DataType::F32));    out_7.allocator() -> init(TensorInfo(fc_7_out_shape, 1, DataType::F32));    act_7.allocator() -> init(TensorInfo(fc_7_out_shape, 1, DataType::F32));    //init_fc_8    constexpr unsigned int fc_8_numoflabel = 1000;    const TensorShape fc_8_weights_shape(fc_7_out_shape.x(), fc_8_numoflabel);    const TensorShape fc_8_biases_shape(fc_8_numoflabel);    const TensorShape fc_8_out_shape(fc_8_numoflabel);    weights_8.allocator() -> init(TensorInfo(fc_8_weights_shape, 1, DataType::F32));    biases_8.allocator() -> init(TensorInfo(fc_8_biases_shape, 1, DataType::F32));    out_8.allocator() -> init(TensorInfo(fc_8_out_shape, 1, DataType::F32));    const TensorShape softmax_shape(fc_8_out_shape.x());    softmax_tensor.allocator() -> init(TensorInfo(softmax_shape, 1, DataType::F32));    /*----------------------------------END:[init_Tensor]----------------------------------*/    /*-----------------------------BEGIN:[Configure Functions]-----------------------------*/    //init_layer    //NEON CPU    NEConvolutionLayer    conv_1_1;    NEConvolutionLayer    conv_1_2;    NEConvolutionLayer    conv_2_1;    NEConvolutionLayer    conv_2_2;    NEConvolutionLayer    conv_3_1;    NEConvolutionLayer    conv_3_2;    NEConvolutionLayer    conv_3_3;    NEConvolutionLayer    conv_4_1;    NEConvolutionLayer    conv_4_2;    NEConvolutionLayer    conv_4_3;    NEConvolutionLayer    conv_5_1;    NEConvolutionLayer    conv_5_2;    NEConvolutionLayer    conv_5_3;    NEActivationLayer     Nact_1_1;    NEActivationLayer     Nact_1_2;    NEActivationLayer     Nact_2_1;    NEActivationLayer     Nact_2_2;    NEActivationLayer     Nact_3_1;    NEActivationLayer     Nact_3_2;    NEActivationLayer     Nact_3_3;    NEActivationLayer     Nact_4_1;    NEActivationLayer     Nact_4_2;    NEActivationLayer     Nact_4_3;    NEActivationLayer     Nact_5_1;    NEActivationLayer     Nact_5_2;    NEActivationLayer     Nact_5_3;    NEActivationLayer     Nact_6;    NEActivationLayer     Nact_7;    NEPoolingLayer        Npool_1;    NEPoolingLayer        Npool_2;    NEPoolingLayer        Npool_3;    NEPoolingLayer        Npool_4;    NEPoolingLayer        Npool_5;    NEFullyConnectedLayer fc_6;    NEFullyConnectedLayer fc_7;    NEFullyConnectedLayer fc_8;    NESoftmaxLayer        softmax;    //conv_1    //in: 224 * 224 * 3, kernel: 3 * 3 * 3 * 64, out: 224 * 224 * 64    conv_1_1.configure(&input, &weights_1_1, &biases_1_1, &out_1_1, PadStrideInfo(1, 1, 1, 1));    //in: 224 * 224 * 64, out: 224 * 224 * 64    Nact_1_1.configure(&out_1_1, &act_1_1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 224 × 224 × 64, kernel: 3 * 3 * 64 * 64, out: 224 * 224 * 64    conv_1_2.configure(&act_1_1, &weights_1_2, &biases_1_2, &out_1_2, PadStrideInfo(1, 1, 1, 1));    //in: 224 * 224 * 64, out: 224 * 224 * 64    Nact_1_2.configure(&out_1_2, &act_1_2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 224 * 224 * 64, out: 112 * 112 * 64    Npool_1.configure(&act_1_2, &pool_1, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2)));    //conv_2    //in: 112 * 112 * 64, kernel: 3 * 3 * 64 * 128. out: 112 * 112 * 128    conv_2_1.configure(&pool_1, &weights_2_1, &biases_2_1, &out_2_1, PadStrideInfo(1, 1, 1, 1));    //in: 112 * 112 * 128, out: 112 * 112 * 128    Nact_2_1.configure(&out_2_1, &act_2_1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 112 * 112 * 128, kernel: 3 * 3 * 128 * 128, out: 112 * 112 * 128    conv_2_2.configure(&act_2_1, &weights_2_2, &biases_2_2, &out_2_2, PadStrideInfo(1, 1, 1, 1));    //in: 112 * 112 * 128, out: 112 * 112 * 128    Nact_2_2.configure(&out_2_2, &act_2_2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 112 * 112 * 128, out: 56 * 56 * 128    Npool_2.configure(&act_2_2, &pool_2, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2)));    //conv_3    //in: 56 * 56 * 128, kernel: 3 * 3 * 128 * 256, out: 56 * 56 * 256    conv_3_1.configure(&pool_2, &weights_3_1, &biases_3_1, &out_3_1, PadStrideInfo(1, 1, 1, 1));    //in: 56 * 56 * 256, out: 56 * 56 * 256    Nact_3_1.configure(&out_3_1, &act_3_1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 56 * 56 * 256, kernel: 3 * 3 * 256 * 256, out: 56 * 56 * 256    conv_3_2.configure(&act_3_1, &weights_3_2, &biases_3_2, &out_3_2, PadStrideInfo(1, 1, 1, 1));    //in: 56 * 56 * 256, out: 56 * 56 * 256    Nact_3_2.configure(&out_3_2, &act_3_2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 56 * 56 * 256, kernel: 3 * 3 * 256 * 256, out: 56 * 56 * 256    conv_3_3.configure(&act_3_2, &weights_3_3, &biases_3_3, &out_3_3, PadStrideInfo(1, 1, 1, 1));    //in: 56 * 56 * 256, out: 56 * 56 * 256    Nact_3_3.configure(&out_3_3, &act_3_3, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 56 * 56 * 256, out: 28 * 28 * 256    Npool_3.configure(&act_3_3, &pool_3, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2)));    //conv_4    //in: 28 * 28 * 256, kernel: 3 * 3 * 256 * 512, out: 28 * 28 * 512    conv_4_1.configure(&pool_3, &weights_4_1, &biases_4_1, &out_4_1, PadStrideInfo(1, 1, 1, 1));    //in: 28 * 28 * 512, out: 28 * 28 * 512    Nact_4_1.configure(&out_4_1, &act_4_1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 28 * 28 * 512, kernel: 3 * 3 * 512 * 512, out: 28 * 28 * 512    conv_4_2.configure(&act_4_1, &weights_4_2, &biases_4_2, &out_4_2, PadStrideInfo(1, 1, 1, 1));    //in: 28 * 28 * 512, out: 28 * 28 * 512    Nact_4_2.configure(&out_4_2, &act_4_2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 28 * 28 * 512, kernel: 3 * 3 * 512 * 512, out: 28 * 28 * 512    conv_4_3.configure(&act_4_2, &weights_4_3, &biases_4_3, &out_4_3, PadStrideInfo(1, 1, 1, 1));    //in: 28 * 28 * 512, out: 28 * 28 * 512    Nact_4_3.configure(&out_4_3, &act_4_3, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 28 * 28 * 512, out: 14 * 14 * 512    Npool_4.configure(&act_4_3, &pool_4, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2)));    //conv_5    //in: 14 * 14 * 512, kernel: 3 * 3 * 512 * 512, out: 14 * 14 * 512    conv_5_1.configure(&pool_4, &weights_5_1, &biases_5_1, &out_5_1, PadStrideInfo(1, 1, 1, 1));    //in: 14 * 14 * 512, out: 14 * 14 * 512    Nact_5_1.configure(&out_5_1, &act_5_1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 14 * 14 * 512, kernel: 3 * 3 * 512 * 512, out: 14 * 14 * 512    conv_5_2.configure(&act_5_1, &weights_5_2, &biases_5_2, &out_5_2, PadStrideInfo(1, 1, 1, 1));    //in: 14 * 14 * 512, out: 14 * 14 * 512    Nact_5_2.configure(&out_5_2, &act_5_2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 14 * 14 * 512 kernel: 3 * 3 * 512 * 512, out: 14 * 14 * 512    conv_5_3.configure(&act_5_2, &weights_5_3, &biases_5_3, &out_5_3, PadStrideInfo(1, 1, 1, 1));    //in: 14 * 14 * 512, out: 14 * 14 * 512    Nact_5_3.configure(&out_5_3, &act_5_3, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //in: 14 * 14 * 512, out: 7 * 7 * 512    Npool_5.configure(&act_5_3, &pool_5, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2)));    //fc_6    //in: 7 * 7 * 512, out: 4096    fc_6.configure(&pool_5, &weights_6, &biases_6, &out_6);    //in: 4096, out: 4096    Nact_6.configure(&out_6, &act_6, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //fc_7    //in: 4096, out: 4096    fc_7.configure(&act_6, &weights_7, &biases_7, &out_7);    //in:4096, out: 4096    Nact_7.configure(&out_7, &act_7, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));    //fc_8    //in: 4096, out: 1000    fc_8.configure(&act_7, &weights_8, &biases_8, &out_8);    //softmax layer: 1000    softmax.configure(&out_8, &softmax_tensor);    /*------------------------------END:[Configure Functions]------------------------------*/    /*------------------------------BEGIN:[Allocate tensors]-------------------------------*/    //input    input.allocator() -> allocate();    //conv_1    weights_1_1.allocator() -> allocate();    biases_1_1.allocator() -> allocate();    out_1_1.allocator() -> allocate();    act_1_1.allocator() -> allocate();    weights_1_2.allocator() -> allocate();    biases_1_2.allocator() -> allocate();    out_1_2.allocator() -> allocate();    act_1_2.allocator() -> allocate();    pool_1.allocator() -> allocate();    //conv_2    weights_2_1.allocator() -> allocate();    biases_2_1.allocator() -> allocate();    out_2_1.allocator() -> allocate();    act_2_1.allocator() -> allocate();    weights_2_2.allocator() -> allocate();    biases_2_2.allocator() -> allocate();    out_2_2.allocator() -> allocate();    act_2_2.allocator() -> allocate();    pool_2.allocator() -> allocate();    //conv_3    weights_3_1.allocator() -> allocate();    biases_3_1.allocator() -> allocate();    out_3_1.allocator() -> allocate();    act_3_1.allocator() -> allocate();    weights_3_2.allocator() -> allocate();    biases_3_2.allocator() -> allocate();    out_3_2.allocator() -> allocate();    act_3_2.allocator() -> allocate();    weights_3_3.allocator() -> allocate();    biases_3_3.allocator() -> allocate();    out_3_3.allocator() -> allocate();    act_3_3.allocator() -> allocate();    pool_3.allocator() -> allocate();    //conv_4    weights_4_1.allocator() -> allocate();    biases_4_1.allocator() -> allocate();    out_4_1.allocator() -> allocate();    act_4_1.allocator() -> allocate();    weights_4_2.allocator() -> allocate();    biases_4_2.allocator() -> allocate();    out_4_2.allocator() -> allocate();    act_4_2.allocator() -> allocate();    weights_4_3.allocator() -> allocate();    biases_4_3.allocator() -> allocate();    out_4_3.allocator() -> allocate();    act_4_3.allocator() -> allocate();    pool_4.allocator() -> allocate();    //conv_5    weights_5_1.allocator() -> allocate();    biases_5_1.allocator() -> allocate();    out_5_1.allocator() -> allocate();    act_5_1.allocator() -> allocate();    weights_5_2.allocator() -> allocate();    biases_5_2.allocator() -> allocate();    out_5_2.allocator() -> allocate();    act_5_2.allocator() -> allocate();    weights_5_3.allocator() -> allocate();    biases_5_3.allocator() -> allocate();    out_5_3.allocator() -> allocate();    act_5_3.allocator() -> allocate();    pool_5.allocator() -> allocate();    //fc_6    weights_6.allocator() -> allocate();    biases_6.allocator() -> allocate();    out_6.allocator() -> allocate();    act_6.allocator() -> allocate();    //fc_7    weights_7.allocator() -> allocate();    biases_7.allocator() -> allocate();    out_7.allocator() -> allocate();    act_7.allocator() -> allocate();    //fc_8    weights_8.allocator() -> allocate();    biases_8.allocator() -> allocate();    out_8.allocator() -> allocate();    softmax_tensor.allocator() -> allocate();    /*------------------------------END:[Allocate tensors]-------------------------------*/    /*-----------------------------BEGIN:[Load the weights]------------------------------*/    //init_pulice_variable    int x, y, z, k;    std::string line;    //conv_1_1    //W: 3 * 3 * 3 * 64    x = y = z = k = 0;    std::ifstream conv_1_1_Wfile("/data/local/tmp/conv1_1_W.csv");    line = "";    while(getline(conv_1_1_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_1_1.buffer() + weights_1_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_1_1_weights_shape.x()) ++ y, x = 0;            if(y == conv_1_1_weights_shape.y()) ++ z, y = 0;            if(z == conv_1_1_weights_shape.z()) ++ k, z = 0;        }    }    conv_1_1_Wfile.close();    //b: 64    x = y = z = k = 0;    std::ifstream conv_1_1_bfile("/data/local/tmp/conv1_1_b.csv");    line = "";    while(getline(conv_1_1_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_1_1.buffer() + biases_1_1.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_1_1_bfile.close();    //conv_1_2    //W: 3 * 3 * 64 * 64    x = y = z = k = 0;    std::ifstream conv_1_2_Wfile("/data/local/tmp/conv1_2_W.csv");    line = "";    while(getline(conv_1_2_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_1_2.buffer() + weights_1_2.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_1_2_weights_shape.x()) ++ y, x = 0;            if(y == conv_1_2_weights_shape.y()) ++ z, y = 0;            if(z == conv_1_2_weights_shape.z()) ++ k, z = 0;        }    }    conv_1_2_Wfile.close();    //b: 64    x = y = z = k = 0;    std::ifstream conv_1_2_bfile("/data/local/tmp/conv1_2_b.csv");    line = "";    while(getline(conv_1_2_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_1_2.buffer() + biases_1_2.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_1_2_bfile.close();    //conv_2_1    //W: 3 * 3 * 64 * 128    x = y = z = k = 0;    std::ifstream conv_2_1_Wfile("/data/local/tmp/conv2_1_W.csv");    line = "";    while(getline(conv_2_1_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_2_1.buffer() + weights_2_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_2_1_weights_shape.x()) ++ y, x = 0;            if(y == conv_2_1_weights_shape.y()) ++ z, y = 0;            if(z == conv_2_1_weights_shape.z()) ++ k, z = 0;        }    }    conv_2_1_Wfile.close();    //b: 128    x = y = z = k = 0;    std::ifstream conv_2_1_bfile("/data/local/tmp/conv2_1_b.csv");    line = "";    while(getline(conv_2_1_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_2_1.buffer() + biases_2_1.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_2_1_bfile.close();    //conv_2_2    //W: 3 * 3 * 128 * 128    x = y = z = k = 0;    std::ifstream conv_2_2_Wfile("/data/local/tmp/conv2_2_W.csv");    line = "";    while(getline(conv_2_2_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_2_2.buffer() + weights_2_2.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_2_2_weights_shape.x()) ++ y, x = 0;            if(y == conv_2_2_weights_shape.y()) ++ z, y = 0;            if(z == conv_2_2_weights_shape.z()) ++ k, z = 0;        }    }    conv_2_2_Wfile.close();    //b: 128    x = y = z = k = 0;    std::ifstream conv_2_2_bfile("/data/local/tmp/conv2_2_b.csv");    line = "";    while(getline(conv_2_2_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_2_2.buffer() + biases_2_2.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_2_2_bfile.close();    //conv_3_1    //W: 3 * 3 * 128 * 256    x = y = z = k = 0;    std::ifstream conv_3_1_Wfile("/data/local/tmp/conv3_1_W.csv");    line = "";    while(getline(conv_3_1_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_3_1.buffer() + weights_3_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_3_1_weights_shape.x()) ++ y, x = 0;            if(y == conv_3_1_weights_shape.y()) ++ z, y = 0;            if(z == conv_3_1_weights_shape.z()) ++ k, z = 0;        }    }    conv_3_1_Wfile.close();    //b: 256    x = y = z = k = 0;    std::ifstream conv_3_1_bfile("/data/local/tmp/conv3_1_b.csv");    line = "";    while(getline(conv_3_1_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_3_1.buffer() + biases_3_1.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_3_1_bfile.close();    //conv_3_2    //W: 3 * 3 * 256 * 256    x = y = z = k = 0;    std::ifstream conv_3_2_Wfile("/data/local/tmp/conv3_2_W.csv");    line = "";    while(getline(conv_3_2_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_3_2.buffer() + weights_3_2.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_3_2_weights_shape.x()) ++ y, x = 0;            if(y == conv_3_2_weights_shape.y()) ++ z, y = 0;            if(z == conv_3_2_weights_shape.z()) ++ k, z = 0;        }    }    conv_3_2_Wfile.close();    //b: 256    x = y = z = k = 0;    std::ifstream conv_3_2_bfile("/data/local/tmp/conv3_2_b.csv");    line = "";    while(getline(conv_3_2_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_3_2.buffer() + biases_3_2.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_3_2_bfile.close();    //conv_3_3    //W: 3 * 3 * 256 * 256    x = y = z = k = 0;    std::ifstream conv_3_3_Wfile("/data/local/tmp/conv3_3_W.csv");    line = "";    while(getline(conv_3_3_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_3_3.buffer() + weights_3_3.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_3_3_weights_shape.x()) ++ y, x = 0;            if(y == conv_3_3_weights_shape.y()) ++ z, y = 0;            if(z == conv_3_3_weights_shape.z()) ++ k, z = 0;        }    }    conv_3_3_Wfile.close();    //b: 256    x = y = z = k = 0;    std::ifstream conv_3_3_bfile("/data/local/tmp/conv3_3_b.csv");    line = "";    while(getline(conv_3_3_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_3_3.buffer() + biases_3_3.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_3_3_bfile.close();    //conv_4_1    //W: 3 * 3 * 256 * 512    x = y = z = k = 0;    std::ifstream conv_4_1_Wfile("/data/local/tmp/conv4_1_W.csv");    line = "";    while(getline(conv_4_1_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_4_1.buffer() + weights_4_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_4_1_weights_shape.x()) ++ y, x = 0;            if(y == conv_4_1_weights_shape.y()) ++ z, y = 0;            if(z == conv_4_1_weights_shape.z()) ++ k, z = 0;        }    }    conv_4_1_Wfile.close();    //b: 512    x = y = z = k = 0;    std::ifstream conv_4_1_bfile("/data/local/tmp/conv4_1_b.csv");    line = "";    while(getline(conv_4_1_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_4_1.buffer() + biases_4_1.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_4_1_bfile.close();    //conv_4_2    //W: 3 * 3 * 512 * 512    x = y = z = k = 0;    std::ifstream conv_4_2_Wfile("/data/local/tmp/conv4_2_W.csv");    line = "";    while(getline(conv_4_2_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_4_2.buffer() + weights_4_2.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_4_2_weights_shape.x()) ++ y, x = 0;            if(y == conv_4_2_weights_shape.y()) ++ z, y = 0;            if(z == conv_4_2_weights_shape.z()) ++ k, z = 0;        }    }    conv_4_2_Wfile.close();    //b: 512    x = y = z = k = 0;    std::ifstream conv_4_2_bfile("/data/local/tmp/conv4_2_b.csv");    line = "";    while(getline(conv_4_2_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_4_2.buffer() + biases_4_2.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_4_2_bfile.close();    //conv_4_3    //W: 3 * 3 * 512 * 512    x = y = z = k = 0;    std::ifstream conv_4_3_Wfile("/data/local/tmp/conv4_3_W.csv");    line = "";    while(getline(conv_4_3_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_4_3.buffer() + weights_4_3.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_4_3_weights_shape.x()) ++ y, x = 0;            if(y == conv_4_3_weights_shape.y()) ++ z, y = 0;            if(z == conv_4_3_weights_shape.z()) ++ k, z = 0;        }    }    conv_4_3_Wfile.close();    //b: 512    x = y = z = k = 0;    std::ifstream conv_4_3_bfile("/data/local/tmp/conv4_3_b.csv");    line = "";    while(getline(conv_4_3_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_4_3.buffer() + biases_4_3.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_4_3_bfile.close();    //conv_5_1    //W: 3 * 3 * 512 * 512    x = y = z = k = 0;    std::ifstream conv_5_1_Wfile("/data/local/tmp/conv5_1_W.csv");    line = "";    while(getline(conv_5_1_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_5_1.buffer() + weights_5_1.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_5_1_weights_shape.x()) ++ y, x = 0;            if(y == conv_5_1_weights_shape.y()) ++ z, y = 0;            if(z == conv_5_1_weights_shape.z()) ++ k, z = 0;        }    }    conv_5_1_Wfile.close();    //b: 512    x = y = z = k = 0;    std::ifstream conv_5_1_bfile("/data/local/tmp/conv5_1_b.csv");    line = "";    while(getline(conv_5_1_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_5_1.buffer() + biases_5_1.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_5_1_bfile.close();    //conv_5_2    //W: 3 * 3 * 512 * 512    x = y = z = k = 0;    std::ifstream conv_5_2_Wfile("/data/local/tmp/conv5_2_W.csv");    line = "";    while(getline(conv_5_2_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_5_2.buffer() + weights_5_2.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_5_2_weights_shape.x()) ++ y, x = 0;            if(y == conv_5_2_weights_shape.y()) ++ z, y = 0;            if(z == conv_5_2_weights_shape.z()) ++ k, z = 0;        }    }    conv_5_2_Wfile.close();    //b: 512    x = y = z = k = 0;    std::ifstream conv_5_2_bfile("/data/local/tmp/conv5_2_b.csv");    line = "";    while(getline(conv_5_2_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_5_2.buffer() + biases_5_2.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_5_2_bfile.close();    //conv_5_3    //W: 3 * 3 * 512 * 512    x = y = z = k = 0;    std::ifstream conv_5_3_Wfile("/data/local/tmp/conv5_3_W.csv");    line = "";    while(getline(conv_5_3_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_5_3.buffer() + weights_5_3.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == conv_5_3_weights_shape.x()) ++ y, x = 0;            if(y == conv_5_3_weights_shape.y()) ++ z, y = 0;            if(z == conv_5_3_weights_shape.z()) ++ k, z = 0;        }    }    conv_5_3_Wfile.close();    //b: 512    x = y = z = k = 0;    std::ifstream conv_5_3_bfile("/data/local/tmp/conv5_3_b.csv");    line = "";    while(getline(conv_5_3_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_5_3.buffer() + biases_5_3.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    conv_5_3_bfile.close();    //fc_6    //W: 7 * 7 * 512 * 4096    x = y = z = k = 0;    std::ifstream fc_6_Wfile("/data/local/tmp/fc6_W.csv");    line = "";    while(getline(fc_6_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_6.buffer() + weights_6.info()->offset_element_in_bytes(Coordinates(x, y, z, k))) = temp;            ++ x;            if(x == fc_6_weights_shape.x()) ++ y, x = 0;            if(y == fc_6_weights_shape.y()) ++ z, y = 0;            if(z == fc_6_weights_shape.z()) ++ k, z = 0;        }    }    fc_6_Wfile.close();    //b: 4096    x = y = z = k = 0;    std::ifstream fc_6_bfile("/data/local/tmp/fc6_b.csv");    line = "";    while(getline(fc_6_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_6.buffer() + biases_6.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    fc_6_bfile.close();    //fc_7    //W: 4096 * 4096    x = y = z = k = 0;    std::ifstream fc_7_Wfile("/data/local/tmp/fc7_W.csv");    line = "";    while(getline(fc_7_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_7.buffer() + weights_7.info()->offset_element_in_bytes(Coordinates(x, y))) = temp;            ++ x;            if(x == fc_7_weights_shape.x()) ++ y, x = 0;        }    }    fc_7_Wfile.close();    //b: 4096    x = y = z = k = 0;    std::ifstream fc_7_bfile("/data/local/tmp/fc7_b.csv");    line = "";    while(getline(fc_7_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_7.buffer() + biases_7.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    fc_7_bfile.close();    //fc_8    //W: 4096 * 1000    x = y = z = k = 0;    std::ifstream fc_8_Wfile("/data/local/tmp/fc8_W.csv");    line = "";    while(getline(fc_8_Wfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(weights_8.buffer() + weights_8.info()->offset_element_in_bytes(Coordinates(x, y))) = temp;            ++ x;            if(x == fc_8_weights_shape.x()) ++ y, x = 0;        }    }    fc_8_Wfile.close();    //b: 1000    x = y = z = k = 0;    std::ifstream fc_8_bfile("/data/local/tmp/fc8_b.csv");    line = "";    while(getline(fc_8_bfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(biases_8.buffer() + biases_8.info()->offset_element_in_bytes(Coordinates(x))) = temp;            ++ x;        }    }    fc_8_bfile.close();    /*------------------------------END:[Load the weights]-------------------------------*/    /*-----------------------------------BEGIN:[Input]-----------------------------------*/    x = y = z = k = 0;    float mean[3] = {123.68, 116.779, 103.939};    int t = 0;    std::ifstream inputfile("/data/local/tmp/input.csv");    line = "";int number = 0;    while(getline(inputfile, line)){        std::stringstream strstr(line);        std::string word = "";        while(getline(strstr, word, ',')){            float temp = StringToFloat(word);            *reinterpret_cast<float *>(input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x, y, z))) = temp - mean[t];            t = (t + 1) % 3;            ++ x;            if(x == input_shape.x()) ++ y, x = 0;            if(y == input_shape.y()) ++ z, y = 0;++number;        }    }    inputfile.close();    /*------------------------------------END:[Input]------------------------------------*/    /*--------------------------BEGIN:[Execute the functions]----------------------------*/    //time    struct timeval start, end;    gettimeofday(&start, NULL);    //conv_1    conv_1_1.run();    Nact_1_1.run();    conv_1_2.run();    Nact_1_2.run();    Npool_1.run();    //conv_2    conv_2_1.run();    Nact_2_1.run();    conv_2_2.run();    Nact_2_2.run();    Npool_2.run();    //conv_3    conv_3_1.run();    Nact_3_1.run();    conv_3_2.run();    Nact_3_2.run();    conv_3_3.run();    Nact_3_3.run();    Npool_3.run();    //conv_4    conv_4_1.run();    Nact_4_1.run();    conv_4_2.run();    Nact_4_2.run();    conv_4_3.run();    Nact_4_3.run();    Npool_4.run();    //conv_5    conv_5_1.run();    Nact_5_1.run();    conv_5_2.run();    Nact_5_2.run();    conv_5_3.run();    Nact_5_3.run();    Npool_5.run();    //fc_6    fc_6.run();    Nact_6.run();    //fc_7    fc_7.run();    Nact_7.run();    //fc_8    fc_8.run();    softmax.run();    gettimeofday(&end, NULL);    /*---------------------------END:[Execute the functions]-----------------------------*/    //test    std::cout << std::endl << std::endl << std::endl;    int timeuse = 1000000 * ( end.tv_sec - start.tv_sec ) + end.tv_usec -start.tv_usec;    printf("time: %d us\n", timeuse);    std::map<float, std::string>ma;    std::ifstream outfile("/data/local/tmp/synset.txt");    line = "";    x = 0;    while(getline(outfile, line)){        //std::cout << line << std::endl;        float temp = *reinterpret_cast<float *>(softmax_tensor.buffer() + softmax_tensor.info()->offset_element_in_bytes(Coordinates(x)));        ++ x;        ma[temp] = line;    }    int h = 1;    for(auto it = ma.rbegin(); it != ma.rend(); ++ it){        std::cout << "N0 " << h++ << " " << it->first << " is " << it->second << std::endl;    }    std::cout << "fine! Jason!" << std::endl;}/** Main program for convolution test * @param[in] argc Number of arguments * @param[in] argv Arguments ( [optional] Path to PPM image to process ) */int main(int argc, const char **argv){    return test_helpers::run_example(argc, argv, main_neon_dnn);}