ncnn/tools/quantize/ncnn2table.cpp

// BUG1989 is pleased to support the open source community by supporting ncnn available.
//
// author:BUG1989 (https://github.com/BUG1989/) Long-term support.
// author:JansonZhu (https://github.com/JansonZhu) Implemented the function of entropy calibration.
//
// Copyright (C) 2019 BUG1989. All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
// in compliance with the License. You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// Unless required by applicable law or agreed to in writing, software distributed
// 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 and limitations under the License.

#include <stdio.h>
#include <unistd.h>
#include <getopt.h>
#include <string.h>
#include <vector>
#include <iostream>
#include <fstream>
#include <dirent.h>
#include <stdlib.h>
#include <algorithm>
#include <map>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>

// ncnn public header
#include "platform.h"
#include "net.h"
#include "cpu.h"
#include "benchmark.h"

// ncnn private header
#include "layer/convolution.h"
#include "layer/convolutiondepthwise.h"
#include "layer/innerproduct.h"

static ncnn::Option g_default_option;
static ncnn::UnlockedPoolAllocator g_blob_pool_allocator;
static ncnn::PoolAllocator g_workspace_pool_allocator;

// Get the filenames from direct path
int parse_images_dir(const char *base_path, std::vector<std::string>& file_path)
{
    DIR *dir;
    struct dirent *ptr;

    if ((dir=opendir(base_path)) == NULL)
    {
        perror("Open dir error...");
        exit(1);
    }

    while ((ptr=readdir(dir)) != NULL)
    {
        if(strcmp(ptr->d_name,".")==0 || strcmp(ptr->d_name,"..")==0)    ///current dir OR parrent dir
        {
            continue;
        } 

        std::string path = base_path;
        file_path.push_back(path + ptr->d_name);
    }
    closedir(dir);

    return 0;
}

class QuantNet : public ncnn::Net
{
public:
    int get_conv_names();
    int get_conv_bottom_blob_names();
    int get_conv_weight_blob_scales();

public:
    std::vector<std::string> conv_names;
    std::map<std::string,std::string> conv_bottom_blob_names;
    std::map<std::string,std::vector<float> > weight_scales;
};

int QuantNet::get_conv_names()
{
    for (size_t i=0; i<layers.size(); i++)
    {
        ncnn::Layer* layer = layers[i];
        
        if (layer->type == "Convolution" || layer->type == "ConvolutionDepthWise" || layer->type == "InnerProduct")
        {
            std::string name = layer->name;
            conv_names.push_back(name);
        }
    }        

    return 0;
}

int QuantNet::get_conv_bottom_blob_names()
{
    // find conv bottom name or index
    for (size_t i=0; i<layers.size(); i++)
    {
        ncnn::Layer* layer = layers[i];
        
        if (layer->type == "Convolution" || layer->type == "ConvolutionDepthWise" || layer->type == "InnerProduct")
        {
            std::string name = layer->name;
            std::string bottom_blob_name = blobs[layer->bottoms[0]].name;
            conv_bottom_blob_names[name] = bottom_blob_name;
        }
    }

    return 0;
}

int QuantNet::get_conv_weight_blob_scales()
{
    for (size_t i=0; i<layers.size(); i++)
    {
        ncnn::Layer* layer = layers[i];
        
        if (layer->type == "Convolution")
        {
            std::string name = layer->name;
            const int weight_data_size_output = ((ncnn::Convolution*)layer)->weight_data_size / ((ncnn::Convolution*)layer)->num_output;
            std::vector<float> scales;

            // int8 winograd F43 needs weight data to use 6bit quantization
            bool quant_6bit = false;
            int kernel_w = ((ncnn::Convolution*)layer)->kernel_w;
            int kernel_h = ((ncnn::Convolution*)layer)->kernel_h;
            int dilation_w = ((ncnn::Convolution*)layer)->dilation_w;
            int dilation_h = ((ncnn::Convolution*)layer)->dilation_h;
            int stride_w = ((ncnn::Convolution*)layer)->stride_w;
            int stride_h = ((ncnn::Convolution*)layer)->stride_h;

            if (kernel_w == 3 && kernel_h == 3 && dilation_w == 1 && dilation_h == 1 && stride_w == 1 && stride_h == 1)
                quant_6bit = true;

            for (int n=0; n<((ncnn::Convolution*)layer)->num_output; n++)
            {
                const ncnn::Mat weight_data_n = ((ncnn::Convolution*)layer)->weight_data.range(weight_data_size_output * n, weight_data_size_output);
                const float *data_n = weight_data_n;
                float max_value = std::numeric_limits<float>::min();

                for (int i = 0; i < weight_data_size_output; i++)
                    max_value = std::max(max_value, std::fabs(data_n[i]));

                if (quant_6bit)
                    scales.push_back(31 / max_value);
                else
                    scales.push_back(127 / max_value);
            }

            weight_scales[name] = scales;
        }
        
        if (layer->type == "ConvolutionDepthWise")
        {
            std::string name = layer->name;
            const int weight_data_size_output = ((ncnn::ConvolutionDepthWise*)layer)->weight_data_size / ((ncnn::ConvolutionDepthWise*)layer)->group;
            std::vector<float> scales;

            for (int n=0; n<((ncnn::ConvolutionDepthWise*)layer)->group; n++)
            {
                const ncnn::Mat weight_data_n = ((ncnn::ConvolutionDepthWise*)layer)->weight_data.range(weight_data_size_output * n, weight_data_size_output);
                const float *data_n = weight_data_n;
                float max_value = std::numeric_limits<float>::min();

                for (int i = 0; i < weight_data_size_output; i++)
                    max_value = std::max(max_value, std::fabs(data_n[i]));

                scales.push_back(127 / max_value); 
            }

            weight_scales[name] = scales;                
        }

        if (layer->type == "InnerProduct")
        {
            std::string name = layer->name;
            const int weight_data_size_output = ((ncnn::InnerProduct*)layer)->weight_data_size / ((ncnn::InnerProduct*)layer)->num_output;
            std::vector<float> scales;

            for (int n=0; n<((ncnn::InnerProduct*)layer)->num_output; n++)
            {
                const ncnn::Mat weight_data_n = ((ncnn::InnerProduct*)layer)->weight_data.range(weight_data_size_output * n, weight_data_size_output);
                const float *data_n = weight_data_n;
                float max_value = std::numeric_limits<float>::min();

                for (int i = 0; i < weight_data_size_output; i++)
                    max_value = std::max(max_value, std::fabs(data_n[i]));

                scales.push_back(127 / max_value);
            }

            weight_scales[name] = scales;            
        }
    }              

    return 0;
}

class QuantizeData
{
public:
    QuantizeData(std::string layer_name, int num);

    int initial_blob_max(ncnn::Mat data);
    int initial_histogram_interval();
    int initial_histogram_value();

    int normalize_histogram(); 
    int update_histogram(ncnn::Mat data);

    float compute_kl_divergence(const std::vector<float> &dist_a, const std::vector<float> &dist_b);
    int threshold_distribution(const std::vector<float> &distribution, const int target_bin=128);
    float get_data_blob_scale();

public:
    std::string name;

    float max_value;
    int num_bins;
    float histogram_interval;
    std::vector<float> histogram;
    
    float threshold;
    int threshold_bin;
    float scale;
};

QuantizeData::QuantizeData(std::string layer_name, int num)
{
    name = layer_name;
    max_value = 0.0;
    num_bins = num;
    histogram_interval = 0.0;
    histogram.resize(num_bins);
    initial_histogram_value();
}

int QuantizeData::initial_blob_max(ncnn::Mat data)
{
    int channel_num = data.c;
    int size = data.w * data.h;

    for (int q=0; q<channel_num; q++)
    {
        const float *data_n = data.channel(q);
        for(int i=0; i<size; i++)
        {
            max_value = std::max(max_value, std::fabs(data_n[i]));
        }
    }

    return 0;
}

int QuantizeData::initial_histogram_interval()
{
    histogram_interval = max_value / num_bins;

    return 0;
}

int QuantizeData::initial_histogram_value()
{
    for (size_t i=0; i<histogram.size(); i++)
    {
        histogram[i] = 0.00001;
    }

    return 0;
}

int QuantizeData::normalize_histogram() 
{
    const int length = histogram.size();
    float sum = 0;
    
    for (int i=0; i<length; i++)
        sum += histogram[i];

    for (int i=0; i<length; i++) 
        histogram[i] /= sum;

    return 0;
}

int QuantizeData::update_histogram(ncnn::Mat data)
{
    int channel_num = data.c;
    int size = data.w * data.h;

    for (int q=0; q<channel_num; q++)
    {
        const float *data_n = data.channel(q);
        for(int i=0; i<size; i++)
        {
            if (data_n[i] == 0)
                continue;

            int index = std::min(static_cast<int>(std::abs(data_n[i]) / histogram_interval), 2047);

            histogram[index]++;
        }
    }        

    return 0;
}

float QuantizeData::compute_kl_divergence(const std::vector<float> &dist_a, const std::vector<float> &dist_b) 
{
    const int length = dist_a.size();
    assert(dist_b.size() == length);
    float result = 0;

    for (int i=0; i<length; i++) 
    {
        if (dist_a[i] != 0) 
        {
            if (dist_b[i] == 0) 
            {
                result += 1;
            } 
            else 
            {
                result += dist_a[i] * log(dist_a[i] / dist_b[i]);
            }
        }
    }

    return result;
}

int QuantizeData::threshold_distribution(const std::vector<float> &distribution, const int target_bin) 
{
    int target_threshold = target_bin;
    float min_kl_divergence = 1000;
    const int length = distribution.size();

    std::vector<float> quantize_distribution(target_bin);

    float threshold_sum = 0;
    for (int threshold=target_bin; threshold<length; threshold++) 
    {
        threshold_sum += distribution[threshold];
    }

    for (int threshold=target_bin; threshold<length; threshold++) 
    {

        std::vector<float> t_distribution(distribution.begin(), distribution.begin()+threshold);
        
        t_distribution[threshold-1] += threshold_sum; 
        threshold_sum -= distribution[threshold];

        // get P
        fill(quantize_distribution.begin(), quantize_distribution.end(), 0);
        
        const float num_per_bin = static_cast<float>(threshold) / target_bin;

        for (int i=0; i<target_bin; i++) 
        {
            const float start = i * num_per_bin;
            const float end = start + num_per_bin;

            const int left_upper = ceil(start);
            if (left_upper > start) 
            {
                const float left_scale = left_upper - start;
                quantize_distribution[i] += left_scale * distribution[left_upper - 1];
            }

            const int right_lower = floor(end);

            if (right_lower < end) 
            {

                const float right_scale = end - right_lower;
                quantize_distribution[i] += right_scale * distribution[right_lower];
            }

            for (int j=left_upper; j<right_lower; j++) 
            {
                quantize_distribution[i] += distribution[j];
            }
        }

        // get Q
        std::vector<float> expand_distribution(threshold, 0);

        for (int i=0; i<target_bin; i++) 
        {
            const float start = i * num_per_bin;
            const float end = start + num_per_bin;

            float count = 0;

            const int left_upper = ceil(start);
            float left_scale = 0;
            if (left_upper > start) 
            {
                left_scale = left_upper - start;
                if (distribution[left_upper - 1] != 0) 
                {
                    count += left_scale;
                }
            }

            const int right_lower = floor(end);
            float right_scale = 0;
            if (right_lower < end) 
            {
                right_scale = end - right_lower;
                if (distribution[right_lower] != 0) 
                {
                    count += right_scale;
                }
            }

            for (int j=left_upper; j<right_lower; j++) 
            {
                if (distribution[j] != 0) 
                {
                    count++;
                }
            }

            const float expand_value = quantize_distribution[i] / count;

            if (left_upper > start) 
            {
                if (distribution[left_upper - 1] != 0) 
                {
                    expand_distribution[left_upper - 1] += expand_value * left_scale;
                }
            }
            if (right_lower < end) 
            {
                if (distribution[right_lower] != 0) 
                {
                    expand_distribution[right_lower] += expand_value * right_scale;
                }
            }
            for (int j=left_upper; j<right_lower; j++) 
            {
                if (distribution[j] != 0) 
                {
                    expand_distribution[j] += expand_value;
                }
            }
        }

        // kl
        float kl_divergence = compute_kl_divergence(t_distribution, expand_distribution);

        // the best num of bin
        if (kl_divergence < min_kl_divergence) 
        {
            min_kl_divergence = kl_divergence;
            target_threshold = threshold;
        }
    }

    return target_threshold;
}

float QuantizeData::get_data_blob_scale()
{   
    normalize_histogram();
    threshold_bin = threshold_distribution(histogram);
    threshold = (threshold_bin + 0.5) * histogram_interval;
    scale = 127 / threshold;
    return scale;
}

struct PreParam
{
    float mean[3];
    float norm[3];
    int weith;
    int height;
};

static int post_training_quantize(const std::vector<std::string> filenames, const char* param_path, const char* bin_path, const char* table_path, struct PreParam per_param)
{
    int size = filenames.size();

    QuantNet net;

    net.load_param(param_path);
    net.load_model(bin_path);

    float mean_vals[3], norm_vals[3];
    int weith = per_param.weith;
    int height = per_param.height;

    mean_vals[0] = per_param.mean[0];
    mean_vals[1] = per_param.mean[1];
    mean_vals[2] = per_param.mean[2];

    norm_vals[0] = per_param.norm[0];
    norm_vals[1] = per_param.norm[1];
    norm_vals[2] = per_param.norm[2];

    g_blob_pool_allocator.clear();
    g_workspace_pool_allocator.clear();

    net.get_conv_names();
    net.get_conv_bottom_blob_names();
    net.get_conv_weight_blob_scales();

    FILE *fp=fopen(table_path, "w");

    // save quantization scale of weight 
    printf("====> Quantize the parameters.\n");    
    for (size_t i=0; i<net.conv_names.size(); i++)
    {
        std::string layer_name = net.conv_names[i];
        std::string blob_name = net.conv_bottom_blob_names[layer_name];
        std::vector<float> weight_scale_n = net.weight_scales[layer_name];

        fprintf(fp, "%s_param_0 ", layer_name.c_str());
        for (size_t j=0; j<weight_scale_n.size(); j++)
            fprintf(fp, "%f ", weight_scale_n[j]);
        fprintf(fp, "\n");        
    }

    // initial quantization data
    std::vector<QuantizeData> quantize_datas;
    
    for (size_t i=0; i<net.conv_names.size(); i++)
    {
        std::string layer_name = net.conv_names[i];

        QuantizeData quantize_data(layer_name, 2048);
        quantize_datas.push_back(quantize_data);
    }    

    // step 1 count the max value
    printf("====> Quantize the activation.\n"); 
    printf("    ====> step 1 : find the max value.\n");

    for (size_t i=0; i<filenames.size(); i++)
    {
        std::string img_name = filenames[i];

        if ((i+1)%100 == 0)
            fprintf(stderr, "          %d/%d\n", (int)(i+1), (int)size);

        cv::Mat bgr = cv::imread(img_name, CV_LOAD_IMAGE_COLOR);
        if (bgr.empty())
        {
            fprintf(stderr, "cv::imread %s failed\n", img_name.c_str());
            return -1;
        }

        ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, weith, height);
        in.substract_mean_normalize(mean_vals, norm_vals);

        ncnn::Extractor ex = net.create_extractor();
        ex.input("data", in);

        for (size_t i=0; i<net.conv_names.size(); i++)
        {
            std::string layer_name = net.conv_names[i];
            std::string blob_name = net.conv_bottom_blob_names[layer_name];

            ncnn::Mat out;
            ex.extract(blob_name.c_str(), out);  

            for (size_t j=0; j<quantize_datas.size(); j++)
            {
                if (quantize_datas[j].name == layer_name)
                {
                    quantize_datas[j].initial_blob_max(out);
                    break;
                }
            }
        }         
    }

    // step 2 histogram_interval
    printf("    ====> step 2 : generatue the histogram_interval.\n");
    for (size_t i=0; i<net.conv_names.size(); i++)
    {
        std::string layer_name = net.conv_names[i];

        for (size_t j=0; j<quantize_datas.size(); j++)
        {
            if (quantize_datas[j].name == layer_name)
            {
                quantize_datas[j].initial_histogram_interval();

                fprintf(stderr, "%-20s : max = %-15f interval = %-10f\n", quantize_datas[j].name.c_str(), quantize_datas[j].max_value, quantize_datas[j].histogram_interval);
                break;
            }
        }
    }    

    // step 3 histogram
    printf("    ====> step 3 : generatue the histogram.\n");
    for (size_t i=0; i<filenames.size(); i++)
    {
        std::string img_name = filenames[i];

        if ((i+1)%100 == 0)
            fprintf(stderr, "          %d/%d\n", (int)(i+1), (int)size);

        cv::Mat bgr = cv::imread(img_name, CV_LOAD_IMAGE_COLOR);
        if (bgr.empty())
        {
            fprintf(stderr, "cv::imread %s failed\n", img_name.c_str());
            return -1;
        }  

        ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, weith, height);
        in.substract_mean_normalize(mean_vals, norm_vals);
      
        ncnn::Extractor ex = net.create_extractor();
        ex.input("data", in);

        for (size_t i=0; i<net.conv_names.size(); i++)
        {
            std::string layer_name = net.conv_names[i];
            std::string blob_name = net.conv_bottom_blob_names[layer_name];

            ncnn::Mat out;
            ex.extract(blob_name.c_str(), out);  

            for (size_t j=0; j<quantize_datas.size(); j++)
            {
                if (quantize_datas[j].name == layer_name)
                {
                    quantize_datas[j].update_histogram(out);
                    break;
                }
            }
        }     
    }

    // step4 kld
    printf("    ====> step 4 : using kld to find the best threshold value.\n");
    for (size_t i=0; i<net.conv_names.size(); i++)
    {
        std::string layer_name = net.conv_names[i];
        std::string blob_name = net.conv_bottom_blob_names[layer_name];
        fprintf(stderr, "%-20s ", layer_name.c_str());

        for (size_t j=0; j<quantize_datas.size(); j++)
        {
            if (quantize_datas[j].name == layer_name)
            {
                quantize_datas[j].get_data_blob_scale();
                fprintf(stderr, "bin : %-8d threshold : %-15f interval : %-10f scale : %-10f\n", \
                                                                quantize_datas[j].threshold_bin, \
                                                                quantize_datas[j].threshold, \
                                                                quantize_datas[j].histogram_interval, \
                                                                quantize_datas[j].scale);

                fprintf(fp, "%s %f\n", layer_name.c_str(), quantize_datas[j].scale);

                break;
            }
        }
    }

    fclose(fp);
    printf("====> Save the calibration table done.\n");

    return 0;
}

// usage
void showUsage() 
{
    std::cout << "usage: ncnn2table [-h] [-p] [-b] [-o] [-m] [-n] [-s] [-t]" << std::endl;
    std::cout << " -h, --help       show this help message and exit" << std::endl;
    std::cout << " -p, --param      path to ncnn.param file" << std::endl;
    std::cout << " -b, --bin        path to ncnn.bin file" << std::endl;
    std::cout << " -o, --output     path to output calibration tbale file" << std::endl;
    std::cout << " -m, --mean       value of mean" << std::endl;
    std::cout << " -n, --norm       value of normalize(scale value,defualt is 1)" << std::endl;
    std::cout << " -s, --size       the size of input image(using the resize the original image,default is w=224,h=224)" << std::endl;
    std::cout << " -t, --thread     number of threads(defalut is 1)" << std::endl;    
    std::cout << "example: ./ncnn2table --param squeezenet-fp32.param --bin squeezenet-fp32.bin --images images/ --output squeezenet.table --mean 104,117,123 --norm 1,1,1 --size 227,227 --thread 2" << std::endl;
}

// string.split('x')
std::vector<std::string> split(const std::string &str,const std::string &pattern)
{
    //const char* convert to char*
    char * strc = new char[strlen(str.c_str())+1];
    strcpy(strc, str.c_str());
    std::vector<std::string> resultVec;
    char* tmpStr = strtok(strc, pattern.c_str());
    while (tmpStr != NULL)
    {
        resultVec.push_back(std::string(tmpStr));
        tmpStr = strtok(NULL, pattern.c_str());
    }

    delete[] strc;

    return resultVec;
}

int main(int argc, char** argv)
{
    std::cout << "--- ncnn post training quantization tool --- " << __TIME__ << " " << __DATE__ << std::endl;

    char* imagepath = NULL;
    char* parampath = NULL;
    char* binpath = NULL;
    char* tablepath = NULL;
    int num_threads = 1;

    struct PreParam pre_param = {
        .mean = {104.f, 117.f, 103.f}, 
        .norm = {1.f, 1.f, 1.f}, 
        .weith = 224, 
        .height =224
    };

    int c;

    while (1) 
    {
        int option_index = 0;
        static struct option long_options[] = 
        {
            {"param",   required_argument, 0,  'p' },
            {"bin",     required_argument, 0,  'b' },
            {"images",  required_argument, 0,  'i' },
            {"output",  required_argument, 0,  'o' },
            {"mean",    required_argument, 0,  'm' },
            {"norm",    required_argument, 0,  'n' },
            {"size",    required_argument, 0,  's' },
            {"thread",  required_argument, 0,  't' },
            {"help",    no_argument,       0,  'h' },
            {0,         0,                 0,  0 }
        };

        c = getopt_long(argc, argv, "p:b:i:o:m:n:s:t:h", long_options, &option_index);
        if (c == -1)
            break;

        switch (c) 
        {
        case 'p':
            printf("param = '%s'\n", optarg);
            parampath = optarg;
            break;

        case 'b':
            printf("bin = '%s'\n", optarg);
            binpath = optarg;
            break;

        case 'i':
            printf("images = '%s'\n", optarg);
            imagepath = optarg;
            break;

        case 'o':
            printf("output = '%s'\n", optarg);
            tablepath = optarg;
            break;

        case 'm':
        {
            printf("mean = '%s'\n", optarg);
            std::string temp(optarg);
            std::vector<std::string> array = split(temp, ",");
            pre_param.mean[0] = atof(array[0].c_str());
            pre_param.mean[1] = atof(array[1].c_str());
            pre_param.mean[2] = atof(array[2].c_str());
        }
            break;

        case 'n':
        {
            printf("norm = '%s'\n", optarg);
            std::string temp(optarg);
            std::vector<std::string> array = split(temp, ",");
            pre_param.norm[0] = atof(array[0].c_str());
            pre_param.norm[1] = atof(array[1].c_str());
            pre_param.norm[2] = atof(array[2].c_str());
        }
            break;

        case 's':
        {
            printf("size = '%s'\n", optarg);
            std::string temp(optarg);
            std::vector<std::string> array = split(temp, ",");
            pre_param.weith = atoi(array[0].c_str());
            pre_param.height = atoi(array[1].c_str());
        }
            break;                        

        case 't':
            printf("thread = '%s'\n", optarg);
            num_threads = atoi(optarg);
            break;            

        case 'h':
        case '?':
            showUsage();
            return 0;

        default:
            showUsage();
        }
    }

    // check the input param
    if (imagepath == NULL || parampath == NULL || binpath == NULL || tablepath == NULL)
    {
        fprintf(stderr, "someone path maybe empty,please check it and try again.\n");
        return 0;
    }

    g_blob_pool_allocator.set_size_compare_ratio(0.0f);
    g_workspace_pool_allocator.set_size_compare_ratio(0.5f);

    // default option
    g_default_option.lightmode = true;
    g_default_option.num_threads = num_threads;
    g_default_option.blob_allocator = &g_blob_pool_allocator;
    g_default_option.workspace_allocator = &g_workspace_pool_allocator;

    g_default_option.use_winograd_convolution = true;
    g_default_option.use_sgemm_convolution = true;
    g_default_option.use_int8_inference = true;
    g_default_option.use_fp16_packed = true;
    g_default_option.use_fp16_storage = true;
    g_default_option.use_fp16_arithmetic = true;
    g_default_option.use_int8_storage = true;
    g_default_option.use_int8_arithmetic = true;

    ncnn::set_cpu_powersave(2);
    ncnn::set_omp_dynamic(0);
    ncnn::set_omp_num_threads(num_threads);  

    std::vector<std::string> filenames;

    // parse the image file.
    parse_images_dir(imagepath, filenames);

    // get the calibration table file, and save it.
    int ret = post_training_quantize(filenames, parampath, binpath, tablepath, pre_param);
    if (!ret)
        fprintf(stderr, "\nNCNN Int8 Calibration table create success, best wish for your INT8 inference has a low accuracy loss...\\(^▽^)/...233...\n");

    return 0;
}