darknet.c

#include "darknet.h"
#include <time.h>
#include <stdlib.h>
#include <stdio.h>
#if defined(_MSC_VER) && defined(_DEBUG)
#include <crtdbg.h>
#endif

#include "parser.h"
#include "utils.h"
#include "dark_cuda.h"
#include "blas.h"
#include "connected_layer.h"


extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
extern void run_voxel(int argc, char **argv);
extern void run_yolo(int argc, char **argv);
extern void run_detector(int argc, char **argv);
extern void run_coco(int argc, char **argv);
extern void run_writing(int argc, char **argv);
extern void run_captcha(int argc, char **argv);
extern void run_nightmare(int argc, char **argv);
extern void run_dice(int argc, char **argv);
extern void run_compare(int argc, char **argv);
extern void run_classifier(int argc, char **argv);
extern void run_char_rnn(int argc, char **argv);
extern void run_vid_rnn(int argc, char **argv);
extern void run_tag(int argc, char **argv);
extern void run_cifar(int argc, char **argv);
extern void run_go(int argc, char **argv);
extern void run_art(int argc, char **argv);
extern void run_super(int argc, char **argv);

void average(int argc, char *argv[])
{
    char *cfgfile = argv[2];
    char *outfile = argv[3];
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    network sum = parse_network_cfg(cfgfile);

    char *weightfile = argv[4];
    load_weights(&sum, weightfile);

    int i, j;
    int n = argc - 5;
    for(i = 0; i < n; ++i){
        weightfile = argv[i+5];
        load_weights(&net, weightfile);
        for(j = 0; j < net.n; ++j){
            layer l = net.layers[j];
            layer out = sum.layers[j];
            if(l.type == CONVOLUTIONAL){
                int num = l.n*l.c*l.size*l.size;
                axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
                axpy_cpu(num, 1, l.weights, 1, out.weights, 1);
                if(l.batch_normalize){
                    axpy_cpu(l.n, 1, l.scales, 1, out.scales, 1);
                    axpy_cpu(l.n, 1, l.rolling_mean, 1, out.rolling_mean, 1);
                    axpy_cpu(l.n, 1, l.rolling_variance, 1, out.rolling_variance, 1);
                }
            }
            if(l.type == CONNECTED){
                axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
                axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1);
            }
        }
    }
    n = n+1;
    for(j = 0; j < net.n; ++j){
        layer l = sum.layers[j];
        if(l.type == CONVOLUTIONAL){
            int num = l.n*l.c*l.size*l.size;
            scal_cpu(l.n, 1./n, l.biases, 1);
            scal_cpu(num, 1./n, l.weights, 1);
                if(l.batch_normalize){
                    scal_cpu(l.n, 1./n, l.scales, 1);
                    scal_cpu(l.n, 1./n, l.rolling_mean, 1);
                    scal_cpu(l.n, 1./n, l.rolling_variance, 1);
                }
        }
        if(l.type == CONNECTED){
            scal_cpu(l.outputs, 1./n, l.biases, 1);
            scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1);
        }
    }
    save_weights(sum, outfile);
}

void speed(char *cfgfile, int tics)
{
    if (tics == 0) tics = 1000;
    network net = parse_network_cfg(cfgfile);
    set_batch_network(&net, 1);
    int i;
    time_t start = time(0);
    image im = make_image(net.w, net.h, net.c);
    for(i = 0; i < tics; ++i){
        network_predict(net, im.data);
    }
    double t = difftime(time(0), start);
    printf("\n%d evals, %f Seconds\n", tics, t);
    printf("Speed: %f sec/eval\n", t/tics);
    printf("Speed: %f Hz\n", tics/t);
}

void operations(char *cfgfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    int i;
    long ops = 0;
    for(i = 0; i < net.n; ++i){
        layer l = net.layers[i];
        if(l.type == CONVOLUTIONAL){
            ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w;
        } else if(l.type == CONNECTED){
            ops += 2l * l.inputs * l.outputs;
		} else if (l.type == RNN){
            ops += 2l * l.input_layer->inputs * l.input_layer->outputs;
            ops += 2l * l.self_layer->inputs * l.self_layer->outputs;
            ops += 2l * l.output_layer->inputs * l.output_layer->outputs;
        } else if (l.type == GRU){
            ops += 2l * l.uz->inputs * l.uz->outputs;
            ops += 2l * l.uh->inputs * l.uh->outputs;
            ops += 2l * l.ur->inputs * l.ur->outputs;
            ops += 2l * l.wz->inputs * l.wz->outputs;
            ops += 2l * l.wh->inputs * l.wh->outputs;
            ops += 2l * l.wr->inputs * l.wr->outputs;
        } else if (l.type == LSTM){
            ops += 2l * l.uf->inputs * l.uf->outputs;
            ops += 2l * l.ui->inputs * l.ui->outputs;
            ops += 2l * l.ug->inputs * l.ug->outputs;
            ops += 2l * l.uo->inputs * l.uo->outputs;
            ops += 2l * l.wf->inputs * l.wf->outputs;
            ops += 2l * l.wi->inputs * l.wi->outputs;
            ops += 2l * l.wg->inputs * l.wg->outputs;
            ops += 2l * l.wo->inputs * l.wo->outputs;
        }
    }
    printf("Floating Point Operations: %ld\n", ops);
    printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
}

void oneoff(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    int oldn = net.layers[net.n - 2].n;
    int c = net.layers[net.n - 2].c;
    net.layers[net.n - 2].n = 9372;
    net.layers[net.n - 2].biases += 5;
    net.layers[net.n - 2].weights += 5*c;
    if(weightfile){
        load_weights(&net, weightfile);
    }
    net.layers[net.n - 2].biases -= 5;
    net.layers[net.n - 2].weights -= 5*c;
    net.layers[net.n - 2].n = oldn;
    printf("%d\n", oldn);
    layer l = net.layers[net.n - 2];
    copy_cpu(l.n/3, l.biases, 1, l.biases +   l.n/3, 1);
    copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1);
    copy_cpu(l.n/3*l.c, l.weights, 1, l.weights +   l.n/3*l.c, 1);
    copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1);
    *net.seen = 0;
    *net.cur_iteration = 0;
    save_weights(net, outfile);
}

void partial(char *cfgfile, char *weightfile, char *outfile, int max)
{
    gpu_index = -1;
    network net = parse_network_cfg_custom(cfgfile, 1, 1);
    if(weightfile){
        load_weights_upto(&net, weightfile, max);
    }
    *net.seen = 0;
    *net.cur_iteration = 0;
    save_weights_upto(net, outfile, max, 0);
}

#include "convolutional_layer.h"
void rescale_net(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    int i;
    for(i = 0; i < net.n; ++i){
        layer l = net.layers[i];
        if(l.type == CONVOLUTIONAL){
            rescale_weights(l, 2, -.5);
            break;
        }
    }
    save_weights(net, outfile);
}

void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    int i;
    for(i = 0; i < net.n; ++i){
        layer l = net.layers[i];
        if(l.type == CONVOLUTIONAL){
            rgbgr_weights(l);
            break;
        }
    }
    save_weights(net, outfile);
}

void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    int i;
    for (i = 0; i < net.n; ++i) {
        layer l = net.layers[i];
        if (l.type == CONVOLUTIONAL && l.batch_normalize) {
            denormalize_convolutional_layer(l);
        }
        if (l.type == CONNECTED && l.batch_normalize) {
            denormalize_connected_layer(l);
        }
        if (l.type == GRU && l.batch_normalize) {
            denormalize_connected_layer(*l.input_z_layer);
            denormalize_connected_layer(*l.input_r_layer);
            denormalize_connected_layer(*l.input_h_layer);
            denormalize_connected_layer(*l.state_z_layer);
            denormalize_connected_layer(*l.state_r_layer);
            denormalize_connected_layer(*l.state_h_layer);
        }
        if (l.type == LSTM && l.batch_normalize) {
            denormalize_connected_layer(*l.wf);
            denormalize_connected_layer(*l.wi);
            denormalize_connected_layer(*l.wg);
            denormalize_connected_layer(*l.wo);
            denormalize_connected_layer(*l.uf);
            denormalize_connected_layer(*l.ui);
            denormalize_connected_layer(*l.ug);
            denormalize_connected_layer(*l.uo);
		}
    }
    save_weights(net, outfile);
}

layer normalize_layer(layer l, int n)
{
    int j;
    l.batch_normalize=1;
    l.scales = (float*)xcalloc(n, sizeof(float));
    for(j = 0; j < n; ++j){
        l.scales[j] = 1;
    }
    l.rolling_mean = (float*)xcalloc(n, sizeof(float));
    l.rolling_variance = (float*)xcalloc(n, sizeof(float));
    return l;
}

void normalize_net(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    int i;
    for(i = 0; i < net.n; ++i){
        layer l = net.layers[i];
        if(l.type == CONVOLUTIONAL && !l.batch_normalize){
            net.layers[i] = normalize_layer(l, l.n);
        }
        if (l.type == CONNECTED && !l.batch_normalize) {
            net.layers[i] = normalize_layer(l, l.outputs);
        }
        if (l.type == GRU && l.batch_normalize) {
            *l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);
            *l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);
            *l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);
            *l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);
            *l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);
            *l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
            net.layers[i].batch_normalize=1;
        }
        if (l.type == LSTM && l.batch_normalize) {
            *l.wf = normalize_layer(*l.wf, l.wf->outputs);
            *l.wi = normalize_layer(*l.wi, l.wi->outputs);
            *l.wg = normalize_layer(*l.wg, l.wg->outputs);
            *l.wo = normalize_layer(*l.wo, l.wo->outputs);
            *l.uf = normalize_layer(*l.uf, l.uf->outputs);
            *l.ui = normalize_layer(*l.ui, l.ui->outputs);
            *l.ug = normalize_layer(*l.ug, l.ug->outputs);
            *l.uo = normalize_layer(*l.uo, l.uo->outputs);
            net.layers[i].batch_normalize=1;
        }
    }
    save_weights(net, outfile);
}

void statistics_net(char *cfgfile, char *weightfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    int i;
    for (i = 0; i < net.n; ++i) {
        layer l = net.layers[i];
        if (l.type == CONNECTED && l.batch_normalize) {
            printf("Connected Layer %d\n", i);
            statistics_connected_layer(l);
        }
        if (l.type == GRU && l.batch_normalize) {
            printf("GRU Layer %d\n", i);
            printf("Input Z\n");
            statistics_connected_layer(*l.input_z_layer);
            printf("Input R\n");
            statistics_connected_layer(*l.input_r_layer);
            printf("Input H\n");
            statistics_connected_layer(*l.input_h_layer);
            printf("State Z\n");
            statistics_connected_layer(*l.state_z_layer);
            printf("State R\n");
            statistics_connected_layer(*l.state_r_layer);
            printf("State H\n");
            statistics_connected_layer(*l.state_h_layer);
        }
        if (l.type == LSTM && l.batch_normalize) {
            printf("LSTM Layer %d\n", i);
            printf("wf\n");
            statistics_connected_layer(*l.wf);
            printf("wi\n");
            statistics_connected_layer(*l.wi);
            printf("wg\n");
            statistics_connected_layer(*l.wg);
            printf("wo\n");
            statistics_connected_layer(*l.wo);
            printf("uf\n");
            statistics_connected_layer(*l.uf);
            printf("ui\n");
            statistics_connected_layer(*l.ui);
            printf("ug\n");
            statistics_connected_layer(*l.ug);
            printf("uo\n");
            statistics_connected_layer(*l.uo);
        }
        printf("\n");
    }
}

void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
{
    gpu_index = -1;
    network net = parse_network_cfg(cfgfile);
    if (weightfile) {
        load_weights(&net, weightfile);
    }
    int i;
    for (i = 0; i < net.n; ++i) {
        layer l = net.layers[i];
        if (l.type == CONVOLUTIONAL && l.batch_normalize) {
            denormalize_convolutional_layer(l);
            net.layers[i].batch_normalize=0;
        }
        if (l.type == CONNECTED && l.batch_normalize) {
            denormalize_connected_layer(l);
            net.layers[i].batch_normalize=0;
        }
        if (l.type == GRU && l.batch_normalize) {
            denormalize_connected_layer(*l.input_z_layer);
            denormalize_connected_layer(*l.input_r_layer);
            denormalize_connected_layer(*l.input_h_layer);
            denormalize_connected_layer(*l.state_z_layer);
            denormalize_connected_layer(*l.state_r_layer);
            denormalize_connected_layer(*l.state_h_layer);
            l.input_z_layer->batch_normalize = 0;
            l.input_r_layer->batch_normalize = 0;
            l.input_h_layer->batch_normalize = 0;
            l.state_z_layer->batch_normalize = 0;
            l.state_r_layer->batch_normalize = 0;
            l.state_h_layer->batch_normalize = 0;
            net.layers[i].batch_normalize=0;
        }
        if (l.type == GRU && l.batch_normalize) {
            denormalize_connected_layer(*l.wf);
            denormalize_connected_layer(*l.wi);
            denormalize_connected_layer(*l.wg);
            denormalize_connected_layer(*l.wo);
            denormalize_connected_layer(*l.uf);
            denormalize_connected_layer(*l.ui);
            denormalize_connected_layer(*l.ug);
            denormalize_connected_layer(*l.uo);
            l.wf->batch_normalize = 0;
            l.wi->batch_normalize = 0;
            l.wg->batch_normalize = 0;
            l.wo->batch_normalize = 0;
            l.uf->batch_normalize = 0;
            l.ui->batch_normalize = 0;
            l.ug->batch_normalize = 0;
            l.uo->batch_normalize = 0;
            net.layers[i].batch_normalize=0;
		}
    }
    save_weights(net, outfile);
}

void visualize(char *cfgfile, char *weightfile)
{
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
        load_weights(&net, weightfile);
    }
    visualize_network(net);
#ifdef OPENCV
    wait_until_press_key_cv();
#endif
}
/*

int main(int argc, char **argv)
{
#ifdef _DEBUG
	_CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
    printf(" _DEBUG is used \n");
#endif

#ifdef DEBUG
    printf(" DEBUG=1 \n");
#endif

	int i;
	for (i = 0; i < argc; ++i) {
		if (!argv[i]) continue;
		strip_args(argv[i]);
	}

    //test_resize("data/bad.jpg");
    //test_box();
    //test_convolutional_layer();
    if(argc < 2){
        fprintf(stderr, "usage: %s <function>\n", argv[0]);
        return 0;
    }
    gpu_index = find_int_arg(argc, argv, "-i", 0);
    if(find_arg(argc, argv, "-nogpu")) {
        gpu_index = -1;
        printf("\n Currently Darknet doesn't support -nogpu flag. If you want to use CPU - please compile Darknet with GPU=0 in the Makefile, or compile darknet_no_gpu.sln on Windows.\n");
        exit(-1);
    }

#ifndef GPU
    gpu_index = -1;
    printf(" GPU isn't used \n");
    init_cpu();
#else   // GPU
    if(gpu_index >= 0){
        cuda_set_device(gpu_index);
        CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync));
    }

    show_cuda_cudnn_info();
    cuda_debug_sync = find_arg(argc, argv, "-cuda_debug_sync");

#ifdef CUDNN_HALF
    printf(" CUDNN_HALF=1 \n");
#endif  // CUDNN_HALF

#endif  // GPU

    show_opencv_info();

    if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "yolo")){
        run_yolo(argc, argv);
    } else if (0 == strcmp(argv[1], "voxel")){
        run_voxel(argc, argv);
    } else if (0 == strcmp(argv[1], "super")){
        run_super(argc, argv);
    } else if (0 == strcmp(argv[1], "detector")){
        run_detector(argc, argv);
    } else if (0 == strcmp(argv[1], "detect")){
        float thresh = find_float_arg(argc, argv, "-thresh", .24);
		int ext_output = find_arg(argc, argv, "-ext_output");
        char *filename = (argc > 4) ? argv[4]: 0;//"E:\\speciality\\darknet_v3_prune\\build\darknet\\x64\\cfg\\coco.data"
        test_detector("E:\\speciality\\darknet_v3_debug_prune\\build\\darknet\\x64\\cfg\\coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0, 0);
    } else if (0 == strcmp(argv[1], "cifar")){
        run_cifar(argc, argv);
    } else if (0 == strcmp(argv[1], "go")){
        run_go(argc, argv);
    } else if (0 == strcmp(argv[1], "rnn")){
        run_char_rnn(argc, argv);
    } else if (0 == strcmp(argv[1], "vid")){
        run_vid_rnn(argc, argv);
    } else if (0 == strcmp(argv[1], "coco")){
        run_coco(argc, argv);
    } else if (0 == strcmp(argv[1], "classify")){
        predict_classifier("cfg/imagenet1k.data", argv[2], argv[3], argv[4], 5);
    } else if (0 == strcmp(argv[1], "classifier")){
        run_classifier(argc, argv);
    } else if (0 == strcmp(argv[1], "art")){
        run_art(argc, argv);
    } else if (0 == strcmp(argv[1], "tag")){
        run_tag(argc, argv);
    } else if (0 == strcmp(argv[1], "compare")){
        run_compare(argc, argv);
    } else if (0 == strcmp(argv[1], "dice")){
        run_dice(argc, argv);
    } else if (0 == strcmp(argv[1], "writing")){
        run_writing(argc, argv);
    } else if (0 == strcmp(argv[1], "3d")){
        composite_3d(argv[2], argv[3], argv[4], (argc > 5) ? atof(argv[5]) : 0);
    } else if (0 == strcmp(argv[1], "test")){
        test_resize(argv[2]);
    } else if (0 == strcmp(argv[1], "captcha")){
        run_captcha(argc, argv);
    } else if (0 == strcmp(argv[1], "nightmare")){
        run_nightmare(argc, argv);
    } else if (0 == strcmp(argv[1], "rgbgr")){
        rgbgr_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "reset")){
        reset_normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "denormalize")){
        denormalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "statistics")){
        statistics_net(argv[2], argv[3]);
    } else if (0 == strcmp(argv[1], "normalize")){
        normalize_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "rescale")){
        rescale_net(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "ops")){
        operations(argv[2]);
    } else if (0 == strcmp(argv[1], "speed")){
        speed(argv[2], (argc > 3 && argv[3]) ? atoi(argv[3]) : 0);
    } else if (0 == strcmp(argv[1], "oneoff")){
        oneoff(argv[2], argv[3], argv[4]);
    } else if (0 == strcmp(argv[1], "partial")){
        partial(argv[2], argv[3], argv[4], atoi(argv[5]));
    } else if (0 == strcmp(argv[1], "visualize")){
        visualize(argv[2], (argc > 3) ? argv[3] : 0);
    } else if (0 == strcmp(argv[1], "imtest")){
        test_resize(argv[2]);
    } else {
        fprintf(stderr, "Not an option: %s\n", argv[1]);
    }
    return 0;
}
*///origional main function
/*
argv[1] cfg
argv[2] weights file
argv[3] nth_layer
argv[4] pertentage
*/
int main(int argc, char** argv)
{
    if (argc < 3)
    {
        printf("usage: .exe nth_layer percentage~~");
        system("pause");
        exit(1);

    }
    network net = parse_network_cfg_custom(argv[1], 1,1);    // set batch=1
    if (argv[2]) {//函数参数为指向net的引用，
        load_weights_debug(&net, argv[2], argv[3], argv[4]);
    }
    char buff[256];
    char *backup_directory = "E:\\speciality\\darknet_new_weights";
    sprintf(buff, "%s\\conv_%s_percent_%s.weights", backup_directory, argv[3], argv[4]);
    //printf("before save weights layer0.n %d\n",net.layers[0].n);
    save_weights(net, buff);
    printf("save weights done\n");
    system("pause");
    return 0;
}