#include "darknet.h"
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#include <time.h>
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#include <stdlib.h>
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#include <stdio.h>
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#if defined(_MSC_VER) && defined(_DEBUG)
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#include <crtdbg.h>
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#endif
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#include "parser.h"
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#include "utils.h"
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#include "dark_cuda.h"
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#include "blas.h"
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#include "connected_layer.h"
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extern void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int top);
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extern void run_voxel(int argc, char **argv);
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extern void run_yolo(int argc, char **argv);
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extern void run_detector(int argc, char **argv);
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extern void run_coco(int argc, char **argv);
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extern void run_writing(int argc, char **argv);
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extern void run_captcha(int argc, char **argv);
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extern void run_nightmare(int argc, char **argv);
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extern void run_dice(int argc, char **argv);
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extern void run_compare(int argc, char **argv);
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extern void run_classifier(int argc, char **argv);
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extern void run_char_rnn(int argc, char **argv);
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extern void run_vid_rnn(int argc, char **argv);
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extern void run_tag(int argc, char **argv);
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extern void run_cifar(int argc, char **argv);
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extern void run_go(int argc, char **argv);
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extern void run_art(int argc, char **argv);
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extern void run_super(int argc, char **argv);
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void average(int argc, char *argv[])
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{
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char *cfgfile = argv[2];
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char *outfile = argv[3];
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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network sum = parse_network_cfg(cfgfile);
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char *weightfile = argv[4];
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load_weights(&sum, weightfile);
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int i, j;
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int n = argc - 5;
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for(i = 0; i < n; ++i){
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weightfile = argv[i+5];
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load_weights(&net, weightfile);
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for(j = 0; j < net.n; ++j){
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layer l = net.layers[j];
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layer out = sum.layers[j];
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if(l.type == CONVOLUTIONAL){
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int num = l.n*l.c*l.size*l.size;
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axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
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axpy_cpu(num, 1, l.weights, 1, out.weights, 1);
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if(l.batch_normalize){
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axpy_cpu(l.n, 1, l.scales, 1, out.scales, 1);
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axpy_cpu(l.n, 1, l.rolling_mean, 1, out.rolling_mean, 1);
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axpy_cpu(l.n, 1, l.rolling_variance, 1, out.rolling_variance, 1);
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}
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}
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if(l.type == CONNECTED){
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axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
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axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1);
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}
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}
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}
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n = n+1;
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for(j = 0; j < net.n; ++j){
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layer l = sum.layers[j];
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if(l.type == CONVOLUTIONAL){
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int num = l.n*l.c*l.size*l.size;
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scal_cpu(l.n, 1./n, l.biases, 1);
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scal_cpu(num, 1./n, l.weights, 1);
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if(l.batch_normalize){
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scal_cpu(l.n, 1./n, l.scales, 1);
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scal_cpu(l.n, 1./n, l.rolling_mean, 1);
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scal_cpu(l.n, 1./n, l.rolling_variance, 1);
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}
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}
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if(l.type == CONNECTED){
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scal_cpu(l.outputs, 1./n, l.biases, 1);
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scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1);
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}
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}
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save_weights(sum, outfile);
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}
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void speed(char *cfgfile, int tics)
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{
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if (tics == 0) tics = 1000;
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network net = parse_network_cfg(cfgfile);
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set_batch_network(&net, 1);
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int i;
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time_t start = time(0);
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image im = make_image(net.w, net.h, net.c);
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for(i = 0; i < tics; ++i){
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network_predict(net, im.data);
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}
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double t = difftime(time(0), start);
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printf("\n%d evals, %f Seconds\n", tics, t);
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printf("Speed: %f sec/eval\n", t/tics);
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printf("Speed: %f Hz\n", tics/t);
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}
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void operations(char *cfgfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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int i;
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long ops = 0;
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL){
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ops += 2l * l.n * l.size*l.size*l.c * l.out_h*l.out_w;
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} else if(l.type == CONNECTED){
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ops += 2l * l.inputs * l.outputs;
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} else if (l.type == RNN){
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ops += 2l * l.input_layer->inputs * l.input_layer->outputs;
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ops += 2l * l.self_layer->inputs * l.self_layer->outputs;
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ops += 2l * l.output_layer->inputs * l.output_layer->outputs;
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} else if (l.type == GRU){
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ops += 2l * l.uz->inputs * l.uz->outputs;
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ops += 2l * l.uh->inputs * l.uh->outputs;
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ops += 2l * l.ur->inputs * l.ur->outputs;
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ops += 2l * l.wz->inputs * l.wz->outputs;
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ops += 2l * l.wh->inputs * l.wh->outputs;
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ops += 2l * l.wr->inputs * l.wr->outputs;
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} else if (l.type == LSTM){
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ops += 2l * l.uf->inputs * l.uf->outputs;
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ops += 2l * l.ui->inputs * l.ui->outputs;
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ops += 2l * l.ug->inputs * l.ug->outputs;
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ops += 2l * l.uo->inputs * l.uo->outputs;
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ops += 2l * l.wf->inputs * l.wf->outputs;
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ops += 2l * l.wi->inputs * l.wi->outputs;
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ops += 2l * l.wg->inputs * l.wg->outputs;
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ops += 2l * l.wo->inputs * l.wo->outputs;
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}
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}
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printf("Floating Point Operations: %ld\n", ops);
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printf("Floating Point Operations: %.2f Bn\n", (float)ops/1000000000.);
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}
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void oneoff(char *cfgfile, char *weightfile, char *outfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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int oldn = net.layers[net.n - 2].n;
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int c = net.layers[net.n - 2].c;
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net.layers[net.n - 2].n = 9372;
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net.layers[net.n - 2].biases += 5;
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net.layers[net.n - 2].weights += 5*c;
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if(weightfile){
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load_weights(&net, weightfile);
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}
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net.layers[net.n - 2].biases -= 5;
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net.layers[net.n - 2].weights -= 5*c;
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net.layers[net.n - 2].n = oldn;
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printf("%d\n", oldn);
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layer l = net.layers[net.n - 2];
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copy_cpu(l.n/3, l.biases, 1, l.biases + l.n/3, 1);
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copy_cpu(l.n/3, l.biases, 1, l.biases + 2*l.n/3, 1);
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copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + l.n/3*l.c, 1);
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copy_cpu(l.n/3*l.c, l.weights, 1, l.weights + 2*l.n/3*l.c, 1);
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*net.seen = 0;
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*net.cur_iteration = 0;
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save_weights(net, outfile);
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}
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void partial(char *cfgfile, char *weightfile, char *outfile, int max)
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{
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gpu_index = -1;
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network net = parse_network_cfg_custom(cfgfile, 1, 1);
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if(weightfile){
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load_weights_upto(&net, weightfile, max);
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}
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*net.seen = 0;
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*net.cur_iteration = 0;
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save_weights_upto(net, outfile, max);
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}
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#include "convolutional_layer.h"
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void rescale_net(char *cfgfile, char *weightfile, char *outfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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int i;
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL){
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rescale_weights(l, 2, -.5);
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break;
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}
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}
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save_weights(net, outfile);
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}
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void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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int i;
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL){
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rgbgr_weights(l);
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break;
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}
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}
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save_weights(net, outfile);
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}
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void reset_normalize_net(char *cfgfile, char *weightfile, char *outfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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if (weightfile) {
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load_weights(&net, weightfile);
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}
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int i;
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for (i = 0; i < net.n; ++i) {
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layer l = net.layers[i];
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if (l.type == CONVOLUTIONAL && l.batch_normalize) {
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denormalize_convolutional_layer(l);
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}
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if (l.type == CONNECTED && l.batch_normalize) {
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denormalize_connected_layer(l);
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}
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if (l.type == GRU && l.batch_normalize) {
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denormalize_connected_layer(*l.input_z_layer);
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denormalize_connected_layer(*l.input_r_layer);
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denormalize_connected_layer(*l.input_h_layer);
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denormalize_connected_layer(*l.state_z_layer);
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denormalize_connected_layer(*l.state_r_layer);
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denormalize_connected_layer(*l.state_h_layer);
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}
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if (l.type == LSTM && l.batch_normalize) {
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denormalize_connected_layer(*l.wf);
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denormalize_connected_layer(*l.wi);
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denormalize_connected_layer(*l.wg);
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denormalize_connected_layer(*l.wo);
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denormalize_connected_layer(*l.uf);
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denormalize_connected_layer(*l.ui);
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denormalize_connected_layer(*l.ug);
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denormalize_connected_layer(*l.uo);
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}
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}
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save_weights(net, outfile);
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}
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layer normalize_layer(layer l, int n)
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{
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int j;
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l.batch_normalize=1;
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l.scales = (float*)xcalloc(n, sizeof(float));
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for(j = 0; j < n; ++j){
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l.scales[j] = 1;
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}
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l.rolling_mean = (float*)xcalloc(n, sizeof(float));
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l.rolling_variance = (float*)xcalloc(n, sizeof(float));
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return l;
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}
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void normalize_net(char *cfgfile, char *weightfile, char *outfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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int i;
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for(i = 0; i < net.n; ++i){
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layer l = net.layers[i];
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if(l.type == CONVOLUTIONAL && !l.batch_normalize){
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net.layers[i] = normalize_layer(l, l.n);
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}
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if (l.type == CONNECTED && !l.batch_normalize) {
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net.layers[i] = normalize_layer(l, l.outputs);
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}
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if (l.type == GRU && l.batch_normalize) {
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*l.input_z_layer = normalize_layer(*l.input_z_layer, l.input_z_layer->outputs);
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*l.input_r_layer = normalize_layer(*l.input_r_layer, l.input_r_layer->outputs);
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*l.input_h_layer = normalize_layer(*l.input_h_layer, l.input_h_layer->outputs);
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*l.state_z_layer = normalize_layer(*l.state_z_layer, l.state_z_layer->outputs);
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*l.state_r_layer = normalize_layer(*l.state_r_layer, l.state_r_layer->outputs);
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*l.state_h_layer = normalize_layer(*l.state_h_layer, l.state_h_layer->outputs);
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net.layers[i].batch_normalize=1;
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}
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if (l.type == LSTM && l.batch_normalize) {
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*l.wf = normalize_layer(*l.wf, l.wf->outputs);
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*l.wi = normalize_layer(*l.wi, l.wi->outputs);
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*l.wg = normalize_layer(*l.wg, l.wg->outputs);
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*l.wo = normalize_layer(*l.wo, l.wo->outputs);
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*l.uf = normalize_layer(*l.uf, l.uf->outputs);
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*l.ui = normalize_layer(*l.ui, l.ui->outputs);
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*l.ug = normalize_layer(*l.ug, l.ug->outputs);
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*l.uo = normalize_layer(*l.uo, l.uo->outputs);
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net.layers[i].batch_normalize=1;
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}
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}
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save_weights(net, outfile);
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}
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void statistics_net(char *cfgfile, char *weightfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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if (weightfile) {
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load_weights(&net, weightfile);
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}
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int i;
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for (i = 0; i < net.n; ++i) {
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layer l = net.layers[i];
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if (l.type == CONNECTED && l.batch_normalize) {
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printf("Connected Layer %d\n", i);
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statistics_connected_layer(l);
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}
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if (l.type == GRU && l.batch_normalize) {
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printf("GRU Layer %d\n", i);
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printf("Input Z\n");
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statistics_connected_layer(*l.input_z_layer);
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printf("Input R\n");
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statistics_connected_layer(*l.input_r_layer);
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printf("Input H\n");
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statistics_connected_layer(*l.input_h_layer);
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printf("State Z\n");
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statistics_connected_layer(*l.state_z_layer);
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printf("State R\n");
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statistics_connected_layer(*l.state_r_layer);
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printf("State H\n");
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statistics_connected_layer(*l.state_h_layer);
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}
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if (l.type == LSTM && l.batch_normalize) {
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printf("LSTM Layer %d\n", i);
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printf("wf\n");
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statistics_connected_layer(*l.wf);
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printf("wi\n");
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statistics_connected_layer(*l.wi);
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printf("wg\n");
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statistics_connected_layer(*l.wg);
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printf("wo\n");
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statistics_connected_layer(*l.wo);
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printf("uf\n");
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statistics_connected_layer(*l.uf);
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printf("ui\n");
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statistics_connected_layer(*l.ui);
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printf("ug\n");
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statistics_connected_layer(*l.ug);
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printf("uo\n");
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statistics_connected_layer(*l.uo);
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}
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printf("\n");
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}
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}
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void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
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{
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gpu_index = -1;
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network net = parse_network_cfg(cfgfile);
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if (weightfile) {
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load_weights(&net, weightfile);
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}
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int i;
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for (i = 0; i < net.n; ++i) {
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layer l = net.layers[i];
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if (l.type == CONVOLUTIONAL && l.batch_normalize) {
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denormalize_convolutional_layer(l);
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net.layers[i].batch_normalize=0;
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}
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if (l.type == CONNECTED && l.batch_normalize) {
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denormalize_connected_layer(l);
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net.layers[i].batch_normalize=0;
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}
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if (l.type == GRU && l.batch_normalize) {
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denormalize_connected_layer(*l.input_z_layer);
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denormalize_connected_layer(*l.input_r_layer);
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denormalize_connected_layer(*l.input_h_layer);
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denormalize_connected_layer(*l.state_z_layer);
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denormalize_connected_layer(*l.state_r_layer);
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denormalize_connected_layer(*l.state_h_layer);
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l.input_z_layer->batch_normalize = 0;
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l.input_r_layer->batch_normalize = 0;
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l.input_h_layer->batch_normalize = 0;
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l.state_z_layer->batch_normalize = 0;
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l.state_r_layer->batch_normalize = 0;
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l.state_h_layer->batch_normalize = 0;
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net.layers[i].batch_normalize=0;
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}
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if (l.type == GRU && l.batch_normalize) {
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denormalize_connected_layer(*l.wf);
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denormalize_connected_layer(*l.wi);
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denormalize_connected_layer(*l.wg);
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denormalize_connected_layer(*l.wo);
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denormalize_connected_layer(*l.uf);
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denormalize_connected_layer(*l.ui);
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denormalize_connected_layer(*l.ug);
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denormalize_connected_layer(*l.uo);
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l.wf->batch_normalize = 0;
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l.wi->batch_normalize = 0;
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l.wg->batch_normalize = 0;
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l.wo->batch_normalize = 0;
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l.uf->batch_normalize = 0;
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l.ui->batch_normalize = 0;
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l.ug->batch_normalize = 0;
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l.uo->batch_normalize = 0;
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net.layers[i].batch_normalize=0;
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}
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}
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save_weights(net, outfile);
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}
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void visualize(char *cfgfile, char *weightfile)
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{
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network net = parse_network_cfg(cfgfile);
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if(weightfile){
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load_weights(&net, weightfile);
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}
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visualize_network(net);
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#ifdef OPENCV
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wait_until_press_key_cv();
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#endif
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}
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//int main(int argc, char **argv)
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//{
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//#ifdef _DEBUG
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// _CrtSetDbgFlag(_CRTDBG_ALLOC_MEM_DF | _CRTDBG_LEAK_CHECK_DF);
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// printf(" _DEBUG is used \n");
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//#endif
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//
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//#ifdef DEBUG
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// printf(" DEBUG=1 \n");
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//#endif
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//
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// int i;
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// for (i = 0; i < argc; ++i) {
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// if (!argv[i]) continue;
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// strip_args(argv[i]);
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// }
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//
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// //test_resize("data/bad.jpg");
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// //test_box();
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// //test_convolutional_layer();
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// if(argc < 2){
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// fprintf(stderr, "usage: %s <function>\n", argv[0]);
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// return 0;
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// }
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// gpu_index = find_int_arg(argc, argv, "-i", 0);
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// if(find_arg(argc, argv, "-nogpu")) {
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// gpu_index = -1;
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// 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");
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// exit(-1);
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// }
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//
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//#ifndef GPU
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// gpu_index = -1;
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// printf(" GPU isn't used \n");
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// init_cpu();
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//#else // GPU
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// if(gpu_index >= 0){
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// cuda_set_device(gpu_index);
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// CHECK_CUDA(cudaSetDeviceFlags(cudaDeviceScheduleBlockingSync));
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// }
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//
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// show_cuda_cudnn_info();
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// cuda_debug_sync = find_arg(argc, argv, "-cuda_debug_sync");
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//
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//#ifdef CUDNN_HALF
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// printf(" CUDNN_HALF=1 \n");
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//#endif // CUDNN_HALF
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//
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//#endif // GPU
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//
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// show_opencv_info();
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//
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// if (0 == strcmp(argv[1], "average")){
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// average(argc, argv);
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// } else if (0 == strcmp(argv[1], "yolo")){
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// run_yolo(argc, argv);
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// } else if (0 == strcmp(argv[1], "voxel")){
|
// run_voxel(argc, argv);
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// } else if (0 == strcmp(argv[1], "super")){
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// run_super(argc, argv);
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// } else if (0 == strcmp(argv[1], "detector")){
|
// run_detector(argc, argv);
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// } else if (0 == strcmp(argv[1], "detect")){
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// float thresh = find_float_arg(argc, argv, "-thresh", .24);
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// int ext_output = find_arg(argc, argv, "-ext_output");
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// char *filename = (argc > 4) ? argv[4]: 0;
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// test_detector("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);
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// } else if (0 == strcmp(argv[1], "go")){
|
// run_go(argc, argv);
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// } 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;
|
//}
|
