mirror of
https://github.com/pjreddie/darknet.git
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MERRY CHRISTMAS I BROKE ALL YOUR DETECTION THINGS
This commit is contained in:
Joseph Redmon
527
examples/lsd.c
527
examples/lsd.c
@ -408,6 +408,8 @@ void test_dcgan(char *cfgfile, char *weightfile)
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for(i = 0; i < im.w*im.h*im.c; ++i){
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im.data[i] = rand_normal();
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}
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float mag = mag_array(im.data, im.w*im.h*im.c);
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//scale_array(im.data, im.w*im.h*im.c, 1./mag);
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float *X = im.data;
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time=clock();
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@ -426,21 +428,10 @@ void test_dcgan(char *cfgfile, char *weightfile)
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}
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}
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void dcgan_batch(network gnet, network anet)
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{
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//float *input = calloc(x_size, sizeof(float));
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}
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void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear, int display, char *train_images)
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{
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#ifdef GPU
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//char *train_images = "/home/pjreddie/data/coco/train1.txt";
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//char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
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//char *train_images = "/home/pjreddie/data/imagenet/imagenet1k.train.list";
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//char *train_images = "data/64.txt";
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//char *train_images = "data/alp.txt";
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//char *train_images = "data/cifar.txt";
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char *backup_directory = "/home/pjreddie/backup/";
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srand(time(0));
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char *base = basecfg(cfg);
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@ -498,7 +489,7 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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//data generated = copy_data(train);
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while (get_current_batch(gnet) < gnet->max_batches) {
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start += 1;
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start += 1;
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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@ -513,8 +504,8 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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data gen = copy_data(train);
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for (j = 0; j < imgs; ++j) {
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train.y.vals[j][0] = .95;
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gen.y.vals[j][0] = .05;
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train.y.vals[j][0] = 1;
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gen.y.vals[j][0] = 0;
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}
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time=clock();
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@ -524,31 +515,35 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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for(z = 0; z < x_size; ++z){
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gnet->input[z] = rand_normal();
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}
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for(z = 0; z < gnet->batch; ++z){
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float mag = mag_array(gnet->input + z*gnet->inputs, gnet->inputs);
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scale_array(gnet->input + z*gnet->inputs, gnet->inputs, 1./mag);
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}
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cuda_push_array(gnet->input_gpu, gnet->input, x_size);
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cuda_push_array(gnet->truth_gpu, gnet->truth, y_size);
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//cuda_push_array(gnet->input_gpu, gnet->input, x_size);
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//cuda_push_array(gnet->truth_gpu, gnet->truth, y_size);
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*gnet->seen += gnet->batch;
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forward_network_gpu(gnet);
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forward_network(gnet);
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fill_gpu(imlayer.outputs*imlayer.batch, 0, imerror, 1);
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fill_gpu(anet->truths*anet->batch, .95, anet->truth_gpu, 1);
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copy_gpu(anet->inputs*anet->batch, imlayer.output_gpu, 1, anet->input_gpu, 1);
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fill_cpu(anet->truths*anet->batch, 1, anet->truth, 1);
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copy_cpu(anet->inputs*anet->batch, imlayer.output, 1, anet->input, 1);
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anet->delta_gpu = imerror;
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forward_network_gpu(anet);
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backward_network_gpu(anet);
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forward_network(anet);
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backward_network(anet);
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float genaloss = *anet->cost / anet->batch;
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printf("%f\n", genaloss);
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scal_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1);
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scal_gpu(imlayer.outputs*imlayer.batch, .00, gnet->layers[gnet->n-1].delta_gpu, 1);
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scal_gpu(imlayer.outputs*imlayer.batch, 0, gnet->layers[gnet->n-1].delta_gpu, 1);
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printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs*imlayer.batch));
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printf("features %f\n", cuda_mag_array(gnet->layers[gnet->n-1].delta_gpu, imlayer.outputs*imlayer.batch));
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axpy_gpu(imlayer.outputs*imlayer.batch, 1, imerror, 1, gnet->layers[gnet->n-1].delta_gpu, 1);
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backward_network_gpu(gnet);
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backward_network(gnet);
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for(k = 0; k < gnet->batch; ++k){
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int index = j*gnet->batch + k;
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@ -565,23 +560,25 @@ void train_dcgan(char *cfg, char *weight, char *acfg, char *aweight, int clear,
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//scale_image(im, .5);
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//translate_image(im2, 1);
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//scale_image(im2, .5);
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#ifdef OPENCV
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#ifdef OPENCV
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if(display){
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image im = float_to_image(anet->w, anet->h, anet->c, gen.X.vals[0]);
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image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
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show_image(im, "gen");
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show_image(im2, "train");
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save_image(im, "gen");
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save_image(im2, "train");
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cvWaitKey(50);
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}
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#endif
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#endif
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/*
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if(aloss < .1){
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anet->learning_rate = 0;
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} else if (aloss > .3){
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anet->learning_rate = orig_rate;
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}
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*/
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/*
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if(aloss < .1){
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anet->learning_rate = 0;
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} else if (aloss > .3){
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anet->learning_rate = orig_rate;
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}
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*/
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update_network_gpu(gnet);
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@ -747,7 +744,7 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
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update_network_gpu(net);
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#ifdef OPENCV
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#ifdef OPENCV
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if(display){
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image im = float_to_image(anet->w, anet->h, anet->c, gray.X.vals[0]);
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image im2 = float_to_image(anet->w, anet->h, anet->c, train.X.vals[0]);
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@ -755,7 +752,7 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
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show_image(im2, "train");
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cvWaitKey(50);
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}
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#endif
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#endif
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free_data(merge);
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free_data(train);
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free_data(gray);
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@ -786,259 +783,259 @@ void train_colorizer(char *cfg, char *weight, char *acfg, char *aweight, int cle
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}
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/*
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void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfile, int clear)
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{
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void train_lsd2(char *cfgfile, char *weightfile, char *acfgfile, char *aweightfile, int clear)
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{
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#ifdef GPU
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char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
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char *backup_directory = "/home/pjreddie/backup/";
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srand(time(0));
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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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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if(clear) *net->seen = 0;
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char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
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char *backup_directory = "/home/pjreddie/backup/";
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srand(time(0));
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char *base = basecfg(cfgfile);
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printf("%s\n", base);
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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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if(clear) *net->seen = 0;
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char *abase = basecfg(acfgfile);
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network anet = parse_network_cfg(acfgfile);
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if(aweightfile){
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load_weights(&anet, aweightfile);
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}
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if(clear) *anet->seen = 0;
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char *abase = basecfg(acfgfile);
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network anet = parse_network_cfg(acfgfile);
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if(aweightfile){
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load_weights(&anet, aweightfile);
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}
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if(clear) *anet->seen = 0;
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int i, j, k;
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layer imlayer = {0};
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for (i = 0; i < net->n; ++i) {
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if (net->layers[i].out_c == 3) {
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imlayer = net->layers[i];
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break;
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int i, j, k;
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layer imlayer = {0};
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for (i = 0; i < net->n; ++i) {
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if (net->layers[i].out_c == 3) {
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imlayer = net->layers[i];
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break;
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}
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}
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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int imgs = net->batch*net->subdivisions;
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i = *net->seen/imgs;
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data train, buffer;
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list *plist = get_paths(train_images);
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//int N = plist->size;
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char **paths = (char **)list_to_array(plist);
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load_args args = {0};
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args.w = net->w;
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args.h = net->h;
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args.paths = paths;
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args.n = imgs;
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args.m = plist->size;
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args.d = &buffer;
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args.min = net->min_crop;
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args.max = net->max_crop;
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args.angle = net->angle;
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args.aspect = net->aspect;
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args.exposure = net->exposure;
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args.saturation = net->saturation;
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args.hue = net->hue;
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args.size = net->w;
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args.type = CLASSIFICATION_DATA;
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args.classes = 1;
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char *ls[1] = {"coco"};
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args.labels = ls;
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pthread_t load_thread = load_data_in_thread(args);
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clock_t time;
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network_state gstate = {0};
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gstate.index = 0;
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gstate.net = net;
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int x_size = get_network_input_size(net)*net->batch;
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int y_size = 1*net->batch;
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gstate.input = cuda_make_array(0, x_size);
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gstate.truth = 0;
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gstate.delta = 0;
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gstate.train = 1;
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float *X = calloc(x_size, sizeof(float));
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float *y = calloc(y_size, sizeof(float));
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network_state astate = {0};
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astate.index = 0;
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astate.net = anet;
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int ay_size = get_network_output_size(anet)*anet->batch;
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astate.input = 0;
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astate.truth = 0;
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astate.delta = 0;
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astate.train = 1;
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float *imerror = cuda_make_array(0, imlayer.outputs);
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float *ones_gpu = cuda_make_array(0, ay_size);
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fill_gpu(ay_size, 1, ones_gpu, 1);
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float aloss_avg = -1;
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float gloss_avg = -1;
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//data generated = copy_data(train);
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while (get_current_batch(net) < net->max_batches) {
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_in_thread(args);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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data generated = copy_data(train);
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time=clock();
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float gloss = 0;
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for(j = 0; j < net->subdivisions; ++j){
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get_next_batch(train, net->batch, j*net->batch, X, y);
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cuda_push_array(gstate.input, X, x_size);
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*net->seen += net->batch;
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forward_network_gpu(net, gstate);
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fill_gpu(imlayer.outputs, 0, imerror, 1);
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astate.input = imlayer.output_gpu;
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astate.delta = imerror;
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astate.truth = ones_gpu;
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forward_network_gpu(anet, astate);
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backward_network_gpu(anet, astate);
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scal_gpu(imlayer.outputs, 1, imerror, 1);
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axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
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backward_network_gpu(net, gstate);
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printf("features %f\n", cuda_mag_array(imlayer.delta_gpu, imlayer.outputs));
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printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
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gloss += get_network_cost(net) /(net->subdivisions*net->batch);
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cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
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for(k = 0; k < net->batch; ++k){
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int index = j*net->batch + k;
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copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
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generated.y.vals[index][0] = 0;
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}
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}
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harmless_update_network_gpu(anet);
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printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
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int imgs = net->batch*net->subdivisions;
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i = *net->seen/imgs;
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data train, buffer;
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data merge = concat_data(train, generated);
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randomize_data(merge);
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float aloss = train_network(anet, merge);
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update_network_gpu(net);
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update_network_gpu(anet);
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free_data(merge);
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free_data(train);
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free_data(generated);
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if (aloss_avg < 0) aloss_avg = aloss;
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aloss_avg = aloss_avg*.9 + aloss*.1;
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gloss_avg = gloss_avg*.9 + gloss*.1;
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list *plist = get_paths(train_images);
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//int N = plist->size;
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char **paths = (char **)list_to_array(plist);
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load_args args = {0};
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args.w = net->w;
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args.h = net->h;
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args.paths = paths;
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args.n = imgs;
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args.m = plist->size;
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args.d = &buffer;
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args.min = net->min_crop;
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args.max = net->max_crop;
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args.angle = net->angle;
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args.aspect = net->aspect;
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args.exposure = net->exposure;
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args.saturation = net->saturation;
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args.hue = net->hue;
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args.size = net->w;
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args.type = CLASSIFICATION_DATA;
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args.classes = 1;
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char *ls[1] = {"coco"};
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args.labels = ls;
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pthread_t load_thread = load_data_in_thread(args);
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clock_t time;
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network_state gstate = {0};
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gstate.index = 0;
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gstate.net = net;
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int x_size = get_network_input_size(net)*net->batch;
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int y_size = 1*net->batch;
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gstate.input = cuda_make_array(0, x_size);
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gstate.truth = 0;
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gstate.delta = 0;
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gstate.train = 1;
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float *X = calloc(x_size, sizeof(float));
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float *y = calloc(y_size, sizeof(float));
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network_state astate = {0};
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astate.index = 0;
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astate.net = anet;
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int ay_size = get_network_output_size(anet)*anet->batch;
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astate.input = 0;
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astate.truth = 0;
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astate.delta = 0;
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astate.train = 1;
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float *imerror = cuda_make_array(0, imlayer.outputs);
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float *ones_gpu = cuda_make_array(0, ay_size);
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fill_gpu(ay_size, 1, ones_gpu, 1);
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float aloss_avg = -1;
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float gloss_avg = -1;
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//data generated = copy_data(train);
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while (get_current_batch(net) < net->max_batches) {
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i += 1;
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time=clock();
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pthread_join(load_thread, 0);
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train = buffer;
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load_thread = load_data_in_thread(args);
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printf("Loaded: %lf seconds\n", sec(clock()-time));
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data generated = copy_data(train);
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time=clock();
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float gloss = 0;
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for(j = 0; j < net->subdivisions; ++j){
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get_next_batch(train, net->batch, j*net->batch, X, y);
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cuda_push_array(gstate.input, X, x_size);
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*net->seen += net->batch;
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forward_network_gpu(net, gstate);
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fill_gpu(imlayer.outputs, 0, imerror, 1);
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astate.input = imlayer.output_gpu;
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astate.delta = imerror;
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astate.truth = ones_gpu;
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forward_network_gpu(anet, astate);
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backward_network_gpu(anet, astate);
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scal_gpu(imlayer.outputs, 1, imerror, 1);
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axpy_gpu(imlayer.outputs, 1, imerror, 1, imlayer.delta_gpu, 1);
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backward_network_gpu(net, gstate);
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printf("features %f\n", cuda_mag_array(imlayer.delta_gpu, imlayer.outputs));
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printf("realness %f\n", cuda_mag_array(imerror, imlayer.outputs));
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|
||||
gloss += get_network_cost(net) /(net->subdivisions*net->batch);
|
||||
|
||||
cuda_pull_array(imlayer.output_gpu, imlayer.output, imlayer.outputs*imlayer.batch);
|
||||
for(k = 0; k < net->batch; ++k){
|
||||
int index = j*net->batch + k;
|
||||
copy_cpu(imlayer.outputs, imlayer.output + k*imlayer.outputs, 1, generated.X.vals[index], 1);
|
||||
generated.y.vals[index][0] = 0;
|
||||
}
|
||||
}
|
||||
harmless_update_network_gpu(anet);
|
||||
|
||||
data merge = concat_data(train, generated);
|
||||
randomize_data(merge);
|
||||
float aloss = train_network(anet, merge);
|
||||
|
||||
update_network_gpu(net);
|
||||
update_network_gpu(anet);
|
||||
free_data(merge);
|
||||
free_data(train);
|
||||
free_data(generated);
|
||||
if (aloss_avg < 0) aloss_avg = aloss;
|
||||
aloss_avg = aloss_avg*.9 + aloss*.1;
|
||||
gloss_avg = gloss_avg*.9 + gloss*.1;
|
||||
|
||||
printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
|
||||
if(i%1000==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
||||
save_weights(net, buff);
|
||||
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
|
||||
save_weights(anet, buff);
|
||||
}
|
||||
if(i%100==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
sprintf(buff, "%s/%s.backup", backup_directory, abase);
|
||||
save_weights(anet, buff);
|
||||
}
|
||||
printf("%d: gen: %f, adv: %f | gen_avg: %f, adv_avg: %f, %f rate, %lf seconds, %d images\n", i, gloss, aloss, gloss_avg, aloss_avg, get_current_rate(net), sec(clock()-time), i*imgs);
|
||||
if(i%1000==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
||||
save_weights(net, buff);
|
||||
sprintf(buff, "%s/%s_%d.weights", backup_directory, abase, i);
|
||||
save_weights(anet, buff);
|
||||
}
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
if(i%100==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
sprintf(buff, "%s/%s.backup", backup_directory, abase);
|
||||
save_weights(anet, buff);
|
||||
}
|
||||
}
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
#endif
|
||||
}
|
||||
*/
|
||||
|
||||
/*
|
||||
void train_lsd(char *cfgfile, char *weightfile, int clear)
|
||||
{
|
||||
char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
|
||||
char *backup_directory = "/home/pjreddie/backup/";
|
||||
srand(time(0));
|
||||
char *base = basecfg(cfgfile);
|
||||
printf("%s\n", base);
|
||||
float avg_loss = -1;
|
||||
network net = parse_network_cfg(cfgfile);
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
if(clear) *net->seen = 0;
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
|
||||
int imgs = net->batch*net->subdivisions;
|
||||
int i = *net->seen/imgs;
|
||||
data train, buffer;
|
||||
void train_lsd(char *cfgfile, char *weightfile, int clear)
|
||||
{
|
||||
char *train_images = "/home/pjreddie/data/coco/trainvalno5k.txt";
|
||||
char *backup_directory = "/home/pjreddie/backup/";
|
||||
srand(time(0));
|
||||
char *base = basecfg(cfgfile);
|
||||
printf("%s\n", base);
|
||||
float avg_loss = -1;
|
||||
network net = parse_network_cfg(cfgfile);
|
||||
if(weightfile){
|
||||
load_weights(&net, weightfile);
|
||||
}
|
||||
if(clear) *net->seen = 0;
|
||||
printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net->learning_rate, net->momentum, net->decay);
|
||||
int imgs = net->batch*net->subdivisions;
|
||||
int i = *net->seen/imgs;
|
||||
data train, buffer;
|
||||
|
||||
|
||||
list *plist = get_paths(train_images);
|
||||
//int N = plist->size;
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
list *plist = get_paths(train_images);
|
||||
//int N = plist->size;
|
||||
char **paths = (char **)list_to_array(plist);
|
||||
|
||||
load_args args = {0};
|
||||
args.w = net->w;
|
||||
args.h = net->h;
|
||||
args.paths = paths;
|
||||
args.n = imgs;
|
||||
args.m = plist->size;
|
||||
args.d = &buffer;
|
||||
load_args args = {0};
|
||||
args.w = net->w;
|
||||
args.h = net->h;
|
||||
args.paths = paths;
|
||||
args.n = imgs;
|
||||
args.m = plist->size;
|
||||
args.d = &buffer;
|
||||
|
||||
args.min = net->min_crop;
|
||||
args.max = net->max_crop;
|
||||
args.angle = net->angle;
|
||||
args.aspect = net->aspect;
|
||||
args.exposure = net->exposure;
|
||||
args.saturation = net->saturation;
|
||||
args.hue = net->hue;
|
||||
args.size = net->w;
|
||||
args.type = CLASSIFICATION_DATA;
|
||||
args.classes = 1;
|
||||
char *ls[1] = {"coco"};
|
||||
args.labels = ls;
|
||||
args.min = net->min_crop;
|
||||
args.max = net->max_crop;
|
||||
args.angle = net->angle;
|
||||
args.aspect = net->aspect;
|
||||
args.exposure = net->exposure;
|
||||
args.saturation = net->saturation;
|
||||
args.hue = net->hue;
|
||||
args.size = net->w;
|
||||
args.type = CLASSIFICATION_DATA;
|
||||
args.classes = 1;
|
||||
char *ls[1] = {"coco"};
|
||||
args.labels = ls;
|
||||
|
||||
pthread_t load_thread = load_data_in_thread(args);
|
||||
clock_t time;
|
||||
//while(i*imgs < N*120){
|
||||
while(get_current_batch(net) < net->max_batches){
|
||||
i += 1;
|
||||
time=clock();
|
||||
pthread_join(load_thread, 0);
|
||||
train = buffer;
|
||||
load_thread = load_data_in_thread(args);
|
||||
pthread_t load_thread = load_data_in_thread(args);
|
||||
clock_t time;
|
||||
//while(i*imgs < N*120){
|
||||
while(get_current_batch(net) < net->max_batches){
|
||||
i += 1;
|
||||
time=clock();
|
||||
pthread_join(load_thread, 0);
|
||||
train = buffer;
|
||||
load_thread = load_data_in_thread(args);
|
||||
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
printf("Loaded: %lf seconds\n", sec(clock()-time));
|
||||
|
||||
time=clock();
|
||||
float loss = train_network(net, train);
|
||||
if (avg_loss < 0) avg_loss = loss;
|
||||
avg_loss = avg_loss*.9 + loss*.1;
|
||||
time=clock();
|
||||
float loss = train_network(net, train);
|
||||
if (avg_loss < 0) avg_loss = loss;
|
||||
avg_loss = avg_loss*.9 + loss*.1;
|
||||
|
||||
printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
|
||||
if(i%1000==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
||||
save_weights(net, buff);
|
||||
}
|
||||
if(i%100==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
}
|
||||
free_data(train);
|
||||
}
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
printf("%d: %f, %f avg, %f rate, %lf seconds, %d images\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time), i*imgs);
|
||||
if(i%1000==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
|
||||
save_weights(net, buff);
|
||||
}
|
||||
if(i%100==0){
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s.backup", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
}
|
||||
free_data(train);
|
||||
}
|
||||
char buff[256];
|
||||
sprintf(buff, "%s/%s_final.weights", backup_directory, base);
|
||||
save_weights(net, buff);
|
||||
}
|
||||
*/
|
||||
|
||||
|
||||
Reference in New Issue
Block a user