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https://github.com/pjreddie/darknet.git
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@crossbot I will murder you in your sleep ⚔️
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Joseph Redmon
212
examples/rnn_vid.c
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212
examples/rnn_vid.c
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#include "darknet/network.h"
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#include "darknet/cost_layer.h"
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#include "darknet/utils.h"
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#include "darknet/parser.h"
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#include "darknet/blas.h"
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#ifdef OPENCV
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image get_image_from_stream(CvCapture *cap);
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image ipl_to_image(IplImage* src);
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void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters);
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typedef struct {
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float *x;
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float *y;
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} float_pair;
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float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int steps)
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{
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int b;
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assert(net.batch == steps + 1);
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image out_im = get_network_image(net);
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int output_size = out_im.w*out_im.h*out_im.c;
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printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
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float *feats = calloc(net.batch*batch*output_size, sizeof(float));
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for(b = 0; b < batch; ++b){
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int input_size = net.w*net.h*net.c;
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float *input = calloc(input_size*net.batch, sizeof(float));
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char *filename = files[rand()%n];
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CvCapture *cap = cvCaptureFromFile(filename);
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int frames = cvGetCaptureProperty(cap, CV_CAP_PROP_FRAME_COUNT);
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int index = rand() % (frames - steps - 2);
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if (frames < (steps + 4)){
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--b;
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free(input);
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continue;
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}
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printf("frames: %d, index: %d\n", frames, index);
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cvSetCaptureProperty(cap, CV_CAP_PROP_POS_FRAMES, index);
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int i;
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for(i = 0; i < net.batch; ++i){
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IplImage* src = cvQueryFrame(cap);
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image im = ipl_to_image(src);
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rgbgr_image(im);
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image re = resize_image(im, net.w, net.h);
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//show_image(re, "loaded");
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//cvWaitKey(10);
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memcpy(input + i*input_size, re.data, input_size*sizeof(float));
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free_image(im);
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free_image(re);
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}
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float *output = network_predict(net, input);
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free(input);
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for(i = 0; i < net.batch; ++i){
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memcpy(feats + (b + i*batch)*output_size, output + i*output_size, output_size*sizeof(float));
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}
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cvReleaseCapture(&cap);
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}
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//printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
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float_pair p = {0};
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p.x = feats;
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p.y = feats + output_size*batch; //+ out_im.w*out_im.h*out_im.c;
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return p;
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}
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void train_vid_rnn(char *cfgfile, char *weightfile)
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{
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char *train_videos = "data/vid/train.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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float avg_loss = -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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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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int i = *net.seen/imgs;
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list *plist = get_paths(train_videos);
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int N = plist->size;
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char **paths = (char **)list_to_array(plist);
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clock_t time;
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int steps = net.time_steps;
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int batch = net.batch / net.time_steps;
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network extractor = parse_network_cfg("cfg/extractor.cfg");
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load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv");
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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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float_pair p = get_rnn_vid_data(extractor, paths, N, batch, steps);
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memcpy(net.input, p.x, net.inputs*net.batch);
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memcpy(net.truth, p.y, net.truths*net.batch);
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float loss = train_network_datum(net) / (net.batch);
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free(p.x);
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if (avg_loss < 0) avg_loss = loss;
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avg_loss = avg_loss*.9 + loss*.1;
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fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time));
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if(i%100==0){
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char buff[256];
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sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
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save_weights(net, buff);
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}
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if(i%10==0){
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char buff[256];
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sprintf(buff, "%s/%s.backup", backup_directory, base);
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save_weights(net, buff);
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}
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}
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char buff[256];
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sprintf(buff, "%s/%s_final.weights", backup_directory, base);
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save_weights(net, buff);
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}
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image save_reconstruction(network net, image *init, float *feat, char *name, int i)
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{
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image recon;
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if (init) {
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recon = copy_image(*init);
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} else {
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recon = make_random_image(net.w, net.h, 3);
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}
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image update = make_image(net.w, net.h, 3);
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reconstruct_picture(net, feat, recon, update, .01, .9, .1, 2, 50);
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char buff[256];
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sprintf(buff, "%s%d", name, i);
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save_image(recon, buff);
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free_image(update);
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return recon;
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}
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void generate_vid_rnn(char *cfgfile, char *weightfile)
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{
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network extractor = parse_network_cfg("cfg/extractor.recon.cfg");
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load_weights(&extractor, "/home/pjreddie/trained/yolo-coco.conv");
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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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set_batch_network(&extractor, 1);
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set_batch_network(&net, 1);
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int i;
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CvCapture *cap = cvCaptureFromFile("/extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4");
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float *feat;
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float *next;
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image last;
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for(i = 0; i < 25; ++i){
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image im = get_image_from_stream(cap);
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image re = resize_image(im, extractor.w, extractor.h);
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feat = network_predict(extractor, re.data);
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if(i > 0){
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printf("%f %f\n", mean_array(feat, 14*14*512), variance_array(feat, 14*14*512));
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printf("%f %f\n", mean_array(next, 14*14*512), variance_array(next, 14*14*512));
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printf("%f\n", mse_array(feat, 14*14*512));
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axpy_cpu(14*14*512, -1, feat, 1, next, 1);
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printf("%f\n", mse_array(next, 14*14*512));
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}
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next = network_predict(net, feat);
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free_image(im);
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free_image(save_reconstruction(extractor, 0, feat, "feat", i));
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free_image(save_reconstruction(extractor, 0, next, "next", i));
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if (i==24) last = copy_image(re);
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free_image(re);
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}
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for(i = 0; i < 30; ++i){
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next = network_predict(net, next);
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image new = save_reconstruction(extractor, &last, next, "new", i);
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free_image(last);
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last = new;
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}
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}
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void run_vid_rnn(int argc, char **argv)
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{
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if(argc < 4){
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fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
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return;
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}
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char *cfg = argv[3];
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char *weights = (argc > 4) ? argv[4] : 0;
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//char *filename = (argc > 5) ? argv[5]: 0;
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if(0==strcmp(argv[2], "train")) train_vid_rnn(cfg, weights);
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else if(0==strcmp(argv[2], "generate")) generate_vid_rnn(cfg, weights);
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}
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#else
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void run_vid_rnn(int argc, char **argv){}
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#endif
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