From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/rnn_vid.c | 416 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 208 insertions(+), 208 deletions(-)
diff --git a/lib/detecter_tools/darknet/rnn_vid.c b/lib/detecter_tools/darknet/rnn_vid.c
index 723ff0f..a5ff527 100644
--- a/lib/detecter_tools/darknet/rnn_vid.c
+++ b/lib/detecter_tools/darknet/rnn_vid.c
@@ -1,208 +1,208 @@
-#include "network.h"
-#include "cost_layer.h"
-#include "utils.h"
-#include "parser.h"
-#include "blas.h"
-
-#ifdef OPENCV
-void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters);
-
-
-typedef struct {
- float *x;
- float *y;
-} float_pair;
-
-float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int steps)
-{
- int b;
- assert(net.batch == steps + 1);
- image out_im = get_network_image(net);
- int output_size = out_im.w*out_im.h*out_im.c;
- printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
- float* feats = (float*)xcalloc(net.batch * batch * output_size, sizeof(float));
- for(b = 0; b < batch; ++b){
- int input_size = net.w*net.h*net.c;
- float* input = (float*)xcalloc(input_size * net.batch, sizeof(float));
- char *filename = files[rand()%n];
- cap_cv *cap = get_capture_video_stream(filename);
- int frames = get_capture_frame_count_cv(cap);
- int index = rand() % (frames - steps - 2);
- if (frames < (steps + 4)){
- --b;
- free(input);
- continue;
- }
-
- printf("frames: %d, index: %d\n", frames, index);
- set_capture_position_frame_cv(cap, index);
-
- int i;
- for(i = 0; i < net.batch; ++i){
- mat_cv *src = get_capture_frame_cv(cap);
- image im = mat_to_image_cv(src);
- rgbgr_image(im);
- image re = resize_image(im, net.w, net.h);
- //show_image(re, "loaded");
- //cvWaitKey(10);
- memcpy(input + i*input_size, re.data, input_size*sizeof(float));
- free_image(im);
- free_image(re);
- }
- float *output = network_predict(net, input);
-
- free(input);
-
- for(i = 0; i < net.batch; ++i){
- memcpy(feats + (b + i*batch)*output_size, output + i*output_size, output_size*sizeof(float));
- }
-
- release_capture(cap); //cvReleaseCapture(&cap);
- }
-
- //printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
- float_pair p = {0};
- p.x = feats;
- p.y = feats + output_size*batch; //+ out_im.w*out_im.h*out_im.c;
-
- return p;
-}
-
-
-void train_vid_rnn(char *cfgfile, char *weightfile)
-{
- char *train_videos = "data/vid/train.txt";
- char* backup_directory = "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);
- }
- 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;
-
- list *plist = get_paths(train_videos);
- int N = plist->size;
- char **paths = (char **)list_to_array(plist);
- clock_t time;
- int steps = net.time_steps;
- int batch = net.batch / net.time_steps;
-
- network extractor = parse_network_cfg("cfg/extractor.cfg");
- load_weights(&extractor, "trained/yolo-coco.conv");
-
- while(get_current_batch(net) < net.max_batches){
- i += 1;
- time=clock();
- float_pair p = get_rnn_vid_data(extractor, paths, N, batch, steps);
-
- float loss = train_network_datum(net, p.x, p.y) / (net.batch);
-
-
- free(p.x);
- if (avg_loss < 0) avg_loss = loss;
- avg_loss = avg_loss*.9 + loss*.1;
-
- fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time));
- if(i%100==0){
- char buff[256];
- sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
- save_weights(net, buff);
- }
- if(i%10==0){
- char buff[256];
- sprintf(buff, "%s/%s.backup", backup_directory, base);
- save_weights(net, buff);
- }
- }
- char buff[256];
- sprintf(buff, "%s/%s_final.weights", backup_directory, base);
- save_weights(net, buff);
-}
-
-
-image save_reconstruction(network net, image *init, float *feat, char *name, int i)
-{
- image recon;
- if (init) {
- recon = copy_image(*init);
- } else {
- recon = make_random_image(net.w, net.h, 3);
- }
-
- image update = make_image(net.w, net.h, 3);
- reconstruct_picture(net, feat, recon, update, .01, .9, .1, 2, 50);
- char buff[256];
- sprintf(buff, "%s%d", name, i);
- save_image(recon, buff);
- free_image(update);
- return recon;
-}
-
-void generate_vid_rnn(char *cfgfile, char *weightfile)
-{
- network extractor = parse_network_cfg("cfg/extractor.recon.cfg");
- load_weights(&extractor, "trained/yolo-coco.conv");
-
- network net = parse_network_cfg(cfgfile);
- if(weightfile){
- load_weights(&net, weightfile);
- }
- set_batch_network(&extractor, 1);
- set_batch_network(&net, 1);
-
- int i;
- cap_cv *cap = get_capture_video_stream("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4");
- //CvCapture* cap = cvCaptureFromFile("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4");
- float *feat;
- float *next;
- next = NULL;
- image last;
- for(i = 0; i < 25; ++i){
- image im = get_image_from_stream_cpp(cap);
- image re = resize_image(im, extractor.w, extractor.h);
- feat = network_predict(extractor, re.data);
- if(i > 0){
- printf("%f %f\n", mean_array(feat, 14*14*512), variance_array(feat, 14*14*512));
- printf("%f %f\n", mean_array(next, 14*14*512), variance_array(next, 14*14*512));
- printf("%f\n", mse_array(feat, 14*14*512));
- axpy_cpu(14*14*512, -1, feat, 1, next, 1);
- printf("%f\n", mse_array(next, 14*14*512));
- }
- next = network_predict(net, feat);
-
- free_image(im);
-
- free_image(save_reconstruction(extractor, 0, feat, "feat", i));
- free_image(save_reconstruction(extractor, 0, next, "next", i));
- if (i==24) last = copy_image(re);
- free_image(re);
- }
- for(i = 0; i < 30; ++i){
- next = network_predict(net, next);
- image newimage = save_reconstruction(extractor, &last, next, "newimage", i);
- free_image(last);
- last = newimage;
- }
-}
-
-void run_vid_rnn(int argc, char **argv)
-{
- if(argc < 4){
- fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
- return;
- }
-
- char *cfg = argv[3];
- char *weights = (argc > 4) ? argv[4] : 0;
- //char *filename = (argc > 5) ? argv[5]: 0;
- if(0==strcmp(argv[2], "train")) train_vid_rnn(cfg, weights);
- else if(0==strcmp(argv[2], "generate")) generate_vid_rnn(cfg, weights);
-}
-#else
-void run_vid_rnn(int argc, char **argv){}
-#endif
+#include "network.h"
+#include "cost_layer.h"
+#include "utils.h"
+#include "parser.h"
+#include "blas.h"
+
+#ifdef OPENCV
+void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters);
+
+
+typedef struct {
+ float *x;
+ float *y;
+} float_pair;
+
+float_pair get_rnn_vid_data(network net, char **files, int n, int batch, int steps)
+{
+ int b;
+ assert(net.batch == steps + 1);
+ image out_im = get_network_image(net);
+ int output_size = out_im.w*out_im.h*out_im.c;
+ printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
+ float* feats = (float*)xcalloc(net.batch * batch * output_size, sizeof(float));
+ for(b = 0; b < batch; ++b){
+ int input_size = net.w*net.h*net.c;
+ float* input = (float*)xcalloc(input_size * net.batch, sizeof(float));
+ char *filename = files[rand()%n];
+ cap_cv *cap = get_capture_video_stream(filename);
+ int frames = get_capture_frame_count_cv(cap);
+ int index = rand() % (frames - steps - 2);
+ if (frames < (steps + 4)){
+ --b;
+ free(input);
+ continue;
+ }
+
+ printf("frames: %d, index: %d\n", frames, index);
+ set_capture_position_frame_cv(cap, index);
+
+ int i;
+ for(i = 0; i < net.batch; ++i){
+ mat_cv *src = get_capture_frame_cv(cap);
+ image im = mat_to_image_cv(src);
+ rgbgr_image(im);
+ image re = resize_image(im, net.w, net.h);
+ //show_image(re, "loaded");
+ //cvWaitKey(10);
+ memcpy(input + i*input_size, re.data, input_size*sizeof(float));
+ free_image(im);
+ free_image(re);
+ }
+ float *output = network_predict(net, input);
+
+ free(input);
+
+ for(i = 0; i < net.batch; ++i){
+ memcpy(feats + (b + i*batch)*output_size, output + i*output_size, output_size*sizeof(float));
+ }
+
+ release_capture(cap); //cvReleaseCapture(&cap);
+ }
+
+ //printf("%d %d %d\n", out_im.w, out_im.h, out_im.c);
+ float_pair p = {0};
+ p.x = feats;
+ p.y = feats + output_size*batch; //+ out_im.w*out_im.h*out_im.c;
+
+ return p;
+}
+
+
+void train_vid_rnn(char *cfgfile, char *weightfile)
+{
+ char *train_videos = "data/vid/train.txt";
+ char* backup_directory = "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);
+ }
+ 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;
+
+ list *plist = get_paths(train_videos);
+ int N = plist->size;
+ char **paths = (char **)list_to_array(plist);
+ clock_t time;
+ int steps = net.time_steps;
+ int batch = net.batch / net.time_steps;
+
+ network extractor = parse_network_cfg("cfg/extractor.cfg");
+ load_weights(&extractor, "trained/yolo-coco.conv");
+
+ while(get_current_batch(net) < net.max_batches){
+ i += 1;
+ time=clock();
+ float_pair p = get_rnn_vid_data(extractor, paths, N, batch, steps);
+
+ float loss = train_network_datum(net, p.x, p.y) / (net.batch);
+
+
+ free(p.x);
+ if (avg_loss < 0) avg_loss = loss;
+ avg_loss = avg_loss*.9 + loss*.1;
+
+ fprintf(stderr, "%d: %f, %f avg, %f rate, %lf seconds\n", i, loss, avg_loss, get_current_rate(net), sec(clock()-time));
+ if(i%100==0){
+ char buff[256];
+ sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+ save_weights(net, buff);
+ }
+ if(i%10==0){
+ char buff[256];
+ sprintf(buff, "%s/%s.backup", backup_directory, base);
+ save_weights(net, buff);
+ }
+ }
+ char buff[256];
+ sprintf(buff, "%s/%s_final.weights", backup_directory, base);
+ save_weights(net, buff);
+}
+
+
+image save_reconstruction(network net, image *init, float *feat, char *name, int i)
+{
+ image recon;
+ if (init) {
+ recon = copy_image(*init);
+ } else {
+ recon = make_random_image(net.w, net.h, 3);
+ }
+
+ image update = make_image(net.w, net.h, 3);
+ reconstruct_picture(net, feat, recon, update, .01, .9, .1, 2, 50);
+ char buff[256];
+ sprintf(buff, "%s%d", name, i);
+ save_image(recon, buff);
+ free_image(update);
+ return recon;
+}
+
+void generate_vid_rnn(char *cfgfile, char *weightfile)
+{
+ network extractor = parse_network_cfg("cfg/extractor.recon.cfg");
+ load_weights(&extractor, "trained/yolo-coco.conv");
+
+ network net = parse_network_cfg(cfgfile);
+ if(weightfile){
+ load_weights(&net, weightfile);
+ }
+ set_batch_network(&extractor, 1);
+ set_batch_network(&net, 1);
+
+ int i;
+ cap_cv *cap = get_capture_video_stream("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4");
+ //CvCapture* cap = cvCaptureFromFile("extra/vid/ILSVRC2015/Data/VID/snippets/val/ILSVRC2015_val_00007030.mp4");
+ float *feat;
+ float *next;
+ next = NULL;
+ image last;
+ for(i = 0; i < 25; ++i){
+ image im = get_image_from_stream_cpp(cap);
+ image re = resize_image(im, extractor.w, extractor.h);
+ feat = network_predict(extractor, re.data);
+ if(i > 0){
+ printf("%f %f\n", mean_array(feat, 14*14*512), variance_array(feat, 14*14*512));
+ printf("%f %f\n", mean_array(next, 14*14*512), variance_array(next, 14*14*512));
+ printf("%f\n", mse_array(feat, 14*14*512));
+ axpy_cpu(14*14*512, -1, feat, 1, next, 1);
+ printf("%f\n", mse_array(next, 14*14*512));
+ }
+ next = network_predict(net, feat);
+
+ free_image(im);
+
+ free_image(save_reconstruction(extractor, 0, feat, "feat", i));
+ free_image(save_reconstruction(extractor, 0, next, "next", i));
+ if (i==24) last = copy_image(re);
+ free_image(re);
+ }
+ for(i = 0; i < 30; ++i){
+ next = network_predict(net, next);
+ image newimage = save_reconstruction(extractor, &last, next, "newimage", i);
+ free_image(last);
+ last = newimage;
+ }
+}
+
+void run_vid_rnn(int argc, char **argv)
+{
+ if(argc < 4){
+ fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
+ return;
+ }
+
+ char *cfg = argv[3];
+ char *weights = (argc > 4) ? argv[4] : 0;
+ //char *filename = (argc > 5) ? argv[5]: 0;
+ if(0==strcmp(argv[2], "train")) train_vid_rnn(cfg, weights);
+ else if(0==strcmp(argv[2], "generate")) generate_vid_rnn(cfg, weights);
+}
+#else
+void run_vid_rnn(int argc, char **argv){}
+#endif
--
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