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/nightmare.c | 606 +++++++++++++++++++++++++++---------------------------
1 files changed, 303 insertions(+), 303 deletions(-)
diff --git a/lib/detecter_tools/darknet/nightmare.c b/lib/detecter_tools/darknet/nightmare.c
index afb9ae1..5c1ca04 100644
--- a/lib/detecter_tools/darknet/nightmare.c
+++ b/lib/detecter_tools/darknet/nightmare.c
@@ -1,303 +1,303 @@
-
-#include "network.h"
-#include "parser.h"
-#include "blas.h"
-#include "utils.h"
-
-// ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2
-
-float abs_mean(float *x, int n)
-{
- int i;
- float sum = 0;
- for (i = 0; i < n; ++i){
- sum += fabs(x[i]);
- }
- return sum/n;
-}
-
-void calculate_loss(float *output, float *delta, int n, float thresh)
-{
- int i;
- float mean = mean_array(output, n);
- float var = variance_array(output, n);
- for(i = 0; i < n; ++i){
- if(delta[i] > mean + thresh*sqrt(var)) delta[i] = output[i];
- else delta[i] = 0;
- }
-}
-
-void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm)
-{
- //scale_image(orig, 2);
- //translate_image(orig, -1);
- net->n = max_layer + 1;
-
- int dx = rand()%16 - 8;
- int dy = rand()%16 - 8;
- int flip = rand()%2;
-
- image crop = crop_image(orig, dx, dy, orig.w, orig.h);
- image im = resize_image(crop, (int)(orig.w * scale), (int)(orig.h * scale));
- if(flip) flip_image(im);
-
- resize_network(net, im.w, im.h);
- layer last = net->layers[net->n-1];
- //net->layers[net->n - 1].activation = LINEAR;
-
- image delta = make_image(im.w, im.h, im.c);
-
- network_state state = {0};
-
-#ifdef GPU
- state.input = cuda_make_array(im.data, im.w*im.h*im.c);
- state.delta = cuda_make_array(im.data, im.w*im.h*im.c);
-
- forward_network_gpu(*net, state);
- copy_ongpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1);
-
- cuda_pull_array(last.delta_gpu, last.delta, last.outputs);
- calculate_loss(last.delta, last.delta, last.outputs, thresh);
- cuda_push_array(last.delta_gpu, last.delta, last.outputs);
-
- backward_network_gpu(*net, state);
-
- cuda_pull_array(state.delta, delta.data, im.w*im.h*im.c);
- cuda_free(state.input);
- cuda_free(state.delta);
-#else
- state.input = im.data;
- state.delta = delta.data;
- forward_network(*net, state);
- copy_cpu(last.outputs, last.output, 1, last.delta, 1);
- calculate_loss(last.output, last.delta, last.outputs, thresh);
- backward_network(*net, state);
-#endif
-
- if(flip) flip_image(delta);
- //normalize_array(delta.data, delta.w*delta.h*delta.c);
- image resized = resize_image(delta, orig.w, orig.h);
- image out = crop_image(resized, -dx, -dy, orig.w, orig.h);
-
- /*
- image g = grayscale_image(out);
- free_image(out);
- out = g;
- */
-
- //rate = rate / abs_mean(out.data, out.w*out.h*out.c);
-
- if(norm) normalize_array(out.data, out.w*out.h*out.c);
- axpy_cpu(orig.w*orig.h*orig.c, rate, out.data, 1, orig.data, 1);
-
- /*
- normalize_array(orig.data, orig.w*orig.h*orig.c);
- scale_image(orig, sqrt(var));
- translate_image(orig, mean);
- */
-
- //translate_image(orig, 1);
- //scale_image(orig, .5);
- //normalize_image(orig);
-
- constrain_image(orig);
-
- free_image(crop);
- free_image(im);
- free_image(delta);
- free_image(resized);
- free_image(out);
-
-}
-
-void smooth(image recon, image update, float lambda, int num)
-{
- int i, j, k;
- int ii, jj;
- for(k = 0; k < recon.c; ++k){
- for(j = 0; j < recon.h; ++j){
- for(i = 0; i < recon.w; ++i){
- int out_index = i + recon.w*(j + recon.h*k);
- for(jj = j-num; jj <= j + num && jj < recon.h; ++jj){
- if (jj < 0) continue;
- for(ii = i-num; ii <= i + num && ii < recon.w; ++ii){
- if (ii < 0) continue;
- int in_index = ii + recon.w*(jj + recon.h*k);
- update.data[out_index] += lambda * (recon.data[in_index] - recon.data[out_index]);
- }
- }
- }
- }
- }
-}
-
-void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters)
-{
- int iter = 0;
- for (iter = 0; iter < iters; ++iter) {
- image delta = make_image(recon.w, recon.h, recon.c);
-
- network_state state = {0};
-#ifdef GPU
- state.input = cuda_make_array(recon.data, recon.w*recon.h*recon.c);
- state.delta = cuda_make_array(delta.data, delta.w*delta.h*delta.c);
- state.truth = cuda_make_array(features, get_network_output_size(net));
-
- forward_network_gpu(net, state);
- backward_network_gpu(net, state);
-
- cuda_pull_array(state.delta, delta.data, delta.w*delta.h*delta.c);
-
- cuda_free(state.input);
- cuda_free(state.delta);
- cuda_free(state.truth);
-#else
- state.input = recon.data;
- state.delta = delta.data;
- state.truth = features;
-
- forward_network(net, state);
- backward_network(net, state);
-#endif
-
- axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1);
- smooth(recon, update, lambda, smooth_size);
-
- axpy_cpu(recon.w*recon.h*recon.c, rate, update.data, 1, recon.data, 1);
- scal_cpu(recon.w*recon.h*recon.c, momentum, update.data, 1);
-
- //float mag = mag_array(recon.data, recon.w*recon.h*recon.c);
- //scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1);
-
- constrain_image(recon);
- free_image(delta);
- }
-}
-
-
-void run_nightmare(int argc, char **argv)
-{
- srand(time(0));
- if(argc < 4){
- fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]);
- return;
- }
-
- char *cfg = argv[2];
- char *weights = argv[3];
- char *input = argv[4];
- int max_layer = atoi(argv[5]);
-
- int range = find_int_arg(argc, argv, "-range", 1);
- int norm = find_int_arg(argc, argv, "-norm", 1);
- int rounds = find_int_arg(argc, argv, "-rounds", 1);
- int iters = find_int_arg(argc, argv, "-iters", 10);
- int octaves = find_int_arg(argc, argv, "-octaves", 4);
- float zoom = find_float_arg(argc, argv, "-zoom", 1.);
- float rate = find_float_arg(argc, argv, "-rate", .04);
- float thresh = find_float_arg(argc, argv, "-thresh", 1.);
- float rotate = find_float_arg(argc, argv, "-rotate", 0);
- float momentum = find_float_arg(argc, argv, "-momentum", .9);
- float lambda = find_float_arg(argc, argv, "-lambda", .01);
- char *prefix = find_char_arg(argc, argv, "-prefix", 0);
- int reconstruct = find_arg(argc, argv, "-reconstruct");
- int smooth_size = find_int_arg(argc, argv, "-smooth", 1);
-
- network net = parse_network_cfg(cfg);
- load_weights(&net, weights);
- char *cfgbase = basecfg(cfg);
- char *imbase = basecfg(input);
-
- set_batch_network(&net, 1);
- image im = load_image_color(input, 0, 0);
- if(0){
- float scale = 1;
- if(im.w > 512 || im.h > 512){
- if(im.w > im.h) scale = 512.0/im.w;
- else scale = 512.0/im.h;
- }
- image resized = resize_image(im, scale*im.w, scale*im.h);
- free_image(im);
- im = resized;
- }
-
- float *features = 0;
- image update;
- if (reconstruct){
- resize_network(&net, im.w, im.h);
-
- int zz = 0;
- network_predict(net, im.data);
- image out_im = get_network_image(net);
- image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
- //flip_image(crop);
- image f_im = resize_image(crop, out_im.w, out_im.h);
- free_image(crop);
- printf("%d features\n", out_im.w*out_im.h*out_im.c);
-
-
- im = resize_image(im, im.w, im.h);
- f_im = resize_image(f_im, f_im.w, f_im.h);
- features = f_im.data;
-
- int i;
- for(i = 0; i < 14*14*512; ++i){
- features[i] += rand_uniform(-.19, .19);
- }
-
- free_image(im);
- im = make_random_image(im.w, im.h, im.c);
- update = make_image(im.w, im.h, im.c);
-
- }
-
- int e;
- int n;
- for(e = 0; e < rounds; ++e){
- fprintf(stderr, "Iteration: ");
- fflush(stderr);
- for(n = 0; n < iters; ++n){
- fprintf(stderr, "%d, ", n);
- fflush(stderr);
- if(reconstruct){
- reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
- //if ((n+1)%30 == 0) rate *= .5;
- show_image(im, "reconstruction");
-#ifdef OPENCV
- wait_key_cv(10);
-#endif
- }else{
- int layer = max_layer + rand()%range - range/2;
- int octave = rand()%octaves;
- optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm);
- }
- }
- fprintf(stderr, "done\n");
- if(0){
- image g = grayscale_image(im);
- free_image(im);
- im = g;
- }
- char buff[256];
- if (prefix){
- sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e);
- }else{
- sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e);
- }
- printf("%d %s\n", e, buff);
- save_image(im, buff);
- //show_image(im, buff);
- //wait_key_cv(0);
-
- if(rotate){
- image rot = rotate_image(im, rotate);
- free_image(im);
- im = rot;
- }
- image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom);
- image resized = resize_image(crop, im.w, im.h);
- free_image(im);
- free_image(crop);
- im = resized;
- }
-}
+
+#include "network.h"
+#include "parser.h"
+#include "blas.h"
+#include "utils.h"
+
+// ./darknet nightmare cfg/extractor.recon.cfg ~/trained/yolo-coco.conv frame6.png -reconstruct -iters 500 -i 3 -lambda .1 -rate .01 -smooth 2
+
+float abs_mean(float *x, int n)
+{
+ int i;
+ float sum = 0;
+ for (i = 0; i < n; ++i){
+ sum += fabs(x[i]);
+ }
+ return sum/n;
+}
+
+void calculate_loss(float *output, float *delta, int n, float thresh)
+{
+ int i;
+ float mean = mean_array(output, n);
+ float var = variance_array(output, n);
+ for(i = 0; i < n; ++i){
+ if(delta[i] > mean + thresh*sqrt(var)) delta[i] = output[i];
+ else delta[i] = 0;
+ }
+}
+
+void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm)
+{
+ //scale_image(orig, 2);
+ //translate_image(orig, -1);
+ net->n = max_layer + 1;
+
+ int dx = rand()%16 - 8;
+ int dy = rand()%16 - 8;
+ int flip = rand()%2;
+
+ image crop = crop_image(orig, dx, dy, orig.w, orig.h);
+ image im = resize_image(crop, (int)(orig.w * scale), (int)(orig.h * scale));
+ if(flip) flip_image(im);
+
+ resize_network(net, im.w, im.h);
+ layer last = net->layers[net->n-1];
+ //net->layers[net->n - 1].activation = LINEAR;
+
+ image delta = make_image(im.w, im.h, im.c);
+
+ network_state state = {0};
+
+#ifdef GPU
+ state.input = cuda_make_array(im.data, im.w*im.h*im.c);
+ state.delta = cuda_make_array(im.data, im.w*im.h*im.c);
+
+ forward_network_gpu(*net, state);
+ copy_ongpu(last.outputs, last.output_gpu, 1, last.delta_gpu, 1);
+
+ cuda_pull_array(last.delta_gpu, last.delta, last.outputs);
+ calculate_loss(last.delta, last.delta, last.outputs, thresh);
+ cuda_push_array(last.delta_gpu, last.delta, last.outputs);
+
+ backward_network_gpu(*net, state);
+
+ cuda_pull_array(state.delta, delta.data, im.w*im.h*im.c);
+ cuda_free(state.input);
+ cuda_free(state.delta);
+#else
+ state.input = im.data;
+ state.delta = delta.data;
+ forward_network(*net, state);
+ copy_cpu(last.outputs, last.output, 1, last.delta, 1);
+ calculate_loss(last.output, last.delta, last.outputs, thresh);
+ backward_network(*net, state);
+#endif
+
+ if(flip) flip_image(delta);
+ //normalize_array(delta.data, delta.w*delta.h*delta.c);
+ image resized = resize_image(delta, orig.w, orig.h);
+ image out = crop_image(resized, -dx, -dy, orig.w, orig.h);
+
+ /*
+ image g = grayscale_image(out);
+ free_image(out);
+ out = g;
+ */
+
+ //rate = rate / abs_mean(out.data, out.w*out.h*out.c);
+
+ if(norm) normalize_array(out.data, out.w*out.h*out.c);
+ axpy_cpu(orig.w*orig.h*orig.c, rate, out.data, 1, orig.data, 1);
+
+ /*
+ normalize_array(orig.data, orig.w*orig.h*orig.c);
+ scale_image(orig, sqrt(var));
+ translate_image(orig, mean);
+ */
+
+ //translate_image(orig, 1);
+ //scale_image(orig, .5);
+ //normalize_image(orig);
+
+ constrain_image(orig);
+
+ free_image(crop);
+ free_image(im);
+ free_image(delta);
+ free_image(resized);
+ free_image(out);
+
+}
+
+void smooth(image recon, image update, float lambda, int num)
+{
+ int i, j, k;
+ int ii, jj;
+ for(k = 0; k < recon.c; ++k){
+ for(j = 0; j < recon.h; ++j){
+ for(i = 0; i < recon.w; ++i){
+ int out_index = i + recon.w*(j + recon.h*k);
+ for(jj = j-num; jj <= j + num && jj < recon.h; ++jj){
+ if (jj < 0) continue;
+ for(ii = i-num; ii <= i + num && ii < recon.w; ++ii){
+ if (ii < 0) continue;
+ int in_index = ii + recon.w*(jj + recon.h*k);
+ update.data[out_index] += lambda * (recon.data[in_index] - recon.data[out_index]);
+ }
+ }
+ }
+ }
+ }
+}
+
+void reconstruct_picture(network net, float *features, image recon, image update, float rate, float momentum, float lambda, int smooth_size, int iters)
+{
+ int iter = 0;
+ for (iter = 0; iter < iters; ++iter) {
+ image delta = make_image(recon.w, recon.h, recon.c);
+
+ network_state state = {0};
+#ifdef GPU
+ state.input = cuda_make_array(recon.data, recon.w*recon.h*recon.c);
+ state.delta = cuda_make_array(delta.data, delta.w*delta.h*delta.c);
+ state.truth = cuda_make_array(features, get_network_output_size(net));
+
+ forward_network_gpu(net, state);
+ backward_network_gpu(net, state);
+
+ cuda_pull_array(state.delta, delta.data, delta.w*delta.h*delta.c);
+
+ cuda_free(state.input);
+ cuda_free(state.delta);
+ cuda_free(state.truth);
+#else
+ state.input = recon.data;
+ state.delta = delta.data;
+ state.truth = features;
+
+ forward_network(net, state);
+ backward_network(net, state);
+#endif
+
+ axpy_cpu(recon.w*recon.h*recon.c, 1, delta.data, 1, update.data, 1);
+ smooth(recon, update, lambda, smooth_size);
+
+ axpy_cpu(recon.w*recon.h*recon.c, rate, update.data, 1, recon.data, 1);
+ scal_cpu(recon.w*recon.h*recon.c, momentum, update.data, 1);
+
+ //float mag = mag_array(recon.data, recon.w*recon.h*recon.c);
+ //scal_cpu(recon.w*recon.h*recon.c, 600/mag, recon.data, 1);
+
+ constrain_image(recon);
+ free_image(delta);
+ }
+}
+
+
+void run_nightmare(int argc, char **argv)
+{
+ srand(time(0));
+ if(argc < 4){
+ fprintf(stderr, "usage: %s %s [cfg] [weights] [image] [layer] [options! (optional)]\n", argv[0], argv[1]);
+ return;
+ }
+
+ char *cfg = argv[2];
+ char *weights = argv[3];
+ char *input = argv[4];
+ int max_layer = atoi(argv[5]);
+
+ int range = find_int_arg(argc, argv, "-range", 1);
+ int norm = find_int_arg(argc, argv, "-norm", 1);
+ int rounds = find_int_arg(argc, argv, "-rounds", 1);
+ int iters = find_int_arg(argc, argv, "-iters", 10);
+ int octaves = find_int_arg(argc, argv, "-octaves", 4);
+ float zoom = find_float_arg(argc, argv, "-zoom", 1.);
+ float rate = find_float_arg(argc, argv, "-rate", .04);
+ float thresh = find_float_arg(argc, argv, "-thresh", 1.);
+ float rotate = find_float_arg(argc, argv, "-rotate", 0);
+ float momentum = find_float_arg(argc, argv, "-momentum", .9);
+ float lambda = find_float_arg(argc, argv, "-lambda", .01);
+ char *prefix = find_char_arg(argc, argv, "-prefix", 0);
+ int reconstruct = find_arg(argc, argv, "-reconstruct");
+ int smooth_size = find_int_arg(argc, argv, "-smooth", 1);
+
+ network net = parse_network_cfg(cfg);
+ load_weights(&net, weights);
+ char *cfgbase = basecfg(cfg);
+ char *imbase = basecfg(input);
+
+ set_batch_network(&net, 1);
+ image im = load_image_color(input, 0, 0);
+ if(0){
+ float scale = 1;
+ if(im.w > 512 || im.h > 512){
+ if(im.w > im.h) scale = 512.0/im.w;
+ else scale = 512.0/im.h;
+ }
+ image resized = resize_image(im, scale*im.w, scale*im.h);
+ free_image(im);
+ im = resized;
+ }
+
+ float *features = 0;
+ image update;
+ if (reconstruct){
+ resize_network(&net, im.w, im.h);
+
+ int zz = 0;
+ network_predict(net, im.data);
+ image out_im = get_network_image(net);
+ image crop = crop_image(out_im, zz, zz, out_im.w-2*zz, out_im.h-2*zz);
+ //flip_image(crop);
+ image f_im = resize_image(crop, out_im.w, out_im.h);
+ free_image(crop);
+ printf("%d features\n", out_im.w*out_im.h*out_im.c);
+
+
+ im = resize_image(im, im.w, im.h);
+ f_im = resize_image(f_im, f_im.w, f_im.h);
+ features = f_im.data;
+
+ int i;
+ for(i = 0; i < 14*14*512; ++i){
+ features[i] += rand_uniform(-.19, .19);
+ }
+
+ free_image(im);
+ im = make_random_image(im.w, im.h, im.c);
+ update = make_image(im.w, im.h, im.c);
+
+ }
+
+ int e;
+ int n;
+ for(e = 0; e < rounds; ++e){
+ fprintf(stderr, "Iteration: ");
+ fflush(stderr);
+ for(n = 0; n < iters; ++n){
+ fprintf(stderr, "%d, ", n);
+ fflush(stderr);
+ if(reconstruct){
+ reconstruct_picture(net, features, im, update, rate, momentum, lambda, smooth_size, 1);
+ //if ((n+1)%30 == 0) rate *= .5;
+ show_image(im, "reconstruction");
+#ifdef OPENCV
+ wait_key_cv(10);
+#endif
+ }else{
+ int layer = max_layer + rand()%range - range/2;
+ int octave = rand()%octaves;
+ optimize_picture(&net, im, layer, 1/pow(1.33333333, octave), rate, thresh, norm);
+ }
+ }
+ fprintf(stderr, "done\n");
+ if(0){
+ image g = grayscale_image(im);
+ free_image(im);
+ im = g;
+ }
+ char buff[256];
+ if (prefix){
+ sprintf(buff, "%s/%s_%s_%d_%06d",prefix, imbase, cfgbase, max_layer, e);
+ }else{
+ sprintf(buff, "%s_%s_%d_%06d",imbase, cfgbase, max_layer, e);
+ }
+ printf("%d %s\n", e, buff);
+ save_image(im, buff);
+ //show_image(im, buff);
+ //wait_key_cv(0);
+
+ if(rotate){
+ image rot = rotate_image(im, rotate);
+ free_image(im);
+ im = rot;
+ }
+ image crop = crop_image(im, im.w * (1. - zoom)/2., im.h * (1.-zoom)/2., im.w*zoom, im.h*zoom);
+ image resized = resize_image(crop, im.w, im.h);
+ free_image(im);
+ free_image(crop);
+ im = resized;
+ }
+}
--
Gitblit v1.8.0