From 0305fb4d99cf1efc7d4aa4d2ee2d65d54500d437 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 26 Nov 2015 19:48:01 +0000
Subject: [PATCH] Some changes
---
src/network.c | 12 +
src/yolo.c | 36 ---
src/network.h | 1
Makefile | 2
src/yolo_kernels.cu | 4
src/image.c | 77 +++++++-
src/coco_kernels.cu | 40 +++-
src/coco.c | 48 ++--
src/activations.h | 4
src/activation_kernels.cu | 6
src/activations.c | 7
cfg/yolo-coco.cfg | 240 ++++++++++++++++++++++++++
src/image.h | 2
src/layer.h | 7
14 files changed, 397 insertions(+), 89 deletions(-)
diff --git a/Makefile b/Makefile
index 1b6aa80..d5c75e0 100644
--- a/Makefile
+++ b/Makefile
@@ -36,7 +36,7 @@
OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o layer.o compare.o classifier.o local_layer.o
ifeq ($(GPU), 1)
-OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o yolo_kernels.o
+OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o yolo_kernels.o coco_kernels.o
endif
OBJS = $(addprefix $(OBJDIR), $(OBJ))
diff --git a/cfg/yolo-coco.cfg b/cfg/yolo-coco.cfg
new file mode 100644
index 0000000..0c13a31
--- /dev/null
+++ b/cfg/yolo-coco.cfg
@@ -0,0 +1,240 @@
+[net]
+batch=64
+subdivisions=4
+height=448
+width=448
+channels=3
+momentum=0.9
+decay=0.0005
+
+learning_rate=0.0001
+policy=steps
+steps=100,200,300,100000,150000
+scales=2.5,2,2,.1,.1
+max_batches = 300000
+
+[crop]
+crop_width=448
+crop_height=448
+flip=0
+angle=0
+saturation = 1.5
+exposure = 1.5
+
+[convolutional]
+filters=64
+size=7
+stride=2
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=192
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+
+#################################
+
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=2
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[local]
+size=3
+stride=1
+pad=1
+filters=192
+activation=leaky
+
+[dropout]
+probability=.5
+
+[connected]
+output= 4410
+activation=linear
+
+[detection]
+classes=80
+coords=4
+rescore=1
+side=7
+num=2
+softmax=0
+sqrt=1
+jitter=.2
+
+object_scale=1
+noobject_scale=.5
+class_scale=1
+coord_scale=5
+
diff --git a/src/activation_kernels.cu b/src/activation_kernels.cu
index 0ab9fd9..d5607da 100644
--- a/src/activation_kernels.cu
+++ b/src/activation_kernels.cu
@@ -10,6 +10,7 @@
__device__ float linear_activate_kernel(float x){return x;}
__device__ float logistic_activate_kernel(float x){return 1./(1. + exp(-x));}
__device__ float relu_activate_kernel(float x){return x*(x>0);}
+__device__ float elu_activate_kernel(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);}
__device__ float relie_activate_kernel(float x){return x*(x>0);}
__device__ float ramp_activate_kernel(float x){return x*(x>0)+.1*x;}
__device__ float leaky_activate_kernel(float x){return (x>0) ? x : .1*x;}
@@ -24,6 +25,7 @@
__device__ float linear_gradient_kernel(float x){return 1;}
__device__ float logistic_gradient_kernel(float x){return (1-x)*x;}
__device__ float relu_gradient_kernel(float x){return (x>0);}
+__device__ float elu_gradient_kernel(float x){return (x >= 0) + (x < 0)*(x + 1);}
__device__ float relie_gradient_kernel(float x){return (x>0) ? 1 : .01;}
__device__ float ramp_gradient_kernel(float x){return (x>0)+.1;}
__device__ float leaky_gradient_kernel(float x){return (x>0) ? 1 : .1;}
@@ -39,6 +41,8 @@
return logistic_activate_kernel(x);
case RELU:
return relu_activate_kernel(x);
+ case ELU:
+ return elu_activate_kernel(x);
case RELIE:
return relie_activate_kernel(x);
case RAMP:
@@ -62,6 +66,8 @@
return logistic_gradient_kernel(x);
case RELU:
return relu_gradient_kernel(x);
+ case ELU:
+ return elu_gradient_kernel(x);
case RELIE:
return relie_gradient_kernel(x);
case RAMP:
diff --git a/src/activations.c b/src/activations.c
index d31b1e4..5a62ef5 100644
--- a/src/activations.c
+++ b/src/activations.c
@@ -12,6 +12,8 @@
return "logistic";
case RELU:
return "relu";
+ case ELU:
+ return "elu";
case RELIE:
return "relie";
case RAMP:
@@ -34,6 +36,7 @@
{
if (strcmp(s, "logistic")==0) return LOGISTIC;
if (strcmp(s, "relu")==0) return RELU;
+ if (strcmp(s, "elu")==0) return ELU;
if (strcmp(s, "relie")==0) return RELIE;
if (strcmp(s, "plse")==0) return PLSE;
if (strcmp(s, "linear")==0) return LINEAR;
@@ -53,6 +56,8 @@
return logistic_activate(x);
case RELU:
return relu_activate(x);
+ case ELU:
+ return elu_activate(x);
case RELIE:
return relie_activate(x);
case RAMP:
@@ -84,6 +89,8 @@
return logistic_gradient(x);
case RELU:
return relu_gradient(x);
+ case ELU:
+ return elu_gradient(x);
case RELIE:
return relie_gradient(x);
case RAMP:
diff --git a/src/activations.h b/src/activations.h
index 22a713a..d824d1e 100644
--- a/src/activations.h
+++ b/src/activations.h
@@ -4,7 +4,7 @@
#include "math.h"
typedef enum{
- LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY
+ LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU
}ACTIVATION;
ACTIVATION get_activation(char *s);
@@ -22,6 +22,7 @@
static inline float linear_activate(float x){return x;}
static inline float logistic_activate(float x){return 1./(1. + exp(-x));}
static inline float relu_activate(float x){return x*(x>0);}
+static inline float elu_activate(float x){return (x >= 0)*x + (x < 0)*(exp(x)-1);}
static inline float relie_activate(float x){return x*(x>0);}
static inline float ramp_activate(float x){return x*(x>0)+.1*x;}
static inline float leaky_activate(float x){return (x>0) ? x : .1*x;}
@@ -36,6 +37,7 @@
static inline float linear_gradient(float x){return 1;}
static inline float logistic_gradient(float x){return (1-x)*x;}
static inline float relu_gradient(float x){return (x>0);}
+static inline float elu_gradient(float x){return (x >= 0) + (x < 0)*(x + 1);}
static inline float relie_gradient(float x){return (x>0) ? 1 : .01;}
static inline float ramp_gradient(float x){return (x>0)+.1;}
static inline float leaky_gradient(float x){return (x>0) ? 1 : .1;}
diff --git a/src/coco.c b/src/coco.c
index cef6ade..17d0654 100644
--- a/src/coco.c
+++ b/src/coco.c
@@ -15,30 +15,7 @@
int coco_ids[] = {1,2,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19,20,21,22,23,24,25,27,28,31,32,33,34,35,36,37,38,39,40,41,42,43,44,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,67,70,72,73,74,75,76,77,78,79,80,81,82,84,85,86,87,88,89,90};
-void draw_coco(image im, int num, float thresh, box *boxes, float **probs)
-{
- int classes = 80;
- int i;
-
- for(i = 0; i < num; ++i){
- int class = max_index(probs[i], classes);
- float prob = probs[i][class];
- if(prob > thresh){
- int width = sqrt(prob)*5 + 1;
- printf("%f %s\n", prob, coco_classes[class]);
- float red = get_color(0,class,classes);
- float green = get_color(1,class,classes);
- float blue = get_color(2,class,classes);
- box b = boxes[i];
-
- int left = (b.x-b.w/2.)*im.w;
- int right = (b.x+b.w/2.)*im.w;
- int top = (b.y-b.h/2.)*im.h;
- int bot = (b.y+b.h/2.)*im.h;
- draw_box_width(im, left, top, right, bot, width, red, green, blue);
- }
- }
-}
+image coco_labels[80];
void train_coco(char *cfgfile, char *weightfile)
{
@@ -368,6 +345,7 @@
detection_layer l = net.layers[net.n-1];
set_batch_network(&net, 1);
srand(2222222);
+ float nms = .4;
clock_t time;
char buff[256];
char *input = buff;
@@ -392,7 +370,8 @@
float *predictions = network_predict(net, X);
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
convert_coco_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
- draw_coco(im, l.side*l.side*l.n, thresh, boxes, probs);
+ if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
+ draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, coco_labels, 80);
show_image(im, "predictions");
show_image(sized, "resized");
@@ -406,9 +385,23 @@
}
}
+#ifdef OPENCV
+#ifdef GPU
+void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index);
+#endif
+#endif
+
void run_coco(int argc, char **argv)
{
+ int i;
+ for(i = 0; i < 80; ++i){
+ char buff[256];
+ sprintf(buff, "data/labels/%s.png", coco_classes[i]);
+ coco_labels[i] = load_image_color(buff, 0, 0);
+ }
float thresh = find_float_arg(argc, argv, "-thresh", .2);
+ int cam_index = find_int_arg(argc, argv, "-c", 0);
+
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
return;
@@ -421,4 +414,9 @@
else if(0==strcmp(argv[2], "train")) train_coco(cfg, weights);
else if(0==strcmp(argv[2], "valid")) validate_coco(cfg, weights);
else if(0==strcmp(argv[2], "recall")) validate_coco_recall(cfg, weights);
+#ifdef OPENCV
+#ifdef GPU
+ else if(0==strcmp(argv[2], "demo")) demo_coco(cfg, weights, thresh, cam_index);
+#endif
+#endif
}
diff --git a/src/coco_kernels.cu b/src/coco_kernels.cu
index a3b4875..2ec0915 100644
--- a/src/coco_kernels.cu
+++ b/src/coco_kernels.cu
@@ -10,6 +10,7 @@
#include "parser.h"
#include "box.h"
#include "image.h"
+#include <sys/time.h>
}
#ifdef OPENCV
@@ -17,7 +18,9 @@
#include "opencv2/imgproc/imgproc.hpp"
extern "C" image ipl_to_image(IplImage* src);
extern "C" void convert_coco_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
-extern "C" void draw_coco(image im, int num, float thresh, box *boxes, float **probs);
+
+extern "C" char *coco_classes[];
+extern "C" image *coco_labels;
static float **probs;
static box *boxes;
@@ -27,9 +30,10 @@
static image det ;
static image det_s;
static image disp ;
-static cv::VideoCapture cap(0);
+static cv::VideoCapture cap;
+static float fps = 0;
-void *fetch_in_thread(void *ptr)
+void *fetch_in_thread_coco(void *ptr)
{
cv::Mat frame_m;
cap >> frame_m;
@@ -40,7 +44,7 @@
return 0;
}
-void *detect_in_thread(void *ptr)
+void *detect_in_thread_coco(void *ptr)
{
float nms = .4;
float thresh = .2;
@@ -53,12 +57,13 @@
if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
- printf("\nObjects:\n\n");
- draw_coco(det, l.side*l.side*l.n, thresh, boxes, probs);
+ printf("\nFPS:%.0f\n",fps);
+ printf("Objects:\n\n");
+ draw_detections(det, l.side*l.side*l.n, thresh, boxes, probs, coco_classes, coco_labels, 80);
return 0;
}
-extern "C" void demo_coco(char *cfgfile, char *weightfile, float thresh)
+extern "C" void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index)
{
printf("YOLO demo\n");
net = parse_network_cfg(cfgfile);
@@ -69,6 +74,8 @@
srand(2222222);
+ cv::VideoCapture cam(cam_index);
+ cap = cam;
if(!cap.isOpened()) error("Couldn't connect to webcam.\n");
detection_layer l = net.layers[net.n-1];
@@ -81,19 +88,21 @@
pthread_t fetch_thread;
pthread_t detect_thread;
- fetch_in_thread(0);
+ fetch_in_thread_coco(0);
det = in;
det_s = in_s;
- fetch_in_thread(0);
- detect_in_thread(0);
+ fetch_in_thread_coco(0);
+ detect_in_thread_coco(0);
disp = det;
det = in;
det_s = in_s;
while(1){
- if(pthread_create(&fetch_thread, 0, fetch_in_thread, 0)) error("Thread creation failed");
- if(pthread_create(&detect_thread, 0, detect_in_thread, 0)) error("Thread creation failed");
+ struct timeval tval_before, tval_after, tval_result;
+ gettimeofday(&tval_before, NULL);
+ if(pthread_create(&fetch_thread, 0, fetch_in_thread_coco, 0)) error("Thread creation failed");
+ if(pthread_create(&detect_thread, 0, detect_in_thread_coco, 0)) error("Thread creation failed");
show_image(disp, "YOLO");
free_image(disp);
cvWaitKey(1);
@@ -103,10 +112,15 @@
disp = det;
det = in;
det_s = in_s;
+
+ gettimeofday(&tval_after, NULL);
+ timersub(&tval_after, &tval_before, &tval_result);
+ float curr = 1000000.f/((long int)tval_result.tv_usec);
+ fps = .9*fps + .1*curr;
}
}
#else
-extern "C" void demo_coco(char *cfgfile, char *weightfile, float thresh){
+extern "C" void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index){
fprintf(stderr, "YOLO-COCO demo needs OpenCV for webcam images.\n");
}
#endif
diff --git a/src/image.c b/src/image.c
index 8497032..a8a6684 100644
--- a/src/image.c
+++ b/src/image.c
@@ -28,6 +28,26 @@
return r;
}
+void draw_label(image a, int r, int c, image label, const float *rgb)
+{
+ float ratio = (float) label.w / label.h;
+ int h = label.h;
+ int w = ratio * h;
+ image rl = resize_image(label, w, h);
+ if (r - h >= 0) r = r - h;
+
+ int i, j, k;
+ for(j = 0; j < h && j + r < a.h; ++j){
+ for(i = 0; i < w && i + c < a.w; ++i){
+ for(k = 0; k < label.c; ++k){
+ float val = get_pixel(rl, i, j, k);
+ set_pixel(a, i+c, j+r, k, rgb[k] * val);
+ }
+ }
+ }
+ free_image(rl);
+}
+
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b)
{
//normalize_image(a);
@@ -42,25 +62,25 @@
if(y2 < 0) y2 = 0;
if(y2 >= a.h) y2 = a.h-1;
- for(i = x1; i < x2; ++i){
- a.data[i + y1*a.w + 0*a.w*a.h] = b;
- a.data[i + y2*a.w + 0*a.w*a.h] = b;
+ for(i = x1; i <= x2; ++i){
+ a.data[i + y1*a.w + 0*a.w*a.h] = r;
+ a.data[i + y2*a.w + 0*a.w*a.h] = r;
a.data[i + y1*a.w + 1*a.w*a.h] = g;
a.data[i + y2*a.w + 1*a.w*a.h] = g;
- a.data[i + y1*a.w + 2*a.w*a.h] = r;
- a.data[i + y2*a.w + 2*a.w*a.h] = r;
+ a.data[i + y1*a.w + 2*a.w*a.h] = b;
+ a.data[i + y2*a.w + 2*a.w*a.h] = b;
}
- for(i = y1; i < y2; ++i){
- a.data[x1 + i*a.w + 0*a.w*a.h] = b;
- a.data[x2 + i*a.w + 0*a.w*a.h] = b;
+ for(i = y1; i <= y2; ++i){
+ a.data[x1 + i*a.w + 0*a.w*a.h] = r;
+ a.data[x2 + i*a.w + 0*a.w*a.h] = r;
a.data[x1 + i*a.w + 1*a.w*a.h] = g;
a.data[x2 + i*a.w + 1*a.w*a.h] = g;
- a.data[x1 + i*a.w + 2*a.w*a.h] = r;
- a.data[x2 + i*a.w + 2*a.w*a.h] = r;
+ a.data[x1 + i*a.w + 2*a.w*a.h] = b;
+ a.data[x2 + i*a.w + 2*a.w*a.h] = b;
}
}
@@ -85,6 +105,43 @@
}
}
+void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image *labels, int classes)
+{
+ int i;
+
+ for(i = 0; i < num; ++i){
+ int class = max_index(probs[i], classes);
+ float prob = probs[i][class];
+ if(prob > thresh){
+ int width = pow(prob, 1./2.)*10+1;
+ printf("%s: %.2f\n", names[class], prob);
+ int offset = class*17 % classes;
+ float red = get_color(0,offset,classes);
+ float green = get_color(1,offset,classes);
+ float blue = get_color(2,offset,classes);
+ float rgb[3];
+ rgb[0] = red;
+ rgb[1] = green;
+ rgb[2] = blue;
+ box b = boxes[i];
+
+ int left = (b.x-b.w/2.)*im.w;
+ int right = (b.x+b.w/2.)*im.w;
+ int top = (b.y-b.h/2.)*im.h;
+ int bot = (b.y+b.h/2.)*im.h;
+
+ if(left < 0) left = 0;
+ if(right > im.w-1) right = im.w-1;
+ if(top < 0) top = 0;
+ if(bot > im.h-1) bot = im.h-1;
+
+ draw_box_width(im, left, top, right, bot, width, red, green, blue);
+ if (labels) draw_label(im, top + width, left, labels[class], rgb);
+ }
+ }
+}
+
+
void flip_image(image a)
{
int i,j,k;
diff --git a/src/image.h b/src/image.h
index 336cfa1..c3e1a78 100644
--- a/src/image.h
+++ b/src/image.h
@@ -20,6 +20,8 @@
void draw_box(image a, int x1, int y1, int x2, int y2, float r, float g, float b);
void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b);
void draw_bbox(image a, box bbox, int w, float r, float g, float b);
+void draw_label(image a, int r, int c, image label, const float *rgb);
+void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image *labels, int classes);
image image_distance(image a, image b);
void scale_image(image m, float s);
image crop_image(image im, int dx, int dy, int w, int h);
diff --git a/src/layer.h b/src/layer.h
index 2a74437..b3ab627 100644
--- a/src/layer.h
+++ b/src/layer.h
@@ -130,11 +130,12 @@
float * x_gpu;
float * x_norm_gpu;
float * weights_gpu;
- float * biases_gpu;
- float * scales_gpu;
-
float * weight_updates_gpu;
+
+ float * biases_gpu;
float * bias_updates_gpu;
+
+ float * scales_gpu;
float * scale_updates_gpu;
float * output_gpu;
diff --git a/src/network.c b/src/network.c
index 6c7461d..d9585c4 100644
--- a/src/network.c
+++ b/src/network.c
@@ -26,6 +26,17 @@
return batch_num;
}
+void reset_momentum(network net)
+{
+ if (net.momentum == 0) return;
+ net.learning_rate = 0;
+ net.momentum = 0;
+ net.decay = 0;
+ #ifdef GPU
+ if(gpu_index >= 0) update_network_gpu(net);
+ #endif
+}
+
float get_current_rate(network net)
{
int batch_num = get_current_batch(net);
@@ -41,6 +52,7 @@
for(i = 0; i < net.num_steps; ++i){
if(net.steps[i] > batch_num) return rate;
rate *= net.scales[i];
+ if(net.steps[i] > batch_num - 1) reset_momentum(net);
}
return rate;
case EXP:
diff --git a/src/network.h b/src/network.h
index 0ad16ff..428ff52 100644
--- a/src/network.h
+++ b/src/network.h
@@ -51,6 +51,7 @@
float *get_network_output_gpu(network net);
void forward_network_gpu(network net, network_state state);
void backward_network_gpu(network net, network_state state);
+void update_network_gpu(network net);
#endif
float get_current_rate(network net);
diff --git a/src/yolo.c b/src/yolo.c
index 80d85af..86b132b 100644
--- a/src/yolo.c
+++ b/src/yolo.c
@@ -11,40 +11,6 @@
char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
-void draw_yolo(image im, int num, float thresh, box *boxes, float **probs)
-{
- int classes = 20;
- int i;
-
- for(i = 0; i < num; ++i){
- int class = max_index(probs[i], classes);
- float prob = probs[i][class];
- if(prob > thresh){
- int width = pow(prob, 1./2.)*10+1;
- width = 8;
- printf("%s: %.2f\n", voc_names[class], prob);
- class = class * 7 % 20;
- float red = get_color(0,class,classes);
- float green = get_color(1,class,classes);
- float blue = get_color(2,class,classes);
- //red = green = blue = 0;
- box b = boxes[i];
-
- int left = (b.x-b.w/2.)*im.w;
- int right = (b.x+b.w/2.)*im.w;
- int top = (b.y-b.h/2.)*im.h;
- int bot = (b.y+b.h/2.)*im.h;
-
- if(left < 0) left = 0;
- if(right > im.w-1) right = im.w-1;
- if(top < 0) top = 0;
- if(bot > im.h-1) bot = im.h-1;
-
- draw_box_width(im, left, top, right, bot, width, red, green, blue);
- }
- }
-}
-
void train_yolo(char *cfgfile, char *weightfile)
{
char *train_images = "data/voc.0712.trainval";
@@ -377,7 +343,7 @@
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
convert_yolo_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, thresh, probs, boxes, 0);
if (nms) do_nms_sort(boxes, probs, l.side*l.side*l.n, l.classes, nms);
- draw_yolo(im, l.side*l.side*l.n, thresh, boxes, probs);
+ draw_detections(im, l.side*l.side*l.n, thresh, boxes, probs, voc_names, 0, 20);
show_image(im, "predictions");
show_image(sized, "resized");
diff --git a/src/yolo_kernels.cu b/src/yolo_kernels.cu
index 86cdc53..78fedaf 100644
--- a/src/yolo_kernels.cu
+++ b/src/yolo_kernels.cu
@@ -20,6 +20,8 @@
extern "C" void convert_yolo_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
extern "C" void draw_yolo(image im, int num, float thresh, box *boxes, float **probs);
+extern "C" char *voc_names[];
+
static float **probs;
static box *boxes;
static network net;
@@ -57,7 +59,7 @@
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
printf("Objects:\n\n");
- draw_yolo(det, l.side*l.side*l.n, thresh, boxes, probs);
+ draw_detections(det, l.side*l.side*l.n, thresh, boxes, probs, voc_names, 0, 20);
return 0;
}
--
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