mirror/darknet
1
0
Fork 0
mirror of https://github.com/pjreddie/darknet.git synced 2023-08-10 21:13:14 +03:00
Code Issues Projects Releases Wiki Activity

rolling avg demo

Browse Source
This commit is contained in:
Joseph Redmon 2015-11-30 15:04:09 -08:00
parent 2774cd86d4
commit e7d43fd65d
7 changed files with 66 additions and 250 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
cfg/darknet.cfg
View File

@ -7,11 +7,10 @@ channels=3
momentum=0.9
decay=0.0005
learning_rate=0.01
policy=sigmoid
gamma=.00002
step=400000
max_batches=800000
learning_rate=0.1
policy=poly
power=4
max_batches=500000
[crop]
crop_height=224
@ -22,6 +21,7 @@ saturation=1
exposure=1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
@ -33,6 +33,7 @@ size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
@ -44,6 +45,7 @@ size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
@ -55,6 +57,7 @@ size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
@ -66,6 +69,7 @@ size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
@ -77,6 +81,7 @@ size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
@ -88,6 +93,7 @@ size=2
stride=2
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1

BIN
data/labels/.make_labels.py.swp
View File

Binary file not shown.

21
src/coco.c
View File

@ -385,11 +385,15 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
}
}
#ifdef OPENCV
#ifdef GPU
void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index);
#endif
#endif
void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index, char *filename);
static void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, char* filename)
{
#if defined(OPENCV) && defined(GPU)
demo_coco(cfgfile, weightfile, thresh, cam_index, filename);
#else
fprintf(stderr, "Need to compile with GPU and OpenCV for demo.\n");
#endif
}
void run_coco(int argc, char **argv)
{
@ -401,6 +405,7 @@ void run_coco(int argc, char **argv)
}
float thresh = find_float_arg(argc, argv, "-thresh", .2);
int cam_index = find_int_arg(argc, argv, "-c", 0);
char *file = find_char_arg(argc, argv, "-file", 0);
if(argc < 4){
fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
@ -414,9 +419,5 @@ void run_coco(int argc, char **argv)
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
else if(0==strcmp(argv[2], "demo")) demo(cfg, weights, thresh, cam_index, file);
}

33
src/coco_kernels.cu
View File

@ -34,6 +34,12 @@ static cv::VideoCapture cap;
static float fps = 0;
static float demo_thresh = 0;
static const int frames = 3;
static float *predictions[frames];
static int demo_index = 0;
static image images[frames];
static float *avg;
void *fetch_in_thread_coco(void *ptr)
{
cv::Mat frame_m;
@ -51,19 +57,28 @@ void *detect_in_thread_coco(void *ptr)
detection_layer l = net.layers[net.n-1];
float *X = det_s.data;
float *predictions = network_predict(net, X);
float *prediction = network_predict(net, X);
memcpy(predictions[demo_index], prediction, l.outputs*sizeof(float));
mean_arrays(predictions, frames, l.outputs, avg);
free_image(det_s);
convert_coco_detections(predictions, l.classes, l.n, l.sqrt, l.side, 1, 1, demo_thresh, probs, boxes, 0);
convert_coco_detections(avg, l.classes, l.n, l.sqrt, l.side, 1, 1, demo_thresh, probs, boxes, 0);
if (nms > 0) do_nms(boxes, probs, l.side*l.side*l.n, l.classes, nms);
printf("\033[2J");
printf("\033[1;1H");
printf("\nFPS:%.0f\n",fps);
printf("Objects:\n\n");
images[demo_index] = det;
det = images[(demo_index + frames/2 + 1)%frames];
demo_index = (demo_index + 1)%frames;
draw_detections(det, l.side*l.side*l.n, demo_thresh, boxes, probs, coco_classes, coco_labels, 80);
return 0;
}
extern "C" void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index)
extern "C" void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename)
{
demo_thresh = thresh;
printf("YOLO demo\n");
@ -75,13 +90,21 @@ extern "C" void demo_coco(char *cfgfile, char *weightfile, float thresh, int cam
srand(2222222);
cv::VideoCapture cam(cam_index);
cap = cam;
if(filename){
cap.open(filename);
}else{
cap.open(cam_index);
}
if(!cap.isOpened()) error("Couldn't connect to webcam.\n");
detection_layer l = net.layers[net.n-1];
int j;
avg = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < frames; ++j) predictions[j] = (float *) calloc(l.outputs, sizeof(float));
for(j = 0; j < frames; ++j) images[j] = make_image(1,1,3);
boxes = (box *)calloc(l.side*l.side*l.n, sizeof(box));
probs = (float **)calloc(l.side*l.side*l.n, sizeof(float *));
for(j = 0; j < l.side*l.side*l.n; ++j) probs[j] = (float *)calloc(l.classes, sizeof(float *));

230
src/local_kernels.cu
View File

@ -1,230 +0,0 @@
#include "cuda_runtime.h"
#include "curand.h"
#include "cublas_v2.h"
extern "C" {
#include "local_layer.h"
#include "gemm.h"
#include "blas.h"
#include "im2col.h"
#include "col2im.h"
#include "utils.h"
#include "cuda.h"
}
__global__ void scale_bias_kernel(float *output, float *biases, int n, int size)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x;
int filter = blockIdx.y;
int batch = blockIdx.z;
if(offset < size) output[(batch*n+filter)*size + offset] *= biases[filter];
}
void scale_bias_gpu(float *output, float *biases, int batch, int n, int size)
{
dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
dim3 dimBlock(BLOCK, 1, 1);
scale_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
check_error(cudaPeekAtLastError());
}
__global__ void backward_scale_kernel(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
{
__shared__ float part[BLOCK];
int i,b;
int filter = blockIdx.x;
int p = threadIdx.x;
float sum = 0;
for(b = 0; b < batch; ++b){
for(i = 0; i < size; i += BLOCK){
int index = p + i + size*(filter + n*b);
sum += (p+i < size) ? delta[index]*x_norm[index] : 0;
}
}
part[p] = sum;
__syncthreads();
if (p == 0) {
for(i = 0; i < BLOCK; ++i) scale_updates[filter] += part[i];
}
}
void backward_scale_gpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
{
backward_scale_kernel<<<n, BLOCK>>>(x_norm, delta, batch, n, size, scale_updates);
check_error(cudaPeekAtLastError());
}
__global__ void add_bias_kernel(float *output, float *biases, int n, int size)
{
int offset = blockIdx.x * blockDim.x + threadIdx.x;
int filter = blockIdx.y;
int batch = blockIdx.z;
if(offset < size) output[(batch*n+filter)*size + offset] += biases[filter];
}
void add_bias_gpu(float *output, float *biases, int batch, int n, int size)
{
dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
dim3 dimBlock(BLOCK, 1, 1);
add_bias_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);
check_error(cudaPeekAtLastError());
}
__global__ void backward_bias_kernel(float *bias_updates, float *delta, int batch, int n, int size)
{
__shared__ float part[BLOCK];
int i,b;
int filter = blockIdx.x;
int p = threadIdx.x;
float sum = 0;
for(b = 0; b < batch; ++b){
for(i = 0; i < size; i += BLOCK){
int index = p + i + size*(filter + n*b);
sum += (p+i < size) ? delta[index] : 0;
}
}
part[p] = sum;
__syncthreads();
if (p == 0) {
for(i = 0; i < BLOCK; ++i) bias_updates[filter] += part[i];
}
}
void backward_bias_gpu(float *bias_updates, float *delta, int batch, int n, int size)
{
backward_bias_kernel<<<n, BLOCK>>>(bias_updates, delta, batch, n, size);
check_error(cudaPeekAtLastError());
}
void forward_local_layer_gpu(local_layer l, network_state state)
{
int i;
int m = l.n;
int k = l.size*l.size*l.c;
int n = local_out_height(l)*
local_out_width(l);
fill_ongpu(l.outputs*l.batch, 0, l.output_gpu, 1);
for(i = 0; i < l.batch; ++i){
im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu);
float * a = l.filters_gpu;
float * b = l.col_image_gpu;
float * c = l.output_gpu;
gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c+i*m*n,n);
}
if(l.batch_normalize){
if(state.train){
fast_mean_gpu(l.output_gpu, l.batch, l.n, l.out_h*l.out_w, l.spatial_mean_gpu, l.mean_gpu);
fast_variance_gpu(l.output_gpu, l.mean_gpu, l.batch, l.n, l.out_h*l.out_w, l.spatial_variance_gpu, l.variance_gpu);
scal_ongpu(l.n, .95, l.rolling_mean_gpu, 1);
axpy_ongpu(l.n, .05, l.mean_gpu, 1, l.rolling_mean_gpu, 1);
scal_ongpu(l.n, .95, l.rolling_variance_gpu, 1);
axpy_ongpu(l.n, .05, l.variance_gpu, 1, l.rolling_variance_gpu, 1);
// cuda_pull_array(l.variance_gpu, l.mean, l.n);
// printf("%f\n", l.mean[0]);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_gpu, 1);
normalize_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_h*l.out_w);
copy_ongpu(l.outputs*l.batch, l.output_gpu, 1, l.x_norm_gpu, 1);
} else {
normalize_gpu(l.output_gpu, l.rolling_mean_gpu, l.rolling_variance_gpu, l.batch, l.n, l.out_h*l.out_w);
}
scale_bias_gpu(l.output_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w);
}
add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.n, n);
activate_array_ongpu(l.output_gpu, m*n*l.batch, l.activation);
}
void backward_local_layer_gpu(local_layer l, network_state state)
{
int i;
int m = l.n;
int n = l.size*l.size*l.c;
int k = local_out_height(l)*
local_out_width(l);
gradient_array_ongpu(l.output_gpu, m*k*l.batch, l.activation, l.delta_gpu);
backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.n, k);
if(l.batch_normalize){
backward_scale_gpu(l.x_norm_gpu, l.delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.scale_updates_gpu);
scale_bias_gpu(l.delta_gpu, l.scales_gpu, l.batch, l.n, l.out_h*l.out_w);
fast_mean_delta_gpu(l.delta_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.spatial_mean_delta_gpu, l.mean_delta_gpu);
fast_variance_delta_gpu(l.x_gpu, l.delta_gpu, l.mean_gpu, l.variance_gpu, l.batch, l.n, l.out_w*l.out_h, l.spatial_variance_delta_gpu, l.variance_delta_gpu);
normalize_delta_gpu(l.x_gpu, l.mean_gpu, l.variance_gpu, l.mean_delta_gpu, l.variance_delta_gpu, l.batch, l.n, l.out_w*l.out_h, l.delta_gpu);
}
for(i = 0; i < l.batch; ++i){
float * a = l.delta_gpu;
float * b = l.col_image_gpu;
float * c = l.filter_updates_gpu;
im2col_ongpu(state.input + i*l.c*l.h*l.w, l.c, l.h, l.w, l.size, l.stride, l.pad, l.col_image_gpu);
gemm_ongpu(0,1,m,n,k,1,a + i*m*k,k,b,k,1,c,n);
if(state.delta){
float * a = l.filters_gpu;
float * b = l.delta_gpu;
float * c = l.col_image_gpu;
gemm_ongpu(1,0,n,k,m,1,a,n,b + i*k*m,k,0,c,k);
col2im_ongpu(l.col_image_gpu, l.c, l.h, l.w, l.size, l.stride, l.pad, state.delta + i*l.c*l.h*l.w);
}
}
}
void pull_local_layer(local_layer layer)
{
cuda_pull_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.biases_gpu, layer.biases, layer.n);
cuda_pull_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
cuda_pull_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
if (layer.batch_normalize){
cuda_pull_array(layer.scales_gpu, layer.scales, layer.n);
cuda_pull_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
cuda_pull_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
}
}
void push_local_layer(local_layer layer)
{
cuda_push_array(layer.filters_gpu, layer.filters, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.biases_gpu, layer.biases, layer.n);
cuda_push_array(layer.filter_updates_gpu, layer.filter_updates, layer.c*layer.n*layer.size*layer.size);
cuda_push_array(layer.bias_updates_gpu, layer.bias_updates, layer.n);
if (layer.batch_normalize){
cuda_push_array(layer.scales_gpu, layer.scales, layer.n);
cuda_push_array(layer.rolling_mean_gpu, layer.rolling_mean, layer.n);
cuda_push_array(layer.rolling_variance_gpu, layer.rolling_variance, layer.n);
}
}
void update_local_layer_gpu(local_layer layer, int batch, float learning_rate, float momentum, float decay)
{
int size = layer.size*layer.size*layer.c*layer.n;
axpy_ongpu(layer.n, learning_rate/batch, layer.bias_updates_gpu, 1, layer.biases_gpu, 1);
scal_ongpu(layer.n, momentum, layer.bias_updates_gpu, 1);
axpy_ongpu(layer.n, learning_rate/batch, layer.scale_updates_gpu, 1, layer.scales_gpu, 1);
scal_ongpu(layer.n, momentum, layer.scale_updates_gpu, 1);
axpy_ongpu(size, -decay*batch, layer.filters_gpu, 1, layer.filter_updates_gpu, 1);
axpy_ongpu(size, learning_rate/batch, layer.filter_updates_gpu, 1, layer.filters_gpu, 1);
scal_ongpu(size, momentum, layer.filter_updates_gpu, 1);
}

15
src/utils.c
View File

@ -359,6 +359,21 @@ float mean_array(float *a, int n)
return sum_array(a,n)/n;
}
void mean_arrays(float **a, int n, int els, float *avg)
{
int i;
int j;
memset(avg, 0, els*sizeof(float));
for(j = 0; j < n; ++j){
for(i = 0; i < els; ++i){
avg[i] += a[j][i];
}
}
for(i = 0; i < els; ++i){
avg[i] /= n;
}
}
float variance_array(float *a, int n)
{
int i;

1
src/utils.h
View File

@ -37,6 +37,7 @@ float rand_normal();
float rand_uniform();
float sum_array(float *a, int n);
float mean_array(float *a, int n);
void mean_arrays(float **a, int n, int els, float *avg);
float variance_array(float *a, int n);
float mag_array(float *a, int n);
float **one_hot_encode(float *a, int n, int k);