基础准备
ROS下实现darknet_ros(YOLO V3)检测
Github YOLOV3
Stereo-Camera data for real-time dynamic obstacle detection and tracking
编译darknet_ros时报出一个很少见的错误
环境配置
搜了半天都是CPU跑的,不到1帧,真! 遇到很多坑,终于成了,笔记本RTX2070Ti跑到137帧左右.
1.下载
可能没有权限需要配置一下SSH密钥,有很多资料自行百度吧
git clone --recursive git@github.com:leggedrobotics/darknet_ros.git
rock@hero:~$ nvidia-smi
Sun Nov 21 20:11:18 2021
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.74 Driver Version: 470.74 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... Off | 00000000:01:00.0 On | N/A |
| N/A 47C P8 13W / N/A | 496MiB / 7982MiB | 3% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 2654 G /usr/lib/xorg/Xorg 362MiB |
| 0 N/A N/A 3893 G /usr/lib/firefox/firefox 122MiB |
| 0 N/A N/A 4105 G /usr/lib/firefox/firefox 2MiB |
| 0 N/A N/A 8779 G /usr/lib/firefox/firefox 2MiB |
| 0 N/A N/A 8809 G /usr/lib/firefox/firefox 2MiB |
+-----------------------------------------------------------------------------+
rock@hero:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Tue_Sep_15_19:10:02_PDT_2020
Cuda compilation tools, release 11.1, V11.1.74
Build cuda_11.1.TC455_06.29069683_0
rock@hero:~$
我的笔记本 适配cuda11.4所以我安装了cuda11.1版本,对应的cudnn8.x几,所以cudnn8.x以后基本不支持ARCH= -gencode arch=compute_30,code=compute_30,所以导致后面因为cudnn8.x,一系列玄学编译bug…
2.修改~/catkin_ws/src/darknet_ros/darknet/src下
convolutional_layer.c文件 修改后完整文件直接粘贴到这了
#include "convolutional_layer.h"
#include "utils.h"
#include "batchnorm_layer.h"
#include "im2col.h"
#include "col2im.h"
#include "blas.h"
#include "gemm.h"
#include <stdio.h>
#include <time.h>
#define PRINT_CUDNN_ALGO 0
#define MEMORY_LIMIT 2000000000
#ifdef AI2
#include "xnor_layer.h"
#endif
void swap_binary(convolutional_layer *l)
{
float *swap = l->weights;
l->weights = l->binary_weights;
l->binary_weights = swap;
#ifdef GPU
swap = l->weights_gpu;
l->weights_gpu = l->binary_weights_gpu;
l->binary_weights_gpu = swap;
#endif
}
void binarize_weights(float *weights, int n, int size, float *binary)
{
int i, f;
for(f = 0; f < n; ++f){
float mean = 0;
for(i = 0; i < size; ++i){
mean += fabs(weights[f*size + i]);
}
mean = mean / size;
for(i = 0; i < size; ++i){
binary[f*size + i] = (weights[f*size + i] > 0) ? mean : -mean;
}
}
}
void binarize_cpu(float *input, int n, float *binary)
{
int i;
for(i = 0; i < n; ++i){
binary[i] = (input[i] > 0) ? 1 : -1;
}
}
void binarize_input(float *input, int n, int size, float *binary)
{
int i, s;
for(s = 0; s < size; ++s){
float mean = 0;
for(i = 0; i < n; ++i){
mean += fabs(input[i*size + s]);
}
mean = mean / n;
for(i = 0; i < n; ++i){
binary[i*size + s] = (input[i*size + s] > 0) ? mean : -mean;
}
}
}
int convolutional_out_height(convolutional_layer l)
{
return (l.h + 2*l.pad - l.size) / l.stride + 1;
}
int convolutional_out_width(convolutional_layer l)
{
return (l.w + 2*l.pad - l.size) / l.stride + 1;
}
image get_convolutional_image(convolutional_layer l)
{
return float_to_image(l.out_w,l.out_h,l.out_c,l.output);
}
image get_convolutional_delta(convolutional_layer l)
{
return float_to_image(l.out_w,l.out_h,l.out_c,l.delta);
}
static size_t get_workspace_size(layer l){
#ifdef CUDNN
if(gpu_index >= 0){
size_t most = 0;
size_t s = 0;
cudnnGetConvolutionForwardWorkspaceSize(cudnn_handle(),
l.srcTensorDesc,
l.weightDesc,
l.convDesc,
l.dstTensorDesc,
l.fw_algo,
&s);
if (s > most) most = s;
cudnnGetConvolutionBackwardFilterWorkspaceSize(cudnn_handle(),
l.srcTensorDesc,
l.ddstTensorDesc,
l.convDesc,
l.dweightDesc,
l.bf_algo,
&s);
if (s > most) most = s;
cudnnGetConvolutionBackwardDataWorkspaceSize(cudnn_handle(),
l.weightDesc,
l.ddstTensorDesc,
l.convDesc,
l.dsrcTensorDesc,
l.bd_algo,
&s);
if (s > most) most = s;
return most;
}
#endif
return (size_t)l.out_h*l.out_w*l.size*l.size*l.c/l.groups*sizeof(float);
}
#ifdef GPU
#ifdef CUDNN
void cudnn_convolutional_setup(layer *l)
{
cudnnSetTensor4dDescriptor(l->dsrcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w);
cudnnSetTensor4dDescriptor(l->ddstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w);
cudnnSetTensor4dDescriptor(l->srcTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->c, l->h, l->w);
cudnnSetTensor4dDescriptor(l->dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l->batch, l->out_c, l->out_h, l->out_w);
cudnnSetTensor4dDescriptor(l->normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l->out_c, 1, 1);
cudnnSetFilter4dDescriptor(l->dweightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size);
cudnnSetFilter4dDescriptor(l->weightDesc, CUDNN_DATA_FLOAT, CUDNN_TENSOR_NCHW, l->n, l->c/l->groups, l->size, l->size);
#if CUDNN_MAJOR >= 6
cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION, CUDNN_DATA_FLOAT);
#else
cudnnSetConvolution2dDescriptor(l->convDesc, l->pad, l->pad, l->stride, l->stride, 1, 1, CUDNN_CROSS_CORRELATION);
#endif
#if CUDNN_MAJOR >= 7
cudnnSetConvolutionGroupCount(l->convDesc, l->groups);
#else
if(l->groups > 1){
error("CUDNN < 7 doesn't support groups, please upgrade!");
}
#endif
#if CUDNN_MAJOR >= 8
int returnedAlgoCount;
cudnnConvolutionFwdAlgoPerf_t fw_results[2 * CUDNN_CONVOLUTION_FWD_ALGO_COUNT];
cudnnConvolutionBwdDataAlgoPerf_t bd_results[2 * CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT];
cudnnConvolutionBwdFilterAlgoPerf_t bf_results[2 * CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT];
cudnnFindConvolutionForwardAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->weightDesc,
l->convDesc,
l->dstTensorDesc,
CUDNN_CONVOLUTION_FWD_ALGO_COUNT,
&returnedAlgoCount,
fw_results);
for(int algoIndex = 0; algoIndex < returnedAlgoCount; ++algoIndex){
#if PRINT_CUDNN_ALGO > 0
printf("^^^^ %s for Algo %d: %f time requiring %llu memory\n",
cudnnGetErrorString(fw_results[algoIndex].status),
fw_results[algoIndex].algo, fw_results[algoIndex].time,
(unsigned long long)fw_results[algoIndex].memory);
#endif
if( fw_results[algoIndex].memory < MEMORY_LIMIT ){
l->fw_algo = fw_results[algoIndex].algo;
break;
}
}
cudnnFindConvolutionBackwardDataAlgorithm(cudnn_handle(),
l->weightDesc,
l->ddstTensorDesc,
l->convDesc,
l->dsrcTensorDesc,
CUDNN_CONVOLUTION_BWD_DATA_ALGO_COUNT,
&returnedAlgoCount,
bd_results);
for(int algoIndex = 0; algoIndex < returnedAlgoCount; ++algoIndex){
#if PRINT_CUDNN_ALGO > 0
printf("^^^^ %s for Algo %d: %f time requiring %llu memory\n",
cudnnGetErrorString(bd_results[algoIndex].status),
bd_results[algoIndex].algo, bd_results[algoIndex].time,
(unsigned long long)bd_results[algoIndex].memory);
#endif
if( bd_results[algoIndex].memory < MEMORY_LIMIT ){
l->bd_algo = bd_results[algoIndex].algo;
break;
}
}
cudnnFindConvolutionBackwardFilterAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->ddstTensorDesc,
l->convDesc,
l->dweightDesc,
CUDNN_CONVOLUTION_BWD_FILTER_ALGO_COUNT,
&returnedAlgoCount,
bf_results);
for(int algoIndex = 0; algoIndex < returnedAlgoCount; ++algoIndex){
#if PRINT_CUDNN_ALGO > 0
printf("^^^^ %s for Algo %d: %f time requiring %llu memory\n",
cudnnGetErrorString(bf_results[algoIndex].status),
bf_results[algoIndex].algo, bf_results[algoIndex].time,
(unsigned long long)bf_results[algoIndex].memory);
#endif
if( bf_results[algoIndex].memory < MEMORY_LIMIT ){
l->bf_algo = bf_results[algoIndex].algo;
break;
}
}
#else
cudnnGetConvolutionForwardAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->weightDesc,
l->convDesc,
l->dstTensorDesc,
CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
2000000000,
&l->fw_algo);
cudnnGetConvolutionBackwardDataAlgorithm(cudnn_handle(),
l->weightDesc,
l->ddstTensorDesc,
l->convDesc,
l->dsrcTensorDesc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
2000000000,
&l->bd_algo);
cudnnGetConvolutionBackwardFilterAlgorithm(cudnn_handle(),
l->srcTensorDesc,
l->ddstTensorDesc,
l->convDesc,
l->dweightDesc,
CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT,
2000000000,
&l->bf_algo);
#endif
}
#endif
#endif
convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int groups, int size, int stride, int padding, ACTIVATION activation, int batch_normalize, int binary, int xnor, int adam)
{
int i;
convolutional_layer l = {0};
l.type = CONVOLUTIONAL;
l.groups = groups;
l.h = h;
l.w = w;
l.c = c;
l.n = n;
l.binary = binary;
l.xnor = xnor;
l.batch = batch;
l.stride = stride;
l.size = size;
l.pad = padding;
l.batch_normalize = batch_normalize;
l.weights = calloc(c/groups*n*size*size, sizeof(float));
l.weight_updates = calloc(c/groups*n*size*size, sizeof(float));
l.biases = calloc(n, sizeof(float));
l.bias_updates = calloc(n, sizeof(float));
l.nweights = c/groups*n*size*size;
l.nbiases = n;
// float scale = 1./sqrt(size*size*c);
float scale = sqrt(2./(size*size*c/l.groups));
//printf("convscale %f\n", scale);
//scale = .02;
//for(i = 0; i < c*n*size*size; ++i) l.weights[i] = scale*rand_uniform(-1, 1);
for(i = 0; i < l.nweights; ++i) l.weights[i] = scale*rand_normal();
int out_w = convolutional_out_width(l);
int out_h = convolutional_out_height(l);
l.out_h = out_h;
l.out_w = out_w;
l.out_c = n;
l.outputs = l.out_h * l.out_w * l.out_c;
l.inputs = l.w * l.h * l.c;
l.output = calloc(l.batch*l.outputs, sizeof(float));
l.delta = calloc(l.batch*l.outputs, sizeof(float));
l.forward = forward_convolutional_layer;
l.backward = backward_convolutional_layer;
l.update = update_convolutional_layer;
if(binary){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.cweights = calloc(l.nweights, sizeof(char));
l.scales = calloc(n, sizeof(float));
}
if(xnor){
l.binary_weights = calloc(l.nweights, sizeof(float));
l.binary_input = calloc(l.inputs*l.batch, sizeof(float));
}
if(batch_normalize){
l.scales = calloc(n, sizeof(float));
l.scale_updates = calloc(n, sizeof(float));
for(i = 0; i < n; ++i){
l.scales[i] = 1;
}
l.mean = calloc(n, sizeof(float));
l.variance = calloc(n, sizeof(float));
l.mean_delta = calloc(n, sizeof(float));
l.variance_delta = calloc(n, sizeof(float));
l.rolling_mean = calloc(n, sizeof(float));
l.rolling_variance = calloc(n, sizeof(float));
l.x = calloc(l.batch*l.outputs, sizeof(float));
l.x_norm = calloc(l.batch*l.outputs, sizeof(float));
}
if(adam){
l.m = calloc(l.nweights, sizeof(float));
l.v = calloc(l.nweights, sizeof(float));
l.bias_m = calloc(n, sizeof(float));
l.scale_m = calloc(n, sizeof(float));
l.bias_v = calloc(n, sizeof(float));
l.scale_v = calloc(n, sizeof(float));
}
#ifdef GPU
l.forward_gpu = forward_convolutional_layer_gpu;
l.backward_gpu = backward_convolutional_layer_gpu;
l.update_gpu = update_convolutional_layer_gpu;
if(gpu_index >= 0){
if (adam) {
l.m_gpu = cuda_make_array(l.m, l.nweights);
l.v_gpu = cuda_make_array(l.v, l.nweights);
l.bias_m_gpu = cuda_make_array(l.bias_m, n);
l.bias_v_gpu = cuda_make_array(l.bias_v, n);
l.scale_m_gpu = cuda_make_array(l.scale_m, n);
l.scale_v_gpu = cuda_make_array(l.scale_v, n);
}
l.weights_gpu = cuda_make_array(l.weights, l.nweights);
l.weight_updates_gpu = cuda_make_array(l.weight_updates, l.nweights);
l.biases_gpu = cuda_make_array(l.biases, n);
l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);
l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
if(binary){
l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
}
if(xnor){
l.binary_weights_gpu = cuda_make_array(l.weights, l.nweights);
l.binary_input_gpu = cuda_make_array(0, l.inputs*l.batch);
}
if(batch_normalize){
l.mean_gpu = cuda_make_array(l.mean, n);
l.variance_gpu = cuda_make_array(l.variance, n);
l.rolling_mean_gpu = cuda_make_array(l.mean, n);
l.rolling_variance_gpu = cuda_make_array(l.variance, n);
l.mean_delta_gpu = cuda_make_array(l.mean, n);
l.variance_delta_gpu = cuda_make_array(l.variance, n);
l.scales_gpu = cuda_make_array(l.scales, n);
l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);
l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
}
#ifdef CUDNN
cudnnCreateTensorDescriptor(&l.normTensorDesc);
cudnnCreateTensorDescriptor(&l.srcTensorDesc);
cudnnCreateTensorDescriptor(&l.dstTensorDesc);
cudnnCreateFilterDescriptor(&l.weightDesc);
cudnnCreateTensorDescriptor(&l.dsrcTensorDesc);
cudnnCreateTensorDescriptor(&l.ddstTensorDesc);
cudnnCreateFilterDescriptor(&l.dweightDesc);
cudnnCreateConvolutionDescriptor(&l.convDesc);
cudnn_convolutional_setup(&l);
#endif
}
#endif
l.workspace_size = get_workspace_size(l);
l.activation = activation;
fprintf(stderr, "conv %5d %2d x%2d /%2d %4d x%4d x%4d -> %4d x%4d x%4d %5.3f BFLOPs\n", n, size, size, stride, w, h, c, l.out_w, l.out_h, l.out_c, (2.0 * l.n * l.size*l.size*l.c/l.groups * l.out_h*l.out_w)/1000000000.);
return l;
}
void denormalize_convolutional_layer(convolutional_layer l)
{
int i, j;
for(i = 0; i < l.n; ++i){
float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
for(j = 0; j < l.c/l.groups*l.size*l.size; ++j){
l.weights[i*l.c/l.groups*l.size*l.size + j] *= scale;
}
l.biases[i] -= l.rolling_mean[i] * scale;
l.scales[i] = 1;
l.rolling_mean[i] = 0;
l.rolling_variance[i] = 1;
}
}
/*
void test_convolutional_layer()
{
convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0, 0, 0);
l.batch_normalize = 1;
float data[] = {1,1,1,1,1,
1,1,1,1,1,
1,1,1,1,1,
1,1,1,1,1,
1,1,1,1,1,
2,2,2,2,2,
2,2,2,2,2,
2,2,2,2,2,
2,2,2,2,2,
2,2,2,2,2,
3,3,3,3,3,
3,3,3,3,3,
3,3,3,3,3,
3,3,3,3,3,
3,3,3,3,3};
//net.input = data;
//forward_convolutional_layer(l);
}
*/
void resize_convolutional_layer(convolutional_layer *l, int w, int h)
{
l->w = w;
l->h = h;
int out_w = convolutional_out_width(*l);
int out_h = convolutional_out_height(*l);
l->out_w = out_w;
l->out_h = out_h;
l->outputs = l->out_h * l->out_w * l->out_c;
l->inputs = l->w * l->h * l->c;
l->output = realloc(l->output, l->batch*l->outputs*sizeof(float));
l->delta = realloc(l->delta, l->batch*l->outputs*sizeof(float));
if(l->batch_normalize){
l->x = realloc(l->x, l->batch*l->outputs*sizeof(float));
l->x_norm = realloc(l->x_norm, l->batch*l->outputs*sizeof(float));
}
#ifdef GPU
cuda_free(l->delta_gpu);
cuda_free(l->output_gpu);
l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
if(l->batch_normalize){
cuda_free(l->x_gpu);
cuda_free(l->x_norm_gpu);
l->x_gpu = cuda_make_array(l->output, l->batch*l->outputs);
l->x_norm_gpu = cuda_make_array(l->output, l->batch*l->outputs);
}
#ifdef CUDNN
cudnn_convolutional_setup(l);
#endif
#endif
l->workspace_size = get_workspace_size(*l);
}
void add_bias(float *output, float *biases, int batch, int n, int size)
{
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
output[(b*n + i)*size + j] += biases[i];
}
}
}
}
void scale_bias(float *output, float *scales, int batch, int n, int size)
{
int i,j,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
for(j = 0; j < size; ++j){
output[(b*n + i)*size + j] *= scales[i];
}
}
}
}
void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
{
int i,b;
for(b = 0; b < batch; ++b){
for(i = 0; i < n; ++i){
bias_updates[i] += sum_array(delta+size*(i+b*n), size);
}
}
}
void forward_convolutional_layer(convolutional_layer l, network net)
{
int i, j;
fill_cpu(l.outputs*l.batch, 0, l.output, 1);
if(l.xnor){
binarize_weights(l.weights, l.n, l.c/l.groups*l.size*l.size, l.binary_weights);
swap_binary(&l);
binarize_cpu(net.input, l.c*l.h*l.w*l.batch, l.binary_input);
net.input = l.binary_input;
}
int m = l.n/l.groups;
int k = l.size*l.size*l.c/l.groups;
int n = l.out_w*l.out_h;
for(i = 0; i < l.batch; ++i){
for(j = 0; j < l.groups; ++j){
float *a = l.weights + j*l.nweights/l.groups;
float *b = net.workspace;
float *c = l.output + (i*l.groups + j)*n*m;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
if (l.size == 1) {
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, b);
}
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
}
}
if(l.batch_normalize){
forward_batchnorm_layer(l, net);
} else {
add_bias(l.output, l.biases, l.batch, l.n, l.out_h*l.out_w);
}
activate_array(l.output, l.outputs*l.batch, l.activation);
if(l.binary || l.xnor) swap_binary(&l);
}
void backward_convolutional_layer(convolutional_layer l, network net)
{
int i, j;
int m = l.n/l.groups;
int n = l.size*l.size*l.c/l.groups;
int k = l.out_w*l.out_h;
gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
if(l.batch_normalize){
backward_batchnorm_layer(l, net);
} else {
backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);
}
for(i = 0; i < l.batch; ++i){
for(j = 0; j < l.groups; ++j){
float *a = l.delta + (i*l.groups + j)*m*k;
float *b = net.workspace;
float *c = l.weight_updates + j*l.nweights/l.groups;
float *im = net.input + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
float *imd = net.delta + (i*l.groups + j)*l.c/l.groups*l.h*l.w;
if(l.size == 1){
b = im;
} else {
im2col_cpu(im, l.c/l.groups, l.h, l.w,
l.size, l.stride, l.pad, b);
}
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
if (net.delta) {
a = l.weights + j*l.nweights/l.groups;
b = l.delta + (i*l.groups + j)*m*k;
c = net.workspace;
if (l.size == 1) {
c = imd;
}
gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);
if (l.size != 1) {
col2im_cpu(net.workspace, l.c/l.groups, l.h, l.w, l.size, l.stride, l.pad, imd);
}
}
}
}
}
void update_convolutional_layer(convolutional_layer l, update_args a)
{
float learning_rate = a.learning_rate*l.learning_rate_scale;
float momentum = a.momentum;
float decay = a.decay;
int batch = a.batch;
axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
scal_cpu(l.n, momentum, l.bias_updates, 1);
if(l.scales){
axpy_cpu(l.n, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
scal_cpu(l.n, momentum, l.scale_updates, 1);
}
axpy_cpu(l.nweights, -decay*batch, l.weights, 1, l.weight_updates, 1);
axpy_cpu(l.nweights, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
scal_cpu(l.nweights, momentum, l.weight_updates, 1);
}
image get_convolutional_weight(convolutional_layer l, int i)
{
int h = l.size;
int w = l.size;
int c = l.c/l.groups;
return float_to_image(w,h,c,l.weights+i*h*w*c);
}
void rgbgr_weights(convolutional_layer l)
{
int i;
for(i = 0; i < l.n; ++i){
image im = get_convolutional_weight(l, i);
if (im.c == 3) {
rgbgr_image(im);
}
}
}
void rescale_weights(convolutional_layer l, float scale, float trans)
{
int i;
for(i = 0; i < l.n; ++i){
image im = get_convolutional_weight(l, i);
if (im.c == 3) {
scale_image(im, scale);
float sum = sum_array(im.data, im.w*im.h*im.c);
l.biases[i] += sum*trans;
}
}
}
image *get_weights(convolutional_layer l)
{
image *weights = calloc(l.n, sizeof(image));
int i;
for(i = 0; i < l.n; ++i){
weights[i] = copy_image(get_convolutional_weight(l, i));
normalize_image(weights[i]);
/*
char buff[256];
sprintf(buff, "filter%d", i);
save_image(weights[i], buff);
*/
}
//error("hey");
return weights;
}
image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_weights)
{
image *single_weights = get_weights(l);
show_images(single_weights, l.n, window);
image delta = get_convolutional_image(l);
image dc = collapse_image_layers(delta, 1);
char buff[256];
sprintf(buff, "%s: Output", window);
//show_image(dc, buff);
//save_image(dc, buff);
free_image(dc);
return single_weights;
}
3.修改~/catkin_ws/src/darknet_ros/darknet下
Makefile cuda路径 算力 我的2070ti对应75我就直接把其它的都删掉了,免得编译报其他错误
GPU=1
CUDNN=1
OPENCV=1
OPENMP=0
DEBUG=0
ARCH= -gencode arch=compute_30,code=compute_30
# -gencode arch=compute_20,code=[sm_20,sm_21] \ This one is deprecated?
# This is what I use, uncomment if you know your arch and want to specify
ARCH= -gencode arch=compute_75,code=compute_75
VPATH=./src/:./examples
SLIB=libdarknet.so
ALIB=libdarknet.a
EXEC=darknet
OBJDIR=./obj/
CC=gcc
CPP=g++
NVCC=/usr/local/cuda-11.1/bin/nvcc
AR=ar
ARFLAGS=rcs
OPTS=-Ofast
LDFLAGS= -lm -pthread
COMMON= -Iinclude/ -Isrc/
CFLAGS=-Wall -Wno-unused-result -Wno-unknown-pragmas -Wfatal-errors -fPIC
ifeq ($(OPENMP), 1)
CFLAGS+= -fopenmp
endif
ifeq ($(DEBUG), 1)
OPTS=-O0 -g
endif
CFLAGS+=$(OPTS)
ifeq ($(OPENCV), 1)
COMMON+= -DOPENCV
CFLAGS+= -DOPENCV
LDFLAGS+= `pkg-config --libs opencv` -lstdc++
COMMON+= `pkg-config --cflags opencv`
endif
ifeq ($(GPU), 1)
COMMON+= -DGPU -I/usr/local/cuda-11.1/include/
CFLAGS+= -DGPU
LDFLAGS+= -L/usr/local/cuda-11.1/lib64 -lcuda -lcudart -lcublas -lcurand
endif
ifeq ($(CUDNN), 1)
COMMON+= -DCUDNN
CFLAGS+= -DCUDNN
LDFLAGS+= -lcudnn
endif
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 detection_layer.o route_layer.o upsample_layer.o box.o normalization_layer.o avgpool_layer.o layer.o local_layer.o shortcut_layer.o logistic_layer.o activation_layer.o rnn_layer.o gru_layer.o crnn_layer.o demo.o batchnorm_layer.o region_layer.o reorg_layer.o tree.o lstm_layer.o l2norm_layer.o yolo_layer.o iseg_layer.o image_opencv.o
EXECOBJA=captcha.o lsd.o super.o art.o tag.o cifar.o go.o rnn.o segmenter.o regressor.o classifier.o coco.o yolo.o detector.o nightmare.o instance-segmenter.o darknet.o
ifeq ($(GPU), 1)
LDFLAGS+= -lstdc++
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 avgpool_layer_kernels.o
endif
EXECOBJ = $(addprefix $(OBJDIR), $(EXECOBJA))
OBJS = $(addprefix $(OBJDIR), $(OBJ))
DEPS = $(wildcard src/*.h) Makefile include/darknet.h
all: obj backup results $(SLIB) $(ALIB) $(EXEC)
#all: obj results $(SLIB) $(ALIB) $(EXEC)
$(EXEC): $(EXECOBJ) $(ALIB)
$(CC) $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS) $(ALIB)
$(ALIB): $(OBJS)
$(AR) $(ARFLAGS) $@ $^
$(SLIB): $(OBJS)
$(CC) $(CFLAGS) -shared $^ -o $@ $(LDFLAGS)
$(OBJDIR)%.o: %.cpp $(DEPS)
$(CPP) $(COMMON) $(CFLAGS) -c $< -o $@
$(OBJDIR)%.o: %.c $(DEPS)
$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
$(OBJDIR)%.o: %.cu $(DEPS)
$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
obj:
mkdir -p obj
backup:
mkdir -p backup
results:
mkdir -p results
.PHONY: clean
clean:
rm -rf $(OBJS) $(SLIB) $(ALIB) $(EXEC) $(EXECOBJ) $(OBJDIR)/*
4.编辑
在darknet目录下 make,这个时候应该没有问题,通过了
5.修改/home/rock/catkin_ws/src/darknet_ros/darknet_ros下
CMakeLists.txt文件,不然catkin_make时会报 算力_30 的错误
cmake_minimum_required(VERSION 3.5.1)
project(darknet_ros)
# Set c++11 cmake flags
set(CMAKE_CXX_STANDARD 11)
set(CMAKE_C_FLAGS "-Wall -Wno-unused-result -Wno-unknown-pragmas -Wno-unused-variable -Wfatal-errors -fPIC ${CMAKE_C_FLAGS}")
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
# Define path of darknet folder here.
find_path(DARKNET_PATH
NAMES "README.md"
HINTS "${CMAKE_CURRENT_SOURCE_DIR}/../darknet/")
message(STATUS "Darknet path dir = ${DARKNET_PATH}")
add_definitions(-DDARKNET_FILE_PATH="${DARKNET_PATH}")
# Find CUDA
find_package(CUDA QUIET)
if (CUDA_FOUND)
find_package(CUDA REQUIRED)
message(STATUS "CUDA Version: ${CUDA_VERSION_STRINGS}")
message(STATUS "CUDA Libararies: ${CUDA_LIBRARIES}")
set(
CUDA_NVCC_FLAGS
${CUDA_NVCC_FLAGS};
-O3
-gencode arch=compute_75,code=sm_75(这里是修改的地方我只留下了75算力)
)
add_definitions(-DGPU)
else()
list(APPEND LIBRARIES "m")
endif()
# Find X11
message ( STATUS "Searching for X11..." )
find_package ( X11 REQUIRED )
if ( X11_FOUND )
include_directories ( ${X11_INCLUDE_DIR} )
link_libraries ( ${X11_LIBRARIES} )
message ( STATUS " X11_INCLUDE_DIR: " ${X11_INCLUDE_DIR} )
message ( STATUS " X11_LIBRARIES: " ${X11_LIBRARIES} )
endif ( X11_FOUND )
# Find rquired packeges
find_package(Boost REQUIRED COMPONENTS thread)
find_package(OpenCV REQUIRED)
include_directories(${OpenCV_INCLUDE_DIRS})
find_package(catkin REQUIRED
COMPONENTS
cv_bridge
roscpp
rospy
std_msgs
actionlib
darknet_ros_msgs
image_transport
nodelet
)
# Enable OPENCV in darknet
add_definitions(-DOPENCV)
add_definitions(-O4 -g)
catkin_package(
INCLUDE_DIRS
include
LIBRARIES
${PROJECT_NAME}_lib
CATKIN_DEPENDS
cv_bridge
roscpp
actionlib
rospy
std_msgs
darknet_ros_msgs
image_transport
nodelet
DEPENDS
Boost
)
include_directories(
${DARKNET_PATH}/src
${DARKNET_PATH}/include
include
${Boost_INCLUDE_DIRS}
${catkin_INCLUDE_DIRS}
)
set(PROJECT_LIB_FILES
src/YoloObjectDetector.cpp src/image_interface.cpp
)
set(DARKNET_CORE_FILES
${DARKNET_PATH}/src/activation_layer.c ${DARKNET_PATH}/src/im2col.c
${DARKNET_PATH}/src/activations.c ${DARKNET_PATH}/src/image.c
${DARKNET_PATH}/src/avgpool_layer.c ${DARKNET_PATH}/src/layer.c
${DARKNET_PATH}/src/batchnorm_layer.c ${DARKNET_PATH}/src/list.c
${DARKNET_PATH}/src/blas.c ${DARKNET_PATH}/src/local_layer.c
${DARKNET_PATH}/src/box.c ${DARKNET_PATH}/src/lstm_layer.c
${DARKNET_PATH}/src/col2im.c ${DARKNET_PATH}/src/matrix.c
${DARKNET_PATH}/src/connected_layer.c ${DARKNET_PATH}/src/maxpool_layer.c
${DARKNET_PATH}/src/convolutional_layer.c ${DARKNET_PATH}/src/network.c
${DARKNET_PATH}/src/cost_layer.c ${DARKNET_PATH}/src/normalization_layer.c
${DARKNET_PATH}/src/crnn_layer.c ${DARKNET_PATH}/src/option_list.c
${DARKNET_PATH}/src/crop_layer.c ${DARKNET_PATH}/src/parser.c
${DARKNET_PATH}/src/cuda.c ${DARKNET_PATH}/src/region_layer.c
${DARKNET_PATH}/src/data.c ${DARKNET_PATH}/src/reorg_layer.c
${DARKNET_PATH}/src/deconvolutional_layer.c ${DARKNET_PATH}/src/rnn_layer.c
${DARKNET_PATH}/src/demo.c ${DARKNET_PATH}/src/route_layer.c
${DARKNET_PATH}/src/detection_layer.c ${DARKNET_PATH}/src/shortcut_layer.c
${DARKNET_PATH}/src/dropout_layer.c ${DARKNET_PATH}/src/softmax_layer.c
${DARKNET_PATH}/src/gemm.c ${DARKNET_PATH}/src/tree.c
${DARKNET_PATH}/src/gru_layer.c ${DARKNET_PATH}/src/utils.c
${DARKNET_PATH}/src/upsample_layer.c ${DARKNET_PATH}/src/logistic_layer.c
${DARKNET_PATH}/src/l2norm_layer.c ${DARKNET_PATH}/src/yolo_layer.c
${DARKNET_PATH}/src/iseg_layer.c ${DARKNET_PATH}/src/image_opencv.cpp
${DARKNET_PATH}/examples/art.c ${DARKNET_PATH}/examples/lsd.c
${DARKNET_PATH}/examples/nightmare.c ${DARKNET_PATH}/examples/instance-segmenter.c
${DARKNET_PATH}/examples/captcha.c ${DARKNET_PATH}/examples/regressor.c
${DARKNET_PATH}/examples/cifar.c ${DARKNET_PATH}/examples/rnn.c
${DARKNET_PATH}/examples/classifier.c ${DARKNET_PATH}/examples/segmenter.c
${DARKNET_PATH}/examples/coco.c ${DARKNET_PATH}/examples/super.c
${DARKNET_PATH}/examples/darknet.c ${DARKNET_PATH}/examples/tag.c
${DARKNET_PATH}/examples/detector.c ${DARKNET_PATH}/examples/yolo.c
${DARKNET_PATH}/examples/go.c
)
set(DARKNET_CUDA_FILES
${DARKNET_PATH}/src/activation_kernels.cu ${DARKNET_PATH}/src/crop_layer_kernels.cu
${DARKNET_PATH}/src/avgpool_layer_kernels.cu ${DARKNET_PATH}/src/deconvolutional_kernels.cu
${DARKNET_PATH}/src/blas_kernels.cu ${DARKNET_PATH}/src/dropout_layer_kernels.cu
${DARKNET_PATH}/src/col2im_kernels.cu ${DARKNET_PATH}/src/im2col_kernels.cu
${DARKNET_PATH}/src/convolutional_kernels.cu ${DARKNET_PATH}/src/maxpool_layer_kernels.cu
)
if (CUDA_FOUND)
link_directories(
${CUDA_TOOLKIT_ROOT_DIR}/lib64
)
cuda_add_library(${PROJECT_NAME}_lib
${PROJECT_LIB_FILES} ${DARKNET_CORE_FILES}
${DARKNET_CUDA_FILES}
)
target_link_libraries(${PROJECT_NAME}_lib
cuda
cudart
cublas
curand
)
cuda_add_executable(${PROJECT_NAME}
src/yolo_object_detector_node.cpp
)
cuda_add_library(${PROJECT_NAME}_nodelet
src/yolo_object_detector_nodelet.cpp
)
else()
add_library(${PROJECT_NAME}_lib
${PROJECT_LIB_FILES} ${DARKNET_CORE_FILES}
)
add_executable(${PROJECT_NAME}
src/yolo_object_detector_node.cpp
)
add_library(${PROJECT_NAME}_nodelet
src/yolo_object_detector_nodelet.cpp
)
endif()
target_link_libraries(${PROJECT_NAME}_lib
m
pthread
stdc++
${Boost_LIBRARIES}
${OpenCV_LIBRARIES}
${catkin_LIBRARIES}
${OpenCV_LIBS}
)
target_link_libraries(${PROJECT_NAME}
${PROJECT_NAME}_lib
)
target_link_libraries(${PROJECT_NAME}_nodelet
${PROJECT_NAME}_lib
)
add_dependencies(${PROJECT_NAME}_lib
darknet_ros_msgs_generate_messages_cpp
)
install(TARGETS ${PROJECT_NAME}_lib
ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
)
install(TARGETS ${PROJECT_NAME}
RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
)
install(
DIRECTORY include/${PROJECT_NAME}/
DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
FILES_MATCHING PATTERN "*.h"
)
install(DIRECTORY config launch yolo_network_config
DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
)
# Download yolov2-tiny.weights
set(PATH "${CMAKE_CURRENT_SOURCE_DIR}/yolo_network_config/weights")
set(FILE "${PATH}/yolov2-tiny.weights")
message(STATUS "Checking and downloading yolov2-tiny.weights if needed ...")
if (NOT EXISTS "${FILE}")
message(STATUS "... file does not exist. Downloading now ...")
execute_process(COMMAND wget -q https://github.com/leggedrobotics/darknet_ros/releases/download/1.1.4/yolov2-tiny.weights -P ${PATH})
endif()
# Download yolov3.weights
set(FILE "${PATH}/yolov3.weights")
message(STATUS "Checking and downloading yolov3.weights if needed ...")
if (NOT EXISTS "${FILE}")
message(STATUS "... file does not exist. Downloading now ...")
execute_process(COMMAND wget -q https://github.com/leggedrobotics/darknet_ros/releases/download/1.1.4/yolov3.weights -P ${PATH})
endif()
#############
## Testing ##
#############
if(CATKIN_ENABLE_TESTING)
# Download yolov2.weights
set(PATH "${CMAKE_CURRENT_SOURCE_DIR}/yolo_network_config/weights")
set(FILE "${PATH}/yolov2.weights")
message(STATUS "Checking and downloading yolov2.weights if needed ...")
if (NOT EXISTS "${FILE}")
message(STATUS "... file does not exist. Downloading now ...")
execute_process(COMMAND wget -q https://github.com/leggedrobotics/darknet_ros/releases/download/1.1.4/yolov2.weights -P ${PATH})
endif()
find_package(rostest REQUIRED)
# Object detection in images.
add_rostest_gtest(${PROJECT_NAME}_object_detection-test
test/object_detection.test
test/test_main.cpp
test/ObjectDetection.cpp
)
target_link_libraries(${PROJECT_NAME}_object_detection-test
${catkin_LIBRARIES}
)
endif()
#########################
### CLANG TOOLING ###
#########################
find_package(cmake_clang_tools QUIET)
if (cmake_clang_tools_FOUND)
message(STATUS "Run clang tooling")
add_clang_tooling(
TARGETS ${PROJECT_NAME}
SOURCE_DIRS ${CMAKE_CURRENT_LIST_DIR}/src ${CMAKE_CURRENT_LIST_DIR}/include ${CMAKE_CURRENT_LIST_DIR}/test
CT_HEADER_DIRS ${CMAKE_CURRENT_LIST_DIR}/include
CF_WERROR
)
endif (cmake_clang_tools_FOUND)
6.编译 cd ~/catkin_ws/
catkin_make -DCMAKE_BUILD_TYPE=Release
7.修改~/catkin_ws/src/darknet_ros/darknet_ros/config
ros.yaml文件
subscribers:
camera_reading:
topic: /kinect/rgb/image_raw(对应的图像话题名)
queue_size: 1
actions:
camera_reading:
name: /darknet_ros/check_for_objects
publishers:
object_detector:
topic: /darknet_ros/found_object
queue_size: 1
latch: false
bounding_boxes:
topic: /darknet_ros/bounding_boxes
queue_size: 1
latch: false
detection_image:
topic: /darknet_ros/detection_image
queue_size: 1
latch: true
image_view:
enable_opencv: true
wait_key_delay: 1
enable_console_output: true
8.发布摄像头话题,这个看自己文件,也可以用笔记本自带的摄像头
9.运行darknet_ros节点
roslaunch darknet_ros darknet_ros.launch
10.成功.
版权声明：本文为CSDN博主「RockWang.」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.csdn.net/xi_shui/article/details/121379747
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