首页 » 机器视觉 » 正文
机器视觉

ROS darknet_ros gpu cudnn目标检测

文章目录[隐藏]

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.
在这里插入图片描述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

标签： · · ·
点赞 0

RockWang.

我还没有学会写个人说明！

暂无评论

发表评论

相关推荐

YOLOv5实战垃圾分类目标检测
机器视觉 2024-04-11

YOLOv5实战垃圾分类目标检测

使用YOLOv5完成垃圾分类的目标检测 课程链接：https://edu.csdn.net/course/detail/35284 垃圾分类是一项利国利民的民生工程，需要全社会的共同参与。YOLOv5是目前流行的

YOLO3 -- 介绍
机器视觉 2024-03-31

YOLO3 -- 介绍

YOLO介绍 YOLO官网 YOLO(You Only Look Once)是目标检测模型，目标检测是计算机视觉中比较简单的任务，用来在一张图片中找到某些特定的物体，目标检测不仅要求我们识别这些物体的

分享到：