caffe

【更新完这个之后，不再更行yolo的东西，应该也不会再使用yolo框架】
一、添加LeakyLayer层
leaky_layer.hpp

template <typename Dtype>class LeakyLayer : public NeuronLayer<Dtype>{public:    explicit LeakyLayer(const LayerParameter& param)       : NeuronLayer<Dtype>(param) {}    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,          const vector<Blob<Dtype>*>& top);    virtual inline const char* type() const { return "Leaky"; }    virtual inline int ExactNumBottomBlobs() const { return 1; }    virtual inline int ExactNumTopBlobs() const { return 1; }protected:      virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,          const vector<Blob<Dtype>*>& top);      virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,          const vector<Blob<Dtype>*>& top);      virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,          const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom){};//?Yê±?1??êμ??      virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,          const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);};
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leaky_layer.cpp

template <typename Dtype>void LeakyLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,          const vector<Blob<Dtype>*>& top) {    NeuronLayer<Dtype>::LayerSetUp(bottom, top);      CHECK_NE(top[0], bottom[0]) << this->type() << " Layer does not "        "allow in-place computation.";}template <typename Dtype>void LeakyLayer<Dtype>::Forward_cpu(        const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {      const int count = top[0]->count();      Dtype* top_data = top[0]->mutable_cpu_data();      const Dtype* bottom_data = bottom[0]->cpu_data();      for(int i = 0; i < count; ++i){          if(bottom_data[i] > 0)              top_data[i] = bottom_data[i];          else              top_data[i] = 0.1*bottom_data[i];          //top_data[i] = bottom_data[i] > 0￡? bottom_data[i]: (Dtype(0.1)*bottom_data[i]);      }}#ifdef CPU_ONLYSTUB_GPU(LeakyLayer);#endifINSTANTIATE_CLASS(LeakyLayer);REGISTER_LAYER_CLASS(Leaky);
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leaky_layer.cu

template <typename Dtype>__global__ void LeakyForward(const int n, const Dtype* in, Dtype* out){    CUDA_KERNEL_LOOP(index, n){        out[index] = in[index] > 0 ? in[index] : in[index]*0.1;    }}template <typename Dtype>void LeakyLayer<Dtype>::Forward_gpu(    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {  const int count = bottom[0]->count();  const Dtype* bottom_data = bottom[0]->gpu_data();  Dtype* top_data = top[0]->mutable_gpu_data();  LeakyForward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(          count, bottom_data, top_data);  CUDA_POST_KERNEL_CHECK;}template<typename Dtype>__global__ void LeakyBackward(const int n, const Dtype* bottom_data, Dtype* bottom_diff, const Dtype* top_diff){    CUDA_KERNEL_LOOP(index, n){        bottom_diff[index] = bottom_data[index] > 0 ? top_diff[index] : top_diff[index]*0.1;    }}template<typename Dtype>void LeakyLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,          const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom){    const int count = bottom[0]->count();    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();    const Dtype* top_diff = top[0]->mutable_gpu_diff();    const Dtype* bottom_data = bottom[0]->gpu_data();    LeakyBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(          count, bottom_data, bottom_diff, top_diff);}INSTANTIATE_LAYER_GPU_FUNCS(LeakyLayer);
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二、添加detect层(loss)
detect_layer.hpp

template<typename Dtype>class DetectLayer : public Layer<Dtype>{public:    explicit DetectLayer(const LayerParameter& param);    virtual ~DetectLayer(){}    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,            const vector<Blob<Dtype>*>& top);    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,            const vector<Blob<Dtype>*>& top);    virtual inline const char* type() const { return "Detect";}    virtual inline int ExactNumBottomBlobs() const {return 2;}    virtual inline int ExactNumTopBlobs() const { return 1;}protected:    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,            const vector<Blob<Dtype>*>& top);    virtual void Backward_cpu(const vector<Blob<Dtype>*>& bottom,            const vector<bool>& propagate_down,const vector<Blob<Dtype>*>& top);    int classes;    int coords;    int rescore;    int side;    int num;    bool softmax;    bool sqrt;    float jiter;    float object_scale;    float noobject_scale;    float class_scale;    float coord_scale;};
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detect_layer.cpp

template<typename Dtype>Dtype lap(Dtype x1_min,Dtype x1_max,Dtype x2_min,Dtype x2_max){    if(x1_min < x2_min){        if(x1_max < x2_min){            return 0;        }else{            if(x1_max > x2_min){                if(x1_max < x2_max){                    return x1_max - x2_min;                }else{                    return x2_max - x2_min;                }            }else{                return 0;            }        }    }else{        if(x1_min < x2_max){            if(x1_max < x2_max)                return x1_max-x1_min;            else                return x2_max-x1_min;        }else{            return 0;        }    }}template<typename Dtype>Dtype box_iou(const vector<Dtype> box1, const vector<Dtype> box2){    Dtype lap_x = lap(box1[0]-box1[2]/2,box1[0]+box1[2]/2,box2[0]-box2[2]/2,box2[0]+box2[2]/2);    Dtype lap_y = lap(box1[1]-box1[3]/2,box1[1]+box1[3]/2,box2[1]-box2[3]/2,box2[1]+box2[3]/2);    Dtype are = box1[2]*box1[3]+box2[2]*box2[3]-lap_x*lap_y;    if(are < 0.00001)        return 0.0;    else        return (lap_x*lap_y)/are;}template <typename Dtype>DetectLayer<Dtype>::DetectLayer(const LayerParameter& param) : Layer<Dtype>(param){    this->layer_param_.add_propagate_down(true);    this->layer_param_.add_propagate_down(false);    const DetectParameter& detect_param = this->layer_param_.detect_param();    classes = detect_param.classes();    coords = detect_param.coords();    rescore = detect_param.rescore();    side = detect_param.side();    num = detect_param.num();    softmax = detect_param.softmax();    sqrt = detect_param.sqrt();    jiter = detect_param.jitter();    object_scale = detect_param.object_scale();    noobject_scale = detect_param.noobject_scale();    class_scale = detect_param.class_scale();    coord_scale = detect_param.coord_scale();}template <typename Dtype>void DetectLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,        const vector<Blob<Dtype>*>& top){    Layer<Dtype>::LayerSetUp(bottom, top);    this->layer_param_.add_loss_weight(Dtype(1));    int inputs = (side*side*(((1+coords)*num)+classes));    CHECK_EQ(inputs, bottom[0]->count(1)) << "input dimensions error";    CHECK_EQ(top.size(), 1) << "top size must be 1";}template <typename Dtype>void DetectLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,    const vector<Blob<Dtype>*>& top){    vector<int> shape(0);    top[0]->Reshape(shape);}template <typename Dtype>void DetectLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,        const vector<Blob<Dtype>*>& top){    int input_num_each = side*side*(((1+coords)*num)+classes);    int batch = bottom[0]->num();    int locations = side*side;    const Dtype* truth = bottom[1]->cpu_data();    const Dtype* input = bottom[0]->cpu_data();    Dtype* delta = bottom[0]->mutable_cpu_diff();    Dtype& cost = top[0]->mutable_cpu_data()[0];    cost = Dtype(0.0);    for(int i = 0; i < bottom[0]->count(); ++i){        delta[i] = Dtype(0.0);    }    float avg_iou = 0;    float avg_cat = 0;    float avg_allcat = 0;    float avg_obj = 0;    float avg_anyobj = 0;    int count = 0;    for(int b = 0; b < batch; ++b){        int input_index = b*input_num_each;        for(int l = 0; l < locations; ++l){            int truth_index = (b*locations+l)*(1+coords+classes);            Dtype is_obj = truth[truth_index];            for(int n = 0; n < num;++n){                int delta_index = input_index + locations*classes + l*num + n;                delta[delta_index] = noobject_scale*(0 - input[delta_index]);                cost += noobject_scale*pow(input[delta_index],2);                avg_anyobj += input[delta_index];            }            int best_index = 0;            float best_iou = 0;            float best_rmse = 400;            if(is_obj < 0.0001) continue;            int class_index = input_index + l*classes;            for(int j = 0; j < classes; ++j){                delta[class_index+j]= class_scale * (truth[truth_index+1+j] - input[class_index+j]);                if(truth[truth_index+1+j]) avg_cat += input[class_index+j];                avg_allcat += input[class_index+j];            }//classes            vector<float> truth_box;            truth_box.push_back(float(truth[truth_index+1+classes]/side));            truth_box.push_back(float(truth[truth_index+1+classes+1]/side));            truth_box.push_back(float(truth[truth_index+1+classes+2]));            truth_box.push_back(float(truth[truth_index+1+classes+3]));            for(int n = 0; n < num; ++n){                int box_index = input_index + locations*(classes+num)+(l*num+n)*coords;                vector<float> out_box;                out_box.push_back(float(input[box_index]/side));                out_box.push_back(float(input[box_index+1]/side));                if(sqrt){                    out_box.push_back(float(input[box_index+2]*input[box_index+2]));                    out_box.push_back(float(input[box_index+3]*input[box_index+3]));                }else{                    out_box.push_back(float(input[box_index+2]));                    out_box.push_back(float(input[box_index+3]));                }                float iou = box_iou(truth_box, out_box);                float rmse = (pow(truth_box[0]-out_box[0],2)+pow(truth_box[1]-out_box[1],2)+pow(truth_box[2]-out_box[2],2)+pow(truth_box[3]-out_box[3],2));                if(best_iou > 0 || iou > 0){                    if(iou > best_iou){                        best_iou = iou;                        best_index = n;                    }                }else{                    if(rmse < best_rmse){                        best_rmse = rmse;                        best_index = n;                    }                }                   }//for num            int box_index = input_index + locations*(classes+num)+(l*num+best_index)*coords;            int tbox_index = truth_index+1+classes;            vector<float> best_box;            best_box.push_back(float(input[box_index]/side));            best_box.push_back(float(input[box_index+1]/side));            if(sqrt){                best_box.push_back(float(input[box_index+2]*input[box_index+2]));                best_box.push_back(float(input[box_index+3]*input[box_index+3]));            }else{                best_box.push_back(float(input[box_index+2]));                best_box.push_back(float(input[box_index+3]));            }            int p_index = input_index + locations*classes + l*num + best_index;            cost -= noobject_scale*pow(input[p_index],2);            cost += object_scale*pow(1-input[p_index],2);            avg_obj += input[p_index];            delta[p_index] = object_scale*(1. - input[p_index]);            if(rescore){                //delta[p_index] = object_scale*(best_iou - input[p_index]);            }            delta[box_index] = coord_scale*(truth[tbox_index]-input[box_index]);            delta[box_index+1] = coord_scale*(truth[tbox_index+1]-input[box_index+1]);            delta[box_index+2] = coord_scale*(truth[tbox_index+2]-input[box_index+2]);            delta[box_index+3] = coord_scale*(truth[tbox_index+3]-input[box_index+3]);            if(sqrt) {                delta[box_index+2] = coord_scale*(std::sqrt(truth[tbox_index+2])-input[box_index+2]);                delta[box_index+3] = coord_scale*(std::sqrt(truth[tbox_index+3])-input[box_index+3]);            }            cost += pow(1-best_iou, 2);            avg_iou += best_iou;            ++count;        }//locations    }//batch    for(int i = 0; i < bottom[0]->count(); ++i){        delta[i] = -delta[i];    }    //printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou, avg_cat, avg_allcat, avg_obj, avg_anyobj, count);}template <typename Dtype>void DetectLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& bottom,        const vector<bool>& propagate_down,const vector<Blob<Dtype>*>& top){}template float lap(float x1_min,float x1_max,float x2_min,float x2_max);template double lap(double x1_min,double x1_max,double x2_min,double x2_max);template float box_iou(const vector<float> box1, const vector<float> box2);template double box_iou(const vector<double> box1, const vector<double> box2);#ifdef CPU_ONLYSTUB_GPU(DetectLayer);#endifINSTANTIATE_CLASS(DetectLayer);REGISTER_LAYER_CLASS(Detect);
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三、train的prototxt
yolo-train.prototxt

name: "yolo"layer {  name: "data"  type: "Data"  top: "data"  include {    phase: TRAIN  }  data_param {    source: ""    batch_size: 15    backend: LMDB  }  transform_param {    mean_value:127.5    mean_value:127.5    mean_value:127.5    scale: 0.00784  }}layer {  name: "data"  type: "Data"  top: "data"  include {    phase: TEST  }  data_param {    source: ""    batch_size: 15    backend: LMDB  }  transform_param {    mean_value:127.5    mean_value:127.5    mean_value:127.5    scale: 0.00784  }}layer {  name: "truth"  type: "HDF5Data"  top: "label"  include {    phase: TRAIN  }  hdf5_data_param {    source: ""    batch_size: 15  }}layer {  name: "truth"  type: "HDF5Data"  top: "label"  include {    phase: TEST  }  hdf5_data_param {    source: ""    batch_size: 15  }}layer{  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 64    kernel_size: 7    stride: 2    pad: 3  }}layer{  name: "Leaky1"  type: "Leaky"  bottom: "conv1"  top: "Leaky1"}layer{  name: "pool1"  type: "Pooling"  bottom: "Leaky1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer{  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 192    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky2"  type: "Leaky"  bottom: "conv2"  top: "Leaky2"}layer{  name: "pool2"  type: "Pooling"  bottom: "Leaky2"  top: "pool2"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer{  name: "conv3"  type: "Convolution"  bottom: "pool2"  top: "conv3"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 128    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky3"  type: "Leaky"  bottom: "conv3"  top: "Leaky3"}layer{  name: "conv4"  type: "Convolution"  bottom: "Leaky3"  top: "conv4"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 256    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky4"  type: "Leaky"  bottom: "conv4"  top: "Leaky4"}layer{  name: "conv5"  type: "Convolution"  bottom: "Leaky4"  top: "conv5"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 256    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky5"  type: "Leaky"  bottom: "conv5"  top: "Leaky5"}layer{  name: "conv6"  type: "Convolution"  bottom: "Leaky5"  top: "conv6"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 512    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky6"  type: "Leaky"  bottom: "conv6"  top: "Leaky6"}layer{  name: "pool3"  type: "Pooling"  bottom: "Leaky6"  top: "pool3"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer{  name: "conv7"  type: "Convolution"  bottom: "pool3"  top: "conv7"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 256    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky7"  type: "Leaky"  bottom: "conv7"  top: "Leaky7"}layer{  name: "conv8"  type: "Convolution"  bottom: "Leaky7"  top: "conv8"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 512    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky8"  type: "Leaky"  bottom: "conv8"  top: "Leaky8"}layer{  name: "conv9"  type: "Convolution"  bottom: "Leaky8"  top: "conv9"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 256    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky9"  type: "Leaky"  bottom: "conv9"  top: "Leaky9"}layer{  name: "conv10"  type: "Convolution"  bottom: "Leaky9"  top: "conv10"  convolution_param{    num_output: 512    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky10"  type: "Leaky"  bottom: "conv10"  top: "Leaky10"}layer{  name: "conv11"  type: "Convolution"  bottom: "Leaky10"  top: "conv11"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 256    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky11"  type: "Leaky"  bottom: "conv11"  top: "Leaky11"}layer{  name: "conv12"  type: "Convolution"  bottom: "Leaky11"  top: "conv12"  convolution_param{    num_output:512    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky12"  type: "Leaky"  bottom: "conv12"  top: "Leaky12"}layer{  name: "conv13"  type: "Convolution"  bottom: "Leaky12"  top: "conv13"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 256    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky13"  type: "Leaky"  bottom: "conv13"  top: "Leaky13"}layer{  name: "conv14"  type: "Convolution"  bottom: "Leaky13"  top: "conv14"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 512    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky14"  type: "Leaky"  bottom: "conv14"  top: "Leaky14"}layer{  name: "conv15"  type: "Convolution"  bottom: "Leaky14"  top: "conv15"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 512    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky15"  type: "Leaky"  bottom: "conv15"  top: "Leaky15"}layer{  name: "conv16"  type: "Convolution"  bottom: "Leaky15"  top: "conv16"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 1024    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky16"  type: "Leaky"  bottom: "conv16"  top: "Leaky16"}layer{  name: "pool4"  type: "Pooling"  bottom: "Leaky16"  top: "pool4"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layer{  name: "conv17"  type: "Convolution"  bottom: "pool4"  top: "conv17"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 512    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky17"  type: "Leaky"  bottom: "conv17"  top: "Leaky17"}layer{  name: "conv18"  type: "Convolution"  bottom: "Leaky17"  top: "conv18"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 1024    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky18"  type: "Leaky"  bottom: "conv18"  top: "Leaky18"}layer{  name: "conv19"  type: "Convolution"  bottom: "Leaky18"  top: "conv19"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 512    kernel_size: 1    stride: 1    pad: 0  }}layer{  name: "Leaky19"  type: "Leaky"  bottom: "conv19"  top: "Leaky19"}layer{  name: "conv20"  type: "Convolution"  bottom: "Leaky19"  top: "conv20"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 1024    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky20"  type: "Leaky"  bottom: "conv20"  top: "Leaky20"}layer{  name: "conv21"  type: "Convolution"  bottom: "Leaky20"  top: "conv21"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 1024    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky21"  type: "Leaky"  bottom: "conv21"  top: "Leaky21"}layer{  name: "conv22"  type: "Convolution"  bottom: "Leaky21"  top: "conv22"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 1024    kernel_size: 3    stride: 2    pad: 1  }}layer{  name: "Leaky22"  type: "Leaky"  bottom: "conv22"  top: "Leaky22"}layer{  name: "conv23"  type: "Convolution"  bottom: "Leaky22"  top: "conv23"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 1024    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky23"  type: "Leaky"  bottom: "conv23"  top: "Leaky23"}layer{  name: "conv24"  type: "Convolution"  bottom: "Leaky23"  top: "conv24"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  convolution_param{    num_output: 1024    kernel_size: 3    stride: 1    pad: 1  }}layer{  name: "Leaky24"  type: "Leaky"  bottom: "conv24"  top: "Leaky24"}layer {  name: "connect1"  type: "InnerProduct"  bottom: "Leaky24"  top: "connect1"  param {    lr_mult: 10  }  param {    lr_mult: 20  }  inner_product_param {    num_output: 4096  }}layer{  name: "Leaky25"  type: "Leaky"  bottom: "connect1"  top: "Leaky25"}layer {  name: "connect2_add"  type: "InnerProduct"  bottom: "Leaky25"  top: "connect2_add"  param {    lr_mult: 10  }  param {    lr_mult: 20  }  inner_product_param {    num_output: 637    weight_filler {      type: "uniform"      max: 1      min: -1    }    bias_filler {      type: "constant"    }  }}layer {  name: "detect"  type: "Detect"  top: "loss"  bottom: "connect2_add"  bottom: "label"  detect_param {    classes: 3  }}
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