darknet源碼學習：layer結構體

// layer.h
struct layer {
    LAYER_TYPE type;
    ACTIVATION activation;
    COST_TYPE cost_type;
    void(*forward)   (struct layer, struct network_state);
    void(*backward)  (struct layer, struct network_state);
    void(*update)    (struct layer, int, float, float, float);
    void(*forward_gpu)   (struct layer, struct network_state);
    void(*backward_gpu)  (struct layer, struct network_state);
    void(*update_gpu)    (struct layer, int, float, float, float);
    layer *share_layer;
    int batch_normalize;
    int shortcut;
    int batch;
    int forced;
    int flipped;
    int inputs;
    int outputs;
    int nweights;
    int nbiases;
    int extra;
    int truths;
    int h, w, c;
    int out_h, out_w, out_c;
    int n;
    int max_boxes;
    int groups;
    int size;
    int side;
    int stride;
    int stride_x;
    int stride_y;
    int dilation;
    int maxpool_depth;
    int out_channels;
    int reverse;
    int flatten;
    int spatial;
    int pad;
    int sqrt;
    int flip;
    int index;
    int binary;
    int xnor;
    int peephole;
    int use_bin_output;
    int steps;
    int state_constrain;
    int hidden;
    int truth;
    float smooth;
    float dot;
    float angle;
    float jitter;
    float saturation;
    float exposure;
    float shift;
    float ratio;
    float learning_rate_scale;
    float clip;
    int focal_loss;
    int noloss;
    int softmax;
    int classes;
    int coords;
    int background;
    int rescore;
    int objectness;
    int does_cost;
    int joint;
    int noadjust;
    int reorg;
    int log;
    int tanh;
    int *mask;
    int total;
    float bflops;

    int adam;
    float B1;
    float B2;
    float eps;

    int t;

    float alpha;
    float beta;
    float kappa;

    float coord_scale;
    float object_scale;
    float noobject_scale;
    float mask_scale;
    float class_scale;
    int bias_match;
    int random;
    float ignore_thresh;
    float truth_thresh;
    float thresh;
    float focus;
    int classfix;
    int absolute;
    int assisted_excitation;

    int onlyforward;
    int stopbackward;
    int dontload;
    int dontsave;
    int dontloadscales;
    int numload;

    float temperature;
    float probability;
    float scale;

    char  * cweights;
    int   * indexes;
    int   * input_layers;
    int   * input_sizes;
    int   * map;
    int   * counts;
    float ** sums;
    float * rand;
    float * cost;
    float * state;
    float * prev_state;
    float * forgot_state;
    float * forgot_delta;
    float * state_delta;
    float * combine_cpu;
    float * combine_delta_cpu;

    float *concat;
    float *concat_delta;

    float *binary_weights;

    float *biases;
    float *bias_updates;

    float *scales;
    float *scale_updates;

    float *weights;
    float *weight_updates;

    float scale_x_y;
    float iou_normalizer;
    float cls_normalizer;
    IOU_LOSS iou_loss;

    char *align_bit_weights_gpu;
    float *mean_arr_gpu;
    float *align_workspace_gpu;
    float *transposed_align_workspace_gpu;
    int align_workspace_size;

    char *align_bit_weights;
    float *mean_arr;
    int align_bit_weights_size;
    int lda_align;
    int new_lda;
    int bit_align;

    float *col_image;
    float * delta;
    float * output;
    float * output_sigmoid;
    int delta_pinned;
    int output_pinned;
    float * loss;
    float * squared;
    float * norms;

    float * spatial_mean;
    float * mean;
    float * variance;

    float * mean_delta;
    float * variance_delta;

    float * rolling_mean;
    float * rolling_variance;

    float * x;
    float * x_norm;

    float * m;
    float * v;

    float * bias_m;
    float * bias_v;
    float * scale_m;
    float * scale_v;


    float *z_cpu;
    float *r_cpu;
    float *h_cpu;
    float *stored_h_cpu;
    float * prev_state_cpu;

    float *temp_cpu;
    float *temp2_cpu;
    float *temp3_cpu;

    float *dh_cpu;
    float *hh_cpu;
    float *prev_cell_cpu;
    float *cell_cpu;
    float *f_cpu;
    float *i_cpu;
    float *g_cpu;
    float *o_cpu;
    float *c_cpu;
    float *stored_c_cpu;
    float *dc_cpu;

    float *binary_input;
    uint32_t *bin_re_packed_input;
    char *t_bit_input;

    struct layer *input_layer;
    struct layer *self_layer;
    struct layer *output_layer;

    struct layer *reset_layer;
    struct layer *update_layer;
    struct layer *state_layer;

    struct layer *input_gate_layer;
    struct layer *state_gate_layer;
    struct layer *input_save_layer;
    struct layer *state_save_layer;
    struct layer *input_state_layer;
    struct layer *state_state_layer;

    struct layer *input_z_layer;
    struct layer *state_z_layer;

    struct layer *input_r_layer;
    struct layer *state_r_layer;

    struct layer *input_h_layer;
    struct layer *state_h_layer;

    struct layer *wz;
    struct layer *uz;
    struct layer *wr;
    struct layer *ur;
    struct layer *wh;
    struct layer *uh;
    struct layer *uo;
    struct layer *wo;
    struct layer *vo;
    struct layer *uf;
    struct layer *wf;
    struct layer *vf;
    struct layer *ui;
    struct layer *wi;
    struct layer *vi;
    struct layer *ug;
    struct layer *wg;

    tree *softmax_tree;

    size_t workspace_size;

#ifdef GPU
    int *indexes_gpu;

    float *z_gpu;
    float *r_gpu;
    float *h_gpu;
    float *stored_h_gpu;

    float *temp_gpu;
    float *temp2_gpu;
    float *temp3_gpu;

    float *dh_gpu;
    float *hh_gpu;
    float *prev_cell_gpu;
    float *prev_state_gpu;
    float *last_prev_state_gpu;
    float *last_prev_cell_gpu;
    float *cell_gpu;
    float *f_gpu;
    float *i_gpu;
    float *g_gpu;
    float *o_gpu;
    float *c_gpu;
    float *stored_c_gpu;
    float *dc_gpu;

    // adam
    float *m_gpu;
    float *v_gpu;
    float *bias_m_gpu;
    float *scale_m_gpu;
    float *bias_v_gpu;
    float *scale_v_gpu;

    float * combine_gpu;
    float * combine_delta_gpu;

    float * forgot_state_gpu;
    float * forgot_delta_gpu;
    float * state_gpu;
    float * state_delta_gpu;
    float * gate_gpu;
    float * gate_delta_gpu;
    float * save_gpu;
    float * save_delta_gpu;
    float * concat_gpu;
    float * concat_delta_gpu;

    float *binary_input_gpu;
    float *binary_weights_gpu;
    float *bin_conv_shortcut_in_gpu;
    float *bin_conv_shortcut_out_gpu;

    float * mean_gpu;
    float * variance_gpu;

    float * rolling_mean_gpu;
    float * rolling_variance_gpu;

    float * variance_delta_gpu;
    float * mean_delta_gpu;

    float * col_image_gpu;

    float * x_gpu;
    float * x_norm_gpu;
    float * weights_gpu;
    float * weight_updates_gpu;
    float * weight_change_gpu;

    float * weights_gpu16;
    float * weight_updates_gpu16;

    float * biases_gpu;
    float * bias_updates_gpu;
    float * bias_change_gpu;

    float * scales_gpu;
    float * scale_updates_gpu;
    float * scale_change_gpu;

    float * output_gpu;
    float * output_sigmoid_gpu;
    float * loss_gpu;
    float * delta_gpu;
    float * rand_gpu;
    float * squared_gpu;
    float * norms_gpu;
#ifdef CUDNN
    cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
    cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16;
    cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc;
    cudnnTensorDescriptor_t dsrcTensorDesc16, ddstTensorDesc16;
    cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16;
    cudnnFilterDescriptor_t weightDesc, weightDesc16;
    cudnnFilterDescriptor_t dweightDesc, dweightDesc16;
    cudnnConvolutionDescriptor_t convDesc;
    cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16;
    cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16;
    cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16;
    cudnnPoolingDescriptor_t poolingDesc;
#endif  // CUDNN
#endif  // GPU
};