// Gaussian YOLOv3 implementation
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// Author: Jiwoong Choi
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// ICCV 2019 Paper: http://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.html
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// arxiv.org: https://arxiv.org/abs/1904.04620v2
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// source code: https://github.com/jwchoi384/Gaussian_YOLOv3
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#include "gaussian_yolo_layer.h"
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#include "activations.h"
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#include "blas.h"
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#include "box.h"
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#include "dark_cuda.h"
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#include "utils.h"
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#include <stdio.h>
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#include <assert.h>
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#include <string.h>
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#include <stdlib.h>
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#ifndef M_PI
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#define M_PI 3.141592
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#endif
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extern int check_mistakes;
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layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes)
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{
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int i;
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layer l = { (LAYER_TYPE)0 };
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l.type = GAUSSIAN_YOLO;
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l.n = n;
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l.total = total;
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l.batch = batch;
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l.h = h;
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l.w = w;
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l.c = n*(classes + 8 + 1);
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l.out_w = l.w;
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l.out_h = l.h;
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l.out_c = l.c;
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l.classes = classes;
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l.cost = (float*)calloc(1, sizeof(float));
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l.biases = (float*)calloc(total*2, sizeof(float));
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if(mask) l.mask = mask;
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else{
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l.mask = (int*)calloc(n, sizeof(int));
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for(i = 0; i < n; ++i){
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l.mask[i] = i;
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}
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}
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l.bias_updates = (float*)calloc(n*2, sizeof(float));
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l.outputs = h*w*n*(classes + 8 + 1);
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l.inputs = l.outputs;
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l.max_boxes = max_boxes;
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l.truth_size = 4 + 2;
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l.truths = l.max_boxes*l.truth_size;
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l.delta = (float*)calloc(batch*l.outputs, sizeof(float));
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l.output = (float*)calloc(batch*l.outputs, sizeof(float));
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for(i = 0; i < total*2; ++i){
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l.biases[i] = .5;
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}
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l.forward = forward_gaussian_yolo_layer;
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l.backward = backward_gaussian_yolo_layer;
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#ifdef GPU
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l.forward_gpu = forward_gaussian_yolo_layer_gpu;
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l.backward_gpu = backward_gaussian_yolo_layer_gpu;
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l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
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l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
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free(l.output);
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if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
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else {
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cudaGetLastError(); // reset CUDA-error
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l.output = (float*)calloc(batch * l.outputs, sizeof(float));
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}
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free(l.delta);
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if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
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else {
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cudaGetLastError(); // reset CUDA-error
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l.delta = (float*)calloc(batch * l.outputs, sizeof(float));
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}
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#endif
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//fprintf(stderr, "Gaussian_yolo\n");
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srand(time(0));
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return l;
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}
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void resize_gaussian_yolo_layer(layer *l, int w, int h)
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{
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l->w = w;
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l->h = h;
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l->outputs = h*w*l->n*(l->classes + 8 + 1);
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l->inputs = l->outputs;
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//l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float));
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//l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float));
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if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float));
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if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float));
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#ifdef GPU
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if (l->output_pinned) {
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CHECK_CUDA(cudaFreeHost(l->output));
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if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
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cudaGetLastError(); // reset CUDA-error
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l->output = (float*)calloc(l->batch * l->outputs, sizeof(float));
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l->output_pinned = 0;
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}
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}
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if (l->delta_pinned) {
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CHECK_CUDA(cudaFreeHost(l->delta));
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if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
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cudaGetLastError(); // reset CUDA-error
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l->delta = (float*)calloc(l->batch * l->outputs, sizeof(float));
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l->delta_pinned = 0;
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}
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}
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cuda_free(l->delta_gpu);
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cuda_free(l->output_gpu);
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l->delta_gpu = cuda_make_array(l->delta, l->batch*l->outputs);
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l->output_gpu = cuda_make_array(l->output, l->batch*l->outputs);
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#endif
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}
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box get_gaussian_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, YOLO_POINT yolo_point)
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{
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box b;
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b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w;
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b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h;
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b.x = (i + x[index + 0 * stride]) / lw;
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b.y = (j + x[index + 2 * stride]) / lh;
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if (yolo_point == YOLO_CENTER) {
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}
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else if (yolo_point == YOLO_LEFT_TOP) {
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b.x = (i + x[index + 0 * stride]) / lw + b.w / 2;
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b.y = (j + x[index + 2 * stride]) / lh + b.h / 2;
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}
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else if (yolo_point == YOLO_RIGHT_BOTTOM) {
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b.x = (i + x[index + 0 * stride]) / lw - b.w / 2;
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b.y = (j + x[index + 2 * stride]) / lh - b.h / 2;
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}
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return b;
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}
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static inline float fix_nan_inf(float val)
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{
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if (isnan(val) || isinf(val)) val = 0;
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return val;
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}
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static inline float clip_value(float val, const float max_val)
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{
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if (val > max_val) val = max_val;
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else if (val < -max_val) val = -max_val;
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return val;
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}
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float delta_gaussian_yolo_box(box truth, float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, float *delta,
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float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point, float max_delta)
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{
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box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, yolo_point);
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float iou;
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ious all_ious = { 0 };
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all_ious.iou = box_iou(pred, truth);
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all_ious.giou = box_giou(pred, truth);
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all_ious.diou = box_diou(pred, truth);
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all_ious.ciou = box_ciou(pred, truth);
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if (pred.w == 0) { pred.w = 1.0; }
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if (pred.h == 0) { pred.h = 1.0; }
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float sigma_const = 0.3;
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float epsi = pow(10,-9);
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float dx, dy, dw, dh;
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iou = all_ious.iou;
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float tx, ty, tw, th;
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tx = (truth.x*lw - i);
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ty = (truth.y*lh - j);
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tw = log(truth.w*w / biases[2 * n]);
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th = log(truth.h*h / biases[2 * n + 1]);
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if (yolo_point == YOLO_CENTER) {
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}
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else if (yolo_point == YOLO_LEFT_TOP) {
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tx = ((truth.x - truth.w / 2)*lw - i);
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ty = ((truth.y - truth.h / 2)*lh - j);
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}
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else if (yolo_point == YOLO_RIGHT_BOTTOM) {
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tx = ((truth.x + truth.w / 2)*lw - i);
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ty = ((truth.y + truth.h / 2)*lh - j);
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}
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dx = (tx - x[index + 0 * stride]);
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dy = (ty - x[index + 2 * stride]);
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dw = (tw - x[index + 4 * stride]);
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dh = (th - x[index + 6 * stride]);
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// Gaussian
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float in_exp_x = dx / x[index+1*stride];
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float in_exp_x_2 = pow(in_exp_x, 2);
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float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const));
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float in_exp_y = dy / x[index+3*stride];
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float in_exp_y_2 = pow(in_exp_y, 2);
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float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const));
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float in_exp_w = dw / x[index+5*stride];
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float in_exp_w_2 = pow(in_exp_w, 2);
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float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const));
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float in_exp_h = dh / x[index+7*stride];
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float in_exp_h_2 = pow(in_exp_h, 2);
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float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const));
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float temp_x = (1./2.) * 1./(normal_dist_x+epsi) * normal_dist_x * scale;
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float temp_y = (1./2.) * 1./(normal_dist_y+epsi) * normal_dist_y * scale;
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float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale;
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float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale;
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if (!accumulate) {
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delta[index + 0 * stride] = 0;
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delta[index + 1 * stride] = 0;
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delta[index + 2 * stride] = 0;
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delta[index + 3 * stride] = 0;
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delta[index + 4 * stride] = 0;
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delta[index + 5 * stride] = 0;
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delta[index + 6 * stride] = 0;
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delta[index + 7 * stride] = 0;
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}
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float delta_x = temp_x * in_exp_x * (1. / x[index + 1 * stride]);
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float delta_y = temp_y * in_exp_y * (1. / x[index + 3 * stride]);
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float delta_w = temp_w * in_exp_w * (1. / x[index + 5 * stride]);
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float delta_h = temp_h * in_exp_h * (1. / x[index + 7 * stride]);
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float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const));
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float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const));
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float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const));
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float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const));
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if (iou_loss != MSE) {
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// GIoU
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iou = all_ious.giou;
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// https://github.com/generalized-iou/g-darknet
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// https://arxiv.org/abs/1902.09630v2
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// https://giou.stanford.edu/
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// https://arxiv.org/abs/1911.08287v1
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// https://github.com/Zzh-tju/DIoU-darknet
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all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
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float dx, dy, dw, dh;
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dx = all_ious.dx_iou.dt;
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dy = all_ious.dx_iou.db;
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dw = all_ious.dx_iou.dl;
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dh = all_ious.dx_iou.dr;
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if (yolo_point == YOLO_CENTER) {
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}
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else if (yolo_point == YOLO_LEFT_TOP) {
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dx = dx - dw/2;
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dy = dy - dh/2;
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}
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else if (yolo_point == YOLO_RIGHT_BOTTOM) {
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dx = dx + dw / 2;
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dy = dy + dh / 2;
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}
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// jacobian^t (transpose)
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//float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr);
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//float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
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//float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
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//float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
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// predict exponential, apply gradient of e^delta_t ONLY for w,h
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dw *= exp(x[index + 4 * stride]);
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dh *= exp(x[index + 6 * stride]);
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delta_x = dx;
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delta_y = dy;
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delta_w = dw;
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delta_h = dh;
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}
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// normalize iou weight, for GIoU
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delta_x *= iou_normalizer;
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delta_y *= iou_normalizer;
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delta_w *= iou_normalizer;
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delta_h *= iou_normalizer;
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// normalize Uncertainty weight
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delta_ux *= uc_normalizer;
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delta_uy *= uc_normalizer;
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delta_uw *= uc_normalizer;
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delta_uh *= uc_normalizer;
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delta_x = fix_nan_inf(delta_x);
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delta_y = fix_nan_inf(delta_y);
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delta_w = fix_nan_inf(delta_w);
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delta_h = fix_nan_inf(delta_h);
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delta_ux = fix_nan_inf(delta_ux);
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delta_uy = fix_nan_inf(delta_uy);
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delta_uw = fix_nan_inf(delta_uw);
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delta_uh = fix_nan_inf(delta_uh);
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if (max_delta != FLT_MAX) {
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delta_x = clip_value(delta_x, max_delta);
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delta_y = clip_value(delta_y, max_delta);
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delta_w = clip_value(delta_w, max_delta);
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delta_h = clip_value(delta_h, max_delta);
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delta_ux = clip_value(delta_ux, max_delta);
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delta_uy = clip_value(delta_uy, max_delta);
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delta_uw = clip_value(delta_uw, max_delta);
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delta_uh = clip_value(delta_uh, max_delta);
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}
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delta[index + 0 * stride] += delta_x;
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delta[index + 2 * stride] += delta_y;
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delta[index + 4 * stride] += delta_w;
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delta[index + 6 * stride] += delta_h;
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delta[index + 1 * stride] += delta_ux;
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delta[index + 3 * stride] += delta_uy;
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delta[index + 5 * stride] += delta_uw;
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delta[index + 7 * stride] += delta_uh;
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return iou;
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}
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void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta)
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{
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int classes_in_one_box = 0;
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int c;
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for (c = 0; c < classes; ++c) {
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if (delta[class_index + stride*c] > 0) classes_in_one_box++;
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}
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if (classes_in_one_box > 0) {
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delta[box_index + 0 * stride] /= classes_in_one_box;
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delta[box_index + 1 * stride] /= classes_in_one_box;
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delta[box_index + 2 * stride] /= classes_in_one_box;
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delta[box_index + 3 * stride] /= classes_in_one_box;
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delta[box_index + 4 * stride] /= classes_in_one_box;
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delta[box_index + 5 * stride] /= classes_in_one_box;
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delta[box_index + 6 * stride] /= classes_in_one_box;
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delta[box_index + 7 * stride] /= classes_in_one_box;
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}
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}
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void delta_gaussian_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, float label_smooth_eps, float *classes_multipliers, float cls_normalizer)
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{
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int n;
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if (delta[index]){
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float y_true = 1;
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if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
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delta[index + stride*class_id] = y_true - output[index + stride*class_id];
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//delta[index + stride*class_id] = 1 - output[index + stride*class_id];
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if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id];
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if(avg_cat) *avg_cat += output[index + stride*class_id];
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return;
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}
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for(n = 0; n < classes; ++n){
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float y_true = ((n == class_id) ? 1 : 0);
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if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
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delta[index + stride*n] = y_true - output[index + stride*n];
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if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id] * cls_normalizer;
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if(n == class_id && avg_cat) *avg_cat += output[index + stride*n];
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}
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}
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int compare_gaussian_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh)
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{
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int j;
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for (j = 0; j < classes; ++j) {
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//float prob = objectness * output[class_index + stride*j];
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float prob = output[class_index + stride*j];
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if (prob > conf_thresh) {
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return 1;
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}
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}
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return 0;
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}
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static int entry_gaussian_index(layer l, int batch, int location, int entry)
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{
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int n = location / (l.w*l.h);
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int loc = location % (l.w*l.h);
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return batch*l.outputs + n*l.w*l.h*(8+l.classes+1) + entry*l.w*l.h + loc;
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}
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void forward_gaussian_yolo_layer(const layer l, network_state state)
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{
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int i,j,b,t,n;
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memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
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#ifndef GPU
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for (b = 0; b < l.batch; ++b){
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for(n = 0; n < l.n; ++n){
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// x : mu, sigma
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int index = entry_gaussian_index(l, b, n*l.w*l.h, 0);
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activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
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scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x
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// y : mu, sigma
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index = entry_gaussian_index(l, b, n*l.w*l.h, 2);
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activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
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scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale y
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// w : sigma
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index = entry_gaussian_index(l, b, n*l.w*l.h, 5);
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activate_array(l.output + index, l.w*l.h, LOGISTIC);
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// h : sigma
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index = entry_gaussian_index(l, b, n*l.w*l.h, 7);
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activate_array(l.output + index, l.w*l.h, LOGISTIC);
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// objectness & class
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index = entry_gaussian_index(l, b, n*l.w*l.h, 8);
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activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC);
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}
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}
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#endif
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memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
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if (!state.train) return;
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float avg_iou = 0;
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float recall = 0;
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float recall75 = 0;
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float avg_cat = 0;
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float avg_obj = 0;
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float avg_anyobj = 0;
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int count = 0;
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int class_count = 0;
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*(l.cost) = 0;
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for (b = 0; b < l.batch; ++b) {
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for (j = 0; j < l.h; ++j) {
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for (i = 0; i < l.w; ++i) {
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for (n = 0; n < l.n; ++n) {
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const int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
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const int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8);
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const int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
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const int stride = l.w*l.h;
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box pred = get_gaussian_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h, l.yolo_point);
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float best_match_iou = 0;
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int best_match_t = 0;
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float best_iou = 0;
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int best_t = 0;
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for(t = 0; t < l.max_boxes; ++t){
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box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1);
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int class_id = state.truth[t*l.truth_size + b*l.truths + 4];
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if (class_id >= l.classes) {
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printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1);
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printf(" truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id);
|
if (check_mistakes) getchar();
|
continue; // if label contains class_id more than number of classes in the cfg-file
|
}
|
if(!truth.x) break;
|
|
|
float objectness = l.output[obj_index];
|
int class_id_match = compare_gaussian_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f);
|
|
float iou = box_iou(pred, truth);
|
if (iou > best_match_iou && class_id_match == 1) {
|
best_match_iou = iou;
|
best_match_t = t;
|
}
|
if (iou > best_iou) {
|
best_iou = iou;
|
best_t = t;
|
}
|
}
|
|
avg_anyobj += l.output[obj_index];
|
l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]);
|
if (best_match_iou > l.ignore_thresh) {
|
const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh);
|
if (l.objectness_smooth) {
|
l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]);
|
|
int class_id = state.truth[best_match_t*l.truth_size + b*l.truths + 4];
|
if (l.map) class_id = l.map[class_id];
|
delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
|
}
|
else l.delta[obj_index] = 0;
|
}
|
else if (state.net.adversarial) {
|
float scale = pred.w * pred.h;
|
if (scale > 0) scale = sqrt(scale);
|
l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]);
|
int cl_id;
|
for (cl_id = 0; cl_id < l.classes; ++cl_id) {
|
if (l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25)
|
l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]);
|
}
|
}
|
if (best_iou > l.truth_thresh) {
|
const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh);
|
if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]);
|
else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]);
|
//l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]);
|
|
int class_id = state.truth[best_t*l.truth_size + b*l.truths + 4];
|
if (l.map) class_id = l.map[class_id];
|
delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
|
const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
|
if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]);
|
box truth = float_to_box_stride(state.truth + best_t*l.truth_size + b*l.truths, 1);
|
delta_gaussian_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta);
|
}
|
}
|
}
|
}
|
for(t = 0; t < l.max_boxes; ++t){
|
box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1);
|
|
if(!truth.x) break;
|
float best_iou = 0;
|
int best_n = 0;
|
i = (truth.x * l.w);
|
j = (truth.y * l.h);
|
|
if (l.yolo_point == YOLO_CENTER) {
|
}
|
else if (l.yolo_point == YOLO_LEFT_TOP) {
|
i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x - truth.w / 2) * l.w)));
|
j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y - truth.h / 2) * l.h)));
|
}
|
else if (l.yolo_point == YOLO_RIGHT_BOTTOM) {
|
i = min_val_cmp(l.w-1, max_val_cmp(0, ((truth.x + truth.w / 2) * l.w)));
|
j = min_val_cmp(l.h-1, max_val_cmp(0, ((truth.y + truth.h / 2) * l.h)));
|
}
|
|
box truth_shift = truth;
|
truth_shift.x = truth_shift.y = 0;
|
for(n = 0; n < l.total; ++n){
|
box pred = {0};
|
pred.w = l.biases[2*n]/ state.net.w;
|
pred.h = l.biases[2*n+1]/ state.net.h;
|
float iou = box_iou(pred, truth_shift);
|
if (iou > best_iou){
|
best_iou = iou;
|
best_n = n;
|
}
|
}
|
|
int mask_n = int_index(l.mask, best_n, l.n);
|
if(mask_n >= 0){
|
int class_id = state.truth[t*l.truth_size + b*l.truths + 4];
|
if (l.map) class_id = l.map[class_id];
|
|
int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
|
const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
|
float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, best_n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2-truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta);
|
|
int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8);
|
avg_obj += l.output[obj_index];
|
l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
|
|
int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9);
|
delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
|
|
++count;
|
++class_count;
|
if(iou > .5) recall += 1;
|
if(iou > .75) recall75 += 1;
|
avg_iou += iou;
|
}
|
|
|
// iou_thresh
|
for (n = 0; n < l.total; ++n) {
|
int mask_n = int_index(l.mask, n, l.n);
|
if (mask_n >= 0 && n != best_n && l.iou_thresh < 1.0f) {
|
box pred = { 0 };
|
pred.w = l.biases[2 * n] / state.net.w;
|
pred.h = l.biases[2 * n + 1] / state.net.h;
|
float iou = box_iou_kind(pred, truth_shift, l.iou_thresh_kind); // IOU, GIOU, MSE, DIOU, CIOU
|
// iou, n
|
|
if (iou > l.iou_thresh) {
|
int class_id = state.truth[t*l.truth_size + b*l.truths + 4];
|
if (l.map) class_id = l.map[class_id];
|
|
int box_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 0);
|
const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
|
float iou = delta_gaussian_yolo_box(truth, l.output, l.biases, n, box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, l.uc_normalizer, 1, l.yolo_point, l.max_delta);
|
|
int obj_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 8);
|
avg_obj += l.output[obj_index];
|
l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
|
|
int class_index = entry_gaussian_index(l, b, mask_n*l.w*l.h + j*l.w + i, 9);
|
delta_gaussian_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, &avg_cat, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
|
|
++count;
|
++class_count;
|
if (iou > .5) recall += 1;
|
if (iou > .75) recall75 += 1;
|
avg_iou += iou;
|
}
|
}
|
}
|
}
|
|
// averages the deltas obtained by the function: delta_yolo_box()_accumulate
|
for (j = 0; j < l.h; ++j) {
|
for (i = 0; i < l.w; ++i) {
|
for (n = 0; n < l.n; ++n) {
|
int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
|
int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
|
const int stride = l.w*l.h;
|
|
averages_gaussian_yolo_deltas(class_index, box_index, stride, l.classes, l.delta);
|
}
|
}
|
}
|
}
|
|
|
// calculate: Classification-loss, IoU-loss and Uncertainty-loss
|
const int stride = l.w*l.h;
|
float* classification_lost = (float *)calloc(l.batch * l.outputs, sizeof(float));
|
memcpy(classification_lost, l.delta, l.batch * l.outputs * sizeof(float));
|
|
|
for (b = 0; b < l.batch; ++b) {
|
for (j = 0; j < l.h; ++j) {
|
for (i = 0; i < l.w; ++i) {
|
for (n = 0; n < l.n; ++n) {
|
int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
|
|
classification_lost[box_index + 0 * stride] = 0;
|
classification_lost[box_index + 1 * stride] = 0;
|
classification_lost[box_index + 2 * stride] = 0;
|
classification_lost[box_index + 3 * stride] = 0;
|
classification_lost[box_index + 4 * stride] = 0;
|
classification_lost[box_index + 5 * stride] = 0;
|
classification_lost[box_index + 6 * stride] = 0;
|
classification_lost[box_index + 7 * stride] = 0;
|
}
|
}
|
}
|
}
|
float class_loss = pow(mag_array(classification_lost, l.outputs * l.batch), 2);
|
free(classification_lost);
|
|
|
float* except_uncertainty_lost = (float *)calloc(l.batch * l.outputs, sizeof(float));
|
memcpy(except_uncertainty_lost, l.delta, l.batch * l.outputs * sizeof(float));
|
for (b = 0; b < l.batch; ++b) {
|
for (j = 0; j < l.h; ++j) {
|
for (i = 0; i < l.w; ++i) {
|
for (n = 0; n < l.n; ++n) {
|
int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
|
except_uncertainty_lost[box_index + 4 * stride] = 0;
|
except_uncertainty_lost[box_index + 5 * stride] = 0;
|
except_uncertainty_lost[box_index + 6 * stride] = 0;
|
except_uncertainty_lost[box_index + 7 * stride] = 0;
|
}
|
}
|
}
|
}
|
float except_uc_loss = pow(mag_array(except_uncertainty_lost, l.outputs * l.batch), 2);
|
free(except_uncertainty_lost);
|
|
*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
|
|
float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2);
|
float uc_loss = loss - except_uc_loss;
|
float iou_loss = except_uc_loss - class_loss;
|
|
loss /= l.batch;
|
class_loss /= l.batch;
|
uc_loss /= l.batch;
|
iou_loss /= l.batch;
|
|
fprintf(stderr, "Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %.2f, iou_loss = %.2f, uc_loss = %.2f, total_loss = %.2f \n",
|
state.index, avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, recall75/count, count,
|
class_loss, iou_loss, uc_loss, loss);
|
}
|
|
void backward_gaussian_yolo_layer(const layer l, network_state state)
|
{
|
axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
|
}
|
|
void correct_gaussian_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter)
|
{
|
int i;
|
int new_w=0;
|
int new_h=0;
|
if (letter) {
|
if (((float)netw / w) < ((float)neth / h)) {
|
new_w = netw;
|
new_h = (h * netw) / w;
|
}
|
else {
|
new_h = neth;
|
new_w = (w * neth) / h;
|
}
|
}
|
else {
|
new_w = netw;
|
new_h = neth;
|
}
|
/*
|
if (((float)netw/w) < ((float)neth/h)) {
|
new_w = netw;
|
new_h = (h * netw)/w;
|
} else {
|
new_h = neth;
|
new_w = (w * neth)/h;
|
}
|
*/
|
for (i = 0; i < n; ++i){
|
box b = dets[i].bbox;
|
b.x = (b.x - (netw - new_w)/2./netw) / ((float)new_w/netw);
|
b.y = (b.y - (neth - new_h)/2./neth) / ((float)new_h/neth);
|
b.w *= (float)netw/new_w;
|
b.h *= (float)neth/new_h;
|
if(!relative){
|
b.x *= w;
|
b.w *= w;
|
b.y *= h;
|
b.h *= h;
|
}
|
dets[i].bbox = b;
|
}
|
}
|
|
int gaussian_yolo_num_detections(layer l, float thresh)
|
{
|
int i, n;
|
int count = 0;
|
for (i = 0; i < l.w*l.h; ++i){
|
for(n = 0; n < l.n; ++n){
|
int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8);
|
if(l.output[obj_index] > thresh){
|
++count;
|
}
|
}
|
}
|
return count;
|
}
|
|
/*
|
void avg_flipped_gaussian_yolo(layer l)
|
{
|
int i,j,n,z;
|
float *flip = l.output + l.outputs;
|
for (j = 0; j < l.h; ++j) {
|
for (i = 0; i < l.w/2; ++i) {
|
for (n = 0; n < l.n; ++n) {
|
for(z = 0; z < l.classes + 8 + 1; ++z){
|
int i1 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + i;
|
int i2 = z*l.w*l.h*l.n + n*l.w*l.h + j*l.w + (l.w - i - 1);
|
float swap = flip[i1];
|
flip[i1] = flip[i2];
|
flip[i2] = swap;
|
if(z == 0){
|
flip[i1] = -flip[i1];
|
flip[i2] = -flip[i2];
|
}
|
}
|
}
|
}
|
}
|
for(i = 0; i < l.outputs; ++i){
|
l.output[i] = (l.output[i] + flip[i])/2.;
|
}
|
}
|
*/
|
|
int get_gaussian_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter)
|
{
|
int i,j,n;
|
float *predictions = l.output;
|
//if (l.batch == 2) avg_flipped_gaussian_yolo(l);
|
int count = 0;
|
for (i = 0; i < l.w*l.h; ++i){
|
int row = i / l.w;
|
int col = i % l.w;
|
for(n = 0; n < l.n; ++n){
|
int obj_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 8);
|
float objectness = predictions[obj_index];
|
if (objectness <= thresh) continue; // incorrect behavior for Nan values
|
|
if (objectness > thresh) {
|
int box_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 0);
|
dets[count].bbox = get_gaussian_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.yolo_point);
|
dets[count].objectness = objectness;
|
dets[count].classes = l.classes;
|
|
dets[count].uc[0] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 1)]; // tx uncertainty
|
dets[count].uc[1] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 3)]; // ty uncertainty
|
dets[count].uc[2] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 5)]; // tw uncertainty
|
dets[count].uc[3] = predictions[entry_gaussian_index(l, 0, n*l.w*l.h + i, 7)]; // th uncertainty
|
|
dets[count].points = l.yolo_point;
|
//if (l.yolo_point != YOLO_CENTER) dets[count].objectness = objectness = 0;
|
|
for (j = 0; j < l.classes; ++j) {
|
int class_index = entry_gaussian_index(l, 0, n*l.w*l.h + i, 9 + j);
|
float uc_aver = (dets[count].uc[0] + dets[count].uc[1] + dets[count].uc[2] + dets[count].uc[3]) / 4.0;
|
float prob = objectness*predictions[class_index] * (1.0 - uc_aver);
|
dets[count].prob[j] = (prob > thresh) ? prob : 0;
|
}
|
++count;
|
}
|
}
|
}
|
correct_gaussian_yolo_boxes(dets, count, w, h, netw, neth, relative, letter);
|
return count;
|
}
|
|
#ifdef GPU
|
|
void forward_gaussian_yolo_layer_gpu(const layer l, network_state state)
|
{
|
copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
|
int b, n;
|
for (b = 0; b < l.batch; ++b)
|
{
|
for(n = 0; n < l.n; ++n)
|
{
|
// x : mu, sigma
|
int index = entry_gaussian_index(l, b, n*l.w*l.h, 0);
|
activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC);
|
scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale x
|
// y : mu, sigma
|
index = entry_gaussian_index(l, b, n*l.w*l.h, 2);
|
activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC);
|
scal_add_ongpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + index, 1); // scale y
|
// w : sigma
|
index = entry_gaussian_index(l, b, n*l.w*l.h, 5);
|
activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC);
|
// h : sigma
|
index = entry_gaussian_index(l, b, n*l.w*l.h, 7);
|
activate_array_ongpu(l.output_gpu + index, l.w*l.h, LOGISTIC);
|
// objectness & class
|
index = entry_gaussian_index(l, b, n*l.w*l.h, 8);
|
activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC);
|
}
|
}
|
|
if (!state.train || l.onlyforward) {
|
//cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
|
cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs);
|
CHECK_CUDA(cudaPeekAtLastError());
|
return;
|
}
|
|
float *in_cpu = (float *)calloc(l.batch*l.inputs, sizeof(float));
|
cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
|
memcpy(in_cpu, l.output, l.batch*l.outputs * sizeof(float));
|
float *truth_cpu = 0;
|
if (state.truth) {
|
int num_truth = l.batch*l.truths;
|
truth_cpu = (float *)calloc(num_truth, sizeof(float));
|
cuda_pull_array(state.truth, truth_cpu, num_truth);
|
}
|
network_state cpu_state = state;
|
cpu_state.net = state.net;
|
cpu_state.index = state.index;
|
cpu_state.train = state.train;
|
cpu_state.truth = truth_cpu;
|
cpu_state.input = in_cpu;
|
forward_gaussian_yolo_layer(l, cpu_state);
|
//forward_yolo_layer(l, state);
|
cuda_push_array(l.delta_gpu, l.delta, l.batch*l.outputs);
|
free(in_cpu);
|
if (cpu_state.truth) free(cpu_state.truth);
|
}
|
|
void backward_gaussian_yolo_layer_gpu(const layer l, network_state state)
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{
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axpy_ongpu(l.batch*l.inputs, l.delta_normalizer, l.delta_gpu, 1, state.delta, 1);
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}
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#endif
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