From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/gaussian_yolo_layer.c | 1798 +++++++++++++++++++++++++++++-----------------------------
1 files changed, 899 insertions(+), 899 deletions(-)
diff --git a/lib/detecter_tools/darknet/gaussian_yolo_layer.c b/lib/detecter_tools/darknet/gaussian_yolo_layer.c
index 5742e29..bd99a89 100644
--- a/lib/detecter_tools/darknet/gaussian_yolo_layer.c
+++ b/lib/detecter_tools/darknet/gaussian_yolo_layer.c
@@ -1,899 +1,899 @@
-// Gaussian YOLOv3 implementation
-// Author: Jiwoong Choi
-// 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
-// arxiv.org: https://arxiv.org/abs/1904.04620v2
-// source code: https://github.com/jwchoi384/Gaussian_YOLOv3
-
-#include "gaussian_yolo_layer.h"
-#include "activations.h"
-#include "blas.h"
-#include "box.h"
-#include "dark_cuda.h"
-#include "utils.h"
-
-#include <stdio.h>
-#include <assert.h>
-#include <string.h>
-#include <stdlib.h>
-
-#ifndef M_PI
-#define M_PI 3.141592
-#endif
-
-extern int check_mistakes;
-
-layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes)
-{
- int i;
- layer l = { (LAYER_TYPE)0 };
- l.type = GAUSSIAN_YOLO;
-
- l.n = n;
- l.total = total;
- l.batch = batch;
- l.h = h;
- l.w = w;
- l.c = n*(classes + 8 + 1);
- l.out_w = l.w;
- l.out_h = l.h;
- l.out_c = l.c;
- l.classes = classes;
- l.cost = (float*)calloc(1, sizeof(float));
- l.biases = (float*)calloc(total*2, sizeof(float));
- if(mask) l.mask = mask;
- else{
- l.mask = (int*)calloc(n, sizeof(int));
- for(i = 0; i < n; ++i){
- l.mask[i] = i;
- }
- }
- l.bias_updates = (float*)calloc(n*2, sizeof(float));
- l.outputs = h*w*n*(classes + 8 + 1);
- l.inputs = l.outputs;
- l.max_boxes = max_boxes;
- l.truths = l.max_boxes*(4 + 1);
- l.delta = (float*)calloc(batch*l.outputs, sizeof(float));
- l.output = (float*)calloc(batch*l.outputs, sizeof(float));
- for(i = 0; i < total*2; ++i){
- l.biases[i] = .5;
- }
-
- l.forward = forward_gaussian_yolo_layer;
- l.backward = backward_gaussian_yolo_layer;
-#ifdef GPU
- l.forward_gpu = forward_gaussian_yolo_layer_gpu;
- l.backward_gpu = backward_gaussian_yolo_layer_gpu;
- l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
- l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
-
-
- free(l.output);
- if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
- else {
- cudaGetLastError(); // reset CUDA-error
- l.output = (float*)calloc(batch * l.outputs, sizeof(float));
- }
-
- free(l.delta);
- if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
- else {
- cudaGetLastError(); // reset CUDA-error
- l.delta = (float*)calloc(batch * l.outputs, sizeof(float));
- }
-
-#endif
-
- //fprintf(stderr, "Gaussian_yolo\n");
- srand(time(0));
-
- return l;
-}
-
-void resize_gaussian_yolo_layer(layer *l, int w, int h)
-{
- l->w = w;
- l->h = h;
-
- l->outputs = h*w*l->n*(l->classes + 8 + 1);
- l->inputs = l->outputs;
-
- //l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float));
- //l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float));
-
- if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float));
- if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float));
-
-#ifdef GPU
-
- if (l->output_pinned) {
- CHECK_CUDA(cudaFreeHost(l->output));
- if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
- cudaGetLastError(); // reset CUDA-error
- l->output = (float*)calloc(l->batch * l->outputs, sizeof(float));
- l->output_pinned = 0;
- }
- }
-
- if (l->delta_pinned) {
- CHECK_CUDA(cudaFreeHost(l->delta));
- if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
- cudaGetLastError(); // reset CUDA-error
- l->delta = (float*)calloc(l->batch * l->outputs, sizeof(float));
- l->delta_pinned = 0;
- }
- }
-
-
- 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);
-#endif
-}
-
-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)
-{
- box b;
-
- b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w;
- b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h;
- b.x = (i + x[index + 0 * stride]) / lw;
- b.y = (j + x[index + 2 * stride]) / lh;
-
- if (yolo_point == YOLO_CENTER) {
- }
- else if (yolo_point == YOLO_LEFT_TOP) {
- b.x = (i + x[index + 0 * stride]) / lw + b.w / 2;
- b.y = (j + x[index + 2 * stride]) / lh + b.h / 2;
- }
- else if (yolo_point == YOLO_RIGHT_BOTTOM) {
- b.x = (i + x[index + 0 * stride]) / lw - b.w / 2;
- b.y = (j + x[index + 2 * stride]) / lh - b.h / 2;
- }
-
- return b;
-}
-
-static inline float fix_nan_inf(float val)
-{
- if (isnan(val) || isinf(val)) val = 0;
- return val;
-}
-
-static inline float clip_value(float val, const float max_val)
-{
- if (val > max_val) val = max_val;
- else if (val < -max_val) val = -max_val;
- return val;
-}
-
-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,
- float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point, float max_delta)
-{
- box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, yolo_point);
-
- float iou;
- ious all_ious = { 0 };
- all_ious.iou = box_iou(pred, truth);
- all_ious.giou = box_giou(pred, truth);
- all_ious.diou = box_diou(pred, truth);
- all_ious.ciou = box_ciou(pred, truth);
- if (pred.w == 0) { pred.w = 1.0; }
- if (pred.h == 0) { pred.h = 1.0; }
-
- float sigma_const = 0.3;
- float epsi = pow(10,-9);
-
- float dx, dy, dw, dh;
-
- iou = all_ious.iou;
-
- float tx, ty, tw, th;
-
- tx = (truth.x*lw - i);
- ty = (truth.y*lh - j);
- tw = log(truth.w*w / biases[2 * n]);
- th = log(truth.h*h / biases[2 * n + 1]);
-
- if (yolo_point == YOLO_CENTER) {
- }
- else if (yolo_point == YOLO_LEFT_TOP) {
- tx = ((truth.x - truth.w / 2)*lw - i);
- ty = ((truth.y - truth.h / 2)*lh - j);
- }
- else if (yolo_point == YOLO_RIGHT_BOTTOM) {
- tx = ((truth.x + truth.w / 2)*lw - i);
- ty = ((truth.y + truth.h / 2)*lh - j);
- }
-
- dx = (tx - x[index + 0 * stride]);
- dy = (ty - x[index + 2 * stride]);
- dw = (tw - x[index + 4 * stride]);
- dh = (th - x[index + 6 * stride]);
-
- // Gaussian
- float in_exp_x = dx / x[index+1*stride];
- float in_exp_x_2 = pow(in_exp_x, 2);
- float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const));
-
- float in_exp_y = dy / x[index+3*stride];
- float in_exp_y_2 = pow(in_exp_y, 2);
- float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const));
-
- float in_exp_w = dw / x[index+5*stride];
- float in_exp_w_2 = pow(in_exp_w, 2);
- float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const));
-
- float in_exp_h = dh / x[index+7*stride];
- float in_exp_h_2 = pow(in_exp_h, 2);
- float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const));
-
- float temp_x = (1./2.) * 1./(normal_dist_x+epsi) * normal_dist_x * scale;
- float temp_y = (1./2.) * 1./(normal_dist_y+epsi) * normal_dist_y * scale;
- float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale;
- float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale;
-
- if (!accumulate) {
- delta[index + 0 * stride] = 0;
- delta[index + 1 * stride] = 0;
- delta[index + 2 * stride] = 0;
- delta[index + 3 * stride] = 0;
- delta[index + 4 * stride] = 0;
- delta[index + 5 * stride] = 0;
- delta[index + 6 * stride] = 0;
- delta[index + 7 * stride] = 0;
- }
-
- float delta_x = temp_x * in_exp_x * (1. / x[index + 1 * stride]);
- float delta_y = temp_y * in_exp_y * (1. / x[index + 3 * stride]);
- float delta_w = temp_w * in_exp_w * (1. / x[index + 5 * stride]);
- float delta_h = temp_h * in_exp_h * (1. / x[index + 7 * stride]);
-
- float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const));
- float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const));
- float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const));
- float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const));
-
- if (iou_loss != MSE) {
- // GIoU
- iou = all_ious.giou;
-
- // https://github.com/generalized-iou/g-darknet
- // https://arxiv.org/abs/1902.09630v2
- // https://giou.stanford.edu/
- // https://arxiv.org/abs/1911.08287v1
- // https://github.com/Zzh-tju/DIoU-darknet
- all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
-
- float dx, dy, dw, dh;
-
- dx = all_ious.dx_iou.dt;
- dy = all_ious.dx_iou.db;
- dw = all_ious.dx_iou.dl;
- dh = all_ious.dx_iou.dr;
-
- if (yolo_point == YOLO_CENTER) {
- }
- else if (yolo_point == YOLO_LEFT_TOP) {
- dx = dx - dw/2;
- dy = dy - dh/2;
- }
- else if (yolo_point == YOLO_RIGHT_BOTTOM) {
- dx = dx + dw / 2;
- dy = dy + dh / 2;
- }
-
- // jacobian^t (transpose)
- //float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr);
- //float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
- //float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
- //float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
-
- // predict exponential, apply gradient of e^delta_t ONLY for w,h
- dw *= exp(x[index + 4 * stride]);
- dh *= exp(x[index + 6 * stride]);
-
- delta_x = dx;
- delta_y = dy;
- delta_w = dw;
- delta_h = dh;
- }
-
- // normalize iou weight, for GIoU
- delta_x *= iou_normalizer;
- delta_y *= iou_normalizer;
- delta_w *= iou_normalizer;
- delta_h *= iou_normalizer;
-
- // normalize Uncertainty weight
- delta_ux *= uc_normalizer;
- delta_uy *= uc_normalizer;
- delta_uw *= uc_normalizer;
- delta_uh *= uc_normalizer;
-
- delta_x = fix_nan_inf(delta_x);
- delta_y = fix_nan_inf(delta_y);
- delta_w = fix_nan_inf(delta_w);
- delta_h = fix_nan_inf(delta_h);
-
- delta_ux = fix_nan_inf(delta_ux);
- delta_uy = fix_nan_inf(delta_uy);
- delta_uw = fix_nan_inf(delta_uw);
- delta_uh = fix_nan_inf(delta_uh);
-
- if (max_delta != FLT_MAX) {
- delta_x = clip_value(delta_x, max_delta);
- delta_y = clip_value(delta_y, max_delta);
- delta_w = clip_value(delta_w, max_delta);
- delta_h = clip_value(delta_h, max_delta);
-
- delta_ux = clip_value(delta_ux, max_delta);
- delta_uy = clip_value(delta_uy, max_delta);
- delta_uw = clip_value(delta_uw, max_delta);
- delta_uh = clip_value(delta_uh, max_delta);
- }
-
- delta[index + 0 * stride] += delta_x;
- delta[index + 2 * stride] += delta_y;
- delta[index + 4 * stride] += delta_w;
- delta[index + 6 * stride] += delta_h;
-
- delta[index + 1 * stride] += delta_ux;
- delta[index + 3 * stride] += delta_uy;
- delta[index + 5 * stride] += delta_uw;
- delta[index + 7 * stride] += delta_uh;
- return iou;
-}
-
-void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta)
-{
-
- int classes_in_one_box = 0;
- int c;
- for (c = 0; c < classes; ++c) {
- if (delta[class_index + stride*c] > 0) classes_in_one_box++;
- }
-
- if (classes_in_one_box > 0) {
- delta[box_index + 0 * stride] /= classes_in_one_box;
- delta[box_index + 1 * stride] /= classes_in_one_box;
- delta[box_index + 2 * stride] /= classes_in_one_box;
- delta[box_index + 3 * stride] /= classes_in_one_box;
- delta[box_index + 4 * stride] /= classes_in_one_box;
- delta[box_index + 5 * stride] /= classes_in_one_box;
- delta[box_index + 6 * stride] /= classes_in_one_box;
- delta[box_index + 7 * stride] /= classes_in_one_box;
- }
-}
-
-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)
-{
- int n;
- if (delta[index]){
- float y_true = 1;
- if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
- delta[index + stride*class_id] = y_true - output[index + stride*class_id];
- //delta[index + stride*class_id] = 1 - output[index + stride*class_id];
-
- if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id];
- if(avg_cat) *avg_cat += output[index + stride*class_id];
- return;
- }
- for(n = 0; n < classes; ++n){
- float y_true = ((n == class_id) ? 1 : 0);
- if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
- delta[index + stride*n] = y_true - output[index + stride*n];
-
- if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id];
- if(n == class_id && avg_cat) *avg_cat += output[index + stride*n];
- }
-}
-
-int compare_gaussian_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh)
-{
- int j;
- for (j = 0; j < classes; ++j) {
- //float prob = objectness * output[class_index + stride*j];
- float prob = output[class_index + stride*j];
- if (prob > conf_thresh) {
- return 1;
- }
- }
- return 0;
-}
-
-static int entry_gaussian_index(layer l, int batch, int location, int entry)
-{
- int n = location / (l.w*l.h);
- int loc = location % (l.w*l.h);
- return batch*l.outputs + n*l.w*l.h*(8+l.classes+1) + entry*l.w*l.h + loc;
-}
-
-void forward_gaussian_yolo_layer(const layer l, network_state state)
-{
- int i,j,b,t,n;
- memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
-
-#ifndef GPU
- 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(l.output + index, 2*l.w*l.h, LOGISTIC);
- scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x
- // y : mu, sigma
- index = entry_gaussian_index(l, b, n*l.w*l.h, 2);
- activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
- scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale y
- // w : sigma
- index = entry_gaussian_index(l, b, n*l.w*l.h, 5);
- activate_array(l.output + index, l.w*l.h, LOGISTIC);
- // h : sigma
- index = entry_gaussian_index(l, b, n*l.w*l.h, 7);
- activate_array(l.output + index, l.w*l.h, LOGISTIC);
- // objectness & class
- index = entry_gaussian_index(l, b, n*l.w*l.h, 8);
- activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC);
- }
- }
-#endif
-
- memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
- if (!state.train) return;
- float avg_iou = 0;
- float recall = 0;
- float recall75 = 0;
- float avg_cat = 0;
- float avg_obj = 0;
- float avg_anyobj = 0;
- int count = 0;
- int class_count = 0;
- *(l.cost) = 0;
- 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) {
- const int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
- const int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8);
- const int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
- const int stride = l.w*l.h;
- 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);
- float best_match_iou = 0;
- int best_match_t = 0;
- float best_iou = 0;
- int best_t = 0;
- for(t = 0; t < l.max_boxes; ++t){
- box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1);
- int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
- if (class_id >= l.classes) {
- 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);
- 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.cls_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.cls_normalizer * (iou_multiplier - l.output[obj_index]);
-
- int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4];
- if (l.map) class_id = l.map[class_id];
- const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
- l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]);
- }
- 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.cls_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.cls_normalizer * (iou_multiplier - l.output[obj_index]);
- else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
- //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
-
- int class_id = state.truth[best_t*(4 + 1) + 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);
- 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*(4 + 1) + 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*(4 + 1) + 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*(4 + 1) + 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.cls_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);
-
- ++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*(4 + 1) + 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.cls_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);
-
- ++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)
-{
- axpy_ongpu(l.batch*l.inputs, 1, l.delta_gpu, 1, state.delta, 1);
-}
-#endif
+// Gaussian YOLOv3 implementation
+// Author: Jiwoong Choi
+// 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
+// arxiv.org: https://arxiv.org/abs/1904.04620v2
+// source code: https://github.com/jwchoi384/Gaussian_YOLOv3
+
+#include "gaussian_yolo_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "dark_cuda.h"
+#include "utils.h"
+
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+#ifndef M_PI
+#define M_PI 3.141592
+#endif
+
+extern int check_mistakes;
+
+layer make_gaussian_yolo_layer(int batch, int w, int h, int n, int total, int *mask, int classes, int max_boxes)
+{
+ int i;
+ layer l = { (LAYER_TYPE)0 };
+ l.type = GAUSSIAN_YOLO;
+
+ l.n = n;
+ l.total = total;
+ l.batch = batch;
+ l.h = h;
+ l.w = w;
+ l.c = n*(classes + 8 + 1);
+ l.out_w = l.w;
+ l.out_h = l.h;
+ l.out_c = l.c;
+ l.classes = classes;
+ l.cost = (float*)calloc(1, sizeof(float));
+ l.biases = (float*)calloc(total*2, sizeof(float));
+ if(mask) l.mask = mask;
+ else{
+ l.mask = (int*)calloc(n, sizeof(int));
+ for(i = 0; i < n; ++i){
+ l.mask[i] = i;
+ }
+ }
+ l.bias_updates = (float*)calloc(n*2, sizeof(float));
+ l.outputs = h*w*n*(classes + 8 + 1);
+ l.inputs = l.outputs;
+ l.max_boxes = max_boxes;
+ l.truth_size = 4 + 2;
+ l.truths = l.max_boxes*l.truth_size;
+ l.delta = (float*)calloc(batch*l.outputs, sizeof(float));
+ l.output = (float*)calloc(batch*l.outputs, sizeof(float));
+ for(i = 0; i < total*2; ++i){
+ l.biases[i] = .5;
+ }
+
+ l.forward = forward_gaussian_yolo_layer;
+ l.backward = backward_gaussian_yolo_layer;
+#ifdef GPU
+ l.forward_gpu = forward_gaussian_yolo_layer_gpu;
+ l.backward_gpu = backward_gaussian_yolo_layer_gpu;
+ l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+ l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
+
+
+ free(l.output);
+ if (cudaSuccess == cudaHostAlloc(&l.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;
+ else {
+ cudaGetLastError(); // reset CUDA-error
+ l.output = (float*)calloc(batch * l.outputs, sizeof(float));
+ }
+
+ free(l.delta);
+ if (cudaSuccess == cudaHostAlloc(&l.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;
+ else {
+ cudaGetLastError(); // reset CUDA-error
+ l.delta = (float*)calloc(batch * l.outputs, sizeof(float));
+ }
+
+#endif
+
+ //fprintf(stderr, "Gaussian_yolo\n");
+ srand(time(0));
+
+ return l;
+}
+
+void resize_gaussian_yolo_layer(layer *l, int w, int h)
+{
+ l->w = w;
+ l->h = h;
+
+ l->outputs = h*w*l->n*(l->classes + 8 + 1);
+ l->inputs = l->outputs;
+
+ //l->output = (float *)realloc(l->output, l->batch*l->outputs * sizeof(float));
+ //l->delta = (float *)realloc(l->delta, l->batch*l->outputs * sizeof(float));
+
+ if (!l->output_pinned) l->output = (float*)realloc(l->output, l->batch*l->outputs * sizeof(float));
+ if (!l->delta_pinned) l->delta = (float*)realloc(l->delta, l->batch*l->outputs * sizeof(float));
+
+#ifdef GPU
+
+ if (l->output_pinned) {
+ CHECK_CUDA(cudaFreeHost(l->output));
+ if (cudaSuccess != cudaHostAlloc(&l->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
+ cudaGetLastError(); // reset CUDA-error
+ l->output = (float*)calloc(l->batch * l->outputs, sizeof(float));
+ l->output_pinned = 0;
+ }
+ }
+
+ if (l->delta_pinned) {
+ CHECK_CUDA(cudaFreeHost(l->delta));
+ if (cudaSuccess != cudaHostAlloc(&l->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {
+ cudaGetLastError(); // reset CUDA-error
+ l->delta = (float*)calloc(l->batch * l->outputs, sizeof(float));
+ l->delta_pinned = 0;
+ }
+ }
+
+
+ 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);
+#endif
+}
+
+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)
+{
+ box b;
+
+ b.w = exp(x[index + 4 * stride]) * biases[2 * n] / w;
+ b.h = exp(x[index + 6 * stride]) * biases[2 * n + 1] / h;
+ b.x = (i + x[index + 0 * stride]) / lw;
+ b.y = (j + x[index + 2 * stride]) / lh;
+
+ if (yolo_point == YOLO_CENTER) {
+ }
+ else if (yolo_point == YOLO_LEFT_TOP) {
+ b.x = (i + x[index + 0 * stride]) / lw + b.w / 2;
+ b.y = (j + x[index + 2 * stride]) / lh + b.h / 2;
+ }
+ else if (yolo_point == YOLO_RIGHT_BOTTOM) {
+ b.x = (i + x[index + 0 * stride]) / lw - b.w / 2;
+ b.y = (j + x[index + 2 * stride]) / lh - b.h / 2;
+ }
+
+ return b;
+}
+
+static inline float fix_nan_inf(float val)
+{
+ if (isnan(val) || isinf(val)) val = 0;
+ return val;
+}
+
+static inline float clip_value(float val, const float max_val)
+{
+ if (val > max_val) val = max_val;
+ else if (val < -max_val) val = -max_val;
+ return val;
+}
+
+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,
+ float scale, int stride, float iou_normalizer, IOU_LOSS iou_loss, float uc_normalizer, int accumulate, YOLO_POINT yolo_point, float max_delta)
+{
+ box pred = get_gaussian_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, yolo_point);
+
+ float iou;
+ ious all_ious = { 0 };
+ all_ious.iou = box_iou(pred, truth);
+ all_ious.giou = box_giou(pred, truth);
+ all_ious.diou = box_diou(pred, truth);
+ all_ious.ciou = box_ciou(pred, truth);
+ if (pred.w == 0) { pred.w = 1.0; }
+ if (pred.h == 0) { pred.h = 1.0; }
+
+ float sigma_const = 0.3;
+ float epsi = pow(10,-9);
+
+ float dx, dy, dw, dh;
+
+ iou = all_ious.iou;
+
+ float tx, ty, tw, th;
+
+ tx = (truth.x*lw - i);
+ ty = (truth.y*lh - j);
+ tw = log(truth.w*w / biases[2 * n]);
+ th = log(truth.h*h / biases[2 * n + 1]);
+
+ if (yolo_point == YOLO_CENTER) {
+ }
+ else if (yolo_point == YOLO_LEFT_TOP) {
+ tx = ((truth.x - truth.w / 2)*lw - i);
+ ty = ((truth.y - truth.h / 2)*lh - j);
+ }
+ else if (yolo_point == YOLO_RIGHT_BOTTOM) {
+ tx = ((truth.x + truth.w / 2)*lw - i);
+ ty = ((truth.y + truth.h / 2)*lh - j);
+ }
+
+ dx = (tx - x[index + 0 * stride]);
+ dy = (ty - x[index + 2 * stride]);
+ dw = (tw - x[index + 4 * stride]);
+ dh = (th - x[index + 6 * stride]);
+
+ // Gaussian
+ float in_exp_x = dx / x[index+1*stride];
+ float in_exp_x_2 = pow(in_exp_x, 2);
+ float normal_dist_x = exp(in_exp_x_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+1*stride]+sigma_const));
+
+ float in_exp_y = dy / x[index+3*stride];
+ float in_exp_y_2 = pow(in_exp_y, 2);
+ float normal_dist_y = exp(in_exp_y_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+3*stride]+sigma_const));
+
+ float in_exp_w = dw / x[index+5*stride];
+ float in_exp_w_2 = pow(in_exp_w, 2);
+ float normal_dist_w = exp(in_exp_w_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+5*stride]+sigma_const));
+
+ float in_exp_h = dh / x[index+7*stride];
+ float in_exp_h_2 = pow(in_exp_h, 2);
+ float normal_dist_h = exp(in_exp_h_2*(-1./2.))/(sqrt(M_PI * 2.0)*(x[index+7*stride]+sigma_const));
+
+ float temp_x = (1./2.) * 1./(normal_dist_x+epsi) * normal_dist_x * scale;
+ float temp_y = (1./2.) * 1./(normal_dist_y+epsi) * normal_dist_y * scale;
+ float temp_w = (1./2.) * 1./(normal_dist_w+epsi) * normal_dist_w * scale;
+ float temp_h = (1./2.) * 1./(normal_dist_h+epsi) * normal_dist_h * scale;
+
+ if (!accumulate) {
+ delta[index + 0 * stride] = 0;
+ delta[index + 1 * stride] = 0;
+ delta[index + 2 * stride] = 0;
+ delta[index + 3 * stride] = 0;
+ delta[index + 4 * stride] = 0;
+ delta[index + 5 * stride] = 0;
+ delta[index + 6 * stride] = 0;
+ delta[index + 7 * stride] = 0;
+ }
+
+ float delta_x = temp_x * in_exp_x * (1. / x[index + 1 * stride]);
+ float delta_y = temp_y * in_exp_y * (1. / x[index + 3 * stride]);
+ float delta_w = temp_w * in_exp_w * (1. / x[index + 5 * stride]);
+ float delta_h = temp_h * in_exp_h * (1. / x[index + 7 * stride]);
+
+ float delta_ux = temp_x * (in_exp_x_2 / x[index + 1 * stride] - 1. / (x[index + 1 * stride] + sigma_const));
+ float delta_uy = temp_y * (in_exp_y_2 / x[index + 3 * stride] - 1. / (x[index + 3 * stride] + sigma_const));
+ float delta_uw = temp_w * (in_exp_w_2 / x[index + 5 * stride] - 1. / (x[index + 5 * stride] + sigma_const));
+ float delta_uh = temp_h * (in_exp_h_2 / x[index + 7 * stride] - 1. / (x[index + 7 * stride] + sigma_const));
+
+ if (iou_loss != MSE) {
+ // GIoU
+ iou = all_ious.giou;
+
+ // https://github.com/generalized-iou/g-darknet
+ // https://arxiv.org/abs/1902.09630v2
+ // https://giou.stanford.edu/
+ // https://arxiv.org/abs/1911.08287v1
+ // https://github.com/Zzh-tju/DIoU-darknet
+ all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
+
+ float dx, dy, dw, dh;
+
+ dx = all_ious.dx_iou.dt;
+ dy = all_ious.dx_iou.db;
+ dw = all_ious.dx_iou.dl;
+ dh = all_ious.dx_iou.dr;
+
+ if (yolo_point == YOLO_CENTER) {
+ }
+ else if (yolo_point == YOLO_LEFT_TOP) {
+ dx = dx - dw/2;
+ dy = dy - dh/2;
+ }
+ else if (yolo_point == YOLO_RIGHT_BOTTOM) {
+ dx = dx + dw / 2;
+ dy = dy + dh / 2;
+ }
+
+ // jacobian^t (transpose)
+ //float dx = (all_ious.dx_iou.dl + all_ious.dx_iou.dr);
+ //float dy = (all_ious.dx_iou.dt + all_ious.dx_iou.db);
+ //float dw = ((-0.5 * all_ious.dx_iou.dl) + (0.5 * all_ious.dx_iou.dr));
+ //float dh = ((-0.5 * all_ious.dx_iou.dt) + (0.5 * all_ious.dx_iou.db));
+
+ // predict exponential, apply gradient of e^delta_t ONLY for w,h
+ dw *= exp(x[index + 4 * stride]);
+ dh *= exp(x[index + 6 * stride]);
+
+ delta_x = dx;
+ delta_y = dy;
+ delta_w = dw;
+ delta_h = dh;
+ }
+
+ // normalize iou weight, for GIoU
+ delta_x *= iou_normalizer;
+ delta_y *= iou_normalizer;
+ delta_w *= iou_normalizer;
+ delta_h *= iou_normalizer;
+
+ // normalize Uncertainty weight
+ delta_ux *= uc_normalizer;
+ delta_uy *= uc_normalizer;
+ delta_uw *= uc_normalizer;
+ delta_uh *= uc_normalizer;
+
+ delta_x = fix_nan_inf(delta_x);
+ delta_y = fix_nan_inf(delta_y);
+ delta_w = fix_nan_inf(delta_w);
+ delta_h = fix_nan_inf(delta_h);
+
+ delta_ux = fix_nan_inf(delta_ux);
+ delta_uy = fix_nan_inf(delta_uy);
+ delta_uw = fix_nan_inf(delta_uw);
+ delta_uh = fix_nan_inf(delta_uh);
+
+ if (max_delta != FLT_MAX) {
+ delta_x = clip_value(delta_x, max_delta);
+ delta_y = clip_value(delta_y, max_delta);
+ delta_w = clip_value(delta_w, max_delta);
+ delta_h = clip_value(delta_h, max_delta);
+
+ delta_ux = clip_value(delta_ux, max_delta);
+ delta_uy = clip_value(delta_uy, max_delta);
+ delta_uw = clip_value(delta_uw, max_delta);
+ delta_uh = clip_value(delta_uh, max_delta);
+ }
+
+ delta[index + 0 * stride] += delta_x;
+ delta[index + 2 * stride] += delta_y;
+ delta[index + 4 * stride] += delta_w;
+ delta[index + 6 * stride] += delta_h;
+
+ delta[index + 1 * stride] += delta_ux;
+ delta[index + 3 * stride] += delta_uy;
+ delta[index + 5 * stride] += delta_uw;
+ delta[index + 7 * stride] += delta_uh;
+ return iou;
+}
+
+void averages_gaussian_yolo_deltas(int class_index, int box_index, int stride, int classes, float *delta)
+{
+
+ int classes_in_one_box = 0;
+ int c;
+ for (c = 0; c < classes; ++c) {
+ if (delta[class_index + stride*c] > 0) classes_in_one_box++;
+ }
+
+ if (classes_in_one_box > 0) {
+ delta[box_index + 0 * stride] /= classes_in_one_box;
+ delta[box_index + 1 * stride] /= classes_in_one_box;
+ delta[box_index + 2 * stride] /= classes_in_one_box;
+ delta[box_index + 3 * stride] /= classes_in_one_box;
+ delta[box_index + 4 * stride] /= classes_in_one_box;
+ delta[box_index + 5 * stride] /= classes_in_one_box;
+ delta[box_index + 6 * stride] /= classes_in_one_box;
+ delta[box_index + 7 * stride] /= classes_in_one_box;
+ }
+}
+
+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)
+{
+ int n;
+ if (delta[index]){
+ float y_true = 1;
+ if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
+ delta[index + stride*class_id] = y_true - output[index + stride*class_id];
+ //delta[index + stride*class_id] = 1 - output[index + stride*class_id];
+
+ if (classes_multipliers) delta[index + stride*class_id] *= classes_multipliers[class_id];
+ if(avg_cat) *avg_cat += output[index + stride*class_id];
+ return;
+ }
+ for(n = 0; n < classes; ++n){
+ float y_true = ((n == class_id) ? 1 : 0);
+ if (label_smooth_eps) y_true = y_true * (1 - label_smooth_eps) + 0.5*label_smooth_eps;
+ delta[index + stride*n] = y_true - output[index + stride*n];
+
+ if (classes_multipliers && n == class_id) delta[index + stride*class_id] *= classes_multipliers[class_id] * cls_normalizer;
+ if(n == class_id && avg_cat) *avg_cat += output[index + stride*n];
+ }
+}
+
+int compare_gaussian_yolo_class(float *output, int classes, int class_index, int stride, float objectness, int class_id, float conf_thresh)
+{
+ int j;
+ for (j = 0; j < classes; ++j) {
+ //float prob = objectness * output[class_index + stride*j];
+ float prob = output[class_index + stride*j];
+ if (prob > conf_thresh) {
+ return 1;
+ }
+ }
+ return 0;
+}
+
+static int entry_gaussian_index(layer l, int batch, int location, int entry)
+{
+ int n = location / (l.w*l.h);
+ int loc = location % (l.w*l.h);
+ return batch*l.outputs + n*l.w*l.h*(8+l.classes+1) + entry*l.w*l.h + loc;
+}
+
+void forward_gaussian_yolo_layer(const layer l, network_state state)
+{
+ int i,j,b,t,n;
+ memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
+
+#ifndef GPU
+ 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(l.output + index, 2*l.w*l.h, LOGISTIC);
+ scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x
+ // y : mu, sigma
+ index = entry_gaussian_index(l, b, n*l.w*l.h, 2);
+ activate_array(l.output + index, 2*l.w*l.h, LOGISTIC);
+ scal_add_cpu(l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale y
+ // w : sigma
+ index = entry_gaussian_index(l, b, n*l.w*l.h, 5);
+ activate_array(l.output + index, l.w*l.h, LOGISTIC);
+ // h : sigma
+ index = entry_gaussian_index(l, b, n*l.w*l.h, 7);
+ activate_array(l.output + index, l.w*l.h, LOGISTIC);
+ // objectness & class
+ index = entry_gaussian_index(l, b, n*l.w*l.h, 8);
+ activate_array(l.output + index, (1+l.classes)*l.w*l.h, LOGISTIC);
+ }
+ }
+#endif
+
+ memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+ if (!state.train) return;
+ float avg_iou = 0;
+ float recall = 0;
+ float recall75 = 0;
+ float avg_cat = 0;
+ float avg_obj = 0;
+ float avg_anyobj = 0;
+ int count = 0;
+ int class_count = 0;
+ *(l.cost) = 0;
+ 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) {
+ const int class_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 9);
+ const int obj_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 8);
+ const int box_index = entry_gaussian_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+ const int stride = l.w*l.h;
+ 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);
+ float best_match_iou = 0;
+ int best_match_t = 0;
+ float best_iou = 0;
+ int best_t = 0;
+ for(t = 0; t < l.max_boxes; ++t){
+ box truth = float_to_box_stride(state.truth + t*l.truth_size + b*l.truths, 1);
+ int class_id = state.truth[t*l.truth_size + b*l.truths + 4];
+ if (class_id >= l.classes) {
+ 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);
+ 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)
+{
+ axpy_ongpu(l.batch*l.inputs, l.delta_normalizer, l.delta_gpu, 1, state.delta, 1);
+}
+#endif
--
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