From 50c1d55a34ae01049c3a0f8d549c55203bf88da7 Mon Sep 17 00:00:00 2001
From: ZouJiu1 <1069679911@qq.com>
Date: Tue, 8 Aug 2023 16:16:45 +0800
Subject: [PATCH 01/11] if one anchor is asigned to multi label, then should
 choose the max iou truth label

---
 include/darknet.h |  15 +++++-
 src/yolo_layer.c  | 130 +++++++++++++++++++++++++---------------------
 2 files changed, 86 insertions(+), 59 deletions(-)

diff --git a/include/darknet.h b/include/darknet.h
index 2517d3fbd05..018f6893183 100644
--- a/include/darknet.h
+++ b/include/darknet.h
@@ -101,7 +101,6 @@ typedef struct tree {
     int *group_offset;
 } tree;
 
-
 // activations.h
 typedef enum {
     LOGISTIC, RELU, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, REVLEAKY, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, GELU, SWISH, MISH, HARD_MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL
@@ -912,6 +911,20 @@ typedef struct detection{
     int track_id;
 } detection;
 
+// multi_label_one_anchor_choose_maxiou
+typedef struct mloam { 
+    box truth;
+    int use_or_not;
+    float best_iou;
+    int x;
+    int y;
+    int class_id;
+    int track_id;
+    int best_n;
+    int mask_n;
+    int t;
+} mloam;
+
 // network.c -batch inference
 typedef struct det_num_pair {
     int num;
diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index 0eae2fc8feb..4864328e06e 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -4,6 +4,7 @@
 #include "box.h"
 #include "dark_cuda.h"
 #include "utils.h"
+#include "darknet.h"
 
 #include <math.h>
 #include <stdio.h>
@@ -384,6 +385,7 @@ typedef struct train_yolo_args {
     float tot_iou_loss;
     int count;
     int class_count;
+    mloam* mloam_ptr;
 } train_yolo_args;
 
 void *process_batch(void* ptr)
@@ -505,6 +507,8 @@ void *process_batch(void* ptr)
                 }
             }
         }
+        mloam* mloam_ptr = args->mloam_ptr;   // multi_label_one_anchor_choose_maxiou
+        int number = 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);
             if (!truth.x) break;  // continue;
@@ -539,19 +543,79 @@ void *process_batch(void* ptr)
             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];
+                if(number==0) {
+                    mloam choose = {truth, 1, best_iou, i, j, class_id, 1, best_n, mask_n, t};
+                    mloam_ptr[number++] = choose;
+                }
+                for(int mi = 0; mi < number; mi++) {
+                    mloam mp = mloam_ptr[mi];
+                    if(mp.mask_n == mask_n && mp.use_or_not > 0 && mp.best_iou < best_iou) {
+                        mloam choose = {truth, 1, best_iou, i, j, class_id, 1, best_n, mask_n, t};
+                        mloam_ptr[number++] = choose;
+                        mp.use_or_not = -6;
+                        break;
+                    }
+                }
+            }
+            // 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];
+                        if(number==0) {
+                            mloam choose = {truth, 1, best_iou, i, j, class_id, 1, best_n, mask_n, t};
+                            mloam_ptr[number++] = choose;
+                        }
+                        for(int mi = 0; mi < number; mi++) {
+                            mloam mp = mloam_ptr[mi];
+                            if(mp.mask_n == mask_n && mp.use_or_not > 0 && mp.best_iou < iou) {
+                                mloam choose = {truth, 1, iou, i, j, class_id, -1, n, mask_n, t};
+                                mloam_ptr[number++] = choose;
+                                mp.use_or_not = -6;
+                                break;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        for(int ni = 0; ni < number; ni++) {
+            mloam mp   = mloam_ptr[ni];
+            box truth      = mp.truth;
+            int use_or_not = mp.use_or_not;
+            float best_iou = mp.best_iou;
+            int i          = mp.x;
+            int j          = mp.y;
+            int class_id   = mp.class_id;
+            int track_id   = mp.track_id;
+            int best_n     = mp.best_n;
+            int mask_n     = mp.mask_n;
+            int t          = mp.t;
+            if (use_or_not > 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_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;
                 ious all_ious = delta_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, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords);
                 (*state.net.total_bbox)++;
 
-                const int truth_in_index = t * l.truth_size + b * l.truths + 5;
-                const int track_id = state.truth[truth_in_index];
-                const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i;
-                l.labels[truth_out_index] = track_id;
-                l.class_ids[truth_out_index] = class_id;
-                //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index);
-
+                if(track_id > 0) {
+                    const int truth_in_index = t * l.truth_size + b * l.truths + 5;
+                    const int track_id = state.truth[truth_in_index];
+                    const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i;
+                    l.labels[truth_out_index] = track_id;
+                    l.class_ids[truth_out_index] = class_id;
+                    //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index);
+                }
                 // range is 0 <= 1
                 args->tot_iou += all_ious.iou;
                 args->tot_iou_loss += 1 - all_ious.iou;
@@ -584,57 +648,6 @@ void *process_batch(void* ptr)
                 if (all_ious.iou > .5) recall += 1;
                 if (all_ious.iou > .75) recall75 += 1;
             }
-
-            // 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_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;
-                        ious all_ious = delta_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, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords);
-                        (*state.net.total_bbox)++;
-
-                        // range is 0 <= 1
-                        args->tot_iou += all_ious.iou;
-                        args->tot_iou_loss += 1 - all_ious.iou;
-                        // range is -1 <= giou <= 1
-                        tot_giou += all_ious.giou;
-                        args->tot_giou_loss += 1 - all_ious.giou;
-
-                        tot_diou += all_ious.diou;
-                        tot_diou_loss += 1 - all_ious.diou;
-
-                        tot_ciou += all_ious.ciou;
-                        tot_ciou_loss += 1 - all_ious.ciou;
-
-                        int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4);
-                        avg_obj += l.output[obj_index];
-                        if (l.objectness_smooth) {
-                            float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
-                            if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj;
-                        }
-                        else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
-
-                        int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1);
-                        delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
-
-                        ++(args->count);
-                        ++(args->class_count);
-                        if (all_ious.iou > .5) recall += 1;
-                        if (all_ious.iou > .75) recall75 += 1;
-                    }
-                }
-            }
         }
 
         if (l.iou_thresh < 1.0f) {
@@ -726,6 +739,7 @@ void forward_yolo_layer(const layer l, network_state state)
         yolo_args[b].tot_giou_loss = 0;
         yolo_args[b].count = 0;
         yolo_args[b].class_count = 0;
+        yolo_args[b].mloam_ptr = (mloam *)xcalloc(l.max_boxes * 3 + 60, sizeof(mloam));     // multi_label_one_anchor_choose_maxiou
 
         if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed", DARKNET_LOC);
     }

From 4320968ce2f15a652c65023e2fb26bb8b5002502 Mon Sep 17 00:00:00 2001
From: ZouJiu1 <1069679911@qq.com>
Date: Tue, 8 Aug 2023 16:37:34 +0800
Subject: [PATCH 02/11] fix a mistake

---
 src/yolo_layer.c | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index 4864328e06e..db7729e040c 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -571,7 +571,7 @@ void *process_batch(void* ptr)
                         int class_id = state.truth[t * l.truth_size + b * l.truths + 4];
                         if (l.map) class_id = l.map[class_id];
                         if(number==0) {
-                            mloam choose = {truth, 1, best_iou, i, j, class_id, 1, best_n, mask_n, t};
+                            mloam choose = {truth, 1, iou, i, j, class_id, -1, n, mask_n, t};
                             mloam_ptr[number++] = choose;
                         }
                         for(int mi = 0; mi < number; mi++) {

From 4b72b929a5cdf6537e5957a79f1fb4bfa92d0aec Mon Sep 17 00:00:00 2001
From: ZouJiu <1069679911@qq.com>
Date: Tue, 8 Aug 2023 16:59:41 +0800
Subject: [PATCH 03/11] remove duplicated parts and free xcalloc ptr

---
 include/darknet.h | 1 -
 1 file changed, 1 deletion(-)

diff --git a/include/darknet.h b/include/darknet.h
index 018f6893183..1f9afedee06 100644
--- a/include/darknet.h
+++ b/include/darknet.h
@@ -918,7 +918,6 @@ typedef struct mloam {
     float best_iou;
     int x;
     int y;
-    int class_id;
     int track_id;
     int best_n;
     int mask_n;

From f305bf6408f9923ede8f4ad31016a5d0c78a349e Mon Sep 17 00:00:00 2001
From: ZouJiu <1069679911@qq.com>
Date: Tue, 8 Aug 2023 17:18:27 +0800
Subject: [PATCH 04/11] remove duplicated parts and free xcalloc ptr

---
 src/yolo_layer.c | 15 +++++----------
 1 file changed, 5 insertions(+), 10 deletions(-)

diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index db7729e040c..43fd18852e4 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -541,16 +541,14 @@ void *process_batch(void* ptr)
 
             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];
                 if(number==0) {
-                    mloam choose = {truth, 1, best_iou, i, j, class_id, 1, best_n, mask_n, t};
+                    mloam choose = {truth, 1, best_iou, i, j, 1, best_n, mask_n, t};
                     mloam_ptr[number++] = choose;
                 }
                 for(int mi = 0; mi < number; mi++) {
                     mloam mp = mloam_ptr[mi];
                     if(mp.mask_n == mask_n && mp.use_or_not > 0 && mp.best_iou < best_iou) {
-                        mloam choose = {truth, 1, best_iou, i, j, class_id, 1, best_n, mask_n, t};
+                        mloam choose = {truth, 1, best_iou, i, j, 1, best_n, mask_n, t};
                         mloam_ptr[number++] = choose;
                         mp.use_or_not = -6;
                         break;
@@ -568,16 +566,14 @@ void *process_batch(void* ptr)
                     // 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];
                         if(number==0) {
-                            mloam choose = {truth, 1, iou, i, j, class_id, -1, n, mask_n, t};
+                            mloam choose = {truth, 1, iou, i, j, -1, n, mask_n, t};
                             mloam_ptr[number++] = choose;
                         }
                         for(int mi = 0; mi < number; mi++) {
                             mloam mp = mloam_ptr[mi];
                             if(mp.mask_n == mask_n && mp.use_or_not > 0 && mp.best_iou < iou) {
-                                mloam choose = {truth, 1, iou, i, j, class_id, -1, n, mask_n, t};
+                                mloam choose = {truth, 1, iou, i, j, -1, n, mask_n, t};
                                 mloam_ptr[number++] = choose;
                                 mp.use_or_not = -6;
                                 break;
@@ -591,10 +587,8 @@ void *process_batch(void* ptr)
             mloam mp   = mloam_ptr[ni];
             box truth      = mp.truth;
             int use_or_not = mp.use_or_not;
-            float best_iou = mp.best_iou;
             int i          = mp.x;
             int j          = mp.y;
-            int class_id   = mp.class_id;
             int track_id   = mp.track_id;
             int best_n     = mp.best_n;
             int mask_n     = mp.mask_n;
@@ -753,6 +747,7 @@ void forward_yolo_layer(const layer l, network_state state)
         tot_giou_loss += yolo_args[b].tot_giou_loss;
         count += yolo_args[b].count;
         class_count += yolo_args[b].class_count;
+        free(yolo_args[b].mloam_ptr);
     }
 
     free(yolo_args);

From 7ddd724c9f6951647642879595a6579f18f4adee Mon Sep 17 00:00:00 2001
From: ZouJiu <1069679911@qq.com>
Date: Tue, 8 Aug 2023 18:11:13 +0800
Subject: [PATCH 05/11] del use_or_not param

---
 src/yolo_layer.c | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index 43fd18852e4..500b39a6d64 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -586,14 +586,13 @@ void *process_batch(void* ptr)
         for(int ni = 0; ni < number; ni++) {
             mloam mp   = mloam_ptr[ni];
             box truth      = mp.truth;
-            int use_or_not = mp.use_or_not;
             int i          = mp.x;
             int j          = mp.y;
             int track_id   = mp.track_id;
             int best_n     = mp.best_n;
             int mask_n     = mp.mask_n;
             int t          = mp.t;
-            if (use_or_not > 0) {
+            if (mp.use_or_not > 0) {
                 int class_id = state.truth[t * l.truth_size + b * l.truths + 4];
                 if (l.map) class_id = l.map[class_id];
 

From 8a7c413ff55b2fb0d1a2217936d4819d9a77b843 Mon Sep 17 00:00:00 2001
From: ZouJiu <1069679911@qq.com>
Date: Wed, 9 Aug 2023 07:48:58 +0800
Subject: [PATCH 06/11] fix logical problem

---
 include/darknet.h |   1 -
 src/yolo_layer.c  | 137 ++++++++++++++++++++++++++--------------------
 2 files changed, 77 insertions(+), 61 deletions(-)

diff --git a/include/darknet.h b/include/darknet.h
index 1f9afedee06..cec68e9de8e 100644
--- a/include/darknet.h
+++ b/include/darknet.h
@@ -914,7 +914,6 @@ typedef struct detection{
 // multi_label_one_anchor_choose_maxiou
 typedef struct mloam { 
     box truth;
-    int use_or_not;
     float best_iou;
     int x;
     int y;
diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index 500b39a6d64..b8a571583ce 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -542,16 +542,25 @@ void *process_batch(void* ptr)
             int mask_n = int_index(l.mask, best_n, l.n);
             if (mask_n >= 0) {
                 if(number==0) {
-                    mloam choose = {truth, 1, best_iou, i, j, 1, best_n, mask_n, t};
+                    mloam choose = {truth, best_iou, i, j, 1, best_n, mask_n, t};
                     mloam_ptr[number++] = choose;
                 }
-                for(int mi = 0; mi < number; mi++) {
-                    mloam mp = mloam_ptr[mi];
-                    if(mp.mask_n == mask_n && mp.use_or_not > 0 && mp.best_iou < best_iou) {
-                        mloam choose = {truth, 1, best_iou, i, j, 1, best_n, mask_n, t};
+                else {
+                    int markable = 666;
+                    for(int mi = 0; mi < number; mi++) {
+                        mloam mp = mloam_ptr[mi];
+                        if(mp.mask_n == mask_n && mp.x==i && mp.y==j) {
+                            markable = -666;
+                            if(mp.best_iou < best_iou) {
+                                mloam choose = {truth, best_iou, i, j, 1, best_n, mask_n, t};
+                                mloam_ptr[mi] = choose;
+                                break;
+                            }
+                        }
+                    }
+                    if(markable > 0) {
+                        mloam choose = {truth, best_iou, i, j, 1, best_n, mask_n, t};
                         mloam_ptr[number++] = choose;
-                        mp.use_or_not = -6;
-                        break;
                     }
                 }
             }
@@ -567,16 +576,25 @@ void *process_batch(void* ptr)
 
                     if (iou > l.iou_thresh) {
                         if(number==0) {
-                            mloam choose = {truth, 1, iou, i, j, -1, n, mask_n, t};
+                            mloam choose = {truth, iou, i, j, -1, n, mask_n, t};
                             mloam_ptr[number++] = choose;
                         }
-                        for(int mi = 0; mi < number; mi++) {
-                            mloam mp = mloam_ptr[mi];
-                            if(mp.mask_n == mask_n && mp.use_or_not > 0 && mp.best_iou < iou) {
-                                mloam choose = {truth, 1, iou, i, j, -1, n, mask_n, t};
+                        else {
+                            int markable = 666;
+                            for(int mi = 0; mi < number; mi++) {
+                                mloam mp = mloam_ptr[mi];
+                                if(mp.mask_n == mask_n && mp.x==i && mp.y==j) {
+                                    markable = -666;
+                                    if(mp.best_iou < iou) {
+                                        mloam choose = {truth, iou, i, j, -1, n, mask_n, t};
+                                        mloam_ptr[mi] = choose;
+                                        break;
+                                    }
+                                }
+                            }
+                            if(markable > 0) {
+                                mloam choose = {truth, iou, i, j, -1, n, mask_n, t};
                                 mloam_ptr[number++] = choose;
-                                mp.use_or_not = -6;
-                                break;
                             }
                         }
                     }
@@ -592,55 +610,54 @@ void *process_batch(void* ptr)
             int best_n     = mp.best_n;
             int mask_n     = mp.mask_n;
             int t          = mp.t;
-            if (mp.use_or_not > 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_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;
-                ious all_ious = delta_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, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords);
-                (*state.net.total_bbox)++;
-
-                if(track_id > 0) {
-                    const int truth_in_index = t * l.truth_size + b * l.truths + 5;
-                    const int track_id = state.truth[truth_in_index];
-                    const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i;
-                    l.labels[truth_out_index] = track_id;
-                    l.class_ids[truth_out_index] = class_id;
-                    //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index);
-                }
-                // range is 0 <= 1
-                args->tot_iou += all_ious.iou;
-                args->tot_iou_loss += 1 - all_ious.iou;
-                // range is -1 <= giou <= 1
-                tot_giou += all_ious.giou;
-                args->tot_giou_loss += 1 - all_ious.giou;
-
-                tot_diou += all_ious.diou;
-                tot_diou_loss += 1 - all_ious.diou;
-
-                tot_ciou += all_ious.ciou;
-                tot_ciou_loss += 1 - all_ious.ciou;
-
-                int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4);
-                avg_obj += l.output[obj_index];
-                if (l.objectness_smooth) {
-                    float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
-                    if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj;
-                }
-                else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
 
-                int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1);
-                delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
+            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_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;
+            ious all_ious = delta_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, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords);
+            (*state.net.total_bbox)++;
+
+            if(track_id > 0) {
+                const int truth_in_index = t * l.truth_size + b * l.truths + 5;
+                const int track_id = state.truth[truth_in_index];
+                const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i;
+                l.labels[truth_out_index] = track_id;
+                l.class_ids[truth_out_index] = class_id;
+                //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index);
+            }
+            // range is 0 <= 1
+            args->tot_iou += all_ious.iou;
+            args->tot_iou_loss += 1 - all_ious.iou;
+            // range is -1 <= giou <= 1
+            tot_giou += all_ious.giou;
+            args->tot_giou_loss += 1 - all_ious.giou;
+
+            tot_diou += all_ious.diou;
+            tot_diou_loss += 1 - all_ious.diou;
+
+            tot_ciou += all_ious.ciou;
+            tot_ciou_loss += 1 - all_ious.ciou;
+
+            int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4);
+            avg_obj += l.output[obj_index];
+            if (l.objectness_smooth) {
+                float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
+                if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj;
+            }
+            else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]);
+
+            int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1);
+            delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
 
-                //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h);
-                //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]);
+            //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h);
+            //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]);
 
-                ++(args->count);
-                ++(args->class_count);
-                if (all_ious.iou > .5) recall += 1;
-                if (all_ious.iou > .75) recall75 += 1;
-            }
+            ++(args->count);
+            ++(args->class_count);
+            if (all_ious.iou > .5) recall += 1;
+            if (all_ious.iou > .75) recall75 += 1;
         }
 
         if (l.iou_thresh < 1.0f) {

From 638206a4682891e2577efa426a9f6587833b22d0 Mon Sep 17 00:00:00 2001
From: ZouJiu1 <34758215+ZouJiu1@users.noreply.github.com>
Date: Fri, 11 Aug 2023 20:19:24 +0800
Subject: [PATCH 07/11] Create yolo_layer.cc

---
 src/yolo_layer.cc | 1124 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1124 insertions(+)
 create mode 100644 src/yolo_layer.cc

diff --git a/src/yolo_layer.cc b/src/yolo_layer.cc
new file mode 100644
index 00000000000..423ca8f980c
--- /dev/null
+++ b/src/yolo_layer.cc
@@ -0,0 +1,1124 @@
+#include "yolo_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "dark_cuda.h"
+#include "utils.h"
+#include "darknet.h"
+
+#include <math.h>
+#include <stdio.h>
+#include <assert.h>
+#include <string.h>
+#include <stdlib.h>
+
+extern int check_mistakes;
+
+layer make_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 = YOLO;
+
+    l.n = n;
+    l.total = total;
+    l.batch = batch;
+    l.h = h;
+    l.w = w;
+    l.c = n*(classes + 4 + 1);
+    l.out_w = l.w;
+    l.out_h = l.h;
+    l.out_c = l.c;
+    l.classes = classes;
+    l.cost = (float*)xcalloc(1, sizeof(float));
+    l.biases = (float*)xcalloc(total * 2, sizeof(float));
+    if(mask) l.mask = mask;
+    else{
+        l.mask = (int*)xcalloc(n, sizeof(int));
+        for(i = 0; i < n; ++i){
+            l.mask[i] = i;
+        }
+    }
+    l.bias_updates = (float*)xcalloc(n * 2, sizeof(float));
+    l.outputs = h*w*n*(classes + 4 + 1);
+    l.inputs = l.outputs;
+    l.max_boxes = max_boxes;
+    l.truth_size = 4 + 2;
+    l.truths = l.max_boxes*l.truth_size;    // 90*(4 + 1);
+    l.labels = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int));
+    for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.labels[i] = -1;
+    l.class_ids = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int));
+    for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1;
+
+    l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float));
+    l.output = (float*)xcalloc(batch * l.outputs, sizeof(float));
+    for(i = 0; i < total*2; ++i){
+        l.biases[i] = .5;
+    }
+
+    l.forward = forward_yolo_layer;
+    l.backward = backward_yolo_layer;
+#ifdef GPU
+    l.forward_gpu = forward_yolo_layer_gpu;
+    l.backward_gpu = backward_yolo_layer_gpu;
+    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
+    l.output_avg_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*)xcalloc(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*)xcalloc(batch * l.outputs, sizeof(float));
+    }
+#endif
+
+    fprintf(stderr, "yolo\n");
+    srand(time(0));
+
+    return l;
+}
+
+void resize_yolo_layer(layer *l, int w, int h)
+{
+    l->w = w;
+    l->h = h;
+
+    l->outputs = h*w*l->n*(l->classes + 4 + 1);
+    l->inputs = l->outputs;
+
+    if (l->embedding_output) l->embedding_output = (float*)xrealloc(l->output, l->batch * l->embedding_size * l->n * l->h * l->w * sizeof(float));
+    if (l->labels) l->labels = (int*)xrealloc(l->labels, l->batch * l->n * l->h * l->w * sizeof(int));
+    if (l->class_ids) l->class_ids = (int*)xrealloc(l->class_ids, l->batch * l->n * l->h * l->w * sizeof(int));
+
+    if (!l->output_pinned) l->output = (float*)xrealloc(l->output, l->batch*l->outputs * sizeof(float));
+    if (!l->delta_pinned) l->delta = (float*)xrealloc(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*)xcalloc(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*)xcalloc(l->batch * l->outputs, sizeof(float));
+            l->delta_pinned = 0;
+        }
+    }
+
+    cuda_free(l->delta_gpu);
+    cuda_free(l->output_gpu);
+    cuda_free(l->output_avg_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);
+    l->output_avg_gpu = cuda_make_array(l->output, l->batch*l->outputs);
+#endif
+}
+
+box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, int new_coords)
+{
+    box b;
+    // ln - natural logarithm (base = e)
+    // x` = t.x * lw - i;   // x = ln(x`/(1-x`))   // x - output of previous conv-layer
+    // y` = t.y * lh - i;   // y = ln(y`/(1-y`))   // y - output of previous conv-layer
+    // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer
+    // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer
+    if (new_coords) {
+        b.x = (i + x[index + 0 * stride]) / lw;
+        b.y = (j + x[index + 1 * stride]) / lh;
+        b.w = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w;
+        b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h;
+    }
+    else {
+        b.x = (i + x[index + 0 * stride]) / lw;
+        b.y = (j + x[index + 1 * stride]) / lh;
+        b.w = exp(x[index + 2 * stride]) * biases[2 * n] / w;
+        b.h = exp(x[index + 3 * stride]) * biases[2 * n + 1] / h;
+    }
+    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) {
+        //printf("\n val = %f > max_val = %f \n", val, max_val);
+        val = max_val;
+    }
+    else if (val < -max_val) {
+        //printf("\n val = %f < -max_val = %f \n", val, -max_val);
+        val = -max_val;
+    }
+    return val;
+}
+
+ious delta_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, int accumulate, float max_delta, int *rewritten_bbox, int new_coords)
+{
+    if (delta[index + 0 * stride] || delta[index + 1 * stride] || delta[index + 2 * stride] || delta[index + 3 * stride]) {
+        (*rewritten_bbox)++;
+    }
+
+    ious all_ious = { 0 };
+    // i - step in layer width
+    // j - step in layer height
+    //  Returns a box in absolute coordinates
+    box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, new_coords);
+    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);
+    // avoid nan in dx_box_iou
+    if (pred.w == 0) { pred.w = 1.0; }
+    if (pred.h == 0) { pred.h = 1.0; }
+    if (iou_loss == MSE)    // old loss
+    {
+        float tx = (truth.x*lw - i);
+        float ty = (truth.y*lh - j);
+        float tw = log(truth.w*w / biases[2 * n]);
+        float th = log(truth.h*h / biases[2 * n + 1]);
+
+        if (new_coords) {
+            //tx = (truth.x*lw - i + 0.5) / 2;
+            //ty = (truth.y*lh - j + 0.5) / 2;
+            tw = sqrt(truth.w*w / (4 * biases[2 * n]));
+            th = sqrt(truth.h*h / (4 * biases[2 * n + 1]));
+        }
+
+        //printf(" tx = %f, ty = %f, tw = %f, th = %f \n", tx, ty, tw, th);
+        //printf(" x = %f, y = %f, w = %f, h = %f \n", x[index + 0 * stride], x[index + 1 * stride], x[index + 2 * stride], x[index + 3 * stride]);
+
+        // accumulate delta
+        delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]) * iou_normalizer;
+        delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]) * iou_normalizer;
+        delta[index + 2 * stride] += scale * (tw - x[index + 2 * stride]) * iou_normalizer;
+        delta[index + 3 * stride] += scale * (th - x[index + 3 * stride]) * iou_normalizer;
+    }
+    else {
+        // https://github.com/generalized-iou/g-darknet
+        // https://arxiv.org/abs/1902.09630v2
+        // https://giou.stanford.edu/
+        all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
+
+        // 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));
+
+        // jacobian^t (transpose)
+        float dx = all_ious.dx_iou.dt;
+        float dy = all_ious.dx_iou.db;
+        float dw = all_ious.dx_iou.dl;
+        float dh = all_ious.dx_iou.dr;
+
+
+        // predict exponential, apply gradient of e^delta_t ONLY for w,h
+        if (new_coords) {
+            //dw *= 8 * x[index + 2 * stride];
+            //dh *= 8 * x[index + 3 * stride];
+            //dw *= 8 * x[index + 2 * stride] * biases[2 * n] / w;
+            //dh *= 8 * x[index + 3 * stride] * biases[2 * n + 1] / h;
+
+            //float grad_w = 8 * exp(-x[index + 2 * stride]) / pow(exp(-x[index + 2 * stride]) + 1, 3);
+            //float grad_h = 8 * exp(-x[index + 3 * stride]) / pow(exp(-x[index + 3 * stride]) + 1, 3);
+            //dw *= grad_w;
+            //dh *= grad_h;
+        }
+        else {
+            dw *= exp(x[index + 2 * stride]);
+            dh *= exp(x[index + 3 * stride]);
+        }
+
+
+        //dw *= exp(x[index + 2 * stride]);
+        //dh *= exp(x[index + 3 * stride]);
+
+        // normalize iou weight
+        dx *= iou_normalizer;
+        dy *= iou_normalizer;
+        dw *= iou_normalizer;
+        dh *= iou_normalizer;
+
+
+        dx = fix_nan_inf(dx);
+        dy = fix_nan_inf(dy);
+        dw = fix_nan_inf(dw);
+        dh = fix_nan_inf(dh);
+
+        if (max_delta != FLT_MAX) {
+            dx = clip_value(dx, max_delta);
+            dy = clip_value(dy, max_delta);
+            dw = clip_value(dw, max_delta);
+            dh = clip_value(dh, max_delta);
+        }
+
+
+        if (!accumulate) {
+            delta[index + 0 * stride] = 0;
+            delta[index + 1 * stride] = 0;
+            delta[index + 2 * stride] = 0;
+            delta[index + 3 * stride] = 0;
+        }
+
+        // accumulate delta
+        delta[index + 0 * stride] += dx;
+        delta[index + 1 * stride] += dy;
+        delta[index + 2 * stride] += dw;
+        delta[index + 3 * stride] += dh;
+    }
+
+    return all_ious;
+}
+
+void averages_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;
+    }
+}
+
+void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss, float label_smooth_eps, float *classes_multipliers, float cls_normalizer)
+{
+    int n;
+    if (delta[index + stride*class_id]){
+        float y_true = 1;
+        if(label_smooth_eps) y_true = y_true *  (1 - label_smooth_eps) + 0.5*label_smooth_eps;
+        float result_delta = y_true - output[index + stride*class_id];
+        if(!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*class_id] = result_delta;
+        //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;
+    }
+    // Focal loss
+    if (focal_loss) {
+        // Focal Loss
+        float alpha = 0.5;    // 0.25 or 0.5
+        //float gamma = 2;    // hardcoded in many places of the grad-formula
+
+        int ti = index + stride*class_id;
+        float pt = output[ti] + 0.000000000000001F;
+        // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d
+        float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1);    // http://blog.csdn.net/linmingan/article/details/77885832
+        //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1);    // https://github.com/unsky/focal-loss
+
+        for (n = 0; n < classes; ++n) {
+            delta[index + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]);
+
+            delta[index + stride*n] *= alpha*grad;
+
+            if (n == class_id && avg_cat) *avg_cat += output[index + stride*n];
+        }
+    }
+    else {
+        // default
+        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;
+            float result_delta = y_true - output[index + stride*n];
+            if (!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*n] = result_delta;
+
+            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_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_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*(4+l.classes+1) + entry*l.w*l.h + loc;
+}
+
+typedef struct train_yolo_args {
+    layer l;
+    network_state state;
+    int b;
+
+    float tot_iou;
+    float tot_giou_loss;
+    float tot_iou_loss;
+    int count;
+    int class_count;
+    mloam* mloam_ptr;
+} train_yolo_args;
+
+void *process_batch(void* ptr)
+{
+    {
+        train_yolo_args *args = (train_yolo_args*)ptr;
+        const layer l = args->l;
+        network_state state = args->state;
+        int b = args->b;
+
+        int i, j, t, n, ln, x, y;
+
+        //printf(" b = %d \n", b, b);
+
+        //float tot_iou = 0;
+        float tot_giou = 0;
+        float tot_diou = 0;
+        float tot_ciou = 0;
+        //float tot_iou_loss = 0;
+        //float tot_giou_loss = 0;
+        float tot_diou_loss = 0;
+        float tot_ciou_loss = 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;
+        mloam* mloam_ptr = args->mloam_ptr;   // multi_label_one_anchor_choose_maxiou
+        int number = 0;
+        float* maxiou = (float *)xcalloc(l.max_boxes, sizeof(float));
+        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;  // continue;
+            if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) {
+                char buff[256];
+                printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h);
+                sprintf(buff, "echo \"Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f\" >> bad_label.list",
+                    truth.x, truth.y, truth.w, truth.h);
+                system(buff);
+            }
+            int class_id = state.truth[t * l.truth_size + b * l.truths + 4];
+            if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value
+
+            // x = (truth.x * l.w);
+            // y = (truth.y * l.h);
+            box truth_shift = truth;
+            // truth_shift.x = truth_shift.y = 0;
+            for (ln = 0; ln < l.total; ++ln) {
+                int n = int_index(l.mask, ln, l.n);
+                if (n >= 0) {
+                    box pred = { 0 };
+                    pred.w = l.biases[2 * ln] / state.net.w;
+                    pred.h = l.biases[2 * ln + 1] / state.net.h;
+                    for (j = 0; j < l.h; ++j) {
+                        for (i = 0; i < l.w; ++i) {
+                            pred.x = i / (float)(l.w);
+                            pred.y = j / (float)(l.h);
+                            float iou = box_iou_kind(pred, truth_shift, l.iou_thresh_kind); // IOU, GIOU, MSE, DIOU, CIOU
+
+                            const int obj_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4);
+                            l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]);
+                            if(iou > 0.3) {
+                                if(number==0) {
+                                    mloam choose = {truth, iou, i, j, 1, ln, n, t};
+                                    mloam_ptr[number++] = choose;
+                                    if(iou > maxiou[t]) {
+                                        maxiou[t] = iou;
+                                    }
+                                }
+                                else {
+                                    int markable = 666;
+                                    for(int mi = 0; mi < number; mi++) {
+                                        mloam mp = mloam_ptr[mi];
+                                        if(mp.mask_n == n && mp.x==i && mp.y==j) {
+                                            markable = -666;
+                                            if(mp.best_iou < iou) {
+                                                mloam choose = {truth, iou, i, j, 1, ln, n, t};
+                                                mloam_ptr[mi] = choose;
+                                                if(iou > maxiou[t]) {
+                                                    maxiou[t] = iou;
+                                                }
+                                                break;
+                                            }
+                                        }
+                                    }
+                                    if(markable > 0) {
+                                        mloam choose = {truth, iou, i, j, 1, ln, n, t};
+                                        mloam_ptr[number++] = choose;
+                                        if(iou > maxiou[t]) {
+                                            maxiou[t] = iou;
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        
+        for(int ni = 0; ni < number; ni++) {
+            mloam mp   = mloam_ptr[ni];
+            box truth      = mp.truth;
+            int i          = mp.x;
+            int j          = mp.y;
+            int track_id   = mp.track_id;
+            int best_n     = mp.best_n;
+            int mask_n     = mp.mask_n;
+            int t          = mp.t;
+            float iou_scale = mp.best_iou / maxiou[t];
+            // printf("%f", iou_scale);
+            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_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;
+            ious all_ious = delta_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 * iou_scale, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords);
+            (*state.net.total_bbox)++;
+
+            if(track_id > 0) {
+                const int truth_in_index = t * l.truth_size + b * l.truths + 5;
+                const int track_id = state.truth[truth_in_index];
+                const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i;
+                l.labels[truth_out_index] = track_id;
+                l.class_ids[truth_out_index] = class_id;
+                //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index);
+            }
+            // range is 0 <= 1
+            args->tot_iou += all_ious.iou;
+            args->tot_iou_loss += 1 - all_ious.iou;
+            // range is -1 <= giou <= 1
+            tot_giou += all_ious.giou;
+            args->tot_giou_loss += 1 - all_ious.giou;
+
+            tot_diou += all_ious.diou;
+            tot_diou_loss += 1 - all_ious.diou;
+
+            tot_ciou += all_ious.ciou;
+            tot_ciou_loss += 1 - all_ious.ciou;
+
+            int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4);
+            avg_obj += l.output[obj_index];
+            if (l.objectness_smooth) {
+                float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]) * iou_scale;
+                if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj;
+            }
+            else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]) * iou_scale;
+
+            int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1);
+            delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer * iou_scale);
+
+            //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h);
+            //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]);
+
+            ++(args->count);
+            ++(args->class_count);
+            if (all_ious.iou > .5) recall += 1;
+            if (all_ious.iou > .75) recall75 += 1;
+        }
+    }
+
+    return 0;
+}
+
+
+
+void forward_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));
+    int b, n;
+
+#ifndef GPU
+    for (b = 0; b < l.batch; ++b) {
+        for (n = 0; n < l.n; ++n) {
+            int bbox_index = entry_index(l, b, n*l.w*l.h, 0);
+            if (l.new_coords) {
+                //activate_array(l.output + bbox_index, 4 * l.w*l.h, LOGISTIC);    // x,y,w,h
+            }
+            else {
+                activate_array(l.output + bbox_index, 2 * l.w*l.h, LOGISTIC);        // x,y,
+                int obj_index = entry_index(l, b, n*l.w*l.h, 4);
+                activate_array(l.output + obj_index, (1 + l.classes)*l.w*l.h, LOGISTIC);
+            }
+            scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + bbox_index, 1);    // scale x,y
+        }
+    }
+#endif
+
+    // delta is zeroed
+    memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
+    if (!state.train) return;
+
+    int i;
+    for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1;
+    for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1;
+    //float avg_iou = 0;
+    float tot_iou = 0;
+    float tot_giou = 0;
+    float tot_diou = 0;
+    float tot_ciou = 0;
+    float tot_iou_loss = 0;
+    float tot_giou_loss = 0;
+    float tot_diou_loss = 0;
+    float tot_ciou_loss = 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;
+
+
+    int num_threads = l.batch;
+    pthread_t* threads = (pthread_t*)calloc(num_threads, sizeof(pthread_t));
+
+    struct train_yolo_args* yolo_args = (train_yolo_args*)xcalloc(l.batch, sizeof(struct train_yolo_args));
+
+    for (b = 0; b < l.batch; b++)
+    {
+        yolo_args[b].l = l;
+        yolo_args[b].state = state;
+        yolo_args[b].b = b;
+
+        yolo_args[b].tot_iou = 0;
+        yolo_args[b].tot_iou_loss = 0;
+        yolo_args[b].tot_giou_loss = 0;
+        yolo_args[b].count = 0;
+        yolo_args[b].class_count = 0;
+        yolo_args[b].mloam_ptr = (mloam *)xcalloc(l.max_boxes * 10 + 60, sizeof(mloam));     // multi_label_one_anchor_choose_maxiou
+
+        if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed", DARKNET_LOC);
+    }
+
+    for (b = 0; b < l.batch; b++)
+    {
+        pthread_join(threads[b], 0);
+
+        tot_iou += yolo_args[b].tot_iou;
+        tot_iou_loss += yolo_args[b].tot_iou_loss;
+        tot_giou_loss += yolo_args[b].tot_giou_loss;
+        count += yolo_args[b].count;
+        class_count += yolo_args[b].class_count;
+        free(yolo_args[b].mloam_ptr);
+    }
+
+    free(yolo_args);
+    free(threads);
+
+    // Search for an equidistant point from the distant boundaries of the local minimum
+    int iteration_num = get_current_iteration(state.net);
+    const int start_point = state.net.max_batches * 3 / 4;
+    //printf(" equidistant_point ep = %d, it = %d \n", state.net.equidistant_point, iteration_num);
+
+    if ((state.net.badlabels_rejection_percentage && start_point < iteration_num) ||
+        (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) ||
+        (state.net.equidistant_point && state.net.equidistant_point < iteration_num))
+    {
+        const float progress_it = iteration_num - state.net.equidistant_point;
+        const float progress = progress_it / (state.net.max_batches - state.net.equidistant_point);
+        float ep_loss_threshold = (*state.net.delta_rolling_avg) * progress * 1.4;
+
+        float cur_max = 0;
+        float cur_avg = 0;
+        float counter = 0;
+        for (i = 0; i < l.batch * l.outputs; ++i) {
+
+            if (l.delta[i] != 0) {
+                counter++;
+                cur_avg += fabs(l.delta[i]);
+
+                if (cur_max < fabs(l.delta[i]))
+                    cur_max = fabs(l.delta[i]);
+            }
+        }
+
+        cur_avg = cur_avg / counter;
+
+        if (*state.net.delta_rolling_max == 0) *state.net.delta_rolling_max = cur_max;
+        *state.net.delta_rolling_max = *state.net.delta_rolling_max * 0.99 + cur_max * 0.01;
+        *state.net.delta_rolling_avg = *state.net.delta_rolling_avg * 0.99 + cur_avg * 0.01;
+
+        // reject high loss to filter bad labels
+        if (state.net.num_sigmas_reject_badlabels && start_point < iteration_num)
+        {
+            const float rolling_std = (*state.net.delta_rolling_std);
+            const float rolling_max = (*state.net.delta_rolling_max);
+            const float rolling_avg = (*state.net.delta_rolling_avg);
+            const float progress_badlabels = (float)(iteration_num - start_point) / (start_point);
+
+            float cur_std = 0;
+            float counter = 0;
+            for (i = 0; i < l.batch * l.outputs; ++i) {
+                if (l.delta[i] != 0) {
+                    counter++;
+                    cur_std += pow(l.delta[i] - rolling_avg, 2);
+                }
+            }
+            cur_std = sqrt(cur_std / counter);
+
+            *state.net.delta_rolling_std = *state.net.delta_rolling_std * 0.99 + cur_std * 0.01;
+
+            float final_badlebels_threshold = rolling_avg + rolling_std * state.net.num_sigmas_reject_badlabels;
+            float badlabels_threshold = rolling_max - progress_badlabels * fabs(rolling_max - final_badlebels_threshold);
+            badlabels_threshold = max_val_cmp(final_badlebels_threshold, badlabels_threshold);
+            for (i = 0; i < l.batch * l.outputs; ++i) {
+                if (fabs(l.delta[i]) > badlabels_threshold)
+                    l.delta[i] = 0;
+            }
+            printf(" rolling_std = %f, rolling_max = %f, rolling_avg = %f \n", rolling_std, rolling_max, rolling_avg);
+            printf(" badlabels loss_threshold = %f, start_it = %d, progress = %f \n", badlabels_threshold, start_point, progress_badlabels *100);
+
+            ep_loss_threshold = min_val_cmp(final_badlebels_threshold, rolling_avg) * progress;
+        }
+
+
+        // reject some percent of the highest deltas to filter bad labels
+        if (state.net.badlabels_rejection_percentage && start_point < iteration_num) {
+            if (*state.net.badlabels_reject_threshold == 0)
+                *state.net.badlabels_reject_threshold = *state.net.delta_rolling_max;
+
+            printf(" badlabels_reject_threshold = %f \n", *state.net.badlabels_reject_threshold);
+
+            const float num_deltas_per_anchor = (l.classes + 4 + 1);
+            float counter_reject = 0;
+            float counter_all = 0;
+            for (i = 0; i < l.batch * l.outputs; ++i) {
+                if (l.delta[i] != 0) {
+                    counter_all++;
+                    if (fabs(l.delta[i]) > (*state.net.badlabels_reject_threshold)) {
+                        counter_reject++;
+                        l.delta[i] = 0;
+                    }
+                }
+            }
+            float cur_percent = 100 * (counter_reject*num_deltas_per_anchor / counter_all);
+            if (cur_percent > state.net.badlabels_rejection_percentage) {
+                *state.net.badlabels_reject_threshold += 0.01;
+                printf(" increase!!! \n");
+            }
+            else if (*state.net.badlabels_reject_threshold > 0.01) {
+                *state.net.badlabels_reject_threshold -= 0.01;
+                printf(" decrease!!! \n");
+            }
+
+            printf(" badlabels_reject_threshold = %f, cur_percent = %f, badlabels_rejection_percentage = %f, delta_rolling_max = %f \n",
+                *state.net.badlabels_reject_threshold, cur_percent, state.net.badlabels_rejection_percentage, *state.net.delta_rolling_max);
+        }
+
+
+        // reject low loss to find equidistant point
+        if (state.net.equidistant_point && state.net.equidistant_point < iteration_num) {
+            printf(" equidistant_point loss_threshold = %f, start_it = %d, progress = %3.1f %% \n", ep_loss_threshold, state.net.equidistant_point, progress * 100);
+            for (i = 0; i < l.batch * l.outputs; ++i) {
+                if (fabs(l.delta[i]) < ep_loss_threshold)
+                    l.delta[i] = 0;
+            }
+        }
+    }
+
+    if (count == 0) count = 1;
+    if (class_count == 0) class_count = 1;
+
+    if (l.show_details == 0) {
+        float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+        *(l.cost) = loss;
+
+        loss /= l.batch;
+
+        fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, total_loss = %f \n",
+            (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, loss);
+    }
+    else {
+        // show detailed output
+
+        int stride = l.w*l.h;
+        float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float));
+        memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float));
+
+
+        int j, n;
+        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 index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+                        no_iou_loss_delta[index + 0 * stride] = 0;
+                        no_iou_loss_delta[index + 1 * stride] = 0;
+                        no_iou_loss_delta[index + 2 * stride] = 0;
+                        no_iou_loss_delta[index + 3 * stride] = 0;
+                    }
+                }
+            }
+        }
+
+        float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2);
+        free(no_iou_loss_delta);
+        float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+        float iou_loss = loss - classification_loss;
+
+        float avg_iou_loss = 0;
+        *(l.cost) = loss;
+
+        // gIOU loss + MSE (objectness) loss
+        if (l.iou_loss == MSE) {
+            *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
+        }
+        else {
+            // Always compute classification loss both for iou + cls loss and for logging with mse loss
+            // TODO: remove IOU loss fields before computing MSE on class
+            //   probably split into two arrays
+            if (l.iou_loss == GIOU) {
+                avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0;
+            }
+            else {
+                avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0;
+            }
+            *(l.cost) = avg_iou_loss + classification_loss;
+        }
+
+
+        loss /= l.batch;
+        classification_loss /= l.batch;
+        iou_loss /= l.batch;
+
+        fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n",
+            (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, classification_loss, iou_loss, loss);
+
+        //fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n",
+        //    (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count,
+        //    classification_loss, iou_loss, loss);
+    }
+}
+
+void backward_yolo_layer(const layer l, network_state state)
+{
+   axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
+}
+
+// Converts output of the network to detection boxes
+// w,h: image width,height
+// netw,neth: network width,height
+// relative: 1 (all callers seems to pass TRUE)
+void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter)
+{
+    int i;
+    // network height (or width)
+    int new_w = 0;
+    // network height (or width)
+    int new_h = 0;
+    // Compute scale given image w,h vs network w,h
+    // I think this "rotates" the image to match network to input image w/h ratio
+    // new_h and new_w are really just network width and height
+    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;
+    }
+    // difference between network width and "rotated" width
+    float deltaw = netw - new_w;
+    // difference between network height and "rotated" height
+    float deltah = neth - new_h;
+    // ratio between rotated network width and network width
+    float ratiow = (float)new_w / netw;
+    // ratio between rotated network width and network width
+    float ratioh = (float)new_h / neth;
+    for (i = 0; i < n; ++i) {
+
+        box b = dets[i].bbox;
+        // x = ( x - (deltaw/2)/netw ) / ratiow;
+        //   x - [(1/2 the difference of the network width and rotated width) / (network width)]
+        b.x = (b.x - deltaw / 2. / netw) / ratiow;
+        b.y = (b.y - deltah / 2. / neth) / ratioh;
+        // scale to match rotation of incoming image
+        b.w *= 1 / ratiow;
+        b.h *= 1 / ratioh;
+
+        // relative seems to always be == 1, I don't think we hit this condition, ever.
+        if (!relative) {
+            b.x *= w;
+            b.w *= w;
+            b.y *= h;
+            b.h *= h;
+        }
+
+        dets[i].bbox = b;
+    }
+}
+
+/*
+void correct_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;
+    }
+    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 yolo_num_detections(layer l, float thresh)
+{
+    int i, n;
+    int count = 0;
+    for(n = 0; n < l.n; ++n){
+        for (i = 0; i < l.w*l.h; ++i) {
+            int obj_index  = entry_index(l, 0, n*l.w*l.h + i, 4);
+            if(l.output[obj_index] > thresh){
+                ++count;
+            }
+        }
+    }
+    return count;
+}
+
+int yolo_num_detections_batch(layer l, float thresh, int batch)
+{
+    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_index(l, batch, n*l.w*l.h + i, 4);
+            if(l.output[obj_index] > thresh){
+                ++count;
+            }
+        }
+    }
+    return count;
+}
+
+void avg_flipped_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 + 4 + 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_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter)
+{
+    //printf("\n l.batch = %d, l.w = %d, l.h = %d, l.n = %d \n", l.batch, l.w, l.h, l.n);
+    int i,j,n;
+    float *predictions = l.output;
+    // This snippet below is not necessary
+    // Need to comment it in order to batch processing >= 2 images
+    //if (l.batch == 2) avg_flipped_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_index(l, 0, n*l.w*l.h + i, 4);
+            float objectness = predictions[obj_index];
+            //if(objectness <= thresh) continue;    // incorrect behavior for Nan values
+            if (objectness > thresh) {
+                //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n);
+                int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
+                dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.new_coords);
+                dets[count].objectness = objectness;
+                dets[count].classes = l.classes;
+                if (l.embedding_output) {
+                    get_embedding(l.embedding_output, l.w, l.h, l.n*l.embedding_size, l.embedding_size, col, row, n, 0, dets[count].embeddings);
+                }
+
+                for (j = 0; j < l.classes; ++j) {
+                    int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j);
+                    float prob = objectness*predictions[class_index];
+                    dets[count].prob[j] = (prob > thresh) ? prob : 0;
+                }
+                ++count;
+            }
+        }
+    }
+    correct_yolo_boxes(dets, count, w, h, netw, neth, relative, letter);
+    return count;
+}
+
+int get_yolo_detections_batch(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter, int batch)
+{
+    int i,j,n;
+    float *predictions = l.output;
+    //if (l.batch == 2) avg_flipped_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_index(l, batch, n*l.w*l.h + i, 4);
+            float objectness = predictions[obj_index];
+            //if(objectness <= thresh) continue;    // incorrect behavior for Nan values
+            if (objectness > thresh) {
+                //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n);
+                int box_index = entry_index(l, batch, n*l.w*l.h + i, 0);
+                dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.new_coords);
+                dets[count].objectness = objectness;
+                dets[count].classes = l.classes;
+                if (l.embedding_output) {
+                    get_embedding(l.embedding_output, l.w, l.h, l.n*l.embedding_size, l.embedding_size, col, row, n, batch, dets[count].embeddings);
+                }
+
+                for (j = 0; j < l.classes; ++j) {
+                    int class_index = entry_index(l, batch, n*l.w*l.h + i, 4 + 1 + j);
+                    float prob = objectness*predictions[class_index];
+                    dets[count].prob[j] = (prob > thresh) ? prob : 0;
+                }
+                ++count;
+            }
+        }
+    }
+    correct_yolo_boxes(dets, count, w, h, netw, neth, relative, letter);
+    return count;
+}
+
+#ifdef GPU
+
+void forward_yolo_layer_gpu(const layer l, network_state state)
+{
+    if (l.embedding_output) {
+        layer le = state.net.layers[l.embedding_layer_id];
+        cuda_pull_array_async(le.output_gpu, l.embedding_output, le.batch*le.outputs);
+    }
+
+    //copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
+    simple_copy_ongpu(l.batch*l.inputs, state.input, l.output_gpu);
+    int b, n;
+    for (b = 0; b < l.batch; ++b){
+        for(n = 0; n < l.n; ++n){
+            int bbox_index = entry_index(l, b, n*l.w*l.h, 0);
+            // y = 1./(1. + exp(-x))
+            // x = ln(y/(1-y))  // ln - natural logarithm (base = e)
+            // if(y->1) x -> inf
+            // if(y->0) x -> -inf
+            if (l.new_coords) {
+                //activate_array_ongpu(l.output_gpu + bbox_index, 4 * l.w*l.h, LOGISTIC);    // x,y,w,h
+            }
+            else {
+                activate_array_ongpu(l.output_gpu + bbox_index, 2 * l.w*l.h, LOGISTIC);    // x,y
+
+                int obj_index = entry_index(l, b, n*l.w*l.h, 4);
+                activate_array_ongpu(l.output_gpu + obj_index, (1 + l.classes)*l.w*l.h, LOGISTIC); // classes and objectness
+            }
+            if (l.scale_x_y != 1) scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + bbox_index, 1);      // scale x,y
+        }
+    }
+    if(!state.train || l.onlyforward){
+        //cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
+        if (l.mean_alpha && l.output_avg_gpu) mean_array_gpu(l.output_gpu, l.batch*l.outputs, l.mean_alpha, l.output_avg_gpu);
+        cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs);
+        CHECK_CUDA(cudaPeekAtLastError());
+        return;
+    }
+
+    float *in_cpu = (float *)xcalloc(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 *)xcalloc(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_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_yolo_layer_gpu(const layer l, network_state state)
+{
+    axpy_ongpu(l.batch*l.inputs, state.net.loss_scale * l.delta_normalizer, l.delta_gpu, 1, state.delta, 1);
+}
+#endif

From 27d3762ccb897c415dc5692139576b3ebc35ce0b Mon Sep 17 00:00:00 2001
From: ZouJiu1 <34758215+ZouJiu1@users.noreply.github.com>
Date: Sat, 12 Aug 2023 10:15:45 +0800
Subject: [PATCH 08/11] Delete yolo_layer.cc

---
 src/yolo_layer.cc | 1124 ---------------------------------------------
 1 file changed, 1124 deletions(-)
 delete mode 100644 src/yolo_layer.cc

diff --git a/src/yolo_layer.cc b/src/yolo_layer.cc
deleted file mode 100644
index 423ca8f980c..00000000000
--- a/src/yolo_layer.cc
+++ /dev/null
@@ -1,1124 +0,0 @@
-#include "yolo_layer.h"
-#include "activations.h"
-#include "blas.h"
-#include "box.h"
-#include "dark_cuda.h"
-#include "utils.h"
-#include "darknet.h"
-
-#include <math.h>
-#include <stdio.h>
-#include <assert.h>
-#include <string.h>
-#include <stdlib.h>
-
-extern int check_mistakes;
-
-layer make_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 = YOLO;
-
-    l.n = n;
-    l.total = total;
-    l.batch = batch;
-    l.h = h;
-    l.w = w;
-    l.c = n*(classes + 4 + 1);
-    l.out_w = l.w;
-    l.out_h = l.h;
-    l.out_c = l.c;
-    l.classes = classes;
-    l.cost = (float*)xcalloc(1, sizeof(float));
-    l.biases = (float*)xcalloc(total * 2, sizeof(float));
-    if(mask) l.mask = mask;
-    else{
-        l.mask = (int*)xcalloc(n, sizeof(int));
-        for(i = 0; i < n; ++i){
-            l.mask[i] = i;
-        }
-    }
-    l.bias_updates = (float*)xcalloc(n * 2, sizeof(float));
-    l.outputs = h*w*n*(classes + 4 + 1);
-    l.inputs = l.outputs;
-    l.max_boxes = max_boxes;
-    l.truth_size = 4 + 2;
-    l.truths = l.max_boxes*l.truth_size;    // 90*(4 + 1);
-    l.labels = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int));
-    for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.labels[i] = -1;
-    l.class_ids = (int*)xcalloc(batch * l.w*l.h*l.n, sizeof(int));
-    for (i = 0; i < batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1;
-
-    l.delta = (float*)xcalloc(batch * l.outputs, sizeof(float));
-    l.output = (float*)xcalloc(batch * l.outputs, sizeof(float));
-    for(i = 0; i < total*2; ++i){
-        l.biases[i] = .5;
-    }
-
-    l.forward = forward_yolo_layer;
-    l.backward = backward_yolo_layer;
-#ifdef GPU
-    l.forward_gpu = forward_yolo_layer_gpu;
-    l.backward_gpu = backward_yolo_layer_gpu;
-    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
-    l.output_avg_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*)xcalloc(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*)xcalloc(batch * l.outputs, sizeof(float));
-    }
-#endif
-
-    fprintf(stderr, "yolo\n");
-    srand(time(0));
-
-    return l;
-}
-
-void resize_yolo_layer(layer *l, int w, int h)
-{
-    l->w = w;
-    l->h = h;
-
-    l->outputs = h*w*l->n*(l->classes + 4 + 1);
-    l->inputs = l->outputs;
-
-    if (l->embedding_output) l->embedding_output = (float*)xrealloc(l->output, l->batch * l->embedding_size * l->n * l->h * l->w * sizeof(float));
-    if (l->labels) l->labels = (int*)xrealloc(l->labels, l->batch * l->n * l->h * l->w * sizeof(int));
-    if (l->class_ids) l->class_ids = (int*)xrealloc(l->class_ids, l->batch * l->n * l->h * l->w * sizeof(int));
-
-    if (!l->output_pinned) l->output = (float*)xrealloc(l->output, l->batch*l->outputs * sizeof(float));
-    if (!l->delta_pinned) l->delta = (float*)xrealloc(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*)xcalloc(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*)xcalloc(l->batch * l->outputs, sizeof(float));
-            l->delta_pinned = 0;
-        }
-    }
-
-    cuda_free(l->delta_gpu);
-    cuda_free(l->output_gpu);
-    cuda_free(l->output_avg_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);
-    l->output_avg_gpu = cuda_make_array(l->output, l->batch*l->outputs);
-#endif
-}
-
-box get_yolo_box(float *x, float *biases, int n, int index, int i, int j, int lw, int lh, int w, int h, int stride, int new_coords)
-{
-    box b;
-    // ln - natural logarithm (base = e)
-    // x` = t.x * lw - i;   // x = ln(x`/(1-x`))   // x - output of previous conv-layer
-    // y` = t.y * lh - i;   // y = ln(y`/(1-y`))   // y - output of previous conv-layer
-    // w = ln(t.w * net.w / anchors_w); // w - output of previous conv-layer
-    // h = ln(t.h * net.h / anchors_h); // h - output of previous conv-layer
-    if (new_coords) {
-        b.x = (i + x[index + 0 * stride]) / lw;
-        b.y = (j + x[index + 1 * stride]) / lh;
-        b.w = x[index + 2 * stride] * x[index + 2 * stride] * 4 * biases[2 * n] / w;
-        b.h = x[index + 3 * stride] * x[index + 3 * stride] * 4 * biases[2 * n + 1] / h;
-    }
-    else {
-        b.x = (i + x[index + 0 * stride]) / lw;
-        b.y = (j + x[index + 1 * stride]) / lh;
-        b.w = exp(x[index + 2 * stride]) * biases[2 * n] / w;
-        b.h = exp(x[index + 3 * stride]) * biases[2 * n + 1] / h;
-    }
-    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) {
-        //printf("\n val = %f > max_val = %f \n", val, max_val);
-        val = max_val;
-    }
-    else if (val < -max_val) {
-        //printf("\n val = %f < -max_val = %f \n", val, -max_val);
-        val = -max_val;
-    }
-    return val;
-}
-
-ious delta_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, int accumulate, float max_delta, int *rewritten_bbox, int new_coords)
-{
-    if (delta[index + 0 * stride] || delta[index + 1 * stride] || delta[index + 2 * stride] || delta[index + 3 * stride]) {
-        (*rewritten_bbox)++;
-    }
-
-    ious all_ious = { 0 };
-    // i - step in layer width
-    // j - step in layer height
-    //  Returns a box in absolute coordinates
-    box pred = get_yolo_box(x, biases, n, index, i, j, lw, lh, w, h, stride, new_coords);
-    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);
-    // avoid nan in dx_box_iou
-    if (pred.w == 0) { pred.w = 1.0; }
-    if (pred.h == 0) { pred.h = 1.0; }
-    if (iou_loss == MSE)    // old loss
-    {
-        float tx = (truth.x*lw - i);
-        float ty = (truth.y*lh - j);
-        float tw = log(truth.w*w / biases[2 * n]);
-        float th = log(truth.h*h / biases[2 * n + 1]);
-
-        if (new_coords) {
-            //tx = (truth.x*lw - i + 0.5) / 2;
-            //ty = (truth.y*lh - j + 0.5) / 2;
-            tw = sqrt(truth.w*w / (4 * biases[2 * n]));
-            th = sqrt(truth.h*h / (4 * biases[2 * n + 1]));
-        }
-
-        //printf(" tx = %f, ty = %f, tw = %f, th = %f \n", tx, ty, tw, th);
-        //printf(" x = %f, y = %f, w = %f, h = %f \n", x[index + 0 * stride], x[index + 1 * stride], x[index + 2 * stride], x[index + 3 * stride]);
-
-        // accumulate delta
-        delta[index + 0 * stride] += scale * (tx - x[index + 0 * stride]) * iou_normalizer;
-        delta[index + 1 * stride] += scale * (ty - x[index + 1 * stride]) * iou_normalizer;
-        delta[index + 2 * stride] += scale * (tw - x[index + 2 * stride]) * iou_normalizer;
-        delta[index + 3 * stride] += scale * (th - x[index + 3 * stride]) * iou_normalizer;
-    }
-    else {
-        // https://github.com/generalized-iou/g-darknet
-        // https://arxiv.org/abs/1902.09630v2
-        // https://giou.stanford.edu/
-        all_ious.dx_iou = dx_box_iou(pred, truth, iou_loss);
-
-        // 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));
-
-        // jacobian^t (transpose)
-        float dx = all_ious.dx_iou.dt;
-        float dy = all_ious.dx_iou.db;
-        float dw = all_ious.dx_iou.dl;
-        float dh = all_ious.dx_iou.dr;
-
-
-        // predict exponential, apply gradient of e^delta_t ONLY for w,h
-        if (new_coords) {
-            //dw *= 8 * x[index + 2 * stride];
-            //dh *= 8 * x[index + 3 * stride];
-            //dw *= 8 * x[index + 2 * stride] * biases[2 * n] / w;
-            //dh *= 8 * x[index + 3 * stride] * biases[2 * n + 1] / h;
-
-            //float grad_w = 8 * exp(-x[index + 2 * stride]) / pow(exp(-x[index + 2 * stride]) + 1, 3);
-            //float grad_h = 8 * exp(-x[index + 3 * stride]) / pow(exp(-x[index + 3 * stride]) + 1, 3);
-            //dw *= grad_w;
-            //dh *= grad_h;
-        }
-        else {
-            dw *= exp(x[index + 2 * stride]);
-            dh *= exp(x[index + 3 * stride]);
-        }
-
-
-        //dw *= exp(x[index + 2 * stride]);
-        //dh *= exp(x[index + 3 * stride]);
-
-        // normalize iou weight
-        dx *= iou_normalizer;
-        dy *= iou_normalizer;
-        dw *= iou_normalizer;
-        dh *= iou_normalizer;
-
-
-        dx = fix_nan_inf(dx);
-        dy = fix_nan_inf(dy);
-        dw = fix_nan_inf(dw);
-        dh = fix_nan_inf(dh);
-
-        if (max_delta != FLT_MAX) {
-            dx = clip_value(dx, max_delta);
-            dy = clip_value(dy, max_delta);
-            dw = clip_value(dw, max_delta);
-            dh = clip_value(dh, max_delta);
-        }
-
-
-        if (!accumulate) {
-            delta[index + 0 * stride] = 0;
-            delta[index + 1 * stride] = 0;
-            delta[index + 2 * stride] = 0;
-            delta[index + 3 * stride] = 0;
-        }
-
-        // accumulate delta
-        delta[index + 0 * stride] += dx;
-        delta[index + 1 * stride] += dy;
-        delta[index + 2 * stride] += dw;
-        delta[index + 3 * stride] += dh;
-    }
-
-    return all_ious;
-}
-
-void averages_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;
-    }
-}
-
-void delta_yolo_class(float *output, float *delta, int index, int class_id, int classes, int stride, float *avg_cat, int focal_loss, float label_smooth_eps, float *classes_multipliers, float cls_normalizer)
-{
-    int n;
-    if (delta[index + stride*class_id]){
-        float y_true = 1;
-        if(label_smooth_eps) y_true = y_true *  (1 - label_smooth_eps) + 0.5*label_smooth_eps;
-        float result_delta = y_true - output[index + stride*class_id];
-        if(!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*class_id] = result_delta;
-        //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;
-    }
-    // Focal loss
-    if (focal_loss) {
-        // Focal Loss
-        float alpha = 0.5;    // 0.25 or 0.5
-        //float gamma = 2;    // hardcoded in many places of the grad-formula
-
-        int ti = index + stride*class_id;
-        float pt = output[ti] + 0.000000000000001F;
-        // http://fooplot.com/#W3sidHlwZSI6MCwiZXEiOiItKDEteCkqKDIqeCpsb2coeCkreC0xKSIsImNvbG9yIjoiIzAwMDAwMCJ9LHsidHlwZSI6MTAwMH1d
-        float grad = -(1 - pt) * (2 * pt*logf(pt) + pt - 1);    // http://blog.csdn.net/linmingan/article/details/77885832
-        //float grad = (1 - pt) * (2 * pt*logf(pt) + pt - 1);    // https://github.com/unsky/focal-loss
-
-        for (n = 0; n < classes; ++n) {
-            delta[index + stride*n] = (((n == class_id) ? 1 : 0) - output[index + stride*n]);
-
-            delta[index + stride*n] *= alpha*grad;
-
-            if (n == class_id && avg_cat) *avg_cat += output[index + stride*n];
-        }
-    }
-    else {
-        // default
-        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;
-            float result_delta = y_true - output[index + stride*n];
-            if (!isnan(result_delta) && !isinf(result_delta)) delta[index + stride*n] = result_delta;
-
-            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_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_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*(4+l.classes+1) + entry*l.w*l.h + loc;
-}
-
-typedef struct train_yolo_args {
-    layer l;
-    network_state state;
-    int b;
-
-    float tot_iou;
-    float tot_giou_loss;
-    float tot_iou_loss;
-    int count;
-    int class_count;
-    mloam* mloam_ptr;
-} train_yolo_args;
-
-void *process_batch(void* ptr)
-{
-    {
-        train_yolo_args *args = (train_yolo_args*)ptr;
-        const layer l = args->l;
-        network_state state = args->state;
-        int b = args->b;
-
-        int i, j, t, n, ln, x, y;
-
-        //printf(" b = %d \n", b, b);
-
-        //float tot_iou = 0;
-        float tot_giou = 0;
-        float tot_diou = 0;
-        float tot_ciou = 0;
-        //float tot_iou_loss = 0;
-        //float tot_giou_loss = 0;
-        float tot_diou_loss = 0;
-        float tot_ciou_loss = 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;
-        mloam* mloam_ptr = args->mloam_ptr;   // multi_label_one_anchor_choose_maxiou
-        int number = 0;
-        float* maxiou = (float *)xcalloc(l.max_boxes, sizeof(float));
-        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;  // continue;
-            if (truth.x < 0 || truth.y < 0 || truth.x > 1 || truth.y > 1 || truth.w < 0 || truth.h < 0) {
-                char buff[256];
-                printf(" Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", truth.x, truth.y, truth.w, truth.h);
-                sprintf(buff, "echo \"Wrong label: truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f\" >> bad_label.list",
-                    truth.x, truth.y, truth.w, truth.h);
-                system(buff);
-            }
-            int class_id = state.truth[t * l.truth_size + b * l.truths + 4];
-            if (class_id >= l.classes || class_id < 0) continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value
-
-            // x = (truth.x * l.w);
-            // y = (truth.y * l.h);
-            box truth_shift = truth;
-            // truth_shift.x = truth_shift.y = 0;
-            for (ln = 0; ln < l.total; ++ln) {
-                int n = int_index(l.mask, ln, l.n);
-                if (n >= 0) {
-                    box pred = { 0 };
-                    pred.w = l.biases[2 * ln] / state.net.w;
-                    pred.h = l.biases[2 * ln + 1] / state.net.h;
-                    for (j = 0; j < l.h; ++j) {
-                        for (i = 0; i < l.w; ++i) {
-                            pred.x = i / (float)(l.w);
-                            pred.y = j / (float)(l.h);
-                            float iou = box_iou_kind(pred, truth_shift, l.iou_thresh_kind); // IOU, GIOU, MSE, DIOU, CIOU
-
-                            const int obj_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4);
-                            l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]);
-                            if(iou > 0.3) {
-                                if(number==0) {
-                                    mloam choose = {truth, iou, i, j, 1, ln, n, t};
-                                    mloam_ptr[number++] = choose;
-                                    if(iou > maxiou[t]) {
-                                        maxiou[t] = iou;
-                                    }
-                                }
-                                else {
-                                    int markable = 666;
-                                    for(int mi = 0; mi < number; mi++) {
-                                        mloam mp = mloam_ptr[mi];
-                                        if(mp.mask_n == n && mp.x==i && mp.y==j) {
-                                            markable = -666;
-                                            if(mp.best_iou < iou) {
-                                                mloam choose = {truth, iou, i, j, 1, ln, n, t};
-                                                mloam_ptr[mi] = choose;
-                                                if(iou > maxiou[t]) {
-                                                    maxiou[t] = iou;
-                                                }
-                                                break;
-                                            }
-                                        }
-                                    }
-                                    if(markable > 0) {
-                                        mloam choose = {truth, iou, i, j, 1, ln, n, t};
-                                        mloam_ptr[number++] = choose;
-                                        if(iou > maxiou[t]) {
-                                            maxiou[t] = iou;
-                                        }
-                                    }
-                                }
-                            }
-                        }
-                    }
-                }
-            }
-        }
-        
-        for(int ni = 0; ni < number; ni++) {
-            mloam mp   = mloam_ptr[ni];
-            box truth      = mp.truth;
-            int i          = mp.x;
-            int j          = mp.y;
-            int track_id   = mp.track_id;
-            int best_n     = mp.best_n;
-            int mask_n     = mp.mask_n;
-            int t          = mp.t;
-            float iou_scale = mp.best_iou / maxiou[t];
-            // printf("%f", iou_scale);
-            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_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;
-            ious all_ious = delta_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 * iou_scale, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords);
-            (*state.net.total_bbox)++;
-
-            if(track_id > 0) {
-                const int truth_in_index = t * l.truth_size + b * l.truths + 5;
-                const int track_id = state.truth[truth_in_index];
-                const int truth_out_index = b * l.n * l.w * l.h + mask_n * l.w * l.h + j * l.w + i;
-                l.labels[truth_out_index] = track_id;
-                l.class_ids[truth_out_index] = class_id;
-                //printf(" track_id = %d, t = %d, b = %d, truth_in_index = %d, truth_out_index = %d \n", track_id, t, b, truth_in_index, truth_out_index);
-            }
-            // range is 0 <= 1
-            args->tot_iou += all_ious.iou;
-            args->tot_iou_loss += 1 - all_ious.iou;
-            // range is -1 <= giou <= 1
-            tot_giou += all_ious.giou;
-            args->tot_giou_loss += 1 - all_ious.giou;
-
-            tot_diou += all_ious.diou;
-            tot_diou_loss += 1 - all_ious.diou;
-
-            tot_ciou += all_ious.ciou;
-            tot_ciou_loss += 1 - all_ious.ciou;
-
-            int obj_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4);
-            avg_obj += l.output[obj_index];
-            if (l.objectness_smooth) {
-                float delta_obj = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]) * iou_scale;
-                if (l.delta[obj_index] == 0) l.delta[obj_index] = delta_obj;
-            }
-            else l.delta[obj_index] = class_multiplier * l.obj_normalizer * (1 - l.output[obj_index]) * iou_scale;
-
-            int class_index = entry_index(l, b, mask_n * l.w * l.h + j * l.w + i, 4 + 1);
-            delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, &avg_cat, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer * iou_scale);
-
-            //printf(" label: class_id = %d, truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f \n", class_id, truth.x, truth.y, truth.w, truth.h);
-            //printf(" mask_n = %d, l.output[obj_index] = %f, l.output[class_index + class_id] = %f \n\n", mask_n, l.output[obj_index], l.output[class_index + class_id]);
-
-            ++(args->count);
-            ++(args->class_count);
-            if (all_ious.iou > .5) recall += 1;
-            if (all_ious.iou > .75) recall75 += 1;
-        }
-    }
-
-    return 0;
-}
-
-
-
-void forward_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));
-    int b, n;
-
-#ifndef GPU
-    for (b = 0; b < l.batch; ++b) {
-        for (n = 0; n < l.n; ++n) {
-            int bbox_index = entry_index(l, b, n*l.w*l.h, 0);
-            if (l.new_coords) {
-                //activate_array(l.output + bbox_index, 4 * l.w*l.h, LOGISTIC);    // x,y,w,h
-            }
-            else {
-                activate_array(l.output + bbox_index, 2 * l.w*l.h, LOGISTIC);        // x,y,
-                int obj_index = entry_index(l, b, n*l.w*l.h, 4);
-                activate_array(l.output + obj_index, (1 + l.classes)*l.w*l.h, LOGISTIC);
-            }
-            scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + bbox_index, 1);    // scale x,y
-        }
-    }
-#endif
-
-    // delta is zeroed
-    memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
-    if (!state.train) return;
-
-    int i;
-    for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.labels[i] = -1;
-    for (i = 0; i < l.batch * l.w*l.h*l.n; ++i) l.class_ids[i] = -1;
-    //float avg_iou = 0;
-    float tot_iou = 0;
-    float tot_giou = 0;
-    float tot_diou = 0;
-    float tot_ciou = 0;
-    float tot_iou_loss = 0;
-    float tot_giou_loss = 0;
-    float tot_diou_loss = 0;
-    float tot_ciou_loss = 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;
-
-
-    int num_threads = l.batch;
-    pthread_t* threads = (pthread_t*)calloc(num_threads, sizeof(pthread_t));
-
-    struct train_yolo_args* yolo_args = (train_yolo_args*)xcalloc(l.batch, sizeof(struct train_yolo_args));
-
-    for (b = 0; b < l.batch; b++)
-    {
-        yolo_args[b].l = l;
-        yolo_args[b].state = state;
-        yolo_args[b].b = b;
-
-        yolo_args[b].tot_iou = 0;
-        yolo_args[b].tot_iou_loss = 0;
-        yolo_args[b].tot_giou_loss = 0;
-        yolo_args[b].count = 0;
-        yolo_args[b].class_count = 0;
-        yolo_args[b].mloam_ptr = (mloam *)xcalloc(l.max_boxes * 10 + 60, sizeof(mloam));     // multi_label_one_anchor_choose_maxiou
-
-        if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed", DARKNET_LOC);
-    }
-
-    for (b = 0; b < l.batch; b++)
-    {
-        pthread_join(threads[b], 0);
-
-        tot_iou += yolo_args[b].tot_iou;
-        tot_iou_loss += yolo_args[b].tot_iou_loss;
-        tot_giou_loss += yolo_args[b].tot_giou_loss;
-        count += yolo_args[b].count;
-        class_count += yolo_args[b].class_count;
-        free(yolo_args[b].mloam_ptr);
-    }
-
-    free(yolo_args);
-    free(threads);
-
-    // Search for an equidistant point from the distant boundaries of the local minimum
-    int iteration_num = get_current_iteration(state.net);
-    const int start_point = state.net.max_batches * 3 / 4;
-    //printf(" equidistant_point ep = %d, it = %d \n", state.net.equidistant_point, iteration_num);
-
-    if ((state.net.badlabels_rejection_percentage && start_point < iteration_num) ||
-        (state.net.num_sigmas_reject_badlabels && start_point < iteration_num) ||
-        (state.net.equidistant_point && state.net.equidistant_point < iteration_num))
-    {
-        const float progress_it = iteration_num - state.net.equidistant_point;
-        const float progress = progress_it / (state.net.max_batches - state.net.equidistant_point);
-        float ep_loss_threshold = (*state.net.delta_rolling_avg) * progress * 1.4;
-
-        float cur_max = 0;
-        float cur_avg = 0;
-        float counter = 0;
-        for (i = 0; i < l.batch * l.outputs; ++i) {
-
-            if (l.delta[i] != 0) {
-                counter++;
-                cur_avg += fabs(l.delta[i]);
-
-                if (cur_max < fabs(l.delta[i]))
-                    cur_max = fabs(l.delta[i]);
-            }
-        }
-
-        cur_avg = cur_avg / counter;
-
-        if (*state.net.delta_rolling_max == 0) *state.net.delta_rolling_max = cur_max;
-        *state.net.delta_rolling_max = *state.net.delta_rolling_max * 0.99 + cur_max * 0.01;
-        *state.net.delta_rolling_avg = *state.net.delta_rolling_avg * 0.99 + cur_avg * 0.01;
-
-        // reject high loss to filter bad labels
-        if (state.net.num_sigmas_reject_badlabels && start_point < iteration_num)
-        {
-            const float rolling_std = (*state.net.delta_rolling_std);
-            const float rolling_max = (*state.net.delta_rolling_max);
-            const float rolling_avg = (*state.net.delta_rolling_avg);
-            const float progress_badlabels = (float)(iteration_num - start_point) / (start_point);
-
-            float cur_std = 0;
-            float counter = 0;
-            for (i = 0; i < l.batch * l.outputs; ++i) {
-                if (l.delta[i] != 0) {
-                    counter++;
-                    cur_std += pow(l.delta[i] - rolling_avg, 2);
-                }
-            }
-            cur_std = sqrt(cur_std / counter);
-
-            *state.net.delta_rolling_std = *state.net.delta_rolling_std * 0.99 + cur_std * 0.01;
-
-            float final_badlebels_threshold = rolling_avg + rolling_std * state.net.num_sigmas_reject_badlabels;
-            float badlabels_threshold = rolling_max - progress_badlabels * fabs(rolling_max - final_badlebels_threshold);
-            badlabels_threshold = max_val_cmp(final_badlebels_threshold, badlabels_threshold);
-            for (i = 0; i < l.batch * l.outputs; ++i) {
-                if (fabs(l.delta[i]) > badlabels_threshold)
-                    l.delta[i] = 0;
-            }
-            printf(" rolling_std = %f, rolling_max = %f, rolling_avg = %f \n", rolling_std, rolling_max, rolling_avg);
-            printf(" badlabels loss_threshold = %f, start_it = %d, progress = %f \n", badlabels_threshold, start_point, progress_badlabels *100);
-
-            ep_loss_threshold = min_val_cmp(final_badlebels_threshold, rolling_avg) * progress;
-        }
-
-
-        // reject some percent of the highest deltas to filter bad labels
-        if (state.net.badlabels_rejection_percentage && start_point < iteration_num) {
-            if (*state.net.badlabels_reject_threshold == 0)
-                *state.net.badlabels_reject_threshold = *state.net.delta_rolling_max;
-
-            printf(" badlabels_reject_threshold = %f \n", *state.net.badlabels_reject_threshold);
-
-            const float num_deltas_per_anchor = (l.classes + 4 + 1);
-            float counter_reject = 0;
-            float counter_all = 0;
-            for (i = 0; i < l.batch * l.outputs; ++i) {
-                if (l.delta[i] != 0) {
-                    counter_all++;
-                    if (fabs(l.delta[i]) > (*state.net.badlabels_reject_threshold)) {
-                        counter_reject++;
-                        l.delta[i] = 0;
-                    }
-                }
-            }
-            float cur_percent = 100 * (counter_reject*num_deltas_per_anchor / counter_all);
-            if (cur_percent > state.net.badlabels_rejection_percentage) {
-                *state.net.badlabels_reject_threshold += 0.01;
-                printf(" increase!!! \n");
-            }
-            else if (*state.net.badlabels_reject_threshold > 0.01) {
-                *state.net.badlabels_reject_threshold -= 0.01;
-                printf(" decrease!!! \n");
-            }
-
-            printf(" badlabels_reject_threshold = %f, cur_percent = %f, badlabels_rejection_percentage = %f, delta_rolling_max = %f \n",
-                *state.net.badlabels_reject_threshold, cur_percent, state.net.badlabels_rejection_percentage, *state.net.delta_rolling_max);
-        }
-
-
-        // reject low loss to find equidistant point
-        if (state.net.equidistant_point && state.net.equidistant_point < iteration_num) {
-            printf(" equidistant_point loss_threshold = %f, start_it = %d, progress = %3.1f %% \n", ep_loss_threshold, state.net.equidistant_point, progress * 100);
-            for (i = 0; i < l.batch * l.outputs; ++i) {
-                if (fabs(l.delta[i]) < ep_loss_threshold)
-                    l.delta[i] = 0;
-            }
-        }
-    }
-
-    if (count == 0) count = 1;
-    if (class_count == 0) class_count = 1;
-
-    if (l.show_details == 0) {
-        float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2);
-        *(l.cost) = loss;
-
-        loss /= l.batch;
-
-        fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, total_loss = %f \n",
-            (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, loss);
-    }
-    else {
-        // show detailed output
-
-        int stride = l.w*l.h;
-        float* no_iou_loss_delta = (float *)calloc(l.batch * l.outputs, sizeof(float));
-        memcpy(no_iou_loss_delta, l.delta, l.batch * l.outputs * sizeof(float));
-
-
-        int j, n;
-        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 index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
-                        no_iou_loss_delta[index + 0 * stride] = 0;
-                        no_iou_loss_delta[index + 1 * stride] = 0;
-                        no_iou_loss_delta[index + 2 * stride] = 0;
-                        no_iou_loss_delta[index + 3 * stride] = 0;
-                    }
-                }
-            }
-        }
-
-        float classification_loss = l.obj_normalizer * pow(mag_array(no_iou_loss_delta, l.outputs * l.batch), 2);
-        free(no_iou_loss_delta);
-        float loss = pow(mag_array(l.delta, l.outputs * l.batch), 2);
-        float iou_loss = loss - classification_loss;
-
-        float avg_iou_loss = 0;
-        *(l.cost) = loss;
-
-        // gIOU loss + MSE (objectness) loss
-        if (l.iou_loss == MSE) {
-            *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
-        }
-        else {
-            // Always compute classification loss both for iou + cls loss and for logging with mse loss
-            // TODO: remove IOU loss fields before computing MSE on class
-            //   probably split into two arrays
-            if (l.iou_loss == GIOU) {
-                avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_giou_loss / count) : 0;
-            }
-            else {
-                avg_iou_loss = count > 0 ? l.iou_normalizer * (tot_iou_loss / count) : 0;
-            }
-            *(l.cost) = avg_iou_loss + classification_loss;
-        }
-
-
-        loss /= l.batch;
-        classification_loss /= l.batch;
-        iou_loss /= l.batch;
-
-        fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, obj: %.2f, cls: %.2f) Region %d Avg (IOU: %f), count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n",
-            (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, state.index, tot_iou / count, count, classification_loss, iou_loss, loss);
-
-        //fprintf(stderr, "v3 (%s loss, Normalizer: (iou: %.2f, cls: %.2f) Region %d Avg (IOU: %f, GIOU: %f), Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d, class_loss = %f, iou_loss = %f, total_loss = %f \n",
-        //    (l.iou_loss == MSE ? "mse" : (l.iou_loss == GIOU ? "giou" : "iou")), l.iou_normalizer, l.obj_normalizer, state.index, tot_iou / count, tot_giou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count,
-        //    classification_loss, iou_loss, loss);
-    }
-}
-
-void backward_yolo_layer(const layer l, network_state state)
-{
-   axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
-}
-
-// Converts output of the network to detection boxes
-// w,h: image width,height
-// netw,neth: network width,height
-// relative: 1 (all callers seems to pass TRUE)
-void correct_yolo_boxes(detection *dets, int n, int w, int h, int netw, int neth, int relative, int letter)
-{
-    int i;
-    // network height (or width)
-    int new_w = 0;
-    // network height (or width)
-    int new_h = 0;
-    // Compute scale given image w,h vs network w,h
-    // I think this "rotates" the image to match network to input image w/h ratio
-    // new_h and new_w are really just network width and height
-    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;
-    }
-    // difference between network width and "rotated" width
-    float deltaw = netw - new_w;
-    // difference between network height and "rotated" height
-    float deltah = neth - new_h;
-    // ratio between rotated network width and network width
-    float ratiow = (float)new_w / netw;
-    // ratio between rotated network width and network width
-    float ratioh = (float)new_h / neth;
-    for (i = 0; i < n; ++i) {
-
-        box b = dets[i].bbox;
-        // x = ( x - (deltaw/2)/netw ) / ratiow;
-        //   x - [(1/2 the difference of the network width and rotated width) / (network width)]
-        b.x = (b.x - deltaw / 2. / netw) / ratiow;
-        b.y = (b.y - deltah / 2. / neth) / ratioh;
-        // scale to match rotation of incoming image
-        b.w *= 1 / ratiow;
-        b.h *= 1 / ratioh;
-
-        // relative seems to always be == 1, I don't think we hit this condition, ever.
-        if (!relative) {
-            b.x *= w;
-            b.w *= w;
-            b.y *= h;
-            b.h *= h;
-        }
-
-        dets[i].bbox = b;
-    }
-}
-
-/*
-void correct_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;
-    }
-    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 yolo_num_detections(layer l, float thresh)
-{
-    int i, n;
-    int count = 0;
-    for(n = 0; n < l.n; ++n){
-        for (i = 0; i < l.w*l.h; ++i) {
-            int obj_index  = entry_index(l, 0, n*l.w*l.h + i, 4);
-            if(l.output[obj_index] > thresh){
-                ++count;
-            }
-        }
-    }
-    return count;
-}
-
-int yolo_num_detections_batch(layer l, float thresh, int batch)
-{
-    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_index(l, batch, n*l.w*l.h + i, 4);
-            if(l.output[obj_index] > thresh){
-                ++count;
-            }
-        }
-    }
-    return count;
-}
-
-void avg_flipped_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 + 4 + 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_yolo_detections(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter)
-{
-    //printf("\n l.batch = %d, l.w = %d, l.h = %d, l.n = %d \n", l.batch, l.w, l.h, l.n);
-    int i,j,n;
-    float *predictions = l.output;
-    // This snippet below is not necessary
-    // Need to comment it in order to batch processing >= 2 images
-    //if (l.batch == 2) avg_flipped_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_index(l, 0, n*l.w*l.h + i, 4);
-            float objectness = predictions[obj_index];
-            //if(objectness <= thresh) continue;    // incorrect behavior for Nan values
-            if (objectness > thresh) {
-                //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n);
-                int box_index = entry_index(l, 0, n*l.w*l.h + i, 0);
-                dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.new_coords);
-                dets[count].objectness = objectness;
-                dets[count].classes = l.classes;
-                if (l.embedding_output) {
-                    get_embedding(l.embedding_output, l.w, l.h, l.n*l.embedding_size, l.embedding_size, col, row, n, 0, dets[count].embeddings);
-                }
-
-                for (j = 0; j < l.classes; ++j) {
-                    int class_index = entry_index(l, 0, n*l.w*l.h + i, 4 + 1 + j);
-                    float prob = objectness*predictions[class_index];
-                    dets[count].prob[j] = (prob > thresh) ? prob : 0;
-                }
-                ++count;
-            }
-        }
-    }
-    correct_yolo_boxes(dets, count, w, h, netw, neth, relative, letter);
-    return count;
-}
-
-int get_yolo_detections_batch(layer l, int w, int h, int netw, int neth, float thresh, int *map, int relative, detection *dets, int letter, int batch)
-{
-    int i,j,n;
-    float *predictions = l.output;
-    //if (l.batch == 2) avg_flipped_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_index(l, batch, n*l.w*l.h + i, 4);
-            float objectness = predictions[obj_index];
-            //if(objectness <= thresh) continue;    // incorrect behavior for Nan values
-            if (objectness > thresh) {
-                //printf("\n objectness = %f, thresh = %f, i = %d, n = %d \n", objectness, thresh, i, n);
-                int box_index = entry_index(l, batch, n*l.w*l.h + i, 0);
-                dets[count].bbox = get_yolo_box(predictions, l.biases, l.mask[n], box_index, col, row, l.w, l.h, netw, neth, l.w*l.h, l.new_coords);
-                dets[count].objectness = objectness;
-                dets[count].classes = l.classes;
-                if (l.embedding_output) {
-                    get_embedding(l.embedding_output, l.w, l.h, l.n*l.embedding_size, l.embedding_size, col, row, n, batch, dets[count].embeddings);
-                }
-
-                for (j = 0; j < l.classes; ++j) {
-                    int class_index = entry_index(l, batch, n*l.w*l.h + i, 4 + 1 + j);
-                    float prob = objectness*predictions[class_index];
-                    dets[count].prob[j] = (prob > thresh) ? prob : 0;
-                }
-                ++count;
-            }
-        }
-    }
-    correct_yolo_boxes(dets, count, w, h, netw, neth, relative, letter);
-    return count;
-}
-
-#ifdef GPU
-
-void forward_yolo_layer_gpu(const layer l, network_state state)
-{
-    if (l.embedding_output) {
-        layer le = state.net.layers[l.embedding_layer_id];
-        cuda_pull_array_async(le.output_gpu, l.embedding_output, le.batch*le.outputs);
-    }
-
-    //copy_ongpu(l.batch*l.inputs, state.input, 1, l.output_gpu, 1);
-    simple_copy_ongpu(l.batch*l.inputs, state.input, l.output_gpu);
-    int b, n;
-    for (b = 0; b < l.batch; ++b){
-        for(n = 0; n < l.n; ++n){
-            int bbox_index = entry_index(l, b, n*l.w*l.h, 0);
-            // y = 1./(1. + exp(-x))
-            // x = ln(y/(1-y))  // ln - natural logarithm (base = e)
-            // if(y->1) x -> inf
-            // if(y->0) x -> -inf
-            if (l.new_coords) {
-                //activate_array_ongpu(l.output_gpu + bbox_index, 4 * l.w*l.h, LOGISTIC);    // x,y,w,h
-            }
-            else {
-                activate_array_ongpu(l.output_gpu + bbox_index, 2 * l.w*l.h, LOGISTIC);    // x,y
-
-                int obj_index = entry_index(l, b, n*l.w*l.h, 4);
-                activate_array_ongpu(l.output_gpu + obj_index, (1 + l.classes)*l.w*l.h, LOGISTIC); // classes and objectness
-            }
-            if (l.scale_x_y != 1) scal_add_ongpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output_gpu + bbox_index, 1);      // scale x,y
-        }
-    }
-    if(!state.train || l.onlyforward){
-        //cuda_pull_array(l.output_gpu, l.output, l.batch*l.outputs);
-        if (l.mean_alpha && l.output_avg_gpu) mean_array_gpu(l.output_gpu, l.batch*l.outputs, l.mean_alpha, l.output_avg_gpu);
-        cuda_pull_array_async(l.output_gpu, l.output, l.batch*l.outputs);
-        CHECK_CUDA(cudaPeekAtLastError());
-        return;
-    }
-
-    float *in_cpu = (float *)xcalloc(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 *)xcalloc(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_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_yolo_layer_gpu(const layer l, network_state state)
-{
-    axpy_ongpu(l.batch*l.inputs, state.net.loss_scale * l.delta_normalizer, l.delta_gpu, 1, state.delta, 1);
-}
-#endif

From a65af16ae0aa4c22537c4615f8d1d276b56261c5 Mon Sep 17 00:00:00 2001
From: ZouJiu <1069679911@qq.com>
Date: Mon, 21 Aug 2023 08:34:24 +0800
Subject: [PATCH 09/11] one predict corresponding to multi truth label

---
 src/yolo_layer.c | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/yolo_layer.c b/src/yolo_layer.c
index b8a571583ce..e12109597a5 100644
--- a/src/yolo_layer.c
+++ b/src/yolo_layer.c
@@ -549,7 +549,7 @@ void *process_batch(void* ptr)
                     int markable = 666;
                     for(int mi = 0; mi < number; mi++) {
                         mloam mp = mloam_ptr[mi];
-                        if(mp.mask_n == mask_n && mp.x==i && mp.y==j) {
+                        if(mp.best_n==best_n && mp.mask_n == mask_n && mp.x==i && mp.y==j) {
                             markable = -666;
                             if(mp.best_iou < best_iou) {
                                 mloam choose = {truth, best_iou, i, j, 1, best_n, mask_n, t};
@@ -583,7 +583,7 @@ void *process_batch(void* ptr)
                             int markable = 666;
                             for(int mi = 0; mi < number; mi++) {
                                 mloam mp = mloam_ptr[mi];
-                                if(mp.mask_n == mask_n && mp.x==i && mp.y==j) {
+                                if(mp.best_n==n && mp.mask_n == mask_n && mp.x==i && mp.y==j) {
                                     markable = -666;
                                     if(mp.best_iou < iou) {
                                         mloam choose = {truth, iou, i, j, -1, n, mask_n, t};

From a99d68631a6979802aef20e70fd837e13b7e9f8d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E4=B9=9D=E6=98=AF=E5=90=A6=E9=9A=8F=E6=84=8F=E7=9A=84?=
 =?UTF-8?q?=E7=A7=B0=E5=91=BC?= <1069679911@qq.com>
Date: Sat, 1 Feb 2025 19:21:30 +0800
Subject: [PATCH 10/11] Update LICENSE

---
 LICENSE | 686 +++++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 674 insertions(+), 12 deletions(-)

diff --git a/LICENSE b/LICENSE
index a50f7d700ba..c75e5d2f28b 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,12 +1,674 @@
-                                  YOLO LICENSE
-                             Version 2, July 29 2016
-
-THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER
-SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN
-TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES
-LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY. NOW HERE'S
-THE REAL LICENSE:
-
-0. Darknet is public domain.
-1. Do whatever you want with it.
-2. Stop emailing me about it!
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+  
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+  The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works.  By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users.  We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors.  You can apply it to
+your programs, too.
+
+  When we speak of free software, we are referring to freedom, not
+price.  Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
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+                     END OF TERMS AND CONDITIONS
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+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

From 70cc46af752c6cf8b0455d305570195195dacb1f Mon Sep 17 00:00:00 2001
From: ZouJiu1 <1069679911@qq.com>
Date: Sun, 2 Feb 2025 08:51:11 +0800
Subject: [PATCH 11/11] Revert "Update LICENSE"

This reverts commit a99d68631a6979802aef20e70fd837e13b7e9f8d.
---
 LICENSE | 686 +-------------------------------------------------------
 1 file changed, 12 insertions(+), 674 deletions(-)

diff --git a/LICENSE b/LICENSE
index c75e5d2f28b..a50f7d700ba 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,674 +1,12 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
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-any implied license or other defenses to infringement that may
-otherwise be available to you under applicable patent law.
-
-  12. No Surrender of Others' Freedom.
-
-  If conditions are imposed on you (whether by court order, agreement or
-otherwise) that contradict the conditions of this License, they do not
-excuse you from the conditions of this License.  If you cannot convey a
-covered work so as to satisfy simultaneously your obligations under this
-License and any other pertinent obligations, then as a consequence you may
-not convey it at all.  For example, if you agree to terms that obligate you
-to collect a royalty for further conveying from those to whom you convey
-the Program, the only way you could satisfy both those terms and this
-License would be to refrain entirely from conveying the Program.
-
-  13. Use with the GNU Affero General Public License.
-
-  Notwithstanding any other provision of this License, you have
-permission to link or combine any covered work with a work licensed
-under version 3 of the GNU Affero General Public License into a single
-combined work, and to convey the resulting work.  The terms of this
-License will continue to apply to the part which is the covered work,
-but the special requirements of the GNU Affero General Public License,
-section 13, concerning interaction through a network will apply to the
-combination as such.
-
-  14. Revised Versions of this License.
-
-  The Free Software Foundation may publish revised and/or new versions of
-the GNU General Public License from time to time.  Such new versions will
-be similar in spirit to the present version, but may differ in detail to
-address new problems or concerns.
-
-  Each version is given a distinguishing version number.  If the
-Program specifies that a certain numbered version of the GNU General
-Public License "or any later version" applies to it, you have the
-option of following the terms and conditions either of that numbered
-version or of any later version published by the Free Software
-Foundation.  If the Program does not specify a version number of the
-GNU General Public License, you may choose any version ever published
-by the Free Software Foundation.
-
-  If the Program specifies that a proxy can decide which future
-versions of the GNU General Public License can be used, that proxy's
-public statement of acceptance of a version permanently authorizes you
-to choose that version for the Program.
-
-  Later license versions may give you additional or different
-permissions.  However, no additional obligations are imposed on any
-author or copyright holder as a result of your choosing to follow a
-later version.
-
-  15. Disclaimer of Warranty.
-
-  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
-APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
-HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
-OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
-THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
-  16. Limitation of Liability.
-
-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <https://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<https://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<https://www.gnu.org/licenses/why-not-lgpl.html>.
+                                  YOLO LICENSE
+                             Version 2, July 29 2016
+
+THIS SOFTWARE LICENSE IS PROVIDED "ALL CAPS" SO THAT YOU KNOW IT IS SUPER
+SERIOUS AND YOU DON'T MESS AROUND WITH COPYRIGHT LAW BECAUSE YOU WILL GET IN
+TROUBLE HERE ARE SOME OTHER BUZZWORDS COMMONLY IN THESE THINGS WARRANTIES
+LIABILITY CONTRACT TORT LIABLE CLAIMS RESTRICTION MERCHANTABILITY. NOW HERE'S
+THE REAL LICENSE:
+
+0. Darknet is public domain.
+1. Do whatever you want with it.
+2. Stop emailing me about it!