From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/yolo_layer.c | 2138 ++++++++++++++++++++++++++++++++++-------------------------
1 files changed, 1,229 insertions(+), 909 deletions(-)
diff --git a/lib/detecter_tools/darknet/yolo_layer.c b/lib/detecter_tools/darknet/yolo_layer.c
index 596a502..883d755 100644
--- a/lib/detecter_tools/darknet/yolo_layer.c
+++ b/lib/detecter_tools/darknet/yolo_layer.c
@@ -1,909 +1,1229 @@
-#include "yolo_layer.h"
-#include "activations.h"
-#include "blas.h"
-#include "box.h"
-#include "dark_cuda.h"
-#include "utils.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.truths = l.max_boxes*(4 + 1); // 90*(4 + 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->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)
-{
- 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
- 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)
-{
- 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);
- 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]);
-
- //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
- 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)
-{
- 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];
- 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;
-}
-
-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));
-
-#ifndef GPU
- for (b = 0; b < l.batch; ++b) {
- for (n = 0; n < l.n; ++n) {
- int index = entry_index(l, b, n*l.w*l.h, 0);
- activate_array(l.output + index, 2 * l.w*l.h, LOGISTIC); // x,y,
- scal_add_cpu(2 * l.w*l.h, l.scale_x_y, -0.5*(l.scale_x_y - 1), l.output + index, 1); // scale x,y
- index = entry_index(l, b, n*l.w*l.h, 4);
- activate_array(l.output + index, (1 + l.classes)*l.w*l.h, LOGISTIC);
- }
- }
-#endif
-
- // delta is zeroed
- memset(l.delta, 0, l.outputs * l.batch * sizeof(float));
- if (!state.train) return;
- //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;
- for (b = 0; b < l.batch; ++b) {
- for (j = 0; j < l.h; ++j) {
- for (i = 0; i < l.w; ++i) {
- for (n = 0; n < l.n; ++n) {
- const int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
- const int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
- const int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
- const int stride = l.w*l.h;
- box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w*l.h);
- float best_match_iou = 0;
- int best_match_t = 0;
- float best_iou = 0;
- int best_t = 0;
- for (t = 0; t < l.max_boxes; ++t) {
- box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1);
- int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
- if (class_id >= l.classes || class_id < 0) {
- printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1);
- printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id);
- if (check_mistakes) getchar();
- continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value
- }
- if (!truth.x) break; // continue;
-
- float objectness = l.output[obj_index];
- if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0;
- int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w*l.h, objectness, class_id, 0.25f);
-
- float iou = box_iou(pred, truth);
- if (iou > best_match_iou && class_id_match == 1) {
- best_match_iou = iou;
- best_match_t = t;
- }
- if (iou > best_iou) {
- best_iou = iou;
- best_t = t;
- }
- }
-
- avg_anyobj += l.output[obj_index];
- l.delta[obj_index] = l.cls_normalizer * (0 - l.output[obj_index]);
- if (best_match_iou > l.ignore_thresh) {
- const float iou_multiplier = best_match_iou*best_match_iou;// (best_match_iou - l.ignore_thresh) / (1.0 - l.ignore_thresh);
- if (l.objectness_smooth) {
- l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]);
-
- int class_id = state.truth[best_match_t*(4 + 1) + b*l.truths + 4];
- if (l.map) class_id = l.map[class_id];
- const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
- l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]);
- }
- else l.delta[obj_index] = 0;
- }
- else if (state.net.adversarial) {
- int stride = l.w*l.h;
- float scale = pred.w * pred.h;
- if (scale > 0) scale = sqrt(scale);
- l.delta[obj_index] = scale * l.cls_normalizer * (0 - l.output[obj_index]);
- int cl_id;
- for (cl_id = 0; cl_id < l.classes; ++cl_id) {
- if(l.output[class_index + stride*cl_id] * l.output[obj_index] > 0.25)
- l.delta[class_index + stride*cl_id] = scale * (0 - l.output[class_index + stride*cl_id]);
- }
- }
- if (best_iou > l.truth_thresh) {
- const float iou_multiplier = best_iou*best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh);
- if (l.objectness_smooth) l.delta[obj_index] = l.cls_normalizer * (iou_multiplier - l.output[obj_index]);
- else l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
- //l.delta[obj_index] = l.cls_normalizer * (1 - l.output[obj_index]);
-
- int class_id = state.truth[best_t*(4 + 1) + b*l.truths + 4];
- if (l.map) class_id = l.map[class_id];
- delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w*l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers);
- const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
- if (l.objectness_smooth) l.delta[class_index + stride*class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride*class_id]);
- box truth = float_to_box_stride(state.truth + best_t*(4 + 1) + b*l.truths, 1);
- delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w*truth.h), l.w*l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta);
- }
- }
- }
- }
- for (t = 0; t < l.max_boxes; ++t) {
- box truth = float_to_box_stride(state.truth + t*(4 + 1) + b*l.truths, 1);
- if (truth.x < 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*(4 + 1) + 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
-
- if (!truth.x) break; // continue;
- float best_iou = 0;
- int best_n = 0;
- i = (truth.x * l.w);
- j = (truth.y * l.h);
- box truth_shift = truth;
- truth_shift.x = truth_shift.y = 0;
- for (n = 0; n < l.total; ++n) {
- box pred = { 0 };
- pred.w = l.biases[2 * n] / state.net.w;
- pred.h = l.biases[2 * n + 1] / state.net.h;
- float iou = box_iou(pred, truth_shift);
- if (iou > best_iou) {
- best_iou = iou;
- best_n = n;
- }
- }
-
- int mask_n = int_index(l.mask, best_n, l.n);
- if (mask_n >= 0) {
- int class_id = state.truth[t*(4 + 1) + b*l.truths + 4];
- if (l.map) class_id = l.map[class_id];
-
- int box_index = entry_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);
-
- // range is 0 <= 1
- tot_iou += all_ious.iou;
- tot_iou_loss += 1 - all_ious.iou;
- // range is -1 <= giou <= 1
- tot_giou += all_ious.giou;
- 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];
- l.delta[obj_index] = class_multiplier * l.cls_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);
-
- //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]);
-
- ++count;
- ++class_count;
- 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*(4 + 1) + 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);
-
- // range is 0 <= 1
- tot_iou += all_ious.iou;
- tot_iou_loss += 1 - all_ious.iou;
- // range is -1 <= giou <= 1
- tot_giou += all_ious.giou;
- 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];
- l.delta[obj_index] = class_multiplier * l.cls_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);
-
- ++count;
- ++class_count;
- if (all_ious.iou > .5) recall += 1;
- if (all_ious.iou > .75) recall75 += 1;
- }
- }
- }
- }
-
- // averages the deltas obtained by the function: delta_yolo_box()_accumulate
- for (j = 0; j < l.h; ++j) {
- for (i = 0; i < l.w; ++i) {
- for (n = 0; n < l.n; ++n) {
- int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
- int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
- const int stride = l.w*l.h;
-
- averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta);
- }
- }
- }
- }
-
- if (count == 0) count = 1;
- if (class_count == 0) class_count = 1;
-
- //*(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
- //printf("Region %d Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, .5R: %f, .75R: %f, count: %d\n", state.index, avg_iou / count, avg_cat / class_count, avg_obj / count, avg_anyobj / (l.w*l.h*l.n*l.batch), recall / count, recall75 / count, count);
-
- 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));
- 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.cls_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;
- // 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, 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.cls_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);
- dets[count].objectness = objectness;
- dets[count].classes = l.classes;
- 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);
- dets[count].objectness = objectness;
- dets[count].classes = l.classes;
- 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)
-{
- //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 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
- activate_array_ongpu(l.output_gpu + index, 2*l.w*l.h, LOGISTIC); // x,y
- 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 + index, 1); // scale x,y
- index = entry_index(l, b, n*l.w*l.h, 4);
- activate_array_ongpu(l.output_gpu + index, (1+l.classes)*l.w*l.h, LOGISTIC); // classes and objectness
- }
- }
- 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_gpu, 1, state.delta, 1);
-}
-#endif
+#include "yolo_layer.h"
+#include "activations.h"
+#include "blas.h"
+#include "box.h"
+#include "dark_cuda.h"
+#include "utils.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;
+} 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;
+
+ //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;
+
+ for (j = 0; j < l.h; ++j) {
+ for (i = 0; i < l.w; ++i) {
+ for (n = 0; n < l.n; ++n) {
+ const int class_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4 + 1);
+ const int obj_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 4);
+ const int box_index = entry_index(l, b, n * l.w * l.h + j * l.w + i, 0);
+ const int stride = l.w * l.h;
+ box pred = get_yolo_box(l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.w * l.h, l.new_coords);
+ float best_match_iou = 0;
+ int best_match_t = 0;
+ float best_iou = 0;
+ int best_t = 0;
+ for (t = 0; t < l.max_boxes; ++t) {
+ box truth = float_to_box_stride(state.truth + t * l.truth_size + b * l.truths, 1);
+ if (!truth.x) break; // continue;
+ int class_id = state.truth[t * l.truth_size + b * l.truths + 4];
+ if (class_id >= l.classes || class_id < 0) {
+ printf("\n Warning: in txt-labels class_id=%d >= classes=%d in cfg-file. In txt-labels class_id should be [from 0 to %d] \n", class_id, l.classes, l.classes - 1);
+ printf("\n truth.x = %f, truth.y = %f, truth.w = %f, truth.h = %f, class_id = %d \n", truth.x, truth.y, truth.w, truth.h, class_id);
+ if (check_mistakes) getchar();
+ continue; // if label contains class_id more than number of classes in the cfg-file and class_id check garbage value
+ }
+
+ float objectness = l.output[obj_index];
+ if (isnan(objectness) || isinf(objectness)) l.output[obj_index] = 0;
+ int class_id_match = compare_yolo_class(l.output, l.classes, class_index, l.w * l.h, objectness, class_id, 0.25f);
+
+ float iou = box_iou(pred, truth);
+ if (iou > best_match_iou && class_id_match == 1) {
+ best_match_iou = iou;
+ best_match_t = t;
+ }
+ if (iou > best_iou) {
+ best_iou = iou;
+ best_t = t;
+ }
+ }
+
+ avg_anyobj += l.output[obj_index];
+ l.delta[obj_index] = l.obj_normalizer * (0 - l.output[obj_index]);
+ if (best_match_iou > l.ignore_thresh) {
+ if (l.objectness_smooth) {
+ const float delta_obj = l.obj_normalizer * (best_match_iou - l.output[obj_index]);
+ if (delta_obj > l.delta[obj_index]) l.delta[obj_index] = delta_obj;
+
+ }
+ else l.delta[obj_index] = 0;
+ }
+ else if (state.net.adversarial) {
+ int stride = l.w * l.h;
+ float scale = pred.w * pred.h;
+ if (scale > 0) scale = sqrt(scale);
+ l.delta[obj_index] = scale * l.obj_normalizer * (0 - l.output[obj_index]);
+ int cl_id;
+ int found_object = 0;
+ for (cl_id = 0; cl_id < l.classes; ++cl_id) {
+ if (l.output[class_index + stride * cl_id] * l.output[obj_index] > 0.25) {
+ l.delta[class_index + stride * cl_id] = scale * (0 - l.output[class_index + stride * cl_id]);
+ found_object = 1;
+ }
+ }
+ if (found_object) {
+ // don't use this loop for adversarial attack drawing
+ for (cl_id = 0; cl_id < l.classes; ++cl_id)
+ if (l.output[class_index + stride * cl_id] * l.output[obj_index] < 0.25)
+ l.delta[class_index + stride * cl_id] = scale * (1 - l.output[class_index + stride * cl_id]);
+
+ l.delta[box_index + 0 * stride] += scale * (0 - l.output[box_index + 0 * stride]);
+ l.delta[box_index + 1 * stride] += scale * (0 - l.output[box_index + 1 * stride]);
+ l.delta[box_index + 2 * stride] += scale * (0 - l.output[box_index + 2 * stride]);
+ l.delta[box_index + 3 * stride] += scale * (0 - l.output[box_index + 3 * stride]);
+ }
+ }
+ if (best_iou > l.truth_thresh) {
+ const float iou_multiplier = best_iou * best_iou;// (best_iou - l.truth_thresh) / (1.0 - l.truth_thresh);
+ if (l.objectness_smooth) l.delta[obj_index] = l.obj_normalizer * (iou_multiplier - l.output[obj_index]);
+ else l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]);
+ //l.delta[obj_index] = l.obj_normalizer * (1 - l.output[obj_index]);
+
+ int class_id = state.truth[best_t * l.truth_size + b * l.truths + 4];
+ if (l.map) class_id = l.map[class_id];
+ delta_yolo_class(l.output, l.delta, class_index, class_id, l.classes, l.w * l.h, 0, l.focal_loss, l.label_smooth_eps, l.classes_multipliers, l.cls_normalizer);
+ const float class_multiplier = (l.classes_multipliers) ? l.classes_multipliers[class_id] : 1.0f;
+ if (l.objectness_smooth) l.delta[class_index + stride * class_id] = class_multiplier * (iou_multiplier - l.output[class_index + stride * class_id]);
+ box truth = float_to_box_stride(state.truth + best_t * l.truth_size + b * l.truths, 1);
+ delta_yolo_box(truth, l.output, l.biases, l.mask[n], box_index, i, j, l.w, l.h, state.net.w, state.net.h, l.delta, (2 - truth.w * truth.h), l.w * l.h, l.iou_normalizer * class_multiplier, l.iou_loss, 1, l.max_delta, state.net.rewritten_bbox, l.new_coords);
+ (*state.net.total_bbox)++;
+ }
+ }
+ }
+ }
+ 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
+
+ float best_iou = 0;
+ int best_n = 0;
+ i = (truth.x * l.w);
+ j = (truth.y * l.h);
+ box truth_shift = truth;
+ truth_shift.x = truth_shift.y = 0;
+ for (n = 0; n < l.total; ++n) {
+ box pred = { 0 };
+ pred.w = l.biases[2 * n] / state.net.w;
+ pred.h = l.biases[2 * n + 1] / state.net.h;
+ float iou = box_iou(pred, truth_shift);
+ if (iou > best_iou) {
+ best_iou = iou;
+ best_n = n;
+ }
+ }
+
+ int mask_n = int_index(l.mask, best_n, l.n);
+ if (mask_n >= 0) {
+ int class_id = state.truth[t * l.truth_size + b * l.truths + 4];
+ if (l.map) class_id = l.map[class_id];
+
+ int box_index = entry_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);
+
+ // 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]);
+
+ ++(args->count);
+ ++(args->class_count);
+ 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) {
+ // averages the deltas obtained by the function: delta_yolo_box()_accumulate
+ for (j = 0; j < l.h; ++j) {
+ for (i = 0; i < l.w; ++i) {
+ for (n = 0; n < l.n; ++n) {
+ int obj_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4);
+ int box_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 0);
+ int class_index = entry_index(l, b, n*l.w*l.h + j*l.w + i, 4 + 1);
+ const int stride = l.w*l.h;
+
+ if (l.delta[obj_index] != 0)
+ averages_yolo_deltas(class_index, box_index, stride, l.classes, l.delta);
+ }
+ }
+ }
+ }
+
+ }
+
+ 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;
+
+ if (pthread_create(&threads[b], 0, process_batch, &(yolo_args[b]))) error("Thread creation failed");
+ }
+
+ 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);
+ 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
--
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